In Press
Articles in press have been peer-reviewed and accepted, which are not yet assigned to volumes/issues, but are citable by Digital Object Identifier (DOI).
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, Manuscript accepted 26 July 2024, doi: 10.1007/s00376-024-3144-8
Abstract:
31 May through 2 June 2023 marked the 18th Workshop on Antarctic Meteorology and Climate. The Antarctic Meteorological Research and Data Center hosted this hybrid workshop at the Pyle Center at University of Wisconsin-Madison in Madison, WI, USA. Global members of the Antarctic meteorological community gathered to present and discuss topics from scientific research to operational support within Antarctic meteorology and climate. Students and early career professionals chaired various presentations and discussions while all attendees engaged to share results, build collaborative plans, and discuss further developments. The main workshop topics included updates on the current automatic weather station (AWS) networks, challenges and planning concerning Antarctic forecasting and operational services, Antarctic numerical modeling systems, observational applications and research, and Antarctic community and data sources. Across six sessions, over 25 different presenters elaborated on their work in areas such as field season updates, atmospheric rivers, remote sensing, the Antarctic Mesoscale Prediction System, and forecasting challenges due to data scarcity. Workshop discussions resulted in several key outcomes and action items regarding field work impacts, exploration of field techniques, observation coverage, and communication between operations and research scientists. Future workshops will provide opportunities for continued discussion on the evolution of the AWS network and operational needs while providing a venue to promote collaboration and cooperation for Antarctic meteorology and climate activities.
31 May through 2 June 2023 marked the 18th Workshop on Antarctic Meteorology and Climate. The Antarctic Meteorological Research and Data Center hosted this hybrid workshop at the Pyle Center at University of Wisconsin-Madison in Madison, WI, USA. Global members of the Antarctic meteorological community gathered to present and discuss topics from scientific research to operational support within Antarctic meteorology and climate. Students and early career professionals chaired various presentations and discussions while all attendees engaged to share results, build collaborative plans, and discuss further developments. The main workshop topics included updates on the current automatic weather station (AWS) networks, challenges and planning concerning Antarctic forecasting and operational services, Antarctic numerical modeling systems, observational applications and research, and Antarctic community and data sources. Across six sessions, over 25 different presenters elaborated on their work in areas such as field season updates, atmospheric rivers, remote sensing, the Antarctic Mesoscale Prediction System, and forecasting challenges due to data scarcity. Workshop discussions resulted in several key outcomes and action items regarding field work impacts, exploration of field techniques, observation coverage, and communication between operations and research scientists. Future workshops will provide opportunities for continued discussion on the evolution of the AWS network and operational needs while providing a venue to promote collaboration and cooperation for Antarctic meteorology and climate activities.
, Available online
, Manuscript accepted 26 July 2024, doi: 10.1007/s00376-024-3164-4
Abstract:
Satellite remote sensing is essential for solar energy meteorology. The 14-channel Advanced Geostationary Radiation Imager of the Fengyun-4 series of satellites performs a full-disc scan over greater China every 15 min, providing high-granularity information that allows the retrieval of cloud properties, aerosol optical depth, and precipitable water vapor content, which can facilitate the acquisition of surface solar irradiance components through physical methods. Machine-learning methods have also shown potential in providing accurate end-to-end surface solar radiation retrievals. Albeit the physical principles of irradiance retrieval and machine-learning algorithms are fairly well known, the public service concerning disseminating the irradiance product to the energy and power industry still lacks robustness and consistency. In this perspective article, the status quo of Fengyun-4 irradiance products is first reviewed. Then, from the perspective of solar resource assessment and forecasting, three fundamental characteristics of the kind of irradiance products that are most serviceable to the solar energy sector are identified, namely, coverage, timeliness, and accessibility. Finally, an outlook of the new-generation Fengyun radiation service is put forward, in which the prospective scientific and practical challenges are elaborated.
Satellite remote sensing is essential for solar energy meteorology. The 14-channel Advanced Geostationary Radiation Imager of the Fengyun-4 series of satellites performs a full-disc scan over greater China every 15 min, providing high-granularity information that allows the retrieval of cloud properties, aerosol optical depth, and precipitable water vapor content, which can facilitate the acquisition of surface solar irradiance components through physical methods. Machine-learning methods have also shown potential in providing accurate end-to-end surface solar radiation retrievals. Albeit the physical principles of irradiance retrieval and machine-learning algorithms are fairly well known, the public service concerning disseminating the irradiance product to the energy and power industry still lacks robustness and consistency. In this perspective article, the status quo of Fengyun-4 irradiance products is first reviewed. Then, from the perspective of solar resource assessment and forecasting, three fundamental characteristics of the kind of irradiance products that are most serviceable to the solar energy sector are identified, namely, coverage, timeliness, and accessibility. Finally, an outlook of the new-generation Fengyun radiation service is put forward, in which the prospective scientific and practical challenges are elaborated.
, Available online
, Manuscript accepted 25 July 2024, doi: 10.1007/s00376-024-4107-9
Abstract:
Based on the hindcasts from five sub-seasonal to seasonal (S2S) models participating in the S2S Prediction Project, this study evaluates the performance of the multi-model ensemble (MME) approach in predicting the subseasonal precipitation anomalies during summer in China and reveals the contributions of possible driving factors. The results suggest that while single-model ensembles (SMEs) exhibit constrained predictive skills within a limited forecast lead time of 3 pentads, the MME illustrates an enhanced predictive skill at a lead time of up to 4 pentads, even 6 pentads in southern China. Based on both deterministic and probabilistic verification metrics, the MME consistently outperforms SMEs with a more evident advantage observed in probabilistic forecasting. The superior performance of MME is primarily attributed to the increase in ensemble size, and the enhanced model diversity is also a contributing factor. The reliability of probabilistic skill has been largely improved due to the increase in ensemble members, while the resolution term does not exhibit consistent improvement. Furthermore, the Madden-Julian Oscillation (MJO) has been revealed as the primary driving factor for the successful prediction of summer precipitation in China using the MME. The improvement by the MME is not solely attributed to the enhancement in the inherent predictive capacity of MJO itself, but derived from its capability in capturing the more realistic relationship between the MJO and sub-seasonal precipitation anomalies in China. This study establishes a scientific foundation for acknowledging the advantageous predictive capability of MME in sub-seasonal predictions of summer precipitation in China, and sheds light on further improving the S2S predictions.
Based on the hindcasts from five sub-seasonal to seasonal (S2S) models participating in the S2S Prediction Project, this study evaluates the performance of the multi-model ensemble (MME) approach in predicting the subseasonal precipitation anomalies during summer in China and reveals the contributions of possible driving factors. The results suggest that while single-model ensembles (SMEs) exhibit constrained predictive skills within a limited forecast lead time of 3 pentads, the MME illustrates an enhanced predictive skill at a lead time of up to 4 pentads, even 6 pentads in southern China. Based on both deterministic and probabilistic verification metrics, the MME consistently outperforms SMEs with a more evident advantage observed in probabilistic forecasting. The superior performance of MME is primarily attributed to the increase in ensemble size, and the enhanced model diversity is also a contributing factor. The reliability of probabilistic skill has been largely improved due to the increase in ensemble members, while the resolution term does not exhibit consistent improvement. Furthermore, the Madden-Julian Oscillation (MJO) has been revealed as the primary driving factor for the successful prediction of summer precipitation in China using the MME. The improvement by the MME is not solely attributed to the enhancement in the inherent predictive capacity of MJO itself, but derived from its capability in capturing the more realistic relationship between the MJO and sub-seasonal precipitation anomalies in China. This study establishes a scientific foundation for acknowledging the advantageous predictive capability of MME in sub-seasonal predictions of summer precipitation in China, and sheds light on further improving the S2S predictions.
, Available online
, Manuscript accepted 24 July 2024, doi: 10.1007/s00376-024-4057-2
Abstract:
In this study, we investigate the complex relationship between western disturbances (WDs), the El Niño Southern Oscillation (ENSO), and extreme precipitation events (EPEs) in the Western Himalayas (WH) during the extended winter season (November-March). WDs west of WH coincide with 97% of recorded EPEs, contributing substantially (32% in winter, 11% annually) to total precipitation within WH. WDs are 6% less frequent and 4% more intense during El Niño than La Niña to the west of WH. During El Niño (compared to La Niña) years, WDs co-occurring with EPEs are significantly more intense and associated with 17% higher moisture transport over WH box. This results in doubling of EPEs frequency during El Niño periods than La Niña periods. A substantial southward shift (~180 km) of the subtropical jet (STJ) axis during El Niño brings WD tracks further south towards their primary moisture sources, especially the Arabian Sea. We have shown that WDs that are both more intense and pass to the south of their typical latitudes have higher levels of vertical integrated moisture flux (VIMF) within them. VIMF convergence in the most intense pentile of WDs is 5.7 times higher than in the weakest, and is 3.4 times higher in the second lowest latitude pentile than in the highest. Overall, this study demonstrates a direct link between changes in the latitudinal position and intensity of WDs associated with winter STJ, and moisture convergence, which leads to the occurrence of EPEs over the WH during ENSO phases.
In this study, we investigate the complex relationship between western disturbances (WDs), the El Niño Southern Oscillation (ENSO), and extreme precipitation events (EPEs) in the Western Himalayas (WH) during the extended winter season (November-March). WDs west of WH coincide with 97% of recorded EPEs, contributing substantially (32% in winter, 11% annually) to total precipitation within WH. WDs are 6% less frequent and 4% more intense during El Niño than La Niña to the west of WH. During El Niño (compared to La Niña) years, WDs co-occurring with EPEs are significantly more intense and associated with 17% higher moisture transport over WH box. This results in doubling of EPEs frequency during El Niño periods than La Niña periods. A substantial southward shift (~180 km) of the subtropical jet (STJ) axis during El Niño brings WD tracks further south towards their primary moisture sources, especially the Arabian Sea. We have shown that WDs that are both more intense and pass to the south of their typical latitudes have higher levels of vertical integrated moisture flux (VIMF) within them. VIMF convergence in the most intense pentile of WDs is 5.7 times higher than in the weakest, and is 3.4 times higher in the second lowest latitude pentile than in the highest. Overall, this study demonstrates a direct link between changes in the latitudinal position and intensity of WDs associated with winter STJ, and moisture convergence, which leads to the occurrence of EPEs over the WH during ENSO phases.
, Available online
, Manuscript accepted 17 July 2024, doi: 10.1007/s00376-024-4027-8
Abstract:
The increasing and intensifying extreme high temperature events (EHEs) over the central-eastern China (CEC) in recent decades have caused severe impacts on the social development and people’s livelihood. Using the observed and reanalysis datasets, this study explores the effect of the East Asian subtropical westerly jet stream (EAJ) on the CEC EHEs in summer during 1979-2020. Generally, being located on the right side of the upper layer jet stream exit region, the CEC would suffer more EHEs when the EAJ is relatively stronger and northward shifted in summer. This is owing to the abnormal subsidence induced by the EAJ. However, such an EAJ-EHE connection is unstable for the past four decades, but has an evident inter-decadal change. Before the late-1990s, the inter-annual variation of the EAJ is mainly featured by its meridional displacement in the east of Asia. The atmospheric responses thus are basically located to the east of the CEC, exerting less influence on the CEC EHEs. Since the late-1990s, the EAJ variation is featured with an intensity change in the jet stream center over the northwest side of the CEC, indicating a westward shift in atmospheric responses to cover the CEC region. Therefore, the EAJ would evidently affect the summer CEC EHEs during 2000-2021. Our findings could favor an in-depth understanding on the formation mechanisms of extreme weather/climate events, and thus provide a scientific reference for seasonal climate predictions.
The increasing and intensifying extreme high temperature events (EHEs) over the central-eastern China (CEC) in recent decades have caused severe impacts on the social development and people’s livelihood. Using the observed and reanalysis datasets, this study explores the effect of the East Asian subtropical westerly jet stream (EAJ) on the CEC EHEs in summer during 1979-2020. Generally, being located on the right side of the upper layer jet stream exit region, the CEC would suffer more EHEs when the EAJ is relatively stronger and northward shifted in summer. This is owing to the abnormal subsidence induced by the EAJ. However, such an EAJ-EHE connection is unstable for the past four decades, but has an evident inter-decadal change. Before the late-1990s, the inter-annual variation of the EAJ is mainly featured by its meridional displacement in the east of Asia. The atmospheric responses thus are basically located to the east of the CEC, exerting less influence on the CEC EHEs. Since the late-1990s, the EAJ variation is featured with an intensity change in the jet stream center over the northwest side of the CEC, indicating a westward shift in atmospheric responses to cover the CEC region. Therefore, the EAJ would evidently affect the summer CEC EHEs during 2000-2021. Our findings could favor an in-depth understanding on the formation mechanisms of extreme weather/climate events, and thus provide a scientific reference for seasonal climate predictions.
, Available online
, Manuscript accepted 12 July 2024, doi: 10.1007/s00376-024-4104-z
Abstract:
The North Pacific Storm Track (NPST) is a high-frequency area of extratropical cyclones and an important channel for water vapor and energy transfer between low and mid-high latitudes. Previous weather and dynamic studies in this region have made significant progress, but due to the lack of ocean surface rainfall observation data, there is a lack of statistical research on precipitation in this area. In this study, statistical research on the spatiotemporal distribution characteristics of rainfall in NPST was conducted based on GPM DPR observation data and ERA5 atmospheric parameters, and analysis and explanations were provided based on the atmospheric parameters. The study found that, compared to low-pressure systems, pressure gradients have a greater impact on cyclone activity and rainfall distribution. This feature, along with the meridional distribution of high atmospheric water vapor in the north Pacific Ocean and low in the north, collectively leads to the offset of high-frequency rainfall areas relative to storm tracks. The distribution of sea surface temperatures in the North Pacific Ocean affects the zonal distribution of storm tracks, causing weather disturbances and precipitation along the storm tracks to exhibit a northward extension from west to east. This study deepens the understanding of the role of NPST in global water vapor and energy balance, and it is of great significance for improving the prediction accuracy of rainfall in extratropical cyclones in climate models.
The North Pacific Storm Track (NPST) is a high-frequency area of extratropical cyclones and an important channel for water vapor and energy transfer between low and mid-high latitudes. Previous weather and dynamic studies in this region have made significant progress, but due to the lack of ocean surface rainfall observation data, there is a lack of statistical research on precipitation in this area. In this study, statistical research on the spatiotemporal distribution characteristics of rainfall in NPST was conducted based on GPM DPR observation data and ERA5 atmospheric parameters, and analysis and explanations were provided based on the atmospheric parameters. The study found that, compared to low-pressure systems, pressure gradients have a greater impact on cyclone activity and rainfall distribution. This feature, along with the meridional distribution of high atmospheric water vapor in the north Pacific Ocean and low in the north, collectively leads to the offset of high-frequency rainfall areas relative to storm tracks. The distribution of sea surface temperatures in the North Pacific Ocean affects the zonal distribution of storm tracks, causing weather disturbances and precipitation along the storm tracks to exhibit a northward extension from west to east. This study deepens the understanding of the role of NPST in global water vapor and energy balance, and it is of great significance for improving the prediction accuracy of rainfall in extratropical cyclones in climate models.
, Available online
, Manuscript accepted 08 July 2024, doi: 10.1007/s00376-024-4045-6
Abstract:
Atmospheric de-aliasing is one of the most important background models for recovering Earth’s temporal gravity field from gravity satellite missions. To meet Chinese gravimetric satellite platform, an independent atmospheric de-aliasing model that relies on Chinese meteorological data needs to be developed. The release of CRA-40, as the first generation of Chinese atmospheric reanalysis, provides the opportunity. This study proposes a revised modelling method to calibrate CRA-40 and develops a new atmospheric de-aliasing model (HUST-CRA, 2002-2020). Intensive assessments are made between HUST-CRA and the latest official de-aliasing product of the international gravity satellite mission. The tidal components of the two products demonstrate a high consistency, e.g., the spatial correlation for the major tide S1 is 0.96. The non-tidal components of the two products are also equivalent: (1) the temporal correlation of low-degree terms is higher than 0.97, except for the term of S22 (0.93); (2) the spectral correlation of degree geoid height up to degree/order 100 is as high as 0.99; (3) the confidence interval of the spatial correlation (2002-2020) is [0.971, 0.995] at a confidence level of 95%; (4) the difference of KBRR-residuals is less than 0.08 um/s, the difference of derived temporal gravity field is less than 0.32 mm in terms of geoid height, and both are apparently beyond the ability of current gravity satellite mission. This confirms that CRA-40 is of high quality and that the derived de-aliasing product HUST-CRA is accurate enough to be used in both Chinese and international gravity satellite missions.
Atmospheric de-aliasing is one of the most important background models for recovering Earth’s temporal gravity field from gravity satellite missions. To meet Chinese gravimetric satellite platform, an independent atmospheric de-aliasing model that relies on Chinese meteorological data needs to be developed. The release of CRA-40, as the first generation of Chinese atmospheric reanalysis, provides the opportunity. This study proposes a revised modelling method to calibrate CRA-40 and develops a new atmospheric de-aliasing model (HUST-CRA, 2002-2020). Intensive assessments are made between HUST-CRA and the latest official de-aliasing product of the international gravity satellite mission. The tidal components of the two products demonstrate a high consistency, e.g., the spatial correlation for the major tide S1 is 0.96. The non-tidal components of the two products are also equivalent: (1) the temporal correlation of low-degree terms is higher than 0.97, except for the term of S22 (0.93); (2) the spectral correlation of degree geoid height up to degree/order 100 is as high as 0.99; (3) the confidence interval of the spatial correlation (2002-2020) is [0.971, 0.995] at a confidence level of 95%; (4) the difference of KBRR-residuals is less than 0.08 um/s, the difference of derived temporal gravity field is less than 0.32 mm in terms of geoid height, and both are apparently beyond the ability of current gravity satellite mission. This confirms that CRA-40 is of high quality and that the derived de-aliasing product HUST-CRA is accurate enough to be used in both Chinese and international gravity satellite missions.
, Available online
, Manuscript accepted 08 July 2024, doi: 10.1007/s00376-024-4069-y
Abstract:
Orthogonal conditional nonlinear optimal perturbations (O-CNOPs) are typically used to generate ensemble forecasting members for achieving high forecasting skill of high-impact weather and climate events. However, highly efficient calculations for O-CNOPs are still challenging in the field of ensemble forecasting. In this study, we combine a gradient-based iterative idea with the Gram‒Schmidt orthogonalization, and propose an iterative optimization method to compute O-CNOPs. This method is different from the original sequential optimization method and allows parallel computations of O-CNOPs, thus saving a large amount of computational time. We evaluate this method by using the Lorenz-96 model on the basis of the ensemble forecasting ability achieved and on the time consumed for computing O-CNOPs. The results demonstrate that the parallel iterative method causes O-CNOPs to yield reliable ensemble members and to achieve ensemble forecasting skills similar to or even slightly higher than those produced by the sequential method. Moreover, the parallel method significantly reduces the computational time for O-CNOPs. Therefore, the parallel iterative method provides a highly effective and efficient approach for calculating O-CNOPs for ensemble forecasts. Expectedly, it can play a great role in the application of the O-CNOPs to realistic ensemble forecasts for high-impact weather and climate events.
Orthogonal conditional nonlinear optimal perturbations (O-CNOPs) are typically used to generate ensemble forecasting members for achieving high forecasting skill of high-impact weather and climate events. However, highly efficient calculations for O-CNOPs are still challenging in the field of ensemble forecasting. In this study, we combine a gradient-based iterative idea with the Gram‒Schmidt orthogonalization, and propose an iterative optimization method to compute O-CNOPs. This method is different from the original sequential optimization method and allows parallel computations of O-CNOPs, thus saving a large amount of computational time. We evaluate this method by using the Lorenz-96 model on the basis of the ensemble forecasting ability achieved and on the time consumed for computing O-CNOPs. The results demonstrate that the parallel iterative method causes O-CNOPs to yield reliable ensemble members and to achieve ensemble forecasting skills similar to or even slightly higher than those produced by the sequential method. Moreover, the parallel method significantly reduces the computational time for O-CNOPs. Therefore, the parallel iterative method provides a highly effective and efficient approach for calculating O-CNOPs for ensemble forecasts. Expectedly, it can play a great role in the application of the O-CNOPs to realistic ensemble forecasts for high-impact weather and climate events.
, Available online
, Manuscript accepted 05 July 2024, doi: 10.1007/s00376-024-4029-6
Abstract:
Three extreme cold events have occurred in January 2016 and 2021, and December 2023 in Eastern Asia. As important factors in atmospheric circulation anomalies, the Blocking High and East Asian Trough (BH-ET) structure play key roles during the three extreme cold waves events. Among them, the BH affects the development of cold waves in two different ways: (1) Before the cold waves in 2016 and 2023, BH pushed the cold air southward, resulting in a slow and gradual cooling, with a cooling rate (CR) of 1.34℃/day and 1.2℃/day in Eastern Asia, respectively. (2) In January 2021, the sudden collapse of BH caused the cold air to rapidly attack the mid-latitude region, with a CR of 1.87℃/day. By calculating the CR of the space, the temperature drop in 2021 is 38.8 % and 55% faster than those in 2016 and 2023, respectively. At the same time, the ET affects the wind direction of cold waves through the pressure. Before the cold waves occur, the meridional wind field near ET shows a negative value, forming a northwest or northeast wind, which continues to affect the southern part of East Asia. The meridional wind in January 2021 is stronger than those in 2016 and 2023, which is also the reason for the strong cold wave in 2021. Finally, the Empirical Orthogonal Function (EOF) analysis results of 1980-2023 show obvious BH-ET structure in three cold wave events. This climatological state provides a climatic background for the occurrence of cold waves.
Three extreme cold events have occurred in January 2016 and 2021, and December 2023 in Eastern Asia. As important factors in atmospheric circulation anomalies, the Blocking High and East Asian Trough (BH-ET) structure play key roles during the three extreme cold waves events. Among them, the BH affects the development of cold waves in two different ways: (1) Before the cold waves in 2016 and 2023, BH pushed the cold air southward, resulting in a slow and gradual cooling, with a cooling rate (CR) of 1.34℃/day and 1.2℃/day in Eastern Asia, respectively. (2) In January 2021, the sudden collapse of BH caused the cold air to rapidly attack the mid-latitude region, with a CR of 1.87℃/day. By calculating the CR of the space, the temperature drop in 2021 is 38.8 % and 55% faster than those in 2016 and 2023, respectively. At the same time, the ET affects the wind direction of cold waves through the pressure. Before the cold waves occur, the meridional wind field near ET shows a negative value, forming a northwest or northeast wind, which continues to affect the southern part of East Asia. The meridional wind in January 2021 is stronger than those in 2016 and 2023, which is also the reason for the strong cold wave in 2021. Finally, the Empirical Orthogonal Function (EOF) analysis results of 1980-2023 show obvious BH-ET structure in three cold wave events. This climatological state provides a climatic background for the occurrence of cold waves.
, Available online
, Manuscript accepted 03 July 2024, doi: 10.1007/s00376-024-4064-3
Abstract:
This study investigates extreme rainfall episodes along the eastern foothills of Taihang Mountains in North China from July 30 to August 1, 2023. It focuses on two types of extreme hourly rainfall rates (HRRs), i.e., the maximum regional-average HRR and site-observed HRR, which exhibited sequential development over southern, middle, and northern key regions. These rainfall extremes occurred in an environment where a high-pressure barrier over North China prevented cold air masses from the north, while a northward-moving typhoon remnant vortex and its associated low-level jet (LLJ) transported warm and moist airflow from the south. Two distinct echo evolution modes and convection initiation mechanisms are identified for the two types of extreme HRRs. The maximum regional-average HRR occurred when the LLJ arrived to the east of the key regions, while the maximum site-observed HRR occurred when the regions were influenced by the warmer vortex center. Taking the northern key region as a representation, at the time of the maximum regional-average HRR, slantwise ascent of the airflow along a warm-front-like boundary released symmetrical instability energy, resulting in stratiform rainfall with weak convective cores. The transport of locally initiated convection over the eastern plain region, where the atmospheric stratification was more potentially unstable, also contributed significantly. When the maximum site-observed HRR occurred, terrain lifting of warm and moist southeast airflow led to intense convection over the mountain foothills. Overall, the warm-core typhoon remnant vortex's passage and interaction with Taihang Mountains determined the timing and location of extreme HRRs across the key regions.
This study investigates extreme rainfall episodes along the eastern foothills of Taihang Mountains in North China from July 30 to August 1, 2023. It focuses on two types of extreme hourly rainfall rates (HRRs), i.e., the maximum regional-average HRR and site-observed HRR, which exhibited sequential development over southern, middle, and northern key regions. These rainfall extremes occurred in an environment where a high-pressure barrier over North China prevented cold air masses from the north, while a northward-moving typhoon remnant vortex and its associated low-level jet (LLJ) transported warm and moist airflow from the south. Two distinct echo evolution modes and convection initiation mechanisms are identified for the two types of extreme HRRs. The maximum regional-average HRR occurred when the LLJ arrived to the east of the key regions, while the maximum site-observed HRR occurred when the regions were influenced by the warmer vortex center. Taking the northern key region as a representation, at the time of the maximum regional-average HRR, slantwise ascent of the airflow along a warm-front-like boundary released symmetrical instability energy, resulting in stratiform rainfall with weak convective cores. The transport of locally initiated convection over the eastern plain region, where the atmospheric stratification was more potentially unstable, also contributed significantly. When the maximum site-observed HRR occurred, terrain lifting of warm and moist southeast airflow led to intense convection over the mountain foothills. Overall, the warm-core typhoon remnant vortex's passage and interaction with Taihang Mountains determined the timing and location of extreme HRRs across the key regions.
, Available online
, Manuscript accepted 02 July 2024, doi: 10.1007/s00376-024-3191-1
Abstract:
TC intensity estimation is an essential task in TC observation and forecasting. Deep learning models have recently been applied to estimate TC intensity from satellite images and produce accurate results. This work proposes the ViT-TC model based on the Vision Transformer (ViT) architecture built by the attention mechanism. Satellite images of TCs, including infrared (IR), water vapor (WV), and passive microwave (PMW), are used as inputs for TC intensity estimation. Experiments show that inputting a combination of IR, WV, and PMW can give a more accurate estimation than other combinations of input channels. The ensemble mean technique is applied and improves the model’s estimations to a root mean square error (RMSE) and mean absolute error (MAE) of 9.65 and 6.98 knots, which outperforms traditional methods and is comparable to existing deep learning models. The model assigns high attention weights to areas with high PMW, indicating that PMW magnitude is essential information for the model’s estimation. The model also gives high attention weights to non-cloud areas with high IR and WV, suggesting that the model detects the positive correlation between TC size and intensity and derives this feature from the non-cloud area over the edge of the sample.
TC intensity estimation is an essential task in TC observation and forecasting. Deep learning models have recently been applied to estimate TC intensity from satellite images and produce accurate results. This work proposes the ViT-TC model based on the Vision Transformer (ViT) architecture built by the attention mechanism. Satellite images of TCs, including infrared (IR), water vapor (WV), and passive microwave (PMW), are used as inputs for TC intensity estimation. Experiments show that inputting a combination of IR, WV, and PMW can give a more accurate estimation than other combinations of input channels. The ensemble mean technique is applied and improves the model’s estimations to a root mean square error (RMSE) and mean absolute error (MAE) of 9.65 and 6.98 knots, which outperforms traditional methods and is comparable to existing deep learning models. The model assigns high attention weights to areas with high PMW, indicating that PMW magnitude is essential information for the model’s estimation. The model also gives high attention weights to non-cloud areas with high IR and WV, suggesting that the model detects the positive correlation between TC size and intensity and derives this feature from the non-cloud area over the edge of the sample.
, Available online
, Manuscript accepted 02 July 2024, doi: 10.1007/s00376-024-3264-1
Abstract:
It is fundamental and useful to investigate how the Deep Learning forecasting Model (DLM) performs, compared to the Operational oceanography Forecast System (OFS). However, few studies have intercompared their performances using an identical reference. In this study, three physically reasonable DLMs have been implemented for the forecasting of Sea Surface Temperature (SST), Sea Level Anomaly (SLA) and sea surface velocity in the South China Sea. The DLMs have been validated against the testing dataset and the “OceanPredict” Class 4 dataset, respectively. The results show that the DLM’s RMSEs against the latter will increase by 44%, 245%, 302% and 109% for SST, SLA, current speed and direction, respectively, compared to those against the former. Therefore, different references have significant influences on the validation, and it is necessary to use identical and independent reference to intercompare the DLM and the OFS. Against the Class 4 dataset, the DLMs present significantly better performance for SLA than the OFSs, and slight better performances for other variables. The error patterns of the DLMs and the OFSs show high similarity, which is reasonable from the view of predictability, facilitating further applications of the DLMs. For extreme events of the SLA and speed, the DLMs and the OFSs both present large but similar forecast errors, while for extreme events of SST and direction, the DLMs likely give larger errors. This study provides an evaluation of forecast skills of commonly used DLMs, also provides an example to objectively intercompare different DLMs.
It is fundamental and useful to investigate how the Deep Learning forecasting Model (DLM) performs, compared to the Operational oceanography Forecast System (OFS). However, few studies have intercompared their performances using an identical reference. In this study, three physically reasonable DLMs have been implemented for the forecasting of Sea Surface Temperature (SST), Sea Level Anomaly (SLA) and sea surface velocity in the South China Sea. The DLMs have been validated against the testing dataset and the “OceanPredict” Class 4 dataset, respectively. The results show that the DLM’s RMSEs against the latter will increase by 44%, 245%, 302% and 109% for SST, SLA, current speed and direction, respectively, compared to those against the former. Therefore, different references have significant influences on the validation, and it is necessary to use identical and independent reference to intercompare the DLM and the OFS. Against the Class 4 dataset, the DLMs present significantly better performance for SLA than the OFSs, and slight better performances for other variables. The error patterns of the DLMs and the OFSs show high similarity, which is reasonable from the view of predictability, facilitating further applications of the DLMs. For extreme events of the SLA and speed, the DLMs and the OFSs both present large but similar forecast errors, while for extreme events of SST and direction, the DLMs likely give larger errors. This study provides an evaluation of forecast skills of commonly used DLMs, also provides an example to objectively intercompare different DLMs.
, Available online
, Manuscript accepted 24 June 2024, doi: 10.1007/s00376-024-4102-1
Abstract:
Nusantara, the new capital city of Indonesia, and its surrounding areas experienced intense heavy rainfall on 15−16 March, 2022, leading to devastating and widespread flooding. However, factors triggering such intense heavy rainfall and the underlying physical mechanisms are still not fully understood. Using high-resolution GSMaP data, we that mesoscale convective systems (MCSs) were the primary cause of the heavy rainfall event. The rainfall peak occurred during the MCS’s mature stage at 18Z on 15 March, 2022, and diminished as it entered the dissipation stage. To understand the large-scale environmental factors affecting the MCS event, we analyzed contributions from the MJO, equatorial waves, and low-frequency variability to column water vapor and moisture flux convergence. Results indicate a substantial influence of the MJO and equatorial waves on lower-level (boundary layer) meridional moisture flux convergence during the pre-MCS stage and initiation, with their contributions accounting for up to 80% during the growth phase. Moreover, while La Niña and the Asian monsoon had negligible impacts on MCS moisture supply, we find a large contribution from the residual term of water vapour budget during the maturation and decay phases of MCSs. This suggests that local forcing (such as small-scale convection, local evaporation, land-surface feedback, and topography) also contributes to modulate the intensity and duration of the MCS. The results of this study can help us to understand the potential causes of extreme rainfall in Nusantara and could be leveraged to improve rainstorm forecasting and risk management across the region in the future.
Nusantara, the new capital city of Indonesia, and its surrounding areas experienced intense heavy rainfall on 15−16 March, 2022, leading to devastating and widespread flooding. However, factors triggering such intense heavy rainfall and the underlying physical mechanisms are still not fully understood. Using high-resolution GSMaP data, we that mesoscale convective systems (MCSs) were the primary cause of the heavy rainfall event. The rainfall peak occurred during the MCS’s mature stage at 18Z on 15 March, 2022, and diminished as it entered the dissipation stage. To understand the large-scale environmental factors affecting the MCS event, we analyzed contributions from the MJO, equatorial waves, and low-frequency variability to column water vapor and moisture flux convergence. Results indicate a substantial influence of the MJO and equatorial waves on lower-level (boundary layer) meridional moisture flux convergence during the pre-MCS stage and initiation, with their contributions accounting for up to 80% during the growth phase. Moreover, while La Niña and the Asian monsoon had negligible impacts on MCS moisture supply, we find a large contribution from the residual term of water vapour budget during the maturation and decay phases of MCSs. This suggests that local forcing (such as small-scale convection, local evaporation, land-surface feedback, and topography) also contributes to modulate the intensity and duration of the MCS. The results of this study can help us to understand the potential causes of extreme rainfall in Nusantara and could be leveraged to improve rainstorm forecasting and risk management across the region in the future.
, Available online
, Manuscript accepted 24 June 2024, doi: 10.1007/s00376-024-4070-5
Abstract:
Accurate skin temperature is one of the critical factors in successfully assimilating satellite radiance data over land. However, the model simulated skin temperature may not be accurate enough. To address this issue, an extended skin temperature control variable (TSCV) approach was proposed in a variational assimilation framework which also considered the background error correlation between skin temperature and atmospheric variables. A series of single observation tests and a 10-day cycling assimilation experiment were conducted to evaluate the impact of the TSCV approach on the assimilation of AMSU-A and ATMS microwave temperature-sounding channels over land. The results of the single observation tests show that by applying the TSCV approach, not only the direct analysis of the skin temperature is realized, but also the interaction between the skin temperature and atmospheric variables can be achieved during the assimilation process. The results of the cycling experiment demonstrate that the TSCV approach improves the skin temperature analysis, which in turn reduces the root mean square error (RMSE) of the surface variables and low-level air temperature forecasts. The TSCV approach also reduces the difference between the observed and simulated brightness temperatures of both microwave and infrared window channels over land, suggesting that the approach can facilitate the radiance simulation of these channels, thus contributing to the assimilation of window channels.
Accurate skin temperature is one of the critical factors in successfully assimilating satellite radiance data over land. However, the model simulated skin temperature may not be accurate enough. To address this issue, an extended skin temperature control variable (TSCV) approach was proposed in a variational assimilation framework which also considered the background error correlation between skin temperature and atmospheric variables. A series of single observation tests and a 10-day cycling assimilation experiment were conducted to evaluate the impact of the TSCV approach on the assimilation of AMSU-A and ATMS microwave temperature-sounding channels over land. The results of the single observation tests show that by applying the TSCV approach, not only the direct analysis of the skin temperature is realized, but also the interaction between the skin temperature and atmospheric variables can be achieved during the assimilation process. The results of the cycling experiment demonstrate that the TSCV approach improves the skin temperature analysis, which in turn reduces the root mean square error (RMSE) of the surface variables and low-level air temperature forecasts. The TSCV approach also reduces the difference between the observed and simulated brightness temperatures of both microwave and infrared window channels over land, suggesting that the approach can facilitate the radiance simulation of these channels, thus contributing to the assimilation of window channels.
, Available online
, Manuscript accepted 21 June 2024, doi: 10.1007/s00376-024-4112-z
Abstract:
The Beijing 325 m meteorological tower stands as a pivotal research platform for exploring atmospheric boundary layer physics and atmospheric chemistry. With a legacy spanning 45 years, the tower has played a crucial role in unraveling the complexities of urban air pollution, atmospheric processes, and climate change in Beijing, China. This review paper provides a comprehensive overview of the measurements on the tower over the past two decades. Through long-term comprehensive observations, researchers have elucidated the intricate relationships between anthropogenic emissions, meteorological dynamics, and atmospheric composition, shedding light on the drivers of air pollution and its impacts on public health. The vertical measurements on the tower also enable detailed investigations into boundary layer dynamics, turbulent mixing, and pollutant dispersion, providing invaluable data for validating chemical transport models. Key findings from the tower's research include the identification of positive feedback mechanisms between aerosols and the boundary layer, the characterization of pollutant sources and transport pathways, the determination of fluxes of gaseous and particulate species, and the assessment of the effectiveness of pollution control measures. Additionally, isotopic measurements have provided new insights into the sources and formation processes of particulate matter and reactive nitrogen species. Finally, the paper outlines future directions for tower-based research, emphasizing the need for long-term comprehensive measurements, the development of innovative tower platforms, and integration of emerging technologies.
The Beijing 325 m meteorological tower stands as a pivotal research platform for exploring atmospheric boundary layer physics and atmospheric chemistry. With a legacy spanning 45 years, the tower has played a crucial role in unraveling the complexities of urban air pollution, atmospheric processes, and climate change in Beijing, China. This review paper provides a comprehensive overview of the measurements on the tower over the past two decades. Through long-term comprehensive observations, researchers have elucidated the intricate relationships between anthropogenic emissions, meteorological dynamics, and atmospheric composition, shedding light on the drivers of air pollution and its impacts on public health. The vertical measurements on the tower also enable detailed investigations into boundary layer dynamics, turbulent mixing, and pollutant dispersion, providing invaluable data for validating chemical transport models. Key findings from the tower's research include the identification of positive feedback mechanisms between aerosols and the boundary layer, the characterization of pollutant sources and transport pathways, the determination of fluxes of gaseous and particulate species, and the assessment of the effectiveness of pollution control measures. Additionally, isotopic measurements have provided new insights into the sources and formation processes of particulate matter and reactive nitrogen species. Finally, the paper outlines future directions for tower-based research, emphasizing the need for long-term comprehensive measurements, the development of innovative tower platforms, and integration of emerging technologies.
, Available online
, Manuscript accepted 19 June 2024, doi: 10.1007/s00376-024-3324-6
Abstract:
Climate change is an essential topic in climate science, and the accessibility of accurate,high-resolution datasets in recent years has facilitated the extraction of more insights from big-data resources. Nonetheless, current research predominantly focuses on mean-value changes and largely overlooks changes in the probability distribution. In this study, a novel method called Wasserstein Stability Analysis (WSA) is developed to identify probability density function (PDF) changes, especially the extreme event shift and nonlinear physical value constraint variation in climate change. WSA is applied to the early 21st century and compared with traditional mean-value trend analysis. The results indicate that despite no significant trend, the equatorial eastern Pacific experienced a decline in hot extremes and an increase in cold extremes, indicating a La Niña-like temperature shift. Further analysis at two Arctic locations suggests sea ice severely restricts the hot extremes of surface air temperature. This impact is diminishing as the sea ice melts. By revealing PDF shifts, WSA emerges as a powerful tool to re-examine climate change dynamics, providing enhanced data-driven insights for understanding climate evolution.
Climate change is an essential topic in climate science, and the accessibility of accurate,high-resolution datasets in recent years has facilitated the extraction of more insights from big-data resources. Nonetheless, current research predominantly focuses on mean-value changes and largely overlooks changes in the probability distribution. In this study, a novel method called Wasserstein Stability Analysis (WSA) is developed to identify probability density function (PDF) changes, especially the extreme event shift and nonlinear physical value constraint variation in climate change. WSA is applied to the early 21st century and compared with traditional mean-value trend analysis. The results indicate that despite no significant trend, the equatorial eastern Pacific experienced a decline in hot extremes and an increase in cold extremes, indicating a La Niña-like temperature shift. Further analysis at two Arctic locations suggests sea ice severely restricts the hot extremes of surface air temperature. This impact is diminishing as the sea ice melts. By revealing PDF shifts, WSA emerges as a powerful tool to re-examine climate change dynamics, providing enhanced data-driven insights for understanding climate evolution.
, Available online
, Manuscript accepted 19 June 2024, doi: 10.1007/s00376-024-4073-2
Abstract:
Land–atmosphere coupling and sea surface temperature (SST) anomalies both have essential impacts on weather and climate extremes. Based on the ERA5 reanalysis dataset and the CESM1.2.2 model, this study investigated the influence of land–atmosphere coupling on summer extreme hot-humid events (EHHE) over southern Eurasia under different SST backgrounds. The results suggested that coupling causes the near-surface air temperature increases exceeding 0.5 ℃. From 1961 to 2020, the frequency of EHHE has continuously increased and is closely related to soil moisture anomalies in the northern Indian Peninsula (IDP) and the middle and lower reaches of the Yangtze River (YRB). Numerical simulations further demonstrate that land–atmosphere coupling raises the risk of EHHE by 25.4%. In the typical El Niño SST background, intensified land–atmosphere coupling tends to produce notable increases in the frequency of EHHE. The dominant processes that land–atmosphere coupling affects the EHHE variations are evidently different between these two regions. Land surface thermal anomalies predominate in the IDP, while moisture conditions are more critical in the YRB. When warm SST anomalies exist, dry soil anomalies in the IDP are prominent, and evaporation is constrained, increasing sensible heat flux. Positive geopotential height anomalies are significant, combined with adiabatic warming induced by descending motion and a noticeable warm center in the near-surface atmosphere. The southward shift of the westerly jet enhances divergence over YRB. The anticyclonic circulation anomalies over the western Pacific are conducive to guiding moisture transport to the YRB, providing a favorable circulation background for the development of summer EHHE.
Land–atmosphere coupling and sea surface temperature (SST) anomalies both have essential impacts on weather and climate extremes. Based on the ERA5 reanalysis dataset and the CESM1.2.2 model, this study investigated the influence of land–atmosphere coupling on summer extreme hot-humid events (EHHE) over southern Eurasia under different SST backgrounds. The results suggested that coupling causes the near-surface air temperature increases exceeding 0.5 ℃. From 1961 to 2020, the frequency of EHHE has continuously increased and is closely related to soil moisture anomalies in the northern Indian Peninsula (IDP) and the middle and lower reaches of the Yangtze River (YRB). Numerical simulations further demonstrate that land–atmosphere coupling raises the risk of EHHE by 25.4%. In the typical El Niño SST background, intensified land–atmosphere coupling tends to produce notable increases in the frequency of EHHE. The dominant processes that land–atmosphere coupling affects the EHHE variations are evidently different between these two regions. Land surface thermal anomalies predominate in the IDP, while moisture conditions are more critical in the YRB. When warm SST anomalies exist, dry soil anomalies in the IDP are prominent, and evaporation is constrained, increasing sensible heat flux. Positive geopotential height anomalies are significant, combined with adiabatic warming induced by descending motion and a noticeable warm center in the near-surface atmosphere. The southward shift of the westerly jet enhances divergence over YRB. The anticyclonic circulation anomalies over the western Pacific are conducive to guiding moisture transport to the YRB, providing a favorable circulation background for the development of summer EHHE.
, Available online
, Manuscript accepted 07 June 2024, doi: 10.1007/s00376-024-4046-5
Abstract:
Scientific knowledge on the chemical compositions of fine particulate matter (PM<sub>2.5</sub>) is essential for properly assessing its health and climate effects, and for decision makers to develop efficient mitigation strategies. A high-resolution PM<sub>2.5</sub> chemical composition dataset (CAQRA-aerosol) is developed in this study which provides hourly maps of organic carbon, black carbon, ammonium, nitrate and sulfate in China from 2013 to 2020 with a horizontal resolution of 15 km. This paper describes the method, access and validation results of this dataset. It shows that the CAQRA-aerosol has good consistency with the observations that achieves higher or comparable accuracy with previous PM<sub>2.5</sub> composition datasets. Based on CAQRA-aerosol, spatiotemporal changes of different PM<sub>2.5</sub> compositions were investigated from a national viewpoint, which emphasizes different changes of nitrate from other compositions. The estimated annual rate of population-weighted concentrations of nitrate is 0.23 μg∙m<sup>-3</sup>∙a<sup>-1</sup> from 2015 to 2020, compared with the −0.19 to −1.1 μg∙m<sup>-3</sup>∙a<sup>-1</sup> for other compositions. The whole dataset is freely available from the China Air Pollution Data Center (https://doi.org/10.12423/capdb_PKU.2023.DA).
Scientific knowledge on the chemical compositions of fine particulate matter (PM<sub>2.5</sub>) is essential for properly assessing its health and climate effects, and for decision makers to develop efficient mitigation strategies. A high-resolution PM<sub>2.5</sub> chemical composition dataset (CAQRA-aerosol) is developed in this study which provides hourly maps of organic carbon, black carbon, ammonium, nitrate and sulfate in China from 2013 to 2020 with a horizontal resolution of 15 km. This paper describes the method, access and validation results of this dataset. It shows that the CAQRA-aerosol has good consistency with the observations that achieves higher or comparable accuracy with previous PM<sub>2.5</sub> composition datasets. Based on CAQRA-aerosol, spatiotemporal changes of different PM<sub>2.5</sub> compositions were investigated from a national viewpoint, which emphasizes different changes of nitrate from other compositions. The estimated annual rate of population-weighted concentrations of nitrate is 0.23 μg∙m<sup>-3</sup>∙a<sup>-1</sup> from 2015 to 2020, compared with the −0.19 to −1.1 μg∙m<sup>-3</sup>∙a<sup>-1</sup> for other compositions. The whole dataset is freely available from the China Air Pollution Data Center (https://doi.org/10.12423/capdb_PKU.2023.DA).
, Available online
, Manuscript accepted 06 June 2024, doi: 10.1007/s00376-024-3214-y
Abstract:
Understanding characteristics of heat exchange and evaporation variation of lakes is important for regional water resource management and sustainable development. Based on eddy covariance measurement over Lake Vanajavesi in southern Finland, characteristics of energy fluxes and cold fronts effects on energy exchange were investigated. The lake acted as a heat sink in spring and summer and a heat source in winter. The latent heat flux reached minimum value in the morning, and peaked in the afternoon. The diurnal variation of sensible heat flux was opposite to that of latent heat flux. Impact factors for the sensible heat flux were mainly the lake-air temperature difference, and lake-air temperature difference multiply by wind speed. The latent heat flux was mainly affected by vapor pressure deficit, and vapor pressure deficit multiply by wind speed. The annual mean values of bulk transfer coefficients for momentum, heat, and water vapor were 1.98×10-3, 1.62×10-3, and 1.31×10-3 respectively. Bulk transfer coefficients for heat and water vapor were not equal, indicating parameterization of energy exchange in numerical models, where assume heat coefficient equals to water vapor coefficient, need to be improved. During ice-free season, cold fronts resulted in 28 sensible heat pulse and 17 latent heat pulse, contributing to 50.59% and 34.89% of sensible and latent heat exchange in Lake Vanajavesi. The results indicated cold fronts have significant impacts on the surface energy budget and evaporation over lakes.
Understanding characteristics of heat exchange and evaporation variation of lakes is important for regional water resource management and sustainable development. Based on eddy covariance measurement over Lake Vanajavesi in southern Finland, characteristics of energy fluxes and cold fronts effects on energy exchange were investigated. The lake acted as a heat sink in spring and summer and a heat source in winter. The latent heat flux reached minimum value in the morning, and peaked in the afternoon. The diurnal variation of sensible heat flux was opposite to that of latent heat flux. Impact factors for the sensible heat flux were mainly the lake-air temperature difference, and lake-air temperature difference multiply by wind speed. The latent heat flux was mainly affected by vapor pressure deficit, and vapor pressure deficit multiply by wind speed. The annual mean values of bulk transfer coefficients for momentum, heat, and water vapor were 1.98×10-3, 1.62×10-3, and 1.31×10-3 respectively. Bulk transfer coefficients for heat and water vapor were not equal, indicating parameterization of energy exchange in numerical models, where assume heat coefficient equals to water vapor coefficient, need to be improved. During ice-free season, cold fronts resulted in 28 sensible heat pulse and 17 latent heat pulse, contributing to 50.59% and 34.89% of sensible and latent heat exchange in Lake Vanajavesi. The results indicated cold fronts have significant impacts on the surface energy budget and evaporation over lakes.
, Available online
, Manuscript accepted 05 June 2024, doi: 10.1007/s00376-024-4060-7
Abstract:
Persistent (5-day or longer) extreme cold events (ECEs) over northeastern China during the boreal winter of 1979-2020 are investigated using daily minimum temperature (Tmin) from the China Meteorological Data Network. The extreme cooling area and intensity indices associated with the ECEs exhibit a dominant 10-40-day periodicity. The link of the ECEs to atmospheric intraseasonal oscillations (ISOs) is further examined. The ECEs may be categorized into W- and N- type. In the former, the low-frequency cooling associated with the ISO mode first penetrates into the western boundary of the northeastern China region and gradually occupies over the entire domain at its peak phase. The upper-tropospheric circulation associated with this type is characterized by a northwest-southeast oriented Rossby wave train, expanding from the Ural Mountains to the western Pacific Ocean. In the latter, the cooling invades over the northern boundary first and then penetrates into the entire region. The precursory signal in the upper troposphere associated with this type is a zonally oriented negative geopotential height anomaly, which moves southward. A downward-propagating signal was observed in the stratospheric potential vorticity field prior to the peak cooling, implying a possible stratospheric impact. In addition to the aforementioned W- and N- types, ECEs may occur in a local region over either northern or southern northeastern China.
Persistent (5-day or longer) extreme cold events (ECEs) over northeastern China during the boreal winter of 1979-2020 are investigated using daily minimum temperature (Tmin) from the China Meteorological Data Network. The extreme cooling area and intensity indices associated with the ECEs exhibit a dominant 10-40-day periodicity. The link of the ECEs to atmospheric intraseasonal oscillations (ISOs) is further examined. The ECEs may be categorized into W- and N- type. In the former, the low-frequency cooling associated with the ISO mode first penetrates into the western boundary of the northeastern China region and gradually occupies over the entire domain at its peak phase. The upper-tropospheric circulation associated with this type is characterized by a northwest-southeast oriented Rossby wave train, expanding from the Ural Mountains to the western Pacific Ocean. In the latter, the cooling invades over the northern boundary first and then penetrates into the entire region. The precursory signal in the upper troposphere associated with this type is a zonally oriented negative geopotential height anomaly, which moves southward. A downward-propagating signal was observed in the stratospheric potential vorticity field prior to the peak cooling, implying a possible stratospheric impact. In addition to the aforementioned W- and N- types, ECEs may occur in a local region over either northern or southern northeastern China.
, Available online
, Manuscript accepted 04 June 2024, doi: 10.1007/s00376-024-3256-1
Abstract:
Northern China is a prominent "hotspot" for land−atmosphere interactions, with substantial gradients in both moisture and thermal conditions. Previous studies identified a link between land−atmosphere coupling and the individual roles of each factor, but the synergistic effect of the two factors remains unclear. This study considered the co-variation of evapotranspiration and precipitation to assess evapotranspiration−precipitation (ET−P) coupling across northern China, exploring its spatial variations and their linkage to water and heat factors. Our findings reveal a transition from strongly positive coupling in the northwest to weakly negative coupling in the southeast, peaking in spring. These spatial variations were attributed to water (soil moisture) and heat (air temperature), which explained 39% and 25% of the variability, respectively. The aridity index (AI), a water−heat synergy factor, was the dominant factor, explaining 66% of the spatial variation in ET−P coupling. As the AI increased, ET−P coupling shifted from strongly positive to weakly negative, with an AI around 0.7. This shift was determined by a shift in the evapotranspiration−lifting condensation level (LCL) coupling under an AI change. Regions with an AI below 0.7 experienced water-limited evapotranspiration, where increased soil moisture enhanced evapotranspiration, reduced sensible heat (H), and lowered LCL, resulting in a negative ET-LCL coupling. Conversely, regions with an AI above 0.7 experienced energy-limited evapotranspiration, where the positive ET−LCL coupling reflected a positive H−LCL coupling or a positive impact of LCL on evapotranspiration. This analysis advanced our understanding of the intricate influences of multi-factor surface interactions on the spatial variations of land−atmosphere coupling.
Northern China is a prominent "hotspot" for land−atmosphere interactions, with substantial gradients in both moisture and thermal conditions. Previous studies identified a link between land−atmosphere coupling and the individual roles of each factor, but the synergistic effect of the two factors remains unclear. This study considered the co-variation of evapotranspiration and precipitation to assess evapotranspiration−precipitation (ET−P) coupling across northern China, exploring its spatial variations and their linkage to water and heat factors. Our findings reveal a transition from strongly positive coupling in the northwest to weakly negative coupling in the southeast, peaking in spring. These spatial variations were attributed to water (soil moisture) and heat (air temperature), which explained 39% and 25% of the variability, respectively. The aridity index (AI), a water−heat synergy factor, was the dominant factor, explaining 66% of the spatial variation in ET−P coupling. As the AI increased, ET−P coupling shifted from strongly positive to weakly negative, with an AI around 0.7. This shift was determined by a shift in the evapotranspiration−lifting condensation level (LCL) coupling under an AI change. Regions with an AI below 0.7 experienced water-limited evapotranspiration, where increased soil moisture enhanced evapotranspiration, reduced sensible heat (H), and lowered LCL, resulting in a negative ET-LCL coupling. Conversely, regions with an AI above 0.7 experienced energy-limited evapotranspiration, where the positive ET−LCL coupling reflected a positive H−LCL coupling or a positive impact of LCL on evapotranspiration. This analysis advanced our understanding of the intricate influences of multi-factor surface interactions on the spatial variations of land−atmosphere coupling.
, Available online
, Manuscript accepted 03 June 2024, doi: 10.1007/s00376-024-3209-8
Abstract:
This study examines low-level jets (LLJs) across Northeastern China during both warm (June to September) and cold seasons (December to March) from 1957 to 2021, using fifth generation European Centre for Medium-Range Weather Forecasts reanalysis data with 25-km resolution. LLJs manifest in two prominent regions: one along the leeward flank of the Greater Khingan Mountains in the cold season and another at the center of Northeastern China in the warm season. The intricate interplay between ambient circulation and terrain shapes LLJ distribution, altitudes, wind directions, diurnal cycles, and seasonal diversities. During the warm season, prevailing southwesterly LLJs are found at 925 hPa, while the cold season features stronger and more frequent northwesterly LLJs at 875 hPa. Analysis of the diurnal patterns reveals distinctive behaviors of LLJs between the cold and warm seasons. During the warm season, the single peak in LLJ occurrence emerges around midnight, conversely, in the cold season, LLJs are most frequent shortly before midnight with an additional sub-peak in the morning. A momentum budget analysis establishes mechanisms underlying these two distinct diurnal variations. In both seasons, the diurnal variation of LLJs is predominately driven by inertial oscillation and mountain-valley circulation. However, the sub-peak observed in the cold-season morning arises from the (thermo-)dynamic interaction between the low-level atmosphere and complex terrain.
This study examines low-level jets (LLJs) across Northeastern China during both warm (June to September) and cold seasons (December to March) from 1957 to 2021, using fifth generation European Centre for Medium-Range Weather Forecasts reanalysis data with 25-km resolution. LLJs manifest in two prominent regions: one along the leeward flank of the Greater Khingan Mountains in the cold season and another at the center of Northeastern China in the warm season. The intricate interplay between ambient circulation and terrain shapes LLJ distribution, altitudes, wind directions, diurnal cycles, and seasonal diversities. During the warm season, prevailing southwesterly LLJs are found at 925 hPa, while the cold season features stronger and more frequent northwesterly LLJs at 875 hPa. Analysis of the diurnal patterns reveals distinctive behaviors of LLJs between the cold and warm seasons. During the warm season, the single peak in LLJ occurrence emerges around midnight, conversely, in the cold season, LLJs are most frequent shortly before midnight with an additional sub-peak in the morning. A momentum budget analysis establishes mechanisms underlying these two distinct diurnal variations. In both seasons, the diurnal variation of LLJs is predominately driven by inertial oscillation and mountain-valley circulation. However, the sub-peak observed in the cold-season morning arises from the (thermo-)dynamic interaction between the low-level atmosphere and complex terrain.
, Available online
, Manuscript accepted 03 June 2024, doi: 10.1007/s00376-024-3377-6
Abstract:
In this study, we examined the dynamics and microphysical structures of a deep compact thunderstorm event driving cloud-to-ground (CG) lightning over the Nanjing area located within the Yangtze-Huai River Basin (YHRB) during the monsoon break period. The microphysical structures combined with the dynamics in the glaciated, mixed-phase, and warm phase layers during the formative, intensifying, and mature stages of the thunderstorm were first investigated using C-band polarimetric radar and CG lightning observations. The results showed thunderstorm during the mature stage produced a local cold pool, which collided with southerly warm wind, resulting in strong updraft. The strong updraft favored the lifting of raindrops to the mixed-phase region to form abundant supercooled liquid water and graupel. From the formative stage to the developing stage and further to the mature stage, increased ZH and reduced ZDR within the mixed-phase region has been found, especially within the strong updraft region (> 5 m s-1). This phenomenon suggested the evolution process of supercooled raindrops to large hydrometeors (graupel and hail), indicating strong riming process. These signatures showed a favorable environment for thunderstorm electrification, and resulting in most frequent lightning during the thunderstorm life cycle.
In this study, we examined the dynamics and microphysical structures of a deep compact thunderstorm event driving cloud-to-ground (CG) lightning over the Nanjing area located within the Yangtze-Huai River Basin (YHRB) during the monsoon break period. The microphysical structures combined with the dynamics in the glaciated, mixed-phase, and warm phase layers during the formative, intensifying, and mature stages of the thunderstorm were first investigated using C-band polarimetric radar and CG lightning observations. The results showed thunderstorm during the mature stage produced a local cold pool, which collided with southerly warm wind, resulting in strong updraft. The strong updraft favored the lifting of raindrops to the mixed-phase region to form abundant supercooled liquid water and graupel. From the formative stage to the developing stage and further to the mature stage, increased ZH and reduced ZDR within the mixed-phase region has been found, especially within the strong updraft region (> 5 m s-1). This phenomenon suggested the evolution process of supercooled raindrops to large hydrometeors (graupel and hail), indicating strong riming process. These signatures showed a favorable environment for thunderstorm electrification, and resulting in most frequent lightning during the thunderstorm life cycle.
, Available online
, Manuscript accepted 03 June 2024, doi: 10.1007/s00376-024-3319-3
Abstract:
Grid forecasting can be used to effectively enhance the spatial and temporal density of forecast products, thereby improving the capability of short-term marine disaster forecasting and warnings in terms of proximity. The traditional method that relies on forecasters' subjective correction of station observation data for forecasting has been unable to meet the practical needs of refined forecasting. To address this problem, this paper proposes a transformer-enhanced UNet (TransUNet) model for wave forecast artificial intelligence (AI) correction, which fuses wind and wave information. The Transformer structure is integrated into the encoder of the UNet model, and instead of using the traditional upsampling method, the dual-sampling module is employed in the decoder to enhance feature extraction capability. This paper compares the TransUNet model with the traditional UNet model using wind speed forecast data, wave height forecast data, and significant wave height reanalysis data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). The experimental results indicate that the TransUNet model yields smaller root-mean-square errors, mean errors, and standard deviations of the corrected results for the next 24-hour forecasts than does the UNet model. Specifically, the root-mean-square error decreased by more than 21.55% compared to its precorrection value. According to the statistical analysis, 87.81% of the corrected wave height errors for the next 24-hour forecast were within ±0.2 m, with only 4.56% falling beyond ±0.3 m. This model effectively limits the error range and enhances the ability to forecast wave heights.
Grid forecasting can be used to effectively enhance the spatial and temporal density of forecast products, thereby improving the capability of short-term marine disaster forecasting and warnings in terms of proximity. The traditional method that relies on forecasters' subjective correction of station observation data for forecasting has been unable to meet the practical needs of refined forecasting. To address this problem, this paper proposes a transformer-enhanced UNet (TransUNet) model for wave forecast artificial intelligence (AI) correction, which fuses wind and wave information. The Transformer structure is integrated into the encoder of the UNet model, and instead of using the traditional upsampling method, the dual-sampling module is employed in the decoder to enhance feature extraction capability. This paper compares the TransUNet model with the traditional UNet model using wind speed forecast data, wave height forecast data, and significant wave height reanalysis data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). The experimental results indicate that the TransUNet model yields smaller root-mean-square errors, mean errors, and standard deviations of the corrected results for the next 24-hour forecasts than does the UNet model. Specifically, the root-mean-square error decreased by more than 21.55% compared to its precorrection value. According to the statistical analysis, 87.81% of the corrected wave height errors for the next 24-hour forecast were within ±0.2 m, with only 4.56% falling beyond ±0.3 m. This model effectively limits the error range and enhances the ability to forecast wave heights.
, Available online
, Manuscript accepted 27 May 2024, doi: 10.1007/s00376-024-3344-2
Abstract:
Idealized numerical simulations have been carried out to reveal the complexity in the development of asymmetric convection in a tropical cyclone (TC) under the influence of an environment with either uniform flow, vertical wind shear (VWS), or both. Results show that rainwater is enhanced to the right of the motion in the outer rainband but upshear-left in the inner-core region. Further, due to the asymmetries introduced by the environment flow, wavenumber-1 temperature and height anomalies develop at a radius of ~1000 km at the upper levels. A sub-vortex aside from the TC center encompassing the wavenumber-1 warm center appears, and asymmetric horizontal winds emerge, which in turn changes the storm-scale (within 400 km) VWS. Deep convection in the inner core follows closely the changing storm-scale VWS when its magnitude is larger than 2 m s-1 and is located downshear of storm-scale VWS in all the experiments with environmental flow. In the outer rainbands, the maximum boundary layer convergence is mostly controlled by the motion direction and located in the rear-right quadrant. These results extend the previous studies on three aspects: (1) The discovery of the roughly linear combination effect from uniform flow and large-scale VWS. (2) The development of upper-level asymmetric winds on a thousand-kilometer scale through the interaction between the TC vortex and environmental flow, resulting in changes in the storm-scale VWS pattern within the TC area. (3) The revelation that TC asymmetric convection closely aligns with the direction-varying storm-scale VWS instead of the initially designated VWS.
Idealized numerical simulations have been carried out to reveal the complexity in the development of asymmetric convection in a tropical cyclone (TC) under the influence of an environment with either uniform flow, vertical wind shear (VWS), or both. Results show that rainwater is enhanced to the right of the motion in the outer rainband but upshear-left in the inner-core region. Further, due to the asymmetries introduced by the environment flow, wavenumber-1 temperature and height anomalies develop at a radius of ~1000 km at the upper levels. A sub-vortex aside from the TC center encompassing the wavenumber-1 warm center appears, and asymmetric horizontal winds emerge, which in turn changes the storm-scale (within 400 km) VWS. Deep convection in the inner core follows closely the changing storm-scale VWS when its magnitude is larger than 2 m s-1 and is located downshear of storm-scale VWS in all the experiments with environmental flow. In the outer rainbands, the maximum boundary layer convergence is mostly controlled by the motion direction and located in the rear-right quadrant. These results extend the previous studies on three aspects: (1) The discovery of the roughly linear combination effect from uniform flow and large-scale VWS. (2) The development of upper-level asymmetric winds on a thousand-kilometer scale through the interaction between the TC vortex and environmental flow, resulting in changes in the storm-scale VWS pattern within the TC area. (3) The revelation that TC asymmetric convection closely aligns with the direction-varying storm-scale VWS instead of the initially designated VWS.
, Available online
, Manuscript accepted 27 May 2024, doi: 10.1007/s00376-024-3221-z
Abstract:
Cloud vertical structure (CVS) strongly affects atmospheric circulation and cloud radiative transfer, yet long term ground-based observations are scarce over the Tibetan Plateau (TP) despite its vital role in global climate. This study utilizes ground-based lidar and Ka-band cloud profiling radar (KaCR) measurements at Yangbajing (YBJ), TP, from October 2021 to September 2022, to characterize cloud properties. A novel anomaly detection algorithm (LevelShiftAD) is proposed for lidar and KaCR profiles to identify cloud boundaries, demonstrating satisfactory performance. Cloud base heights (CBH) retrieved from KaCR and lidar observations show good consistency, with a correlation coefficient of 0.78 and a mean difference of -0.06 km. Cloud top heights (CTH) derived from KaCR match well with FengYun-4A and Himawari-8 products. Thus, KaCR measurements serve as the primary dataset for investigating CVSs over the TP. Different diurnal cycles occur in summer and winter. Diurnal cycle exhibits pronounced increase of cloud occurrence frequency in the afternoon and decrease in early morning in winter, while cloud amount remains high all the day, with scattered increases at night in summer. Summer features more frequent clouds with larger geometrical thickness, higher multi-layer ratio, and greater inter-cloud spacing. Around 26% of cloud bases occur below 0.5 km. Winter exhibits bimodal distribution of cloud base heights at 0-0.5 km and 2-2.5 km. Single-layer and geometrically thin clouds prevail at YBJ. This study enriches long-term measurements of CVS over the TP, and the robust anomaly detection method helps quantify cloud macro-physical properties via synergistic lidar and radar observations.
Cloud vertical structure (CVS) strongly affects atmospheric circulation and cloud radiative transfer, yet long term ground-based observations are scarce over the Tibetan Plateau (TP) despite its vital role in global climate. This study utilizes ground-based lidar and Ka-band cloud profiling radar (KaCR) measurements at Yangbajing (YBJ), TP, from October 2021 to September 2022, to characterize cloud properties. A novel anomaly detection algorithm (LevelShiftAD) is proposed for lidar and KaCR profiles to identify cloud boundaries, demonstrating satisfactory performance. Cloud base heights (CBH) retrieved from KaCR and lidar observations show good consistency, with a correlation coefficient of 0.78 and a mean difference of -0.06 km. Cloud top heights (CTH) derived from KaCR match well with FengYun-4A and Himawari-8 products. Thus, KaCR measurements serve as the primary dataset for investigating CVSs over the TP. Different diurnal cycles occur in summer and winter. Diurnal cycle exhibits pronounced increase of cloud occurrence frequency in the afternoon and decrease in early morning in winter, while cloud amount remains high all the day, with scattered increases at night in summer. Summer features more frequent clouds with larger geometrical thickness, higher multi-layer ratio, and greater inter-cloud spacing. Around 26% of cloud bases occur below 0.5 km. Winter exhibits bimodal distribution of cloud base heights at 0-0.5 km and 2-2.5 km. Single-layer and geometrically thin clouds prevail at YBJ. This study enriches long-term measurements of CVS over the TP, and the robust anomaly detection method helps quantify cloud macro-physical properties via synergistic lidar and radar observations.
, Available online
, Manuscript accepted 21 May 2024, doi: 10.1007/s00376-024-3312-x
Abstract:
The three-dimensional ocean subsurface temperature and salinity structures (OST/OSS) in the South China Sea (SCS) play crucial roles in oceanic climate research and disaster mitigation. Traditionally, the real-time OST and OSS are mainly obtained through in-situ ocean observations and simulation by ocean circulation models, which are usu-ally challenging and costly. Recently dynamical, statistical or machine learning models are proposed to invert the OST/OSS from sea surface information, however, these models mainly focused on the inversion of monthly OST and OSS. To address this issue, we apply clustering algorithms and employ a stacking strategy to ensemble three models (XGBoost, Random Forest, and LightGBM), to invert the real-time OST/OSS based on satellite-derived data and Argo dataset. Subsequently, a fusion of temperature and sa-linity is employed to reconstruct the OST and OSS. In the validation dataset, the depth-averaged Correlation (Corr) of estimated OST (OSS) is 0.919 (0.83) and an av-erage Root Mean Square Error (RMSE) is 0.639℃ (0.087 psu), with a depth-averaged coefficient of determination (R^2) of 0.84 (0.68). Notably, at the thermocline where the base models exhibit maximum error, stacking-based fusion model exhibited significant performance enhancement, with a maximum enhancement in OST and OSS inversion exceeding 10%. We further found that the estimated OST and OSS exhibits good agreement with the HYCOM data and BOA_Argo dataset during the passage of a mesoscale eddy. This study shows that the model we proposed can effectively invert the real-time OST and OSS, thereby potentially en-hancing the understanding of multi-scale oceanic processes in the SCS.
The three-dimensional ocean subsurface temperature and salinity structures (OST/OSS) in the South China Sea (SCS) play crucial roles in oceanic climate research and disaster mitigation. Traditionally, the real-time OST and OSS are mainly obtained through in-situ ocean observations and simulation by ocean circulation models, which are usu-ally challenging and costly. Recently dynamical, statistical or machine learning models are proposed to invert the OST/OSS from sea surface information, however, these models mainly focused on the inversion of monthly OST and OSS. To address this issue, we apply clustering algorithms and employ a stacking strategy to ensemble three models (XGBoost, Random Forest, and LightGBM), to invert the real-time OST/OSS based on satellite-derived data and Argo dataset. Subsequently, a fusion of temperature and sa-linity is employed to reconstruct the OST and OSS. In the validation dataset, the depth-averaged Correlation (Corr) of estimated OST (OSS) is 0.919 (0.83) and an av-erage Root Mean Square Error (RMSE) is 0.639℃ (0.087 psu), with a depth-averaged coefficient of determination (R^2) of 0.84 (0.68). Notably, at the thermocline where the base models exhibit maximum error, stacking-based fusion model exhibited significant performance enhancement, with a maximum enhancement in OST and OSS inversion exceeding 10%. We further found that the estimated OST and OSS exhibits good agreement with the HYCOM data and BOA_Argo dataset during the passage of a mesoscale eddy. This study shows that the model we proposed can effectively invert the real-time OST and OSS, thereby potentially en-hancing the understanding of multi-scale oceanic processes in the SCS.
, Available online
, Manuscript accepted 21 May 2024, doi: 10.1007/s00376-024-3367-8
Abstract:
Control of desertification can not only ameliorate the natural environment of arid regions but also convert desertified land into significant terrestrial carbon sinks, thereby bolstering the carbon sequestration capacity of arid ecosystems. However, longstanding neglect of the potential carbon sink benefits of desertification management, and its relationship with environmental factors has limited the exploration of carbon sequestration potential. Based on CO2 flux and environmental factors of artificial protective forest in Taklamakan Desert (TD) from 2018 to 2019, we found that the carbon storage capacity of the desert ecosystem increased approximately 140-fold after the establishment of an artificial shelter forest in the desert, due to plant photosynthesis. Precipitation levels < 2 mm had no impact on carbon exchange in the artificial shelter forest, whereas a precipitation level of approximately 4 mm stimulated a decrease in the vapour pressure deficit over a short period of about three days, promoting photosynthesis and enhancing the carbon absorption of the artificial shelter forest. Precipitation events > 8 mm stimulated soil respiration to release CO2 and promoted plant photosynthesis. In the dynamic equilibrium where precipitation stimulates both soil respiration and photosynthesis, there is a significant threshold value of soil moisture at 5 cm (0.12 m3 m-3), which can serve as a good indicator of the strength of the stimulatory effect of precipitation on both. These results provide important data support for quantifying the contribution of artificial afforestation to carbon sequestration in arid areas, and provide guidance for the development and implementation of artificial forest management measures.
Control of desertification can not only ameliorate the natural environment of arid regions but also convert desertified land into significant terrestrial carbon sinks, thereby bolstering the carbon sequestration capacity of arid ecosystems. However, longstanding neglect of the potential carbon sink benefits of desertification management, and its relationship with environmental factors has limited the exploration of carbon sequestration potential. Based on CO2 flux and environmental factors of artificial protective forest in Taklamakan Desert (TD) from 2018 to 2019, we found that the carbon storage capacity of the desert ecosystem increased approximately 140-fold after the establishment of an artificial shelter forest in the desert, due to plant photosynthesis. Precipitation levels < 2 mm had no impact on carbon exchange in the artificial shelter forest, whereas a precipitation level of approximately 4 mm stimulated a decrease in the vapour pressure deficit over a short period of about three days, promoting photosynthesis and enhancing the carbon absorption of the artificial shelter forest. Precipitation events > 8 mm stimulated soil respiration to release CO2 and promoted plant photosynthesis. In the dynamic equilibrium where precipitation stimulates both soil respiration and photosynthesis, there is a significant threshold value of soil moisture at 5 cm (0.12 m3 m-3), which can serve as a good indicator of the strength of the stimulatory effect of precipitation on both. These results provide important data support for quantifying the contribution of artificial afforestation to carbon sequestration in arid areas, and provide guidance for the development and implementation of artificial forest management measures.
, Available online
, Manuscript accepted 21 May 2024, doi: 10.1007/s00376-024-3301-0
Abstract:
Tropical cyclone (TC) is one of the greatest natural disasters. Accurate TC activity predictions are key to disaster prevention and mitigation. Recently, TC track prediction has made significant progress, but the improvement in intensity prediction is obviously lagging behind. At present, research on TC intensity prediction takes atmospheric reanalysis data as the research object and mines the relationship between TC-related environmental factors and intensity through deep learning. However, the reanalysis data are non-real-time in nature, which does not meet the requirements for operational forecasting applications. Therefore, a TC intensity prediction model named Inten-Pre is proposed, which can simultaneously extract the symmetry degree of strong TC convective cloud and convection intensity, and fuse the deviation-angle variance with satellite images to construct the correlation between TC convection structure and intensity. For TCs’ complex dynamic processes, a convolution neural network (CNN) is used to learn their temporal and spatial features. For real-time intensity estimation, multi-task learning acts as an implicit time-series enhancement. The model is designed with a rolling strategy that aims to moderate the long-term dependent decay problem and improve accuracy for short-term intensity predictions. Since multiple tasks are correlated, the loss function of 12 h and 24 h are corrected. After testing on a sample of TCs in the Northwest Pacific, with a 4.48 kts root mean square error (RMSE) of 6 h intensity prediction, 5.78 kts for 12 h and 13.94 kts for 24 h. TC records from official agencies are used to assess the validity of the Inten-Pre.
Tropical cyclone (TC) is one of the greatest natural disasters. Accurate TC activity predictions are key to disaster prevention and mitigation. Recently, TC track prediction has made significant progress, but the improvement in intensity prediction is obviously lagging behind. At present, research on TC intensity prediction takes atmospheric reanalysis data as the research object and mines the relationship between TC-related environmental factors and intensity through deep learning. However, the reanalysis data are non-real-time in nature, which does not meet the requirements for operational forecasting applications. Therefore, a TC intensity prediction model named Inten-Pre is proposed, which can simultaneously extract the symmetry degree of strong TC convective cloud and convection intensity, and fuse the deviation-angle variance with satellite images to construct the correlation between TC convection structure and intensity. For TCs’ complex dynamic processes, a convolution neural network (CNN) is used to learn their temporal and spatial features. For real-time intensity estimation, multi-task learning acts as an implicit time-series enhancement. The model is designed with a rolling strategy that aims to moderate the long-term dependent decay problem and improve accuracy for short-term intensity predictions. Since multiple tasks are correlated, the loss function of 12 h and 24 h are corrected. After testing on a sample of TCs in the Northwest Pacific, with a 4.48 kts root mean square error (RMSE) of 6 h intensity prediction, 5.78 kts for 12 h and 13.94 kts for 24 h. TC records from official agencies are used to assess the validity of the Inten-Pre.
, Available online
, Manuscript accepted 21 May 2024, doi: 10.1007/s00376-024-3222-y
Abstract:
Aerosol optical depth (AOD) and fine particulate matter with a diameter of less than 2.5μm (PM2.5) play crucial roles in air quality, human health, and climate change. However, the complex correlation of AOD–PM2.5 and the limitations of existing algorithms pose a significant challenge in realizing the accurate joint retrieval of these two parameters at the same location. On this point, a multi-task learning (MTL) model, which enables the joint retrieval of PM2.5 concentration and AOD, is proposed and applied on the top-of-the-atmosphere reflectance (TOAR) data gathered by the Fengyun-4A Advanced Geosynchronous Radiation Imager (FY-4A AGRI), and compared to that of two single-task learning (STL) models, namely, random forest (RF) and deep neural network (DNN). Specifically, The MTL model achieved a coefficient of determination (R2) of 0.88 and a root mean square error (RMSE) of 0.10 in AOD retrieval. In comparison to the RF model, the R2 increased by 0.04, the RMSE decreased by 0.02, and the percentage of retrieval results falling within the expected error range (Within-EE) rose by 5.55%. The R2 and RMSE of PM2.5 retrieval by MTL model are 0.84 and 13.76 μg·m–3, respectively. Compared with the RF model, the R2 increased by 0.06, the RMSE decreased by 4.55 μg·m–3, and the Within-EE increased by 7.28%. Additionally, compared to the DNN model, the MTL model showed an increase of 0.01 in R2 and a decrease of 0.02 in RMSE in AOD retrieval, with a corresponding increase of 2.89% in Within-EE. ......
Aerosol optical depth (AOD) and fine particulate matter with a diameter of less than 2.5μm (PM2.5) play crucial roles in air quality, human health, and climate change. However, the complex correlation of AOD–PM2.5 and the limitations of existing algorithms pose a significant challenge in realizing the accurate joint retrieval of these two parameters at the same location. On this point, a multi-task learning (MTL) model, which enables the joint retrieval of PM2.5 concentration and AOD, is proposed and applied on the top-of-the-atmosphere reflectance (TOAR) data gathered by the Fengyun-4A Advanced Geosynchronous Radiation Imager (FY-4A AGRI), and compared to that of two single-task learning (STL) models, namely, random forest (RF) and deep neural network (DNN). Specifically, The MTL model achieved a coefficient of determination (R2) of 0.88 and a root mean square error (RMSE) of 0.10 in AOD retrieval. In comparison to the RF model, the R2 increased by 0.04, the RMSE decreased by 0.02, and the percentage of retrieval results falling within the expected error range (Within-EE) rose by 5.55%. The R2 and RMSE of PM2.5 retrieval by MTL model are 0.84 and 13.76 μg·m–3, respectively. Compared with the RF model, the R2 increased by 0.06, the RMSE decreased by 4.55 μg·m–3, and the Within-EE increased by 7.28%. Additionally, compared to the DNN model, the MTL model showed an increase of 0.01 in R2 and a decrease of 0.02 in RMSE in AOD retrieval, with a corresponding increase of 2.89% in Within-EE. ......
, Available online
, Manuscript accepted 21 May 2024, doi: 10.1007/s00376-024-4032-y
Abstract:
The Medium-Resolution Spectral Imager-II (MERSI-II) instrument aboard China's Fengyun-3D satellite shares similarities with NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, enabling the retrieval of global aerosol optical depth (AOD). However, there are currently no officially released operational MERSI-II aerosol products over the ocean. This study focuses on adapting the MODIS dark target (DT) ocean algorithm to the MERSI-II sensor. A retrieval test is conducted on the 2019 MERSI data over the global ocean, and the retrieved AODs are validated against ground-based measurements from the automatic Aerosol Robotic Network (AERONET) and the shipborne Maritime Aerosol Network (MAN). The operational MODIS DT aerosol products are also used for comparison purposes. The results show that MERSI-II AOD granule retrievals are in good agreement with MODIS products, boasting high correlation coefficients (R) of up to 0.96 and consistent spatial distribution trends. Furthermore, the MERSI-II retrievals perform well when compared to AERONET and MAN measurements, with high R values (>0.86). However, the low-value retrievals from MERSI-II tend to be slightly overestimated compared to MODIS, despite both AODs displaying a positive bias. Notably, the monthly gridded AODs over the high latitudes of the northern and southern hemispheres suggest that MERSI-II exhibits greater stability in space and time, effectively reducing unrealistically high-value noise in the MODIS products. These results illustrate that the MERSI-II retrievals meet specific accuracy requirements by maintaining the algorithmic framework and most of the algorithmic assumptions, providing a crucial data supplement for aerosol studies and climate change.
The Medium-Resolution Spectral Imager-II (MERSI-II) instrument aboard China's Fengyun-3D satellite shares similarities with NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, enabling the retrieval of global aerosol optical depth (AOD). However, there are currently no officially released operational MERSI-II aerosol products over the ocean. This study focuses on adapting the MODIS dark target (DT) ocean algorithm to the MERSI-II sensor. A retrieval test is conducted on the 2019 MERSI data over the global ocean, and the retrieved AODs are validated against ground-based measurements from the automatic Aerosol Robotic Network (AERONET) and the shipborne Maritime Aerosol Network (MAN). The operational MODIS DT aerosol products are also used for comparison purposes. The results show that MERSI-II AOD granule retrievals are in good agreement with MODIS products, boasting high correlation coefficients (R) of up to 0.96 and consistent spatial distribution trends. Furthermore, the MERSI-II retrievals perform well when compared to AERONET and MAN measurements, with high R values (>0.86). However, the low-value retrievals from MERSI-II tend to be slightly overestimated compared to MODIS, despite both AODs displaying a positive bias. Notably, the monthly gridded AODs over the high latitudes of the northern and southern hemispheres suggest that MERSI-II exhibits greater stability in space and time, effectively reducing unrealistically high-value noise in the MODIS products. These results illustrate that the MERSI-II retrievals meet specific accuracy requirements by maintaining the algorithmic framework and most of the algorithmic assumptions, providing a crucial data supplement for aerosol studies and climate change.
, Available online
, Manuscript accepted 09 May 2024, doi: 10.1007/s00376-024-3217-8
Abstract:
Droughts that occurred in selected areas located in homogeneous regions of South America during the austral springs (SON) and summers (DJF) of the period 1982‒2019 are identified using SPI. Four areas were analyzed for droughts in SON and three areas in DJF. The areas in the Amazon suffered the majority of droughts in El Niño years while most of the droughts that occurred in the area of south Brazil, Uruguay, and north Argentina occurred during La Niña years. In the areas that comprise southeast and central west Brazil, the droughts occurred during both phases of El Niño-Southern Oscillation (ENSO) and in neutral years. Thus, other processes besides ENSO can be related to the observed droughts. The droughts were looked for in each area and month, and composites of atmospheric and oceanic variables during both seasons were analyzed for the selected cases. Regional and large-scale field composites were examined to identify the main processes associated with dry conditions in the different areas. Regional features were related to the influence of high pressure over south and southeast areas and divergence of humidity flux in all areas. Meridional circulations contributed to subsidence over the dry regions. The large-scale characteristics include SST anomalies, wavetrains over the South Pacific Ocean with centers of action over South America that produced subsidence in the study areas, and convection anomalies in the Maritime continent and surroundings. Therefore, the droughts were associated with a combination of regional and large-scale features that produced subsidence over the analyzed regions.
Droughts that occurred in selected areas located in homogeneous regions of South America during the austral springs (SON) and summers (DJF) of the period 1982‒2019 are identified using SPI. Four areas were analyzed for droughts in SON and three areas in DJF. The areas in the Amazon suffered the majority of droughts in El Niño years while most of the droughts that occurred in the area of south Brazil, Uruguay, and north Argentina occurred during La Niña years. In the areas that comprise southeast and central west Brazil, the droughts occurred during both phases of El Niño-Southern Oscillation (ENSO) and in neutral years. Thus, other processes besides ENSO can be related to the observed droughts. The droughts were looked for in each area and month, and composites of atmospheric and oceanic variables during both seasons were analyzed for the selected cases. Regional and large-scale field composites were examined to identify the main processes associated with dry conditions in the different areas. Regional features were related to the influence of high pressure over south and southeast areas and divergence of humidity flux in all areas. Meridional circulations contributed to subsidence over the dry regions. The large-scale characteristics include SST anomalies, wavetrains over the South Pacific Ocean with centers of action over South America that produced subsidence in the study areas, and convection anomalies in the Maritime continent and surroundings. Therefore, the droughts were associated with a combination of regional and large-scale features that produced subsidence over the analyzed regions.
, Available online
, Manuscript accepted 09 May 2024, doi: 10.1007/s00376-024-3339-z
Abstract:
In this study, a latent heat nudging lightning data assimilation (LDA) method independent of the flash rate was developed and tested with data from the Lightning Mapping Imager (LMI) onboard the Feng-Yun-4A (FY-4A) satellite based on the Weather Research and Forecasting (WRF) model. In this LDA method, the positive temperature perturbations at the lightning location are first calculated by the difference between the moist adiabatic temperature of a lifted air parcel and the model temperature. Then the positive temperature perturbations in the mixed-phase region are assimilated by a nudging method to adjust the latent heat within the convective system. Meanwhile, the water vapor mixing ratio is adapted to the temperature perturbations accordingly to maintain the relative humidity unchanged. This method considers the physical nature of the convective system, in contrast with other LDA methods that establish the empirical or statistical relationship between the lightning flash rates and model variables. The impact of this LDA method on short-term (≤6 h) forecasts was evaluated using two severe convective events in eastern China: a multi-region heavy rainfall event and a thunderstorm high wind event. The results showed that LDA could add thermodynamic information associated with the convective system to the WRF model during the nudging period, leading to a more reasonable storm environment. In the forecast fields, the simulations with LDA produced more realistic convective structures, resulting in an improvement in forecasts of precipitation and high winds.
In this study, a latent heat nudging lightning data assimilation (LDA) method independent of the flash rate was developed and tested with data from the Lightning Mapping Imager (LMI) onboard the Feng-Yun-4A (FY-4A) satellite based on the Weather Research and Forecasting (WRF) model. In this LDA method, the positive temperature perturbations at the lightning location are first calculated by the difference between the moist adiabatic temperature of a lifted air parcel and the model temperature. Then the positive temperature perturbations in the mixed-phase region are assimilated by a nudging method to adjust the latent heat within the convective system. Meanwhile, the water vapor mixing ratio is adapted to the temperature perturbations accordingly to maintain the relative humidity unchanged. This method considers the physical nature of the convective system, in contrast with other LDA methods that establish the empirical or statistical relationship between the lightning flash rates and model variables. The impact of this LDA method on short-term (≤6 h) forecasts was evaluated using two severe convective events in eastern China: a multi-region heavy rainfall event and a thunderstorm high wind event. The results showed that LDA could add thermodynamic information associated with the convective system to the WRF model during the nudging period, leading to a more reasonable storm environment. In the forecast fields, the simulations with LDA produced more realistic convective structures, resulting in an improvement in forecasts of precipitation and high winds.
, Available online
, Manuscript accepted 08 May 2024, doi: 10.1007/s00376-024-4034-9.
Abstract:
Wood density (WD) is an important wood quality and functional trait. Despite the relationships between WD and abiotic factors being important to model or predict spatial distributions of functional traits as well as responses of vegetation to climate changes, in current Earth System Models (ESMs) or Dynamic Global Vegetation Models (DGVMs), WD is often oversimplified, being defined as a globally uniform constant either for all plant functional types (PFTs) or for each individual PFT. Such oversimplifications may lead to simulation biases in the morphology of woody PFTs, as well as ecosystem transition and vegetation–atmosphere interactions. Moreover, existing conclusions about the relationships between WD and abiotic factors drawn from field observations remain mixed, making model parameterization improvements difficult. This study systematically investigated the influences of climate and soil factors on WD across various PFTs. Optimal fitting models for predicting WD within each PFT were then constructed by utilizing our collated global database of 138,604 observations. For WDs of tree PFTs, climate emerges as a more influential factor than soil characteristics, whereas for shrub PFTs the effects of climate and soil are of equivalent significance. Across all six PFTs, correlation coefficients between predictions by fitting models and observed WD range from 0.49 to 0.93. The predicted and observed WD exhibit good agreement across climate space. It is expected that the incorporation of our research findings into DGVMs will improve the simulation of tree height and forest fractional coverage, particularly in the central forest areas and forest transition zones.
Wood density (WD) is an important wood quality and functional trait. Despite the relationships between WD and abiotic factors being important to model or predict spatial distributions of functional traits as well as responses of vegetation to climate changes, in current Earth System Models (ESMs) or Dynamic Global Vegetation Models (DGVMs), WD is often oversimplified, being defined as a globally uniform constant either for all plant functional types (PFTs) or for each individual PFT. Such oversimplifications may lead to simulation biases in the morphology of woody PFTs, as well as ecosystem transition and vegetation–atmosphere interactions. Moreover, existing conclusions about the relationships between WD and abiotic factors drawn from field observations remain mixed, making model parameterization improvements difficult. This study systematically investigated the influences of climate and soil factors on WD across various PFTs. Optimal fitting models for predicting WD within each PFT were then constructed by utilizing our collated global database of 138,604 observations. For WDs of tree PFTs, climate emerges as a more influential factor than soil characteristics, whereas for shrub PFTs the effects of climate and soil are of equivalent significance. Across all six PFTs, correlation coefficients between predictions by fitting models and observed WD range from 0.49 to 0.93. The predicted and observed WD exhibit good agreement across climate space. It is expected that the incorporation of our research findings into DGVMs will improve the simulation of tree height and forest fractional coverage, particularly in the central forest areas and forest transition zones.
, Available online
, Manuscript accepted 08 May 2024, doi: 10.1007/s00376-024-3370-0
Abstract:
The East Asian summer monsoon in Northeast Asia (NEA) shows an enhanced trend with increased summer rainfall and delayed rainy season after 2000. Based on the data analyses during 1979–2022, our results show that the increased rainfall amount is associated with the enhancement of Mongolian cyclone (MC) in July–August, as a portion of the Eurasian barotropic Rossby wave train. This wave train is regulated by the sea surface temperature (SST) anomaly in North Atlantic (NA), amplified by the increased warming SST in NA. While the delayed rainy season in September is related to the enhanced anticyclone over the Kuril Islands (ACKI) in Russian Far East. The anticyclone originated from Arctic region, which is possibly induced by the Sea-Ice loss in the East Siberian Sea, which can be detected 2-months in advance. The stronger MC and ACKI jointly resulted in the enhanced East Asian summer monsoon in NEA since 2000, via the ascending motion and moisture supply. Therefore, the SST anomaly in NA is responsible for the intensified rainfall in rainy season in NEA, and the Sea-Ice in the East Siberian Sea provides a potential source for the prediction of rainy season retreat.
The East Asian summer monsoon in Northeast Asia (NEA) shows an enhanced trend with increased summer rainfall and delayed rainy season after 2000. Based on the data analyses during 1979–2022, our results show that the increased rainfall amount is associated with the enhancement of Mongolian cyclone (MC) in July–August, as a portion of the Eurasian barotropic Rossby wave train. This wave train is regulated by the sea surface temperature (SST) anomaly in North Atlantic (NA), amplified by the increased warming SST in NA. While the delayed rainy season in September is related to the enhanced anticyclone over the Kuril Islands (ACKI) in Russian Far East. The anticyclone originated from Arctic region, which is possibly induced by the Sea-Ice loss in the East Siberian Sea, which can be detected 2-months in advance. The stronger MC and ACKI jointly resulted in the enhanced East Asian summer monsoon in NEA since 2000, via the ascending motion and moisture supply. Therefore, the SST anomaly in NA is responsible for the intensified rainfall in rainy season in NEA, and the Sea-Ice in the East Siberian Sea provides a potential source for the prediction of rainy season retreat.
, Available online
, Manuscript accepted 08 May 2024, doi: 10.1007/s00376-024-4004-2
Abstract:
Recently, extreme meteorological droughts have affected China, causing terrible socioeconomic impacts. Despite previous research on the spatiotemporal characteristics and mechanisms of drought, two crucial issues remain seldom explored. First, an event-oriented drought chronology with detailed spatiotemporal evolutions is urgently required. Second, the complex migration patterns and diversity of synchronous temperature extremes need to be quantitatively investigated. Accordingly, the main achievements of our investigation are as follows. We produced an event-oriented set of extreme meteorological droughts over China through the application of a newly developed 3D DBSCAN-based detection method (deposited onhttps://doi.org/10.25452/figshare.plus.25512334 ), which was verified with a historical atlas and monographs on a case-by-case basis. In addition, distinctive migration patterns (i.e., stationary/propagation types) are identified and ranked, considering the differences in latitudinal zones and coastal/inland locations. We also analyze the diversity of synchronous temperature extremes (e.g., hotness and coldness). Notably, an increasing trend in hot droughts occurred over China since the late 1990s, predominantly appearing to the south of 30°N and north of 40°N. All drought events and synchronous temperature extremes are ranked using a comprehensive magnitude index, with the 2022 summer-autumn Yangtze River hot drought being the hottest. Furthermore, Liang-Kleeman information flow-based causality analysis emphasizes key areas where the PDO and AMO influenced decadal variations in coverages of droughts and temperature extremes. We believe that the achievements in this study may offer new insights into sequential mechanism exploration and prediction-related issues.
Recently, extreme meteorological droughts have affected China, causing terrible socioeconomic impacts. Despite previous research on the spatiotemporal characteristics and mechanisms of drought, two crucial issues remain seldom explored. First, an event-oriented drought chronology with detailed spatiotemporal evolutions is urgently required. Second, the complex migration patterns and diversity of synchronous temperature extremes need to be quantitatively investigated. Accordingly, the main achievements of our investigation are as follows. We produced an event-oriented set of extreme meteorological droughts over China through the application of a newly developed 3D DBSCAN-based detection method (deposited on
, Available online
, Manuscript accepted 07 May 2024, doi: 10.1007/s00376-024-4062-5
Abstract:
Tides, as a crucial dynamic process in the ocean, play a vital role in both the marine and atmospheric studies, thus accurate simulation of tidal processes is of utmost importance in tidal circulation models. Based on the sequential data assimilation method and the concept of the Kalman gain matrix, this paper proposes a new Nudging method with spatial-dependent coefficients for the tidal assimilation. The spatial-dependent Nudging method not only retains the advantages of the traditional Nudging method but also facilitates the direct determination of a more reasonable spatial distribution of Nudging coefficients. Utilizing the M2 tidal constituent as an illustration, we conducted the assimilation experiments of sea level data to the barotropic circulation and tide model to assess the global harmonic constants of the M2 constituent. The results demonstrate that the spatial-dependent Nudging method successfully mitigates deviations of tidal phase lag. Following assimilation using the new method, the deviations of the M2 tidal amplitude and phase lag can be reduced by 47% and 18%, respectively, compared to the no-assimilation case, and up to 9% and 11%, respectively, in comparison to the traditional Nudging method. We also applied the S-Nudging method to realistic tidal simulations, and its effectiveness relative to traditional methods is significantly enhanced, making it highly valuable for the modeling of oceanic tidal circulation.
Tides, as a crucial dynamic process in the ocean, play a vital role in both the marine and atmospheric studies, thus accurate simulation of tidal processes is of utmost importance in tidal circulation models. Based on the sequential data assimilation method and the concept of the Kalman gain matrix, this paper proposes a new Nudging method with spatial-dependent coefficients for the tidal assimilation. The spatial-dependent Nudging method not only retains the advantages of the traditional Nudging method but also facilitates the direct determination of a more reasonable spatial distribution of Nudging coefficients. Utilizing the M2 tidal constituent as an illustration, we conducted the assimilation experiments of sea level data to the barotropic circulation and tide model to assess the global harmonic constants of the M2 constituent. The results demonstrate that the spatial-dependent Nudging method successfully mitigates deviations of tidal phase lag. Following assimilation using the new method, the deviations of the M2 tidal amplitude and phase lag can be reduced by 47% and 18%, respectively, compared to the no-assimilation case, and up to 9% and 11%, respectively, in comparison to the traditional Nudging method. We also applied the S-Nudging method to realistic tidal simulations, and its effectiveness relative to traditional methods is significantly enhanced, making it highly valuable for the modeling of oceanic tidal circulation.
, Available online
, Manuscript accepted 07 May 2024, doi: 10.1007/s00376-024-3346-0
Abstract:
The Diurnal Temperature Range (DTR) serves as a vital indicator reflecting both natural climate variability and anthropogenic climate change. This study investigates the historical and projected multi-temporal DTR variations over the Tibetan Plateau. It assesses 23 climate models from the Coupled Model Intercomparison Project 6 (CMIP6) using CN05.1 observational data as validation, evaluating their ability to simulate DTR over the Tibetan Plateau. Then, the evolution of DTR over the Tibetan Plateau under different shared socioeconomic path (SSP) scenarios for the near, middle, and long term of future projection are analyzed using eleven selected robustly performing models. Key findings reveal: (1) Among the models examined, BCC-CSM2-MR, EC-Earth3, EC-Earth3-CC, EC-Earth3-Veg, EC-Earth3-Veg-LR, FGOALS-g3, FIO-ESM-2-0, GFDL-ESM4, MPI-ESM1-2-HR, MPI- ESM1-2-LR, and INM-CM5-0 exhibit superior integrated simulation capability for capturing the spatial-temporal variability of DTR over the Tibetan Plateau. (2) Projection indicates a slightly increasing trend in DTR on the Tibetan Plateau in the SSP1-2.6 scenario, and decreasing trends in the SSP2-4.5, SSP3-7.0, and SPP5-8.5 scenarios. Certain areas, such as southeastern edge Tibetan Plateau, western hinterland Tibetan Plateau, the southern Kunlun, and the Qaidam basins, the changes in DTR are relatively large. (3) Notably, the warming rate of maximum temperature under SSP2-4.5, SSP3-7.0, and SPP5-8.5 scenarios is slower compared to that of minimum temperature, and it emerges as the primary contributor to the projected decrease in DTR over the Tibetan Plateau in the future. Key words: Tibetan Plateau; CMIP6 models; daily temperature range; model assessment; historical period; future projection
The Diurnal Temperature Range (DTR) serves as a vital indicator reflecting both natural climate variability and anthropogenic climate change. This study investigates the historical and projected multi-temporal DTR variations over the Tibetan Plateau. It assesses 23 climate models from the Coupled Model Intercomparison Project 6 (CMIP6) using CN05.1 observational data as validation, evaluating their ability to simulate DTR over the Tibetan Plateau. Then, the evolution of DTR over the Tibetan Plateau under different shared socioeconomic path (SSP) scenarios for the near, middle, and long term of future projection are analyzed using eleven selected robustly performing models. Key findings reveal: (1) Among the models examined, BCC-CSM2-MR, EC-Earth3, EC-Earth3-CC, EC-Earth3-Veg, EC-Earth3-Veg-LR, FGOALS-g3, FIO-ESM-2-0, GFDL-ESM4, MPI-ESM1-2-HR, MPI- ESM1-2-LR, and INM-CM5-0 exhibit superior integrated simulation capability for capturing the spatial-temporal variability of DTR over the Tibetan Plateau. (2) Projection indicates a slightly increasing trend in DTR on the Tibetan Plateau in the SSP1-2.6 scenario, and decreasing trends in the SSP2-4.5, SSP3-7.0, and SPP5-8.5 scenarios. Certain areas, such as southeastern edge Tibetan Plateau, western hinterland Tibetan Plateau, the southern Kunlun, and the Qaidam basins, the changes in DTR are relatively large. (3) Notably, the warming rate of maximum temperature under SSP2-4.5, SSP3-7.0, and SPP5-8.5 scenarios is slower compared to that of minimum temperature, and it emerges as the primary contributor to the projected decrease in DTR over the Tibetan Plateau in the future. Key words: Tibetan Plateau; CMIP6 models; daily temperature range; model assessment; historical period; future projection
, Available online
, Manuscript accepted 29 April 2024, doi: 10.1007/s00376-024-3269-9
Abstract:
Increasing the urban tree cover percentage (TCP) is widely recognized as an efficient way to mitigate the urban heat island effect. The cooling efficiency of urban trees can be either enhanced or attenuated on hotter days, depending on the physiological response of urban trees to rising ambient temperature. However, the response of urban trees’ cooling efficiency to rising urban temperature remains poorly quantified for China’s cities. In this study, we quantify the response of urban trees’ cooling efficiency to rising urban temperature at noontime (~13:30 local time) in 17 summers (June, July, and August) from 2003–2019 in 70 economically developed cities of China based on satellite observations. The results show that urban trees have stronger cooling efficiency with increasing temperature, suggesting additional cooling benefits provided by urban trees on hotter days. The response of urban trees’ cooling efficiency to rising urban temperature ranges from 0.002 to 0.055 ℃∙%^(-1) per 1 ℃ increase in temperature across the selected cities, with larger values for the low-TCP-level cities. The response is also regulated by background temperature and precipitation, as the additional cooling benefit tends to be larger in warmer and wetter cities at the same TCP level. The positive response of urban trees’ cooling efficiency to rising urban temperature is explained mainly by the stronger evapotranspiration of urban trees on hotter days. These results have important implications for alleviating urban heat risk by utilizing urban trees, particularly considering that extreme hot days are becoming more frequent in cities under global warming.
Increasing the urban tree cover percentage (TCP) is widely recognized as an efficient way to mitigate the urban heat island effect. The cooling efficiency of urban trees can be either enhanced or attenuated on hotter days, depending on the physiological response of urban trees to rising ambient temperature. However, the response of urban trees’ cooling efficiency to rising urban temperature remains poorly quantified for China’s cities. In this study, we quantify the response of urban trees’ cooling efficiency to rising urban temperature at noontime (~13:30 local time) in 17 summers (June, July, and August) from 2003–2019 in 70 economically developed cities of China based on satellite observations. The results show that urban trees have stronger cooling efficiency with increasing temperature, suggesting additional cooling benefits provided by urban trees on hotter days. The response of urban trees’ cooling efficiency to rising urban temperature ranges from 0.002 to 0.055 ℃∙%^(-1) per 1 ℃ increase in temperature across the selected cities, with larger values for the low-TCP-level cities. The response is also regulated by background temperature and precipitation, as the additional cooling benefit tends to be larger in warmer and wetter cities at the same TCP level. The positive response of urban trees’ cooling efficiency to rising urban temperature is explained mainly by the stronger evapotranspiration of urban trees on hotter days. These results have important implications for alleviating urban heat risk by utilizing urban trees, particularly considering that extreme hot days are becoming more frequent in cities under global warming.
, Available online
, Manuscript accepted 29 April 2024, doi: 10.1007/s00376-024-3268-x
Abstract:
This study investigates the impact of the salinity barrier layer (BL) on the upper ocean response to Super Typhoon Mangkhut (2018) in the western North Pacific. After the passage of Mangkhut, a noticeable increase (~0.6 psu) in sea surface salinity (SSS) and a weak decrease (<1 ℃) in sea surface temperature (SST) were observed on the right side of the typhoon track. Mangkhut-induced SST change can be divided into the three stages, corresponding to the variations in BL thickness and SST before, during and after the passage of Mangkhut. During the pre-typhoon stage, SST slightly warmed due to the entrainment of BL warm water, which suppressed the cooling induced by surface heat fluxes and horizontal advection. During the forced stage, SST cooling was controlled by entrainment, and the preexisting BL reduced the total cooling by 0.89 ℃/day, thus significantly weakening the overall SST cooling induced by Mangkhut. During the relaxation stage, the SST cooling primarily was caused by the disappearance of BL. Our results indicate that the preexisting BL can limit typhoon-induced SST cooling by suppressing the entrainment of cold thermocline water, which is contributed to Mangkhut becoming the strongest typhoon in 2018.
This study investigates the impact of the salinity barrier layer (BL) on the upper ocean response to Super Typhoon Mangkhut (2018) in the western North Pacific. After the passage of Mangkhut, a noticeable increase (~0.6 psu) in sea surface salinity (SSS) and a weak decrease (<1 ℃) in sea surface temperature (SST) were observed on the right side of the typhoon track. Mangkhut-induced SST change can be divided into the three stages, corresponding to the variations in BL thickness and SST before, during and after the passage of Mangkhut. During the pre-typhoon stage, SST slightly warmed due to the entrainment of BL warm water, which suppressed the cooling induced by surface heat fluxes and horizontal advection. During the forced stage, SST cooling was controlled by entrainment, and the preexisting BL reduced the total cooling by 0.89 ℃/day, thus significantly weakening the overall SST cooling induced by Mangkhut. During the relaxation stage, the SST cooling primarily was caused by the disappearance of BL. Our results indicate that the preexisting BL can limit typhoon-induced SST cooling by suppressing the entrainment of cold thermocline water, which is contributed to Mangkhut becoming the strongest typhoon in 2018.
, Available online
, Manuscript accepted 29 April 2024, doi: 10.1007/s00376-024-4052-7
Abstract:
Cloud base height (CBH) is a crucial parameter for cloud radiative effect estimates, climate change simulations, and aviation guidance. However, due to the limited information on cloud vertical structures included in passive satellite radiometer observations, few operational satellite CBH products are currently available. This study presents a new method for retrieving CBH from satellite radiometers. The method first uses the combined measurements of satellite radiometers and ground-based cloud radars to develop a look-up table (LUT) of effective cloud water content (ECWC), which represents the vertically varying cloud water content. This LUT allows for the conversion of cloud water path to cloud geometric thickness (CGT), enabling the estimation of CBH as the difference between cloud top height and CGT. Detailed comparison analysis of CBH estimates from the state-of-the-art ECWC LUT are conducted against four ground-based millimeter-wave cloud radar (MMCR) measurements, and results show that the mean bias (correlation coefficient) is 0.18±1.79 km (0.73), which is lower (higher) than 0.23±2.11 km (0.67) as derived from the combined measurements of satellite radiometers and satellite radar-lidar (i.e., CloudSat and CALIPSO). Furthermore, the percentages of the CBH biases within 250 m increase by 5% to 10%, which varies by location. This indicates that the CBH estimates from our algorithm are more consistent with ground-based MMCR measurements. Therefore, this algorithm shows great potential for further improvement of the CBH retrievals as ground-based MMCR are being increasingly included in global surface meteorological observing network, and the improved CBH retrievals will contribute to better cloud radiative effect estimates.
Cloud base height (CBH) is a crucial parameter for cloud radiative effect estimates, climate change simulations, and aviation guidance. However, due to the limited information on cloud vertical structures included in passive satellite radiometer observations, few operational satellite CBH products are currently available. This study presents a new method for retrieving CBH from satellite radiometers. The method first uses the combined measurements of satellite radiometers and ground-based cloud radars to develop a look-up table (LUT) of effective cloud water content (ECWC), which represents the vertically varying cloud water content. This LUT allows for the conversion of cloud water path to cloud geometric thickness (CGT), enabling the estimation of CBH as the difference between cloud top height and CGT. Detailed comparison analysis of CBH estimates from the state-of-the-art ECWC LUT are conducted against four ground-based millimeter-wave cloud radar (MMCR) measurements, and results show that the mean bias (correlation coefficient) is 0.18±1.79 km (0.73), which is lower (higher) than 0.23±2.11 km (0.67) as derived from the combined measurements of satellite radiometers and satellite radar-lidar (i.e., CloudSat and CALIPSO). Furthermore, the percentages of the CBH biases within 250 m increase by 5% to 10%, which varies by location. This indicates that the CBH estimates from our algorithm are more consistent with ground-based MMCR measurements. Therefore, this algorithm shows great potential for further improvement of the CBH retrievals as ground-based MMCR are being increasingly included in global surface meteorological observing network, and the improved CBH retrievals will contribute to better cloud radiative effect estimates.
, Available online
, Manuscript accepted 24 April 2024, doi: 10.1007/s00376-024-3266-z
Abstract:
Potential vorticity (PV) streamers are elongated filaments of high PV intrusions that generally exhibit three distinct shapes: ordinarily southwestward, hook and treble-clef shapes, each with significant weather influences. These PV streamers were most frequent over arid and semi-arid Central Asia in the mid-high latitude region. This study applied Mask R-CNN to PV streamers on the dynamical tropopause during the warm season (May to September) over the years 2000–2004 to train a weighted variational model capable of identifying these different shapes. The trained model demonstrated a strong ability to distinguish between the three shapes. A climatology analysis of PV streamers over Central Asia spanning 2000 to 2021 revealed that it featured an increasingly deep and pronounced reversal circulation from ordinary to treble-clef shapes. The treble-clef shape featured a PV tower and distinct cut-off low in the troposphere, but the associated upward motions and precipitation were confined within approximately 1200 km to the east of the PV tower. Although the hook-shape PV streamers were linked to a weaker cut-off low, the extent of upward motion and precipitation was nearly double that of the treble-clef category. In contrast, the ordinary PV streamer was primarily associated with tropopause Rossby wave breaking and exhibited relatively shallow characteristics, which resulted in moderate upward motion and precipitation to 500 km to its east.
Potential vorticity (PV) streamers are elongated filaments of high PV intrusions that generally exhibit three distinct shapes: ordinarily southwestward, hook and treble-clef shapes, each with significant weather influences. These PV streamers were most frequent over arid and semi-arid Central Asia in the mid-high latitude region. This study applied Mask R-CNN to PV streamers on the dynamical tropopause during the warm season (May to September) over the years 2000–2004 to train a weighted variational model capable of identifying these different shapes. The trained model demonstrated a strong ability to distinguish between the three shapes. A climatology analysis of PV streamers over Central Asia spanning 2000 to 2021 revealed that it featured an increasingly deep and pronounced reversal circulation from ordinary to treble-clef shapes. The treble-clef shape featured a PV tower and distinct cut-off low in the troposphere, but the associated upward motions and precipitation were confined within approximately 1200 km to the east of the PV tower. Although the hook-shape PV streamers were linked to a weaker cut-off low, the extent of upward motion and precipitation was nearly double that of the treble-clef category. In contrast, the ordinary PV streamer was primarily associated with tropopause Rossby wave breaking and exhibited relatively shallow characteristics, which resulted in moderate upward motion and precipitation to 500 km to its east.
, Available online
, Manuscript accepted 24 April 2024, doi: 10.1007/s00376-024-4003-3
Abstract:
Accurate seasonal precipitation forecasts, especially for extreme events, are crucial to preventing meteorological hazards and its potential impacts on national development, social activity and security. However, the intensity of summer precipitation is often largely underestimated in many current dynamical models. This study uses a deep learning method called Cycle-Consistent Generative Adversarial Networks (CycleGAN) to improve the seasonal forecasts by Nanjing University of Information Science and Technology Climate Forecast System (NUIST-CFS 1.0) for predicting June-July-August precipitation in southeastern China. The results suggest that the CycleGAN-based model significantly improves the accuracy in predicting the spatiotemporal distribution of summer precipitation compared to traditional quantile mapping (QM) method. Using the unpaired bias-correction model, we can also obtain advanced forecasts of the frequency, intensity, and duration of extreme precipitation events over the dynamical model predictions. This study expands the potential applications of deep learning models to improving seasonal precipitation forecasts.
Accurate seasonal precipitation forecasts, especially for extreme events, are crucial to preventing meteorological hazards and its potential impacts on national development, social activity and security. However, the intensity of summer precipitation is often largely underestimated in many current dynamical models. This study uses a deep learning method called Cycle-Consistent Generative Adversarial Networks (CycleGAN) to improve the seasonal forecasts by Nanjing University of Information Science and Technology Climate Forecast System (NUIST-CFS 1.0) for predicting June-July-August precipitation in southeastern China. The results suggest that the CycleGAN-based model significantly improves the accuracy in predicting the spatiotemporal distribution of summer precipitation compared to traditional quantile mapping (QM) method. Using the unpaired bias-correction model, we can also obtain advanced forecasts of the frequency, intensity, and duration of extreme precipitation events over the dynamical model predictions. This study expands the potential applications of deep learning models to improving seasonal precipitation forecasts.
, Available online
, Manuscript accepted 23 April 2024, doi: 10.1007/s00376-024-3162-6
Abstract:
Utilizing observational data from seven ground gradient stations located on the eastern slope, western slope, and mountaintop of Liupan Mountains (LM) during the rainy seasons from 2020 to 2022, combined with backward trajectory cluster analysis, this study investigated the influence of airflow transport paths on the seasonal rainfall in this mountainous region. The results indicate: (1) LM’s rainy season, characterized by overcast and rainy days, is mainly influenced by cold and moist airflows (CMA) from the westerly direction and warm and moist airflows (WMA) from a slightly southern direction. The precipitation amounts under four airflow transport paths are ranked from largest to smallest as follows: WMA, CMA, warm dry airflows (WDA), and cold dry airflows (CDA). (2) WMA contribute significantly more to the intensity of regional precipitation than the other three types of airflows. During localized precipitation events, warm airflows have higher precipitation intensities at night than cold airflows, while the opposite is true during the afternoon. (3) During regional precipitation events, water vapor content is a primary influencing factor. Precipitation characteristics under humid airflows are mainly affected by high water vapor content, whereas during dry airflow precipitation, dynamic and thermodynamic factors have a more pronounced impact than for humid airflows. (4) During localized precipitation events, the influence of dynamic and thermodynamic factors is more complex than during regional precipitation, with precipitation characteristics of the four airflows closely related to their water vapor content, air temperature and humidity attributes, and orographic lifting. (5) Compared to regional precipitation, the influence of topography is more prominent in localized precipitation processes.
Utilizing observational data from seven ground gradient stations located on the eastern slope, western slope, and mountaintop of Liupan Mountains (LM) during the rainy seasons from 2020 to 2022, combined with backward trajectory cluster analysis, this study investigated the influence of airflow transport paths on the seasonal rainfall in this mountainous region. The results indicate: (1) LM’s rainy season, characterized by overcast and rainy days, is mainly influenced by cold and moist airflows (CMA) from the westerly direction and warm and moist airflows (WMA) from a slightly southern direction. The precipitation amounts under four airflow transport paths are ranked from largest to smallest as follows: WMA, CMA, warm dry airflows (WDA), and cold dry airflows (CDA). (2) WMA contribute significantly more to the intensity of regional precipitation than the other three types of airflows. During localized precipitation events, warm airflows have higher precipitation intensities at night than cold airflows, while the opposite is true during the afternoon. (3) During regional precipitation events, water vapor content is a primary influencing factor. Precipitation characteristics under humid airflows are mainly affected by high water vapor content, whereas during dry airflow precipitation, dynamic and thermodynamic factors have a more pronounced impact than for humid airflows. (4) During localized precipitation events, the influence of dynamic and thermodynamic factors is more complex than during regional precipitation, with precipitation characteristics of the four airflows closely related to their water vapor content, air temperature and humidity attributes, and orographic lifting. (5) Compared to regional precipitation, the influence of topography is more prominent in localized precipitation processes.
, Available online
, Manuscript accepted 23 April 2024, doi: 10.1007/s00376-024-4071-4
Abstract:
We used observed concentrations of air pollutants, reanalyzed meteorological parameters, and results from the Goddard Earth Observing System Chemical Transport Model to examine the relationships between concentrations of maximum daily 8-h average ozone (MDA8 O3), PM2.5 (particulate matter with diameter of 2.5 µm or less), and PM2.5 components and 2-m temperature (T2) or relative humidity (RH), as well as the effectiveness of precursor emission reductions on the control of O3 and PM2.5 in Beijing–Tianjin–Hebei (BTH) under different summertime temperature and humidity conditions. Both observed (simulated) MDA8 O3 and PM2.5 concentrations increased as T2 went up, with linear trends of 4.8 (3.2) ppb °C−1 and 1.9 (1.5) µg m−3 °C−1, respectively. Model results showed that the decreases in MDA8 O3 from precursor emission reductions were more sensitive to T2 than to RH. Reducing a larger proportion of volatile organic compound (VOC) emissions at higher T2 was more effective for the control of summertime O3 in BTH. For the control of summertime PM2.5 in BTH, reducing nitrogen oxides (NOx) combined with a small proportion of VOCs was the best measure. The magnitude of reduction in PM2.5 from reducing precursor emissions was more sensitive to RH than to T2, with the best efficiency at high RH. Results from this study are helpful for formulating effective policies to tackle O3 and PM2.5 pollution in BTH.
We used observed concentrations of air pollutants, reanalyzed meteorological parameters, and results from the Goddard Earth Observing System Chemical Transport Model to examine the relationships between concentrations of maximum daily 8-h average ozone (MDA8 O3), PM2.5 (particulate matter with diameter of 2.5 µm or less), and PM2.5 components and 2-m temperature (T2) or relative humidity (RH), as well as the effectiveness of precursor emission reductions on the control of O3 and PM2.5 in Beijing–Tianjin–Hebei (BTH) under different summertime temperature and humidity conditions. Both observed (simulated) MDA8 O3 and PM2.5 concentrations increased as T2 went up, with linear trends of 4.8 (3.2) ppb °C−1 and 1.9 (1.5) µg m−3 °C−1, respectively. Model results showed that the decreases in MDA8 O3 from precursor emission reductions were more sensitive to T2 than to RH. Reducing a larger proportion of volatile organic compound (VOC) emissions at higher T2 was more effective for the control of summertime O3 in BTH. For the control of summertime PM2.5 in BTH, reducing nitrogen oxides (NOx) combined with a small proportion of VOCs was the best measure. The magnitude of reduction in PM2.5 from reducing precursor emissions was more sensitive to RH than to T2, with the best efficiency at high RH. Results from this study are helpful for formulating effective policies to tackle O3 and PM2.5 pollution in BTH.
, Available online
, Manuscript accepted 10 April 2024, doi: 10.1007/s00376-024-3163-5
Abstract:
High-vertical-resolution radiosonde wind data is highly valuable for describing the dynamics of the meso- and micro-scale atmosphere. However, the current algorithm used in China's L-band radar sounding system for calculating high-vertical-resolution wind vectors excessively smooth the data, resulting in significant underestimation of the calculated kinetic energy of gravity waves compared to similar products from other countries, greatly limiting the effective utilization of the data. To address this issue, this study proposes a novel method to calculate high-vertical-resolution wind vectors that utilizes elevation angle, azimuth angle, and slant range from L-band radar. In order to obtain wind data with stable quality, a two-step automatic quality control procedure, including the root mean square error F (RMSE-F) test and elemental consistency test are first applied to the slant range data, to eliminate continuous erroneous data caused by unstable signals or radar malfunctions. Then a wind calculation scheme based on a sliding second-order polynomial fitting is utilized to derive the high-vertical-resolution radiosonde wind vectors. The evaluation results demonstrate that the wind data obtained through the proposed method shows a high level of consistency with the high-resolution wind data observed using the Vaisala Global Positioning System (GPS) and the data observed by the new Beidou Navigation Sounding System. The calculation of kinetic energy of gravity waves in the recalculate wind data also reaches a level comparable to the Vaisala observations.
High-vertical-resolution radiosonde wind data is highly valuable for describing the dynamics of the meso- and micro-scale atmosphere. However, the current algorithm used in China's L-band radar sounding system for calculating high-vertical-resolution wind vectors excessively smooth the data, resulting in significant underestimation of the calculated kinetic energy of gravity waves compared to similar products from other countries, greatly limiting the effective utilization of the data. To address this issue, this study proposes a novel method to calculate high-vertical-resolution wind vectors that utilizes elevation angle, azimuth angle, and slant range from L-band radar. In order to obtain wind data with stable quality, a two-step automatic quality control procedure, including the root mean square error F (RMSE-F) test and elemental consistency test are first applied to the slant range data, to eliminate continuous erroneous data caused by unstable signals or radar malfunctions. Then a wind calculation scheme based on a sliding second-order polynomial fitting is utilized to derive the high-vertical-resolution radiosonde wind vectors. The evaluation results demonstrate that the wind data obtained through the proposed method shows a high level of consistency with the high-resolution wind data observed using the Vaisala Global Positioning System (GPS) and the data observed by the new Beidou Navigation Sounding System. The calculation of kinetic energy of gravity waves in the recalculate wind data also reaches a level comparable to the Vaisala observations.
, Available online
, Manuscript accepted 09 April 2024, doi: 10.1007/s00376-024-3296-6
Abstract:
Recent observational and numerical studies have revealed the dependence of the intensification rate on the inner-core size of tropical cyclones (TC). In this study, with the initial inner-core size (i.e., the radius of maximum wind—RMW) varied from 20–180 km in idealized simulations using two different numerical models, we found a nonmonotonic dependence of the lifetime maximum intensification rate (LMIR) on the inner-core size. Namely, there is an optimal inner-core size for the LMIR of a TC. Tangential wind budget analysis shows that compared to large TCs, small TCs have large inward flux of absolute vorticity due to large absolute vorticity inside the RMW. However, small TCs also suffer from strong lateral diffusion across the eyewall, which partly offsets the positive contribution from large inward flux of absolute vorticity. These two competing processes ultimately lead to the TC with an intermediate initial inner-core size having the largest LMIR. Results from sensitivity experiments show that the optimal size varies in the range of 40–120 km and increases with higher sea surface temperature, lower latitude, larger horizontal mixing length, and weaker initial TC intensity. The 40–120 km RMW corresponds to the inner-core size most commonly found for intensifying TCs in observations, suggesting the natural selection of initial TC size for intensification. This study highlights the importance of accurate representation of TC inner-core size to TC intensity forecast by numerical weather prediction models.
Recent observational and numerical studies have revealed the dependence of the intensification rate on the inner-core size of tropical cyclones (TC). In this study, with the initial inner-core size (i.e., the radius of maximum wind—RMW) varied from 20–180 km in idealized simulations using two different numerical models, we found a nonmonotonic dependence of the lifetime maximum intensification rate (LMIR) on the inner-core size. Namely, there is an optimal inner-core size for the LMIR of a TC. Tangential wind budget analysis shows that compared to large TCs, small TCs have large inward flux of absolute vorticity due to large absolute vorticity inside the RMW. However, small TCs also suffer from strong lateral diffusion across the eyewall, which partly offsets the positive contribution from large inward flux of absolute vorticity. These two competing processes ultimately lead to the TC with an intermediate initial inner-core size having the largest LMIR. Results from sensitivity experiments show that the optimal size varies in the range of 40–120 km and increases with higher sea surface temperature, lower latitude, larger horizontal mixing length, and weaker initial TC intensity. The 40–120 km RMW corresponds to the inner-core size most commonly found for intensifying TCs in observations, suggesting the natural selection of initial TC size for intensification. This study highlights the importance of accurate representation of TC inner-core size to TC intensity forecast by numerical weather prediction models.
, Available online
, Manuscript accepted 09 April 2024, doi: 10.1007/s00376-024-3314-8
Abstract:
A mesoscale convective system (MCS) occurred over east China coastal provinces and the East China Sea on 30 April 2021, producing damaging surface winds near coastal city Nantong with observed speeds reaching 45 m s-1. A simulation using the Weather Research and Forecasting model at 1.5-km grid spacing generally reproduces the development of convective system and the subsequent organization into an MCS, with an eastward protruding bow segment over the sea. In the simulation, an east-west-oriented high wind swath is generated behind the gust front of the MCS. Descending dry rear-to-front inflows behind the bow and trailing gust front are found to feed the downdrafts in the main precipitation regions. The inflows help establish spreading cold outflows and enhance the downdrafts through evaporative cooling. Meanwhile, front-to-rear inflows from the south are present, associated with severely rearward-tilted updrafts initially forming over the gust front. Such inflows descend behind (north of) the gust front, significantly enhancing the downdrafts and near-surface winds within the cold pool. Consistently, calculated trajectories show that these parcels contributing to the derecho primarily originate from the region ahead (south) of the east-west-oriented gust front, and dry southwesterly flows at mid-lower levels contribute to strong downdrafts within the MCS. Moreover, momentum budget analyses reveal that large westward-directed horizontal pressure gradient force within the simulated cold pool produce rapid flow acceleration towards Nantong. The analyses enrich the understanding of damaging wind characteristics over the East China coast and will be helpful to operational forecasters.
A mesoscale convective system (MCS) occurred over east China coastal provinces and the East China Sea on 30 April 2021, producing damaging surface winds near coastal city Nantong with observed speeds reaching 45 m s-1. A simulation using the Weather Research and Forecasting model at 1.5-km grid spacing generally reproduces the development of convective system and the subsequent organization into an MCS, with an eastward protruding bow segment over the sea. In the simulation, an east-west-oriented high wind swath is generated behind the gust front of the MCS. Descending dry rear-to-front inflows behind the bow and trailing gust front are found to feed the downdrafts in the main precipitation regions. The inflows help establish spreading cold outflows and enhance the downdrafts through evaporative cooling. Meanwhile, front-to-rear inflows from the south are present, associated with severely rearward-tilted updrafts initially forming over the gust front. Such inflows descend behind (north of) the gust front, significantly enhancing the downdrafts and near-surface winds within the cold pool. Consistently, calculated trajectories show that these parcels contributing to the derecho primarily originate from the region ahead (south) of the east-west-oriented gust front, and dry southwesterly flows at mid-lower levels contribute to strong downdrafts within the MCS. Moreover, momentum budget analyses reveal that large westward-directed horizontal pressure gradient force within the simulated cold pool produce rapid flow acceleration towards Nantong. The analyses enrich the understanding of damaging wind characteristics over the East China coast and will be helpful to operational forecasters.
, Available online
, Manuscript accepted 09 April 2024, doi: 10.1007/s00376-024-3114-1
Abstract:
This study examines the effectiveness of adaptive observation experiments using the ensemble transformation sensitivity (ETS) method to improve precipitation forecasts during heavy rainfall events in South China and the Sichuan Basin. High-resolution numerical models are employed to simulate adaptive observations. By identifying sensitive areas of key weather system positions 42 hours before heavy rainfall events, the adaptive observations help improve the prediction of jet streams, strong winds and shear lines, which are essential for accurate heavy rainfall forecasting. This improvement is reflected in both the precipitation structure and location accuracy within the verification region. In South China, targeted observations enhance rainfall predictions by improving water vapor transformation. In the Sichuan Basin, adaptive observations refine water vapor and vortex dynamics adjustments. The research highlights the importance of accurately predicting shear lines and jet streams for forecasting heavy rainfall in these areas. Overall, this study found that adaptive observation enhances the precipitation forecast skills of the structure and location for heavy rainfall in South China and the Sichuan Basin, emphasizing their potential utility in operational numerical weather prediction.
This study examines the effectiveness of adaptive observation experiments using the ensemble transformation sensitivity (ETS) method to improve precipitation forecasts during heavy rainfall events in South China and the Sichuan Basin. High-resolution numerical models are employed to simulate adaptive observations. By identifying sensitive areas of key weather system positions 42 hours before heavy rainfall events, the adaptive observations help improve the prediction of jet streams, strong winds and shear lines, which are essential for accurate heavy rainfall forecasting. This improvement is reflected in both the precipitation structure and location accuracy within the verification region. In South China, targeted observations enhance rainfall predictions by improving water vapor transformation. In the Sichuan Basin, adaptive observations refine water vapor and vortex dynamics adjustments. The research highlights the importance of accurately predicting shear lines and jet streams for forecasting heavy rainfall in these areas. Overall, this study found that adaptive observation enhances the precipitation forecast skills of the structure and location for heavy rainfall in South China and the Sichuan Basin, emphasizing their potential utility in operational numerical weather prediction.
, Available online
, Manuscript accepted 09 April 2024, doi: 10.1007/s00376-024-3345-1
Abstract:
Seasonal rainfall plays a vital role in both environmental dynamics and decision-making for rainfed agriculture in Ethiopia, a country often impacted by extreme climate events such as drought and flooding. Predicting the onset of the rainy season and providing localized rainfall forecasts for Ethiopia is challenging due to the changing spatiotemporal patterns and the country's rugged topography. The CHIRPS, ERA5-Land total precipitation and temperature data are used since 1981-2022 for prediction of spatial rainfall by applying artificial neural network (ANN). The recurrent neural network (RNN) is a nonlinear autoregressive network with exogenous input (NARX), includes feedforward connections and multiple network layers, employing the Levenberg Marquart algorithm. This method is applied to downscale data from European Centre for Medium-range Weather Forecasts fifth-generation seasonal forecast system (ECMWF-SEAS5) and the Euro-Mediterranean Centre for Climate Change (CMCC) to the specific locations of rainfall stations in Ethiopia for the period spanning from 1980 to 2020. Across the stations the results of NARX shows strong association and reduced error. The statistical results indicate that, except for the southwestern Ethiopian highlands, the downscaled monthly precipitation data exhibits high skill scores when compared to the station records, demonstrating the effectiveness of the NARX approach for predicting local seasonal rainfall in Ethiopia's complex terrain. In addition to this spatial ANN of the summer season precipitation, temperature and combined of these two variables shows a promising results compared with CHIRPS products.
Seasonal rainfall plays a vital role in both environmental dynamics and decision-making for rainfed agriculture in Ethiopia, a country often impacted by extreme climate events such as drought and flooding. Predicting the onset of the rainy season and providing localized rainfall forecasts for Ethiopia is challenging due to the changing spatiotemporal patterns and the country's rugged topography. The CHIRPS, ERA5-Land total precipitation and temperature data are used since 1981-2022 for prediction of spatial rainfall by applying artificial neural network (ANN). The recurrent neural network (RNN) is a nonlinear autoregressive network with exogenous input (NARX), includes feedforward connections and multiple network layers, employing the Levenberg Marquart algorithm. This method is applied to downscale data from European Centre for Medium-range Weather Forecasts fifth-generation seasonal forecast system (ECMWF-SEAS5) and the Euro-Mediterranean Centre for Climate Change (CMCC) to the specific locations of rainfall stations in Ethiopia for the period spanning from 1980 to 2020. Across the stations the results of NARX shows strong association and reduced error. The statistical results indicate that, except for the southwestern Ethiopian highlands, the downscaled monthly precipitation data exhibits high skill scores when compared to the station records, demonstrating the effectiveness of the NARX approach for predicting local seasonal rainfall in Ethiopia's complex terrain. In addition to this spatial ANN of the summer season precipitation, temperature and combined of these two variables shows a promising results compared with CHIRPS products.
, Available online
, Manuscript accepted 09 April 2024, doi: 10.1007/s00376-024-3293-9
Abstract:
Coal fired power plants are a major carbon source in China. In order to assess the evaluation of China’s carbon reduction progress with the promise made on the Paris Agreement, it is crucial to monitor the carbon flux intensity from coal-fired power plants. Previous studies have calculated CO2 emissions from point sources based on the Orbiting Carbon Observatory-2 and -3 (OCO-2 and OCO-3) satellite measurements, but the factors affecting CO2 flux estimations are uncertain. In this study, we employ a Gaussian Plume Model to estimate CO2 emissions from three power plants in China based on OCO-3 XCO2 measurements. Moreover, flux uncertainties resulting from wind information, background value, satellite CO2 measurement and atmospheric stability are investigated. Our satellite-based CO2 emission estimates at the Tuoketuo and Nongliushi power plants are generally smaller than the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) inventory, but larger than ODIAC at Baotou. This study highlights the CO2 flux uncertainty derived from the satellite measurements.
Coal fired power plants are a major carbon source in China. In order to assess the evaluation of China’s carbon reduction progress with the promise made on the Paris Agreement, it is crucial to monitor the carbon flux intensity from coal-fired power plants. Previous studies have calculated CO2 emissions from point sources based on the Orbiting Carbon Observatory-2 and -3 (OCO-2 and OCO-3) satellite measurements, but the factors affecting CO2 flux estimations are uncertain. In this study, we employ a Gaussian Plume Model to estimate CO2 emissions from three power plants in China based on OCO-3 XCO2 measurements. Moreover, flux uncertainties resulting from wind information, background value, satellite CO2 measurement and atmospheric stability are investigated. Our satellite-based CO2 emission estimates at the Tuoketuo and Nongliushi power plants are generally smaller than the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) inventory, but larger than ODIAC at Baotou. This study highlights the CO2 flux uncertainty derived from the satellite measurements.
, Available online
, Manuscript accepted 02 April 2024, doi: 10.1007/s00376-024-3240-9
Abstract:
As a new type of wind field detection equipment, coherent Doppler wind lidar (CDWL) still needs more relevant observation experiments to compare and verify whether it can achieve the accuracy and precision of traditional observation equipment in urban areas. In this experiment, a self-developed CDWL was used to observe for 4 months in the Beijing southern area. After the data acquisition time and height match, the wind profile data obtained based on the Doppler beam swinging (DBS) five-beam inversion algorithm were compared with the radiosonde data released from the same location. The standard deviation (SD) of wind speed is 0.8m s-1, and the coefficient of determination R2 is 0.95. The SD of the wind direction is 17.7° and the coefficient of determination R2 is 0.96. Below the height of the roughness sublayer (about 400m), the error in wind speed and wind direction is significantly greater than the error above the height of the boundary layer (about 1500m). In the case of wind speed less than 4 m s-1, the error of wind direction is more significant and is affected by the distribution of surrounding buildings. Averaging at different height levels using suitable time windows can effectively reduce the effects of turbulence and thus reduce the error caused by different measurement methods of the two devices.
As a new type of wind field detection equipment, coherent Doppler wind lidar (CDWL) still needs more relevant observation experiments to compare and verify whether it can achieve the accuracy and precision of traditional observation equipment in urban areas. In this experiment, a self-developed CDWL was used to observe for 4 months in the Beijing southern area. After the data acquisition time and height match, the wind profile data obtained based on the Doppler beam swinging (DBS) five-beam inversion algorithm were compared with the radiosonde data released from the same location. The standard deviation (SD) of wind speed is 0.8m s-1, and the coefficient of determination R2 is 0.95. The SD of the wind direction is 17.7° and the coefficient of determination R2 is 0.96. Below the height of the roughness sublayer (about 400m), the error in wind speed and wind direction is significantly greater than the error above the height of the boundary layer (about 1500m). In the case of wind speed less than 4 m s-1, the error of wind direction is more significant and is affected by the distribution of surrounding buildings. Averaging at different height levels using suitable time windows can effectively reduce the effects of turbulence and thus reduce the error caused by different measurement methods of the two devices.
, Available online
, Manuscript accepted 02 April 2024, doi: 10.1007/s00376-024-3384-7
Abstract:
In this study, a new rain type classification algorithm for the Dual-Frequency Precipitation Radar (DPR) suitable over the Tibetan Plateau (TP) was proposed by analyzing Global Precipitation Measurement (GPM) DPR Level-2 data in summer from 2014 to 2020. It was found that the DPR rain type classification algorithm (simply called DPR algorithm) has mis-identification problems in two aspects in summer TP. In the new algorithm of rain type classification in summer TP, four rain types are classified by using new thresholds, such as the maximum reflectivity factor, the difference between the maximum reflectivity factor and the background maximum reflectivity factor, and the echo top height. In the threshold of the maximum reflectivity factors, 30 dBZ and 18 dBZ are both thresholds to separate strong convective precipitation, weak convective precipitation and weak precipitation. The results illustrate obvious differences of radar reflectivity factor and vertical velocity among the three rain types in summer TP, such as the reflectivity factor of most strong convective precipitation distributes from 15 dBZ to near 35 dBZ from 4 km to 13 km, and increases almost linearly with the decrease in height. For most weak convective precipitation, the reflectivity factor distributes from 15dBZ to 28 dBZ with the height from 4 km to 9 km. For weak precipitation, the reflectivity factor mainly distributes in range of 15–25 dBZ with height within 4–10 km. It is also shows that weak precipitation is the dominant rain type in summer TP, accounting for 40%–80%, followed by weak convective precipitation (25%–40%), and strong convective precipitation has the least proportion (less than 30%).
In this study, a new rain type classification algorithm for the Dual-Frequency Precipitation Radar (DPR) suitable over the Tibetan Plateau (TP) was proposed by analyzing Global Precipitation Measurement (GPM) DPR Level-2 data in summer from 2014 to 2020. It was found that the DPR rain type classification algorithm (simply called DPR algorithm) has mis-identification problems in two aspects in summer TP. In the new algorithm of rain type classification in summer TP, four rain types are classified by using new thresholds, such as the maximum reflectivity factor, the difference between the maximum reflectivity factor and the background maximum reflectivity factor, and the echo top height. In the threshold of the maximum reflectivity factors, 30 dBZ and 18 dBZ are both thresholds to separate strong convective precipitation, weak convective precipitation and weak precipitation. The results illustrate obvious differences of radar reflectivity factor and vertical velocity among the three rain types in summer TP, such as the reflectivity factor of most strong convective precipitation distributes from 15 dBZ to near 35 dBZ from 4 km to 13 km, and increases almost linearly with the decrease in height. For most weak convective precipitation, the reflectivity factor distributes from 15dBZ to 28 dBZ with the height from 4 km to 9 km. For weak precipitation, the reflectivity factor mainly distributes in range of 15–25 dBZ with height within 4–10 km. It is also shows that weak precipitation is the dominant rain type in summer TP, accounting for 40%–80%, followed by weak convective precipitation (25%–40%), and strong convective precipitation has the least proportion (less than 30%).
, Available online
, Manuscript accepted 28 March 2024, doi: 10.1007/s00376-024-3105-2
Abstract:
This paper presents a method for retrieving optical parameters from volcanic sulfate aerosols from the AHI radiometer on board the Himawari-8 satellite. The proposed method is based on optical models for various mixtures of aerosol components from volcanic clouds, including ash particles, ice crystals, water drops, and sulfate aerosol droplets. The application of multi-component optical models of various aerosol compositions allows for the optical thickness and mass loading of sulfate aerosol to be estimated in the sulfuric cloud formed after the Karymsky volcano eruption on 3 November 2021. A comprehensive analysis of the brightness temperatures of the sulfuric cloud in the infrared bands was performed, which revealed that the cloud was composed of a mixture of sulfate aerosol and water droplets. Using models of various aerosol compositions allows for the satellite-based estimation of optical parameters not only for sulfate aerosol but also for the whole aerosol mixture.
This paper presents a method for retrieving optical parameters from volcanic sulfate aerosols from the AHI radiometer on board the Himawari-8 satellite. The proposed method is based on optical models for various mixtures of aerosol components from volcanic clouds, including ash particles, ice crystals, water drops, and sulfate aerosol droplets. The application of multi-component optical models of various aerosol compositions allows for the optical thickness and mass loading of sulfate aerosol to be estimated in the sulfuric cloud formed after the Karymsky volcano eruption on 3 November 2021. A comprehensive analysis of the brightness temperatures of the sulfuric cloud in the infrared bands was performed, which revealed that the cloud was composed of a mixture of sulfate aerosol and water droplets. Using models of various aerosol compositions allows for the satellite-based estimation of optical parameters not only for sulfate aerosol but also for the whole aerosol mixture.
, Available online
, Manuscript accepted 26 March 2024, doi: 10.1007/s00376-024-3277-9
Abstract:
The Tibetan Plateau (TP) region, also known as the “Asian water tower”, provides a vital water resource for downstream regions. Previous studies of water cycle changes over the TP have been conducted with climate models of coarse resolution in which deep convection must be parameterized. In this study, we present results from a first set of high-resolution climate change simulations that permit convection at approximately 3.3-km grid spacing, with a focus on the TP, using the Icosahedral Nonhydrostatic Weather and Climate Model (ICON). Two 12-year simulations were performed, consisting of a retrospective simulation (2008–20) with initial and boundary conditions from ERA5 reanalysis and a pseudo-global warming projection driven by modified reanalysis-derived initial and boundary conditions by adding the monthly CMIP6 ensemble-mean climate change under the SSP5-8.5 scenario. The retrospective simulation shows overall good performance in capturing the seasonal precipitation and surface air temperature. Over the central and eastern TP, the average biases in precipitation (temperature) are less than −0.34 mm d−1 (−1.1°C) throughout the year. The simulated biases over the TP are height-dependent. Cold (wet) biases are found in summer (winter) above 5500 m. The future climate simulation suggests that the TP will be wetter and warmer under the SSP5-8.5 scenario. The general features of projected changes in ICON are comparable to the CMIP6 ensemble projection, but the added value from kilometer-scale modeling is evident in both precipitation and temperature projections over complex topographic regions. These ICON-downscaled climate change simulations provide a high-resolution dataset to the community for the study of regional climate changes and impacts over the TP.
The Tibetan Plateau (TP) region, also known as the “Asian water tower”, provides a vital water resource for downstream regions. Previous studies of water cycle changes over the TP have been conducted with climate models of coarse resolution in which deep convection must be parameterized. In this study, we present results from a first set of high-resolution climate change simulations that permit convection at approximately 3.3-km grid spacing, with a focus on the TP, using the Icosahedral Nonhydrostatic Weather and Climate Model (ICON). Two 12-year simulations were performed, consisting of a retrospective simulation (2008–20) with initial and boundary conditions from ERA5 reanalysis and a pseudo-global warming projection driven by modified reanalysis-derived initial and boundary conditions by adding the monthly CMIP6 ensemble-mean climate change under the SSP5-8.5 scenario. The retrospective simulation shows overall good performance in capturing the seasonal precipitation and surface air temperature. Over the central and eastern TP, the average biases in precipitation (temperature) are less than −0.34 mm d−1 (−1.1°C) throughout the year. The simulated biases over the TP are height-dependent. Cold (wet) biases are found in summer (winter) above 5500 m. The future climate simulation suggests that the TP will be wetter and warmer under the SSP5-8.5 scenario. The general features of projected changes in ICON are comparable to the CMIP6 ensemble projection, but the added value from kilometer-scale modeling is evident in both precipitation and temperature projections over complex topographic regions. These ICON-downscaled climate change simulations provide a high-resolution dataset to the community for the study of regional climate changes and impacts over the TP.
, Available online
, Manuscript accepted 25 March 2024, doi: 10.1007/s00376-024-3300-1
Abstract:
Changes in the activities of the Boreal Summer Intraseasonal Oscillation (BSISO) at the end of 21st century under the SSP5-8.5 scenario are assessed by adopting 17 CMIP6 models and the weak-temperature-gradient assumption. Results show that the intraseasonal variations become more structured. The BSISO-related precipitation anomaly shows a larger zonal scale and propagates further northward. However, there is no broad agreement among models on the changes in the eastward and northward propagation speeds and the frequency of individual phases. In the western North Pacific (WNP), the BSISO precipitation variance is significantly increased, at 4.62% K−1, due to the significantly increased efficiency of vertical moisture transport per unit of BSISO apparent heating. The vertical velocity variance is significantly decreased, at −3.51% K−1, in the middle troposphere, due to the significantly increased mean-state static stability. Changes in the lower-level zonal wind variance are relatively complex, with a significant increase stretching from the northwestern to southeastern WNP, but the opposite in other regions. This is probably due to the combined impacts of the northeastward shift of the BSISO signals and the reduced BSISO vertical velocity variance under global warming. Changes in strong and normal BSISO events in the WNP are also compared. They show same-signed changes in precipitation and large-scale circulation anomalies but opposite changes in the vertical velocity anomalies. This is probably because the precipitation anomaly of strong (normal) events changes at a rate much larger (smaller) than that of the mean-state static stability, causing enhanced (reduced) vertical motion.
Changes in the activities of the Boreal Summer Intraseasonal Oscillation (BSISO) at the end of 21st century under the SSP5-8.5 scenario are assessed by adopting 17 CMIP6 models and the weak-temperature-gradient assumption. Results show that the intraseasonal variations become more structured. The BSISO-related precipitation anomaly shows a larger zonal scale and propagates further northward. However, there is no broad agreement among models on the changes in the eastward and northward propagation speeds and the frequency of individual phases. In the western North Pacific (WNP), the BSISO precipitation variance is significantly increased, at 4.62% K−1, due to the significantly increased efficiency of vertical moisture transport per unit of BSISO apparent heating. The vertical velocity variance is significantly decreased, at −3.51% K−1, in the middle troposphere, due to the significantly increased mean-state static stability. Changes in the lower-level zonal wind variance are relatively complex, with a significant increase stretching from the northwestern to southeastern WNP, but the opposite in other regions. This is probably due to the combined impacts of the northeastward shift of the BSISO signals and the reduced BSISO vertical velocity variance under global warming. Changes in strong and normal BSISO events in the WNP are also compared. They show same-signed changes in precipitation and large-scale circulation anomalies but opposite changes in the vertical velocity anomalies. This is probably because the precipitation anomaly of strong (normal) events changes at a rate much larger (smaller) than that of the mean-state static stability, causing enhanced (reduced) vertical motion.
, Available online
, Manuscript accepted 25 March 2024, doi: 10.1007/s00376-024-3300-1.
Abstract:
Changes in the boreal summer intraseasonal oscillation (BSISO) activities at the end of 21st century under the SSP5-8.5 scenario are assessed here by adopting 17 CMIP6 models and the weak-temperature-gradient assumption. Results show that the intraseasonal variations become more structured. The BSISO related precipitation anomaly shows a larger zonal scale and propagates further northward. However, there has no broad agreement among models on the changes in the eastward and northward propagation speeds and the frequency of individual phases. In the western North Pacific (WNP), the BSISO precipitation variance is significantly increased at 4.62% K-1 due to the significantly increased efficiency of vertical moisture transport by per unit BSISO apparent heating. The vertical velocity variance is significantly decreased at -3.51% K-1 in the middle troposphere due to the significantly increased mean-state static stability. Changes in the lower-level zonal wind variance are relatively complex with a significant increase stretching from northwestern to southeastern WNP but the opposite in other regions. This is probably due to the combined impacts of northeastward shift of the BSISO signals and the reduced BSISO vertical velocity variance under global warming. Changes in strong and normal BSISO events in the WNP are also compared. They show same-signed changes in precipitation and large scale circulation anomalies but opposite changes in the vertical velocity anomalies. This is probably because the precipitation anomaly of strong/normal events changes at a rate much larger/smaller than that of the mean-state static stability, causing enhanced/reduced vertical motion.
Changes in the boreal summer intraseasonal oscillation (BSISO) activities at the end of 21st century under the SSP5-8.5 scenario are assessed here by adopting 17 CMIP6 models and the weak-temperature-gradient assumption. Results show that the intraseasonal variations become more structured. The BSISO related precipitation anomaly shows a larger zonal scale and propagates further northward. However, there has no broad agreement among models on the changes in the eastward and northward propagation speeds and the frequency of individual phases. In the western North Pacific (WNP), the BSISO precipitation variance is significantly increased at 4.62% K-1 due to the significantly increased efficiency of vertical moisture transport by per unit BSISO apparent heating. The vertical velocity variance is significantly decreased at -3.51% K-1 in the middle troposphere due to the significantly increased mean-state static stability. Changes in the lower-level zonal wind variance are relatively complex with a significant increase stretching from northwestern to southeastern WNP but the opposite in other regions. This is probably due to the combined impacts of northeastward shift of the BSISO signals and the reduced BSISO vertical velocity variance under global warming. Changes in strong and normal BSISO events in the WNP are also compared. They show same-signed changes in precipitation and large scale circulation anomalies but opposite changes in the vertical velocity anomalies. This is probably because the precipitation anomaly of strong/normal events changes at a rate much larger/smaller than that of the mean-state static stability, causing enhanced/reduced vertical motion.
, Available online
, Manuscript accepted 25 March 2024, doi: 10.1007/s00376-024-3237-4
Abstract:
There is limited understanding regarding the formation of multiple tropical cyclones (MTCs). This study explores the environmental conditions conducive to MTC formation by objectively determining the atmospheric circulation patterns favorable for MTC formation over the western North Pacific. Based on 199 MTC events occurring from June to October 1980–2020, four distinct circulation patterns are identified: the monsoon trough (MT) pattern, accounting for 40.3% of occurrences, the confluence zone (CON) pattern at 26.2%, the easterly wave (EW) pattern at 17.8%, and the monsoon gyre (MG) pattern at 15.7%. The MT pattern mainly arises from the interaction between the subtropical high and the monsoon trough, with MTCs forming along the monsoon trough and its flanks. The CON pattern is affected by the subtropical high, the South Asian high, and the monsoon trough, with MTCs emerging at the confluence zone where the prevailing southwesterly and southeasterly flows converge. The EW pattern is dominated by easterly flows, with MTCs developing along the easterly wave train. MTCs in the MG pattern arise within a monsoon vortex characterized by strong southwesterly flows. A quantitative analysis further indicates that MTC formation in the MT pattern is primarily governed by mid-level vertical velocity and low-level vorticity, while mid-level humidity and vertical velocity are significantly important in the other patterns. The meridional shear and convergence of zonal winds are essential in converting barotropic energy from the basic flows to disturbance kinetic energy, acting as the primary source for eddy kinetic energy growth.
There is limited understanding regarding the formation of multiple tropical cyclones (MTCs). This study explores the environmental conditions conducive to MTC formation by objectively determining the atmospheric circulation patterns favorable for MTC formation over the western North Pacific. Based on 199 MTC events occurring from June to October 1980–2020, four distinct circulation patterns are identified: the monsoon trough (MT) pattern, accounting for 40.3% of occurrences, the confluence zone (CON) pattern at 26.2%, the easterly wave (EW) pattern at 17.8%, and the monsoon gyre (MG) pattern at 15.7%. The MT pattern mainly arises from the interaction between the subtropical high and the monsoon trough, with MTCs forming along the monsoon trough and its flanks. The CON pattern is affected by the subtropical high, the South Asian high, and the monsoon trough, with MTCs emerging at the confluence zone where the prevailing southwesterly and southeasterly flows converge. The EW pattern is dominated by easterly flows, with MTCs developing along the easterly wave train. MTCs in the MG pattern arise within a monsoon vortex characterized by strong southwesterly flows. A quantitative analysis further indicates that MTC formation in the MT pattern is primarily governed by mid-level vertical velocity and low-level vorticity, while mid-level humidity and vertical velocity are significantly important in the other patterns. The meridional shear and convergence of zonal winds are essential in converting barotropic energy from the basic flows to disturbance kinetic energy, acting as the primary source for eddy kinetic energy growth.
, Available online
, Manuscript accepted 25 March 2024, doi: 10.1007/s00376-024-3327-3
Abstract:
This study investigates the dominant modes of interannual variability of snowfall frequency over the Eurasian continent during autumn and winter, and explores the underlying physical mechanisms. The first EOF mode (EOF1) of snowfall frequency during autumn is mainly characterized by positive anomalies over the Central Siberian Plateau (CSP) and Europe, with opposite anomalies over Central Asia (CA). EOF1 during winter is characterized by positive anomalies in Siberia and negative anomalies in Europe and East Asia (EA). During autumn, EOF1 is associated with the anomalous sea ice in the Kara–Laptev seas (KLS) and sea surface temperature (SST) over the North Atlantic. Increased sea ice in the KLS may cause an increase in the meridional air temperature gradient, resulting in increased synoptic-scale wave activity, thereby inducing increased snowfall frequency over Europe and the CSP. Anomalous increases of both sea ice in the KLS and SST in the North Atlantic may stimulate downstream propagation of Rossby waves and induce an anomalous high in CA corresponding to decreased snowfall frequency. In contrast, EOF1 is mainly affected by the anomalous atmospheric circulation during winter. In the positive phase of the North Atlantic Oscillation (NAO), an anomalous deep cold low (warm high) occurs over Siberia (Europe) leading to increased (decreased) snowfall frequency over Siberia (Europe). The synoptic-scale wave activity excited by the positive NAO can induce downstream Rossby wave propagation and contribute to an anomalous high and descending motion over EA, which may inhibit snowfall. The NAO in winter may be modulated by the Indian Ocean dipole and sea ice in the Barents-Kara-Laptev Seas in autumn.
This study investigates the dominant modes of interannual variability of snowfall frequency over the Eurasian continent during autumn and winter, and explores the underlying physical mechanisms. The first EOF mode (EOF1) of snowfall frequency during autumn is mainly characterized by positive anomalies over the Central Siberian Plateau (CSP) and Europe, with opposite anomalies over Central Asia (CA). EOF1 during winter is characterized by positive anomalies in Siberia and negative anomalies in Europe and East Asia (EA). During autumn, EOF1 is associated with the anomalous sea ice in the Kara–Laptev seas (KLS) and sea surface temperature (SST) over the North Atlantic. Increased sea ice in the KLS may cause an increase in the meridional air temperature gradient, resulting in increased synoptic-scale wave activity, thereby inducing increased snowfall frequency over Europe and the CSP. Anomalous increases of both sea ice in the KLS and SST in the North Atlantic may stimulate downstream propagation of Rossby waves and induce an anomalous high in CA corresponding to decreased snowfall frequency. In contrast, EOF1 is mainly affected by the anomalous atmospheric circulation during winter. In the positive phase of the North Atlantic Oscillation (NAO), an anomalous deep cold low (warm high) occurs over Siberia (Europe) leading to increased (decreased) snowfall frequency over Siberia (Europe). The synoptic-scale wave activity excited by the positive NAO can induce downstream Rossby wave propagation and contribute to an anomalous high and descending motion over EA, which may inhibit snowfall. The NAO in winter may be modulated by the Indian Ocean dipole and sea ice in the Barents-Kara-Laptev Seas in autumn.
, Available online
, Manuscript accepted 21 March 2024, doi: 10.1007/s00376-024-3365-x
Abstract:
The impacts of hydrometeor-related processes on the development and evolution of the “21.7” extremely heavy rainfall in Zhengzhou were investigated using WRF simulations. Surface precipitation was determined by the hydrometeor microphysical processes (all microphysical source sink terms of hydrometeors) and macrophysical processes (local change and flux convergence of hydrometeors). The contribution of hydrometeor macrophysical processes was commonly less than 10%, but could reach 30%–50% in the early stage of precipitation, which was largely dependent on the size of the study area. The macrophysical processes of liquid-phase hydrometeors always presented a promotional effect on rainfall, while the ice-phase hydrometeors played a negative role in the middle and later stages of precipitation. The distributions of microphysical latent heat corresponded well with those of buoyancy and vertical velocity (tendency), indicating that the phase-change heating was the major driver for convective development. Reasonable diagnostic buoyancy was obtained by choosing an area close to the convective size for getting the reference state of air. In addition, a new dynamic equilibrium involving hydrometeors with a tilted airflow was formed during the heavy precipitation period (updraft was not the strongest). The heaviest instantaneous precipitation was mainly produced by the warm-rain processes. Sensitivity experiments further pointed out that the uncertainty of latent heat parameterization (±20%) did not significantly affect the convective rainfall. While when the phase-change heating only altered the temperature tendency, its impact on precipitation was remarkable. The results of this study help to deepen our understanding of heavy rainfall mechanisms from the perspective of hydrometeor processes.
The impacts of hydrometeor-related processes on the development and evolution of the “21.7” extremely heavy rainfall in Zhengzhou were investigated using WRF simulations. Surface precipitation was determined by the hydrometeor microphysical processes (all microphysical source sink terms of hydrometeors) and macrophysical processes (local change and flux convergence of hydrometeors). The contribution of hydrometeor macrophysical processes was commonly less than 10%, but could reach 30%–50% in the early stage of precipitation, which was largely dependent on the size of the study area. The macrophysical processes of liquid-phase hydrometeors always presented a promotional effect on rainfall, while the ice-phase hydrometeors played a negative role in the middle and later stages of precipitation. The distributions of microphysical latent heat corresponded well with those of buoyancy and vertical velocity (tendency), indicating that the phase-change heating was the major driver for convective development. Reasonable diagnostic buoyancy was obtained by choosing an area close to the convective size for getting the reference state of air. In addition, a new dynamic equilibrium involving hydrometeors with a tilted airflow was formed during the heavy precipitation period (updraft was not the strongest). The heaviest instantaneous precipitation was mainly produced by the warm-rain processes. Sensitivity experiments further pointed out that the uncertainty of latent heat parameterization (±20%) did not significantly affect the convective rainfall. While when the phase-change heating only altered the temperature tendency, its impact on precipitation was remarkable. The results of this study help to deepen our understanding of heavy rainfall mechanisms from the perspective of hydrometeor processes.
, Available online
, Manuscript accepted 18 March 2024, doi: 10.1007/s00376-024-3104-3
Abstract:
Besides the rapid retreating trend of Arctic sea-ice extent (SIE), this study found the most outstanding low-frequency variation of SIE to be a 4–6-year periodic variation. Using a clustering analysis algorithm, the SIE in most ice-covered regions was clustered into two special regions: Region-1 around the Barents Sea and Region-2 around the Canadian Basin, which were located on either side of the Arctic Transpolar Drift. Clear 4–6-year periodic variation in these two regions was identified using a novel method called “running linear fitting algorithm”. The rate of temporal variation of the Arctic SIE was related to three driving factors: the regional air temperature, the sea-ice areal flux across the Arctic Transpolar Drift, and the divergence of sea-ice drift. The 4–6-year periodic variation was found to have always been present since 1979, but the SIE responded to different factors under heavy and light ice conditions divided by the year 2005. The joint contribution of the three factors to SIE variation exceeded 83% and 59% in the two regions, respectively, remarkably reflecting their dynamic mechanism. It is proven that the process of El Niño–Southern Oscillation (ENSO) is closely associated with the three factors, being the fundamental source of the 4–6-year periodic variations of Arctic SIE.
Besides the rapid retreating trend of Arctic sea-ice extent (SIE), this study found the most outstanding low-frequency variation of SIE to be a 4–6-year periodic variation. Using a clustering analysis algorithm, the SIE in most ice-covered regions was clustered into two special regions: Region-1 around the Barents Sea and Region-2 around the Canadian Basin, which were located on either side of the Arctic Transpolar Drift. Clear 4–6-year periodic variation in these two regions was identified using a novel method called “running linear fitting algorithm”. The rate of temporal variation of the Arctic SIE was related to three driving factors: the regional air temperature, the sea-ice areal flux across the Arctic Transpolar Drift, and the divergence of sea-ice drift. The 4–6-year periodic variation was found to have always been present since 1979, but the SIE responded to different factors under heavy and light ice conditions divided by the year 2005. The joint contribution of the three factors to SIE variation exceeded 83% and 59% in the two regions, respectively, remarkably reflecting their dynamic mechanism. It is proven that the process of El Niño–Southern Oscillation (ENSO) is closely associated with the three factors, being the fundamental source of the 4–6-year periodic variations of Arctic SIE.
, Available online
, Manuscript accepted 18 March 2024, doi: 10.1007/s00376-024-3261-4
Abstract:
Under global warming, understanding the long-term variation in different types of heatwaves is vital for China’s preparedness against escalating heat stress. This study investigates dry and wet heatwave shifts in eastern China over recent decades. Spatial trend analysis displays pronounced warming in inland midlatitudes and the Yangtze River Valley, with increased humidity in coastal regions. EOF results indicate intensifying dry heatwaves in northern China, while the Yangtze River Valley sees more frequent dry heatwaves. On the other hand, Indochina and regions north of 25°N also experience intensified wet heatwaves, corresponding to regional humidity increases. Composite analysis is conducted based on different situations: strong, frequent dry or wet heatwaves. Strong dry heatwaves are influenced by anticyclonic circulations over northern China, accompanied by warming SST anomalies around the coastal midlatitudes of the western North Pacific (WNP). Frequent dry heatwaves are related to strong subsidence along with a strengthened subtropical high over the WNP. Strong and frequent wet heatwaves show an intensified Okhotsk high at higher latitudes in the lower troposphere, and a negative circumglobal teleconnection wave train pattern in the upper troposphere. Decaying El Niño SST patterns are observed in two kinds of wet heatwave and frequent dry heatwave years. Risk analysis indicates that El Niño events heighten the likelihood of these heatwaves in regions most at risk. As global warming continues, adapting and implementing mitigation strategies toward extreme heatwaves becomes crucial, especially for the aforementioned regions under significant heat stress.
Under global warming, understanding the long-term variation in different types of heatwaves is vital for China’s preparedness against escalating heat stress. This study investigates dry and wet heatwave shifts in eastern China over recent decades. Spatial trend analysis displays pronounced warming in inland midlatitudes and the Yangtze River Valley, with increased humidity in coastal regions. EOF results indicate intensifying dry heatwaves in northern China, while the Yangtze River Valley sees more frequent dry heatwaves. On the other hand, Indochina and regions north of 25°N also experience intensified wet heatwaves, corresponding to regional humidity increases. Composite analysis is conducted based on different situations: strong, frequent dry or wet heatwaves. Strong dry heatwaves are influenced by anticyclonic circulations over northern China, accompanied by warming SST anomalies around the coastal midlatitudes of the western North Pacific (WNP). Frequent dry heatwaves are related to strong subsidence along with a strengthened subtropical high over the WNP. Strong and frequent wet heatwaves show an intensified Okhotsk high at higher latitudes in the lower troposphere, and a negative circumglobal teleconnection wave train pattern in the upper troposphere. Decaying El Niño SST patterns are observed in two kinds of wet heatwave and frequent dry heatwave years. Risk analysis indicates that El Niño events heighten the likelihood of these heatwaves in regions most at risk. As global warming continues, adapting and implementing mitigation strategies toward extreme heatwaves becomes crucial, especially for the aforementioned regions under significant heat stress.
, Available online
, Manuscript accepted 12 March 2024, doi: 10.1007/s00376-024-3323-7
Abstract:
Various approaches have been proposed to minimize the upper-level systematic biases in global numerical weather prediction (NWP) models by using satellite upper-air sounding channels as anchors. However, since the China Meteorological Administration Global Forecast System (CMA-GFS) has a model top near 0.1 hPa (60 km), the upper-level temperature bias may exceed 4 K near 1 hPa and further extend to 5 hPa. In this study, channels 12–14 of the Advanced Microwave Sounding Unit A (AMSU-A) onboard five satellites of NOAA and METOP, whose weighting function peaks range from 10 to 2 hPa are all used as anchor observations in CMA-GFS. It is shown that the new “Anchor” approach can effectively reduce the biases near the model top and their downward propagation in three-month assimilation cycles. The bias growth rate of simulated upper-level channel observations is reduced to ±0.001 K d–1, compared to –0.03 K d–1 derived from the current dynamic correction scheme. The relatively stable bias significantly improves the upper-level analysis field and leads to better global medium-range forecasts up to 10 days with significant reductions in the temperature and geopotential forecast error above 10 hPa.
Various approaches have been proposed to minimize the upper-level systematic biases in global numerical weather prediction (NWP) models by using satellite upper-air sounding channels as anchors. However, since the China Meteorological Administration Global Forecast System (CMA-GFS) has a model top near 0.1 hPa (60 km), the upper-level temperature bias may exceed 4 K near 1 hPa and further extend to 5 hPa. In this study, channels 12–14 of the Advanced Microwave Sounding Unit A (AMSU-A) onboard five satellites of NOAA and METOP, whose weighting function peaks range from 10 to 2 hPa are all used as anchor observations in CMA-GFS. It is shown that the new “Anchor” approach can effectively reduce the biases near the model top and their downward propagation in three-month assimilation cycles. The bias growth rate of simulated upper-level channel observations is reduced to ±0.001 K d–1, compared to –0.03 K d–1 derived from the current dynamic correction scheme. The relatively stable bias significantly improves the upper-level analysis field and leads to better global medium-range forecasts up to 10 days with significant reductions in the temperature and geopotential forecast error above 10 hPa.
, Available online
, Manuscript accepted 07 March 2024, doi: 10.1007/s00376-024-3351-3
Abstract:
Sea salt aerosols play a critical role in regulating the global climate through their interactions with solar radiation. The size distribution of these particles is crucial in determining their bulk optical properties. In this study, we analyzed in situ measured size distributions of sea salt aerosols from four field campaigns and used multi-mode lognormal size distributions to fit the data. We employed super-spheroids and coated super-spheroids to account for the particles’ non-sphericity, inhomogeneity, and hysteresis effect during the deliquescence and crystallization processes. To compute the single-scattering properties of sea salt aerosols, we used the state-of-the-art invariant imbedding T-matrix method, which allows us to obtain accurate optical properties for sea salt aerosols with a maximum volume-equivalent diameter of 12 μm at a wavelength of 532 nm. Our results demonstrated that the particle models developed in this study were successful in replicating both the measured depolarization and lidar ratios at various relative humidity (RH) levels. Importantly, we observed that large-size particles with diameters larger than 4 μm had a substantial impact on the optical properties of sea salt aerosols, which has not been accounted for in previous studies. Specifically, excluding particles with diameters larger than 4 μm led to underestimating the scattering and backscattering coefficients by 27%~38% and 43%~60%, respectively, for the ACE-Asia field campaign. Additionally, the depolarization ratios were underestimated by 0.15 within the 50%~70% RH range. These findings emphasize the necessity of considering large particle sizes for optical modeling of sea salt aerosols.
Sea salt aerosols play a critical role in regulating the global climate through their interactions with solar radiation. The size distribution of these particles is crucial in determining their bulk optical properties. In this study, we analyzed in situ measured size distributions of sea salt aerosols from four field campaigns and used multi-mode lognormal size distributions to fit the data. We employed super-spheroids and coated super-spheroids to account for the particles’ non-sphericity, inhomogeneity, and hysteresis effect during the deliquescence and crystallization processes. To compute the single-scattering properties of sea salt aerosols, we used the state-of-the-art invariant imbedding T-matrix method, which allows us to obtain accurate optical properties for sea salt aerosols with a maximum volume-equivalent diameter of 12 μm at a wavelength of 532 nm. Our results demonstrated that the particle models developed in this study were successful in replicating both the measured depolarization and lidar ratios at various relative humidity (RH) levels. Importantly, we observed that large-size particles with diameters larger than 4 μm had a substantial impact on the optical properties of sea salt aerosols, which has not been accounted for in previous studies. Specifically, excluding particles with diameters larger than 4 μm led to underestimating the scattering and backscattering coefficients by 27%~38% and 43%~60%, respectively, for the ACE-Asia field campaign. Additionally, the depolarization ratios were underestimated by 0.15 within the 50%~70% RH range. These findings emphasize the necessity of considering large particle sizes for optical modeling of sea salt aerosols.
, Available online
, Manuscript accepted 05 March 2024, doi: 10.1007/s00376-024-3281-0
Abstract:
Quantifying differences in secondary organic aerosols (SOAs) between the preindustrial period and the present day is crucial to assess climate forcing and environmental effects resulting from anthropogenic activities. The lack of vegetation information for the preindustrial period and the uncertainties in describing SOA formation are two leading factors preventing simulation of SOA. This study calculated the online emissions of biogenic volatile organic compounds (VOCs) in the Aerosol and Atmospheric Chemistry Model of the Institute of Atmospheric Physics (IAP-AACM) by coupling the Model of Emissions of Gases and Aerosols from Nature (MEGAN), where the input vegetation parameters were simulated by the IAP Dynamic Global Vegetation Model (IAP-DGVM). The volatility basis set (VBS) approach was adopted to simulate SOA formation from the nontraditional pathways, i.e., the oxidation of intermediate VOCs and aging of primary organic aerosol. Although biogenic SOAs (BSOAs) were dominant in SOAs globally in the preindustrial period, the contribution of nontraditional anthropogenic SOAs (ASOAs) to the total SOAs was up to 35.7%. In the present day, the contribution of ASOAs was 2.8 times larger than that in the preindustrial period. The contribution of nontraditional sources of SOAs to SOA was as high as 53.1%. The influence of increased anthropogenic emissions in the present day on BSOA concentrations was greater than that of increased biogenic emission changes. The response of BSOA concentrations to anthropogenic emission changes in the present day was more sensitive than that in the preindustrial period. The nontraditional sources and the atmospheric oxidation capability greatly affect the global SOA change.
Quantifying differences in secondary organic aerosols (SOAs) between the preindustrial period and the present day is crucial to assess climate forcing and environmental effects resulting from anthropogenic activities. The lack of vegetation information for the preindustrial period and the uncertainties in describing SOA formation are two leading factors preventing simulation of SOA. This study calculated the online emissions of biogenic volatile organic compounds (VOCs) in the Aerosol and Atmospheric Chemistry Model of the Institute of Atmospheric Physics (IAP-AACM) by coupling the Model of Emissions of Gases and Aerosols from Nature (MEGAN), where the input vegetation parameters were simulated by the IAP Dynamic Global Vegetation Model (IAP-DGVM). The volatility basis set (VBS) approach was adopted to simulate SOA formation from the nontraditional pathways, i.e., the oxidation of intermediate VOCs and aging of primary organic aerosol. Although biogenic SOAs (BSOAs) were dominant in SOAs globally in the preindustrial period, the contribution of nontraditional anthropogenic SOAs (ASOAs) to the total SOAs was up to 35.7%. In the present day, the contribution of ASOAs was 2.8 times larger than that in the preindustrial period. The contribution of nontraditional sources of SOAs to SOA was as high as 53.1%. The influence of increased anthropogenic emissions in the present day on BSOA concentrations was greater than that of increased biogenic emission changes. The response of BSOA concentrations to anthropogenic emission changes in the present day was more sensitive than that in the preindustrial period. The nontraditional sources and the atmospheric oxidation capability greatly affect the global SOA change.
, Available online
, Manuscript accepted 02 January 2024, doi: 10.1007/s00376-023-3384-z
Abstract:
Central Asia consists of the former Soviet Republics, Kazakhstan, Kyrgyz Republic, Tajikistan, Turkmenistan, and Uzbekistan. The region’s climate is continental, mostly semi-arid to arid. Agriculture is a significant part of the region’s economy. By its nature of intensive water use, agriculture is extremely vulnerable to climate change. Population growth and irrigation development have significantly increased the demand for water in the region. Major climate change issues include melting glaciers and shrinking snowpack that are the foundation of the region’s water resources and a changing precipitation regime. Most glaciers are located in Kyrgyzstan and Tajikistan, leading to transboundary water resource issues. Summer already has extremely high temperatures. Analyses indicate that Central Asia has been warming and precipitation might be increasing. The warming is expected to increase, but its spatial and temporal distribution depends upon specific global scenarios. Forecasts of future precipitation show significant uncertainties in type, amount, and distribution. Regional Hydroclimate Projects (RHP) are an approach to studying these issues. Initial steps to develop a RHP began in 2021 with a widely distributed online survey about these climate issues. It was followed up with an online workshop and then in 2023, an in-person workshop, held in Tashkent, Uzbekistan. Priorities for GEWEX for the region include both observations and modeling, development of better and additional precipitation observations, all topics of the next workshop. A well-designed RHP should lead to reductions in critical climate uncertainties in policy relevant time frames that can influence decisions on necessary investments in climate adaptation.
Central Asia consists of the former Soviet Republics, Kazakhstan, Kyrgyz Republic, Tajikistan, Turkmenistan, and Uzbekistan. The region’s climate is continental, mostly semi-arid to arid. Agriculture is a significant part of the region’s economy. By its nature of intensive water use, agriculture is extremely vulnerable to climate change. Population growth and irrigation development have significantly increased the demand for water in the region. Major climate change issues include melting glaciers and shrinking snowpack that are the foundation of the region’s water resources and a changing precipitation regime. Most glaciers are located in Kyrgyzstan and Tajikistan, leading to transboundary water resource issues. Summer already has extremely high temperatures. Analyses indicate that Central Asia has been warming and precipitation might be increasing. The warming is expected to increase, but its spatial and temporal distribution depends upon specific global scenarios. Forecasts of future precipitation show significant uncertainties in type, amount, and distribution. Regional Hydroclimate Projects (RHP) are an approach to studying these issues. Initial steps to develop a RHP began in 2021 with a widely distributed online survey about these climate issues. It was followed up with an online workshop and then in 2023, an in-person workshop, held in Tashkent, Uzbekistan. Priorities for GEWEX for the region include both observations and modeling, development of better and additional precipitation observations, all topics of the next workshop. A well-designed RHP should lead to reductions in critical climate uncertainties in policy relevant time frames that can influence decisions on necessary investments in climate adaptation.
, Available online
, Manuscript accepted 11 February 2022, doi: 10.1007/s00376-022-0421-y
Abstract:
Using linear regression and composite analyses, this study identifies a pronounced asymmetric connection of sea surface temperature (SST) in the Tasman Sea with the two opposite phases of El Niño-Southern Oscillation (ENSO) during austral summer. In El Niño years, the SST anomalies (SSTAs) in the Tasman Sea exhibit a dipolar pattern with weak warmth in the northwest and modest cooling in the southeast, while during La Niña years the SSTAs exhibit a basin-scale warmth with greater amplitude. Investigations on the underlying mechanism suggest that this asymmetry arises from the oceanic heat transport, especially the anomalous Ekman meridional heat fluxes induced by the zonal wind stress anomalies, rather than the surface heat fluxes on the air-sea interface. A further analysis demonstrates that the asymmetry of oceanic heat transport between El Niño and La Niña years is driven by the asymmetric atmospheric circulation over the Tasman Sea stimulated by the asymmetric diabatic heating in the tropical Pacific between the two opposite ENSO phases.
Using linear regression and composite analyses, this study identifies a pronounced asymmetric connection of sea surface temperature (SST) in the Tasman Sea with the two opposite phases of El Niño-Southern Oscillation (ENSO) during austral summer. In El Niño years, the SST anomalies (SSTAs) in the Tasman Sea exhibit a dipolar pattern with weak warmth in the northwest and modest cooling in the southeast, while during La Niña years the SSTAs exhibit a basin-scale warmth with greater amplitude. Investigations on the underlying mechanism suggest that this asymmetry arises from the oceanic heat transport, especially the anomalous Ekman meridional heat fluxes induced by the zonal wind stress anomalies, rather than the surface heat fluxes on the air-sea interface. A further analysis demonstrates that the asymmetry of oceanic heat transport between El Niño and La Niña years is driven by the asymmetric atmospheric circulation over the Tasman Sea stimulated by the asymmetric diabatic heating in the tropical Pacific between the two opposite ENSO phases.
Abstract:
Previous numerical studies have focused on the combined effect of momentum and scalar eddy diffusivity on the intensity and structure of tropical cyclones. The separate impact of each eddy diffusivity estimated by planetary boundary layer (PBL) parameterization on the tropical cyclones has not yet been systematically examined. We therefore examined the separate impacts of moisture eddy diffusion on idealized tropical cyclones using Advanced Research Weather Research and Forecasting model with the Yonsei University PBL scheme. Our results show nonlinear effects of moisture eddy diffusivity on the simulation of idealized tropical cyclones. Increasing the moisture eddy diffusion increases the moisture content of the PBL, with three different effects on tropical cyclones: (1) an increase in the depth of the PBL; (2) an increase in convection in the inner rain band and eyewall; (3) drying of the lowest region of the PBL and then increasing the surface latent heat flux. These three processes have different effects on the intensity and structure of the tropical cyclone through various physical mechanisms. The increased surface latent heat flux is mainly responsible for the decrease in pressure. Results show that moisture eddy diffusivity has clear effects on the pressure in tropical cyclones, but contributes little to the wind intensity. This largely influences the wind–pressure relationship, which is crucial in tropical cyclones simulation. These results improve our understanding of moisture eddy diffusivity in the PBL and its influence on tropical cyclones and provides guidance for interpreting the variation of the PBL moisture in tropical cyclone simulations.
Previous numerical studies have focused on the combined effect of momentum and scalar eddy diffusivity on the intensity and structure of tropical cyclones. The separate impact of each eddy diffusivity estimated by planetary boundary layer (PBL) parameterization on the tropical cyclones has not yet been systematically examined. We therefore examined the separate impacts of moisture eddy diffusion on idealized tropical cyclones using Advanced Research Weather Research and Forecasting model with the Yonsei University PBL scheme. Our results show nonlinear effects of moisture eddy diffusivity on the simulation of idealized tropical cyclones. Increasing the moisture eddy diffusion increases the moisture content of the PBL, with three different effects on tropical cyclones: (1) an increase in the depth of the PBL; (2) an increase in convection in the inner rain band and eyewall; (3) drying of the lowest region of the PBL and then increasing the surface latent heat flux. These three processes have different effects on the intensity and structure of the tropical cyclone through various physical mechanisms. The increased surface latent heat flux is mainly responsible for the decrease in pressure. Results show that moisture eddy diffusivity has clear effects on the pressure in tropical cyclones, but contributes little to the wind intensity. This largely influences the wind–pressure relationship, which is crucial in tropical cyclones simulation. These results improve our understanding of moisture eddy diffusivity in the PBL and its influence on tropical cyclones and provides guidance for interpreting the variation of the PBL moisture in tropical cyclone simulations.
, Available online
, Manuscript accepted 10 November 2020, doi: 10.1007/s00376-020-0169-5
Abstract:
Accurate estimates of land surface characteristic parameters and turbulent heat fluxes play an important role in the understanding of land–atmosphere interaction. In this study, Fengyun-4A (FY-4A) Advanced Geostationary Radiation Imager (AGRI) satellite data and the China Land Data Assimilation System (CLDAS) meteorological forcing dataset CLDAS-V2.0 were applied for the retrieval of broadband albedo, land surface temperature (LST), radiation flux components, and turbulent heat fluxes over the Tibetan Plateau (TP). The FY-4A/AGRI and CLDAS-V2.0 data from 12 March 2018 to 30 April 2018 were first used to estimate the hourly turbulent heat fluxes over the TP. The time series data of in-situ measurements from the Tibetan Observation and Research Platform were divided into two halves—one for developing retrieval algorithms for broadband albedo and LST based on FY-4A, and the other for the cross validation. Results show the root-mean-square errors (RMSEs) of the FY-4A retrieved broadband albedo and LST were 0.0309 and 3.85 K, respectively, which verifies the applicability of the retrieval method. The RMSEs of the downwelling/upwelling shortwave radiation flux and downwelling/upwelling longwave radiation flux were 138.87/32.78 W m−2 and 51.55/17.92 W m−2, respectively, and the RMSEs of net radiation flux, sensible heat flux, and latent heat flux were 58.88 W m−2, 82.56 W m−2 and 72.46 W m−2, respectively. The spatial distributions and diurnal variations of LST and turbulent heat fluxes were further analyzed in detail.
Accurate estimates of land surface characteristic parameters and turbulent heat fluxes play an important role in the understanding of land–atmosphere interaction. In this study, Fengyun-4A (FY-4A) Advanced Geostationary Radiation Imager (AGRI) satellite data and the China Land Data Assimilation System (CLDAS) meteorological forcing dataset CLDAS-V2.0 were applied for the retrieval of broadband albedo, land surface temperature (LST), radiation flux components, and turbulent heat fluxes over the Tibetan Plateau (TP). The FY-4A/AGRI and CLDAS-V2.0 data from 12 March 2018 to 30 April 2018 were first used to estimate the hourly turbulent heat fluxes over the TP. The time series data of in-situ measurements from the Tibetan Observation and Research Platform were divided into two halves—one for developing retrieval algorithms for broadband albedo and LST based on FY-4A, and the other for the cross validation. Results show the root-mean-square errors (RMSEs) of the FY-4A retrieved broadband albedo and LST were 0.0309 and 3.85 K, respectively, which verifies the applicability of the retrieval method. The RMSEs of the downwelling/upwelling shortwave radiation flux and downwelling/upwelling longwave radiation flux were 138.87/32.78 W m−2 and 51.55/17.92 W m−2, respectively, and the RMSEs of net radiation flux, sensible heat flux, and latent heat flux were 58.88 W m−2, 82.56 W m−2 and 72.46 W m−2, respectively. The spatial distributions and diurnal variations of LST and turbulent heat fluxes were further analyzed in detail.
, Available online
, Manuscript accepted 27 June 2024, doi: 10.1007/s00376-024-4175-x
Abstract:
While being successful in the detection and attribution of climate change, the optimal fingerprinting method (OFM) may have some limitations from a physics-and-dynamics-based viewpoint. Here, an analysis is made on the linearity, non-interaction, and stationary-variability assumptions adopted by OFM. It is suggested that furthering OFM needs a viewpoint beyond statistical science, and the method should be combined with theoretical tools in the dynamics and physics of the Earth system, so as to be applied for the detection and attribution of nonlinear climate change including tipping elements within the Earth system.
While being successful in the detection and attribution of climate change, the optimal fingerprinting method (OFM) may have some limitations from a physics-and-dynamics-based viewpoint. Here, an analysis is made on the linearity, non-interaction, and stationary-variability assumptions adopted by OFM. It is suggested that furthering OFM needs a viewpoint beyond statistical science, and the method should be combined with theoretical tools in the dynamics and physics of the Earth system, so as to be applied for the detection and attribution of nonlinear climate change including tipping elements within the Earth system.
, Available online
, Manuscript accepted 16 May 2024, doi: 10.1007/s00376-024-4139-1
Abstract:
Extreme cold temperatures were observed in July and August 2023, coinciding with the WINFLY (winter fly-in) period of mid to late August into September 2023, meaning aircraft operations into McMurdo Station and Phoenix Airfield were adversely impacted. Specifically, with temperatures below −50°C, safe flight operation was not possible because of the risk of failing hydraulics and fuel turning to gel onboard the aircraft. The cold temperatures were measured across a broad area of the Antarctic, from East Antarctica toward the Ross Ice Shelf, and stretching across West Antarctica to the Antarctic Peninsula. A review of automatic weather station measurements and staffed station observations revealed a series of sites recording new record low temperatures. Four separate cold phases were identified, each a few days in duration and occurring from mid-July to the end of August 2023. A brief analysis of 500-hPa geopotential height anomalies shows how the mid-tropospheric atmospheric environment evolves in relation to these extreme cold temperatures. The monthly 500-hPa geopotential height anomalies show strong negative anomalies in August. Examination of composite geopotential height anomalies during each of the four cold phases suggests various factors leading to cold temperatures, including both southerly off-content flow and calm atmospheric conditions. Understanding the atmospheric environment that leads to such extreme cold temperatures can improve prediction of such events and benefit Antarctic operations and the study of Antarctic meteorology and climatology.
Extreme cold temperatures were observed in July and August 2023, coinciding with the WINFLY (winter fly-in) period of mid to late August into September 2023, meaning aircraft operations into McMurdo Station and Phoenix Airfield were adversely impacted. Specifically, with temperatures below −50°C, safe flight operation was not possible because of the risk of failing hydraulics and fuel turning to gel onboard the aircraft. The cold temperatures were measured across a broad area of the Antarctic, from East Antarctica toward the Ross Ice Shelf, and stretching across West Antarctica to the Antarctic Peninsula. A review of automatic weather station measurements and staffed station observations revealed a series of sites recording new record low temperatures. Four separate cold phases were identified, each a few days in duration and occurring from mid-July to the end of August 2023. A brief analysis of 500-hPa geopotential height anomalies shows how the mid-tropospheric atmospheric environment evolves in relation to these extreme cold temperatures. The monthly 500-hPa geopotential height anomalies show strong negative anomalies in August. Examination of composite geopotential height anomalies during each of the four cold phases suggests various factors leading to cold temperatures, including both southerly off-content flow and calm atmospheric conditions. Understanding the atmospheric environment that leads to such extreme cold temperatures can improve prediction of such events and benefit Antarctic operations and the study of Antarctic meteorology and climatology.
, Available online
, Manuscript accepted 08 April 2024, doi: 10.1007/s00376-024-4055-4
Abstract:
Air pollution in China covers a large area with complex sources and formation mechanisms, making it a unique place to conduct air pollution and atmospheric chemistry research. The National Natural Science Foundation of China’s Major Research Plan entitled “Fundamental Researches on the Formation and Response Mechanism of the Air Pollution Complex in China” (or the Plan) has funded 76 research projects to explore the causes of air pollution in China, and the key processes of air pollution in atmospheric physics and atmospheric chemistry. In order to summarize the abundant data from the Plan and exhibit the long-term impacts domestically and internationally, an integration project is responsible for collecting the various types of data generated by the 76 projects of the Plan. This project has classified and integrated these data, forming eight categories containing 258 datasets and 15 technical reports in total. The integration project has led to the successful establishment of the China Air Pollution Data Center (CAPDC) platform, providing storage, retrieval, and download services for the eight categories. This platform has distinct features including data visualization, related project information querying, and bilingual services in both English and Chinese, which allows for rapid searching and downloading of data and provides a solid foundation of data and support for future related research. Air pollution control in China, especially in the past decade, is undeniably a global exemplar, and this data center is the first in China to focus on research into the country’s air pollution complex.
Air pollution in China covers a large area with complex sources and formation mechanisms, making it a unique place to conduct air pollution and atmospheric chemistry research. The National Natural Science Foundation of China’s Major Research Plan entitled “Fundamental Researches on the Formation and Response Mechanism of the Air Pollution Complex in China” (or the Plan) has funded 76 research projects to explore the causes of air pollution in China, and the key processes of air pollution in atmospheric physics and atmospheric chemistry. In order to summarize the abundant data from the Plan and exhibit the long-term impacts domestically and internationally, an integration project is responsible for collecting the various types of data generated by the 76 projects of the Plan. This project has classified and integrated these data, forming eight categories containing 258 datasets and 15 technical reports in total. The integration project has led to the successful establishment of the China Air Pollution Data Center (CAPDC) platform, providing storage, retrieval, and download services for the eight categories. This platform has distinct features including data visualization, related project information querying, and bilingual services in both English and Chinese, which allows for rapid searching and downloading of data and provides a solid foundation of data and support for future related research. Air pollution control in China, especially in the past decade, is undeniably a global exemplar, and this data center is the first in China to focus on research into the country’s air pollution complex.
, Available online
, Manuscript accepted 11 May 2024, doi: 10.1007/s00376-024-4014-0
Abstract:
An unstably stratified flow entering into a stably stratified flow is referred to as penetrative convection, which is crucial to many physical processes and has been thought of as a key factor for extreme weather conditions. Past theoretical, numerical, and experimental studies on penetrative convection are reviewed, along with field studies providing insights into turbulence modeling. The physical factors that initiate penetrative convection, including internal heat sources, nonlinear constitutive relationships, centrifugal forces and other complicated factors are summarized. Cutting-edge methods for understanding transport mechanisms and statistical properties of penetrative turbulence are also documented, e.g., the variational approach and quasilinear approach, which derive scaling laws embedded in penetrative turbulence. Exploring these scaling laws in penetrative convection can improve our understanding of large-scale geophysical and astrophysical motions. To better the model of penetrative turbulence towards a practical situation, new directions, e.g., penetrative convection in spheres, and radiation-forced convection, are proposed.
An unstably stratified flow entering into a stably stratified flow is referred to as penetrative convection, which is crucial to many physical processes and has been thought of as a key factor for extreme weather conditions. Past theoretical, numerical, and experimental studies on penetrative convection are reviewed, along with field studies providing insights into turbulence modeling. The physical factors that initiate penetrative convection, including internal heat sources, nonlinear constitutive relationships, centrifugal forces and other complicated factors are summarized. Cutting-edge methods for understanding transport mechanisms and statistical properties of penetrative turbulence are also documented, e.g., the variational approach and quasilinear approach, which derive scaling laws embedded in penetrative turbulence. Exploring these scaling laws in penetrative convection can improve our understanding of large-scale geophysical and astrophysical motions. To better the model of penetrative turbulence towards a practical situation, new directions, e.g., penetrative convection in spheres, and radiation-forced convection, are proposed.
, Available online
, Manuscript accepted 06 March 2024, doi: 10.1007/s00376-024-3159-1
Abstract:
Due to the considerable uncertainties inherent in the datasets describing the spatiotemporal distributions of precipitation in the drylands of China, this study presents a new merged monthly precipitation product with a spatial resolution of approximately 0.2° × 0.2° during 1980–2019. The newly developed precipitation product was validated at different temporal scales (e.g., monthly, seasonally, and annually). The results show that the new product consistently aligns with the spatiotemporal distributions reported by the Chinese Meteorological Administration Land Data Assimilation System (CLDAS) product and Multi-Source Weighted Ensemble Precipitation (MSWEP). The merged product exhibits exceptional quality in describing the drylands of China, with a bias of –2.19 mm month–1 relative to MSWEP. In addition, the annual trend of the merged product (0.09 mm month–1 yr-1) also closely aligns with that of the MSWEP (0.11 mm month–1 yr-1) during 1980–2019. The increasing trend indicates that the water cycle and wetting process intensified in the drylands of China during this period. In particular, there was an increase in wetting during the period from 2001–2019. Generally, the merged product exhibits potential value for improving our understanding of the climate and water cycle in the drylands of China.
Due to the considerable uncertainties inherent in the datasets describing the spatiotemporal distributions of precipitation in the drylands of China, this study presents a new merged monthly precipitation product with a spatial resolution of approximately 0.2° × 0.2° during 1980–2019. The newly developed precipitation product was validated at different temporal scales (e.g., monthly, seasonally, and annually). The results show that the new product consistently aligns with the spatiotemporal distributions reported by the Chinese Meteorological Administration Land Data Assimilation System (CLDAS) product and Multi-Source Weighted Ensemble Precipitation (MSWEP). The merged product exhibits exceptional quality in describing the drylands of China, with a bias of –2.19 mm month–1 relative to MSWEP. In addition, the annual trend of the merged product (0.09 mm month–1 yr-1) also closely aligns with that of the MSWEP (0.11 mm month–1 yr-1) during 1980–2019. The increasing trend indicates that the water cycle and wetting process intensified in the drylands of China during this period. In particular, there was an increase in wetting during the period from 2001–2019. Generally, the merged product exhibits potential value for improving our understanding of the climate and water cycle in the drylands of China.
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