2023 Vol. 40, No. 6
Display Method:
2023, 40(6): 1-1.
Abstract:
2023, 40(6): 953-962.
doi: 10.1007/s00376-023-2384-3
Abstract:
From 17 November to 27 December 2022, extremely cold snowstorms frequently swept across North America and Eurasia. Diagnostic analysis reveals that these extreme cold events were closely related to the establishment of blocking circulations. Alaska Blocking (AB) and subsequent Ural Blocking (UB) episodes are linked to the phase transition of the North Atlantic Oscillation (NAO) and represent the main atmospheric regimes in the Northern Hemisphere. The downstream dispersion and propagation of Rossby wave packets from Alaska to East Asia provide a large-scale connection between AB and UB episodes. Based on the nonlinear multi-scale interaction (NMI) model, we found that the meridional potential vorticity gradient (PVy) in November and December of 2022 was anomalously weak in the mid-high latitudes from North America to Eurasia and provided a favorable background for the prolonged maintenance of UB and AB events and the generation of associated severe extreme snowstorms. However, the difference in the UB in terms of its persistence, location, and strength between November and December is related to the positive (negative) NAO in November (December). During the La Niña winter of 2022, the UB and AB events are related to the downward propagation of stratospheric anomalies, in addition to contributions by La Niña and low Arctic sea ice concentrations as they pertain to reducing PVy in mid-latitudes.
From 17 November to 27 December 2022, extremely cold snowstorms frequently swept across North America and Eurasia. Diagnostic analysis reveals that these extreme cold events were closely related to the establishment of blocking circulations. Alaska Blocking (AB) and subsequent Ural Blocking (UB) episodes are linked to the phase transition of the North Atlantic Oscillation (NAO) and represent the main atmospheric regimes in the Northern Hemisphere. The downstream dispersion and propagation of Rossby wave packets from Alaska to East Asia provide a large-scale connection between AB and UB episodes. Based on the nonlinear multi-scale interaction (NMI) model, we found that the meridional potential vorticity gradient (PVy) in November and December of 2022 was anomalously weak in the mid-high latitudes from North America to Eurasia and provided a favorable background for the prolonged maintenance of UB and AB events and the generation of associated severe extreme snowstorms. However, the difference in the UB in terms of its persistence, location, and strength between November and December is related to the positive (negative) NAO in November (December). During the La Niña winter of 2022, the UB and AB events are related to the downward propagation of stratospheric anomalies, in addition to contributions by La Niña and low Arctic sea ice concentrations as they pertain to reducing PVy in mid-latitudes.
2023, 40(6): 963-974.
doi: 10.1007/s00376-023-2385-2
Abstract:
Changes in ocean heat content (OHC), salinity, and stratification provide critical indicators for changes in Earth’s energy and water cycles. These cycles have been profoundly altered due to the emission of greenhouse gasses and other anthropogenic substances by human activities, driving pervasive changes in Earth’s climate system. In 2022, the world’s oceans, as given by OHC, were again the hottest in the historical record and exceeded the previous 2021 record maximum. According to IAP/CAS data, the 0–2000 m OHC in 2022 exceeded that of 2021 by 10.9 ± 8.3 ZJ (1 Zetta Joules = 1021 Joules); and according to NCEI/NOAA data, by 9.1 ± 8.7 ZJ. Among seven regions, four basins (the North Pacific, North Atlantic, the Mediterranean Sea, and southern oceans) recorded their highest OHC since the 1950s. The salinity-contrast index, a quantification of the “salty gets saltier–fresh gets fresher” pattern, also reached its highest level on record in 2022, implying continued amplification of the global hydrological cycle. Regional OHC and salinity changes in 2022 were dominated by a strong La Niña event. Global upper-ocean stratification continued its increasing trend and was among the top seven in 2022.
Changes in ocean heat content (OHC), salinity, and stratification provide critical indicators for changes in Earth’s energy and water cycles. These cycles have been profoundly altered due to the emission of greenhouse gasses and other anthropogenic substances by human activities, driving pervasive changes in Earth’s climate system. In 2022, the world’s oceans, as given by OHC, were again the hottest in the historical record and exceeded the previous 2021 record maximum. According to IAP/CAS data, the 0–2000 m OHC in 2022 exceeded that of 2021 by 10.9 ± 8.3 ZJ (1 Zetta Joules = 1021 Joules); and according to NCEI/NOAA data, by 9.1 ± 8.7 ZJ. Among seven regions, four basins (the North Pacific, North Atlantic, the Mediterranean Sea, and southern oceans) recorded their highest OHC since the 1950s. The salinity-contrast index, a quantification of the “salty gets saltier–fresh gets fresher” pattern, also reached its highest level on record in 2022, implying continued amplification of the global hydrological cycle. Regional OHC and salinity changes in 2022 were dominated by a strong La Niña event. Global upper-ocean stratification continued its increasing trend and was among the top seven in 2022.
2023, 40(6): 975-987.
doi: 10.1007/s00376-022-2086-2
Abstract:
This study assesses the reproducibility of 31 historical simulations from 1850 to 2014 in the Coupled Model Inter-comparison Project phase 6 (CMIP6) for the subsurface (Sub-IOD) and surface Indian Ocean Dipole (IOD) and their association with El Niño-Southern Oscillation (ENSO). Most CMIP6 models can reproduce the leading east-west dipole oscillation mode of heat content anomalies in the tropical Indian Ocean (TIO) but largely overestimate the amplitude and the dominant period of the Sub-IOD. Associated with the much steeper west-to-east thermocline tilt of the TIO, the vertical coupling between the Sub-IOD and IOD is overly strong in most CMIP6 models compared to that in the Ocean Reanalysis System 4 (ORAS4). Related to this, most models also show a much tighter association of Sub-IOD and IOD events with the canonical ENSO than observations. This explains the more (less) regular Sub-IOD and IOD events in autumn in those models with stronger (weaker) surface-subsurface coupling in TIO. Though all model simulations feature a consistently low bias regarding the percentage of the winter–spring Sub-IOD events co-occurring with a Central Pacific (CP) ENSO, the linkage between a westward-centered CP-ENSO and the Sub-IOD that occurs in winter–spring, independent of the IOD, is well reproduced.
This study assesses the reproducibility of 31 historical simulations from 1850 to 2014 in the Coupled Model Inter-comparison Project phase 6 (CMIP6) for the subsurface (Sub-IOD) and surface Indian Ocean Dipole (IOD) and their association with El Niño-Southern Oscillation (ENSO). Most CMIP6 models can reproduce the leading east-west dipole oscillation mode of heat content anomalies in the tropical Indian Ocean (TIO) but largely overestimate the amplitude and the dominant period of the Sub-IOD. Associated with the much steeper west-to-east thermocline tilt of the TIO, the vertical coupling between the Sub-IOD and IOD is overly strong in most CMIP6 models compared to that in the Ocean Reanalysis System 4 (ORAS4). Related to this, most models also show a much tighter association of Sub-IOD and IOD events with the canonical ENSO than observations. This explains the more (less) regular Sub-IOD and IOD events in autumn in those models with stronger (weaker) surface-subsurface coupling in TIO. Though all model simulations feature a consistently low bias regarding the percentage of the winter–spring Sub-IOD events co-occurring with a Central Pacific (CP) ENSO, the linkage between a westward-centered CP-ENSO and the Sub-IOD that occurs in winter–spring, independent of the IOD, is well reproduced.
2023, 40(6): 988-1004.
doi: 10.1007/s00376-022-2100-8
Abstract:
Coastal urban areas are prone to serious disasters caused by landfalling tropical cyclones (TCs). Despite the crucial role of urban forcing in precipitation, how fine-scale urban features impact landfalling TC precipitation remains poorly understood. In this study, high-resolution ensemble simulations of Typhoon Rumbia (2018), which crossed the Yangtze River Delta urban agglomeration, were conducted to analyze the potential urban impact on TC precipitation. Results show that the inner-core rainfall of Rumbia is strengthened by approximately 10% due to the urban impact near the landfall, whereas minor differences in outer-core rainfall are found when the urban impact is excluded. Further diagnostic analyses indicate that low-level upward motion is crucial for precipitation evolution, as both co-vary during landfall. Moreover, the frictionally induced upward motion plays a decisive role in enhancing the rainfall when the urban impacts are included. Urban surface friction can decelerate the tangential wind and therefore destroy the gradient balance and strengthen the radial wind within the boundary layer and thus can enhance upward motion. This study demonstrates that urban surface friction and related physical processes make the most significant contribution to landfalling TC rainfall enhancement.
Coastal urban areas are prone to serious disasters caused by landfalling tropical cyclones (TCs). Despite the crucial role of urban forcing in precipitation, how fine-scale urban features impact landfalling TC precipitation remains poorly understood. In this study, high-resolution ensemble simulations of Typhoon Rumbia (2018), which crossed the Yangtze River Delta urban agglomeration, were conducted to analyze the potential urban impact on TC precipitation. Results show that the inner-core rainfall of Rumbia is strengthened by approximately 10% due to the urban impact near the landfall, whereas minor differences in outer-core rainfall are found when the urban impact is excluded. Further diagnostic analyses indicate that low-level upward motion is crucial for precipitation evolution, as both co-vary during landfall. Moreover, the frictionally induced upward motion plays a decisive role in enhancing the rainfall when the urban impacts are included. Urban surface friction can decelerate the tangential wind and therefore destroy the gradient balance and strengthen the radial wind within the boundary layer and thus can enhance upward motion. This study demonstrates that urban surface friction and related physical processes make the most significant contribution to landfalling TC rainfall enhancement.
2023, 40(6): 1005-1020.
doi: 10.1007/s00376-022-2135-x
Abstract:
In this paper, the evolution of the microphysical characteristics in different regions (eyewall, inner core, and outer rainbands) and different quadrants [downshear left (DL), downshear right (DR), upshear left (UL), and upshear right (UR)] during the final landfall of Typhoon Ewiniar (2018) is analyzed using two-dimensional video disdrometer and S-band polarimetric radar data collected in Guangdong, China. Due to the different types of underlying surfaces, the periods before landfall (mainly dominated by underlying sea surface) and after landfall (mainly dominated by underlying land surface) are also analyzed. Both before landfall and after landfall, the downshear quadrants had the dominate typhoon precipitation. The outer rainbands had more graupel than the inner core, resulting in a larger radar reflectivity, differential reflectivity, specific differential phase shift, and mass-weighted mean diameter below the melting layer. Compared with other regions, the eyewall region had the smallest mean logarithmic normalized intercept parameter before landfall and the smallest mean mass-weighted mean diameter and the largest mean logarithmic normalized intercept parameter after landfall. The hydrometeor size sorting was obvious in the eyewall and inner core (especially in the eyewall) after landfall. A high concentration of large raindrops fell in the DL quadrant, and more small raindrops fell in the UR quadrant. Although the ice-phase process and warm rain process were both important in the formation of tropical cyclone precipitation, the warm rain process (ice-phase process) contributed more liquid water before landfall (after landfall). This investigation provides a reference for improving the microphysical parameterization scheme in numerical models.
In this paper, the evolution of the microphysical characteristics in different regions (eyewall, inner core, and outer rainbands) and different quadrants [downshear left (DL), downshear right (DR), upshear left (UL), and upshear right (UR)] during the final landfall of Typhoon Ewiniar (2018) is analyzed using two-dimensional video disdrometer and S-band polarimetric radar data collected in Guangdong, China. Due to the different types of underlying surfaces, the periods before landfall (mainly dominated by underlying sea surface) and after landfall (mainly dominated by underlying land surface) are also analyzed. Both before landfall and after landfall, the downshear quadrants had the dominate typhoon precipitation. The outer rainbands had more graupel than the inner core, resulting in a larger radar reflectivity, differential reflectivity, specific differential phase shift, and mass-weighted mean diameter below the melting layer. Compared with other regions, the eyewall region had the smallest mean logarithmic normalized intercept parameter before landfall and the smallest mean mass-weighted mean diameter and the largest mean logarithmic normalized intercept parameter after landfall. The hydrometeor size sorting was obvious in the eyewall and inner core (especially in the eyewall) after landfall. A high concentration of large raindrops fell in the DL quadrant, and more small raindrops fell in the UR quadrant. Although the ice-phase process and warm rain process were both important in the formation of tropical cyclone precipitation, the warm rain process (ice-phase process) contributed more liquid water before landfall (after landfall). This investigation provides a reference for improving the microphysical parameterization scheme in numerical models.
2023, 40(6): 1021-1042.
doi: 10.1007/s00376-022-2069-3
Abstract:
Predecessor rain events (PREs) in the Yangtze River Delta (YRD) region associated with the South China Sea and Northwest Pacific Ocean (SCS-WNPO) tropical cyclones (TCs) are investigated during the period from 2010 to 2019. Results indicate that approximately 10% of TCs making landfall in China produce PREs over the YRD region; however, they are seldom forecasted. PREs often occur over the YRD region when TCs begin to be active in the SCS-WNPO with westward paths, whilst the cold air is still existing or beginning to be present. PREs are more likely to peak in June and September. The distances between the PRE centers and the parent TC range from 900 to 1700 km. The median value of rain amounts and the median lifetime of PREs is approximately 200 mm and 24 h, respectively. Composite results suggest that PREs form in the equatorward jet-entrance region of the upper-level westerly jet (WJ), where a 925-hPa equivalent potential temperature ridge is located east of a 500-hPa trough. Deep moisture is transported from the TC vicinity to the remote PREs region. The ascent of this deep moist air in front of the 500-hPa trough and frontogenesis beneath the equatorward entrance region of the WJ is advantageous for the occurrence of PREs in the YRD region. The upper-level WJ may be affected by the subtropical high and westerly trough in the Northwest Pacific Ocean, and the occurrence of PREs may favor the maintenance of the upper-level WJ. The upper-level outflow of TCs in the SCS plays a secondary role.
Predecessor rain events (PREs) in the Yangtze River Delta (YRD) region associated with the South China Sea and Northwest Pacific Ocean (SCS-WNPO) tropical cyclones (TCs) are investigated during the period from 2010 to 2019. Results indicate that approximately 10% of TCs making landfall in China produce PREs over the YRD region; however, they are seldom forecasted. PREs often occur over the YRD region when TCs begin to be active in the SCS-WNPO with westward paths, whilst the cold air is still existing or beginning to be present. PREs are more likely to peak in June and September. The distances between the PRE centers and the parent TC range from 900 to 1700 km. The median value of rain amounts and the median lifetime of PREs is approximately 200 mm and 24 h, respectively. Composite results suggest that PREs form in the equatorward jet-entrance region of the upper-level westerly jet (WJ), where a 925-hPa equivalent potential temperature ridge is located east of a 500-hPa trough. Deep moisture is transported from the TC vicinity to the remote PREs region. The ascent of this deep moist air in front of the 500-hPa trough and frontogenesis beneath the equatorward entrance region of the WJ is advantageous for the occurrence of PREs in the YRD region. The upper-level WJ may be affected by the subtropical high and westerly trough in the Northwest Pacific Ocean, and the occurrence of PREs may favor the maintenance of the upper-level WJ. The upper-level outflow of TCs in the SCS plays a secondary role.
2023, 40(6): 1043-1057.
doi: 10.1007/s00376-022-2127-x
Abstract:
The Gated Recurrent Unit (GRU) neural network has great potential in estimating and predicting a variable. In addition to radar reflectivity (Z), radar echo-top height (ET) is also a good indicator of rainfall rate (R). In this study, we propose a new method, GRU_Z-ET, by introducing Z and ET as two independent variables into the GRU neural network to conduct the quantitative single-polarization radar precipitation estimation. The performance of GRU_Z-ET is compared with that of the other three methods in three heavy rainfall cases in China during 2018, namely, the traditional Z-R relationship (Z=300R1.4), the optimal Z-R relationship (Z=79R1.68) and the GRU neural network with only Z as the independent input variable (GRU_Z). The results indicate that the GRU_Z-ET performs the best, while the traditional Z-R relationship performs the worst. The performances of the rest two methods are similar. To further evaluate the performance of the GRU_Z-ET, 200 rainfall events with 21882 total samples during May–July of 2018 are used for statistical analysis. Results demonstrate that the spatial correlation coefficients, threat scores and probability of detection between the observed and estimated precipitation are the largest for the GRU_Z-ET and the smallest for the traditional Z-R relationship, and the root mean square error is just the opposite. In addition, these statistics of GRU_Z are similar to those of optimal Z-R relationship. Thus, it can be concluded that the performance of the GRU_Z-ET is the best in the four methods for the quantitative precipitation estimation.
The Gated Recurrent Unit (GRU) neural network has great potential in estimating and predicting a variable. In addition to radar reflectivity (Z), radar echo-top height (ET) is also a good indicator of rainfall rate (R). In this study, we propose a new method, GRU_Z-ET, by introducing Z and ET as two independent variables into the GRU neural network to conduct the quantitative single-polarization radar precipitation estimation. The performance of GRU_Z-ET is compared with that of the other three methods in three heavy rainfall cases in China during 2018, namely, the traditional Z-R relationship (Z=300R1.4), the optimal Z-R relationship (Z=79R1.68) and the GRU neural network with only Z as the independent input variable (GRU_Z). The results indicate that the GRU_Z-ET performs the best, while the traditional Z-R relationship performs the worst. The performances of the rest two methods are similar. To further evaluate the performance of the GRU_Z-ET, 200 rainfall events with 21882 total samples during May–July of 2018 are used for statistical analysis. Results demonstrate that the spatial correlation coefficients, threat scores and probability of detection between the observed and estimated precipitation are the largest for the GRU_Z-ET and the smallest for the traditional Z-R relationship, and the root mean square error is just the opposite. In addition, these statistics of GRU_Z are similar to those of optimal Z-R relationship. Thus, it can be concluded that the performance of the GRU_Z-ET is the best in the four methods for the quantitative precipitation estimation.
2023, 40(6): 1058-1072.
doi: 10.1007/s00376-022-2052-z
Abstract:
Using the global navigation satellite system (GNSS) and radio occultation (RO) refractivity data from the Constellation Observing System for Meteorology Ionosphere and Climate-2 (COSMIC-2) mission from January 2020 to December 2021, the spatial and temporal variability of Marine Boundary Layer Heights (MBLHs) over the tropical and subtropical oceans are investigated. The MBLH detection method is based on the wavelet covariance transform (WCT) algorithm, while the distinctness (DT) parameter, which reflects the significance of the maximum WCT function values, is introduced. For the refractivity profiles with indistinct maximum WCT function values, the available surrounding RO-derived MBLHs are used as auxiliary information, which helps to improve the objectiveness of the inversion process. The RO-derived MBLHs are validated with the MBLHs derived from the collocated high-vertical-resolution radiosonde observations, and the seasonal distributions of the RO-derived MBLHs are presented. Further comparisons of the magnitudes and the distributions of the RO-derived MBLHs with those derived from two model datasets, the European Centre for Medium-Range Weather Forecasts (ECMWF) analyses and the National Centers for Environmental Prediction (NCEP) Aviation (AVN) 12-hour forecast data, reveal that although high correlations exist between the RO-derived and the model-derived MBLHs, the model-derived ones are generally lower than the RO-derived ones in most parts of the tropics and sub-tropic ocean areas during different seasons, which should be partially attributed to the limited vertical resolutions of the model datasets. The correlation analyses between the MBLHs and near-surface wind speeds demonstrate that over the Pacific Ocean, near-surface wind speed is an important factor that influences the variations of the MBLHs.
Using the global navigation satellite system (GNSS) and radio occultation (RO) refractivity data from the Constellation Observing System for Meteorology Ionosphere and Climate-2 (COSMIC-2) mission from January 2020 to December 2021, the spatial and temporal variability of Marine Boundary Layer Heights (MBLHs) over the tropical and subtropical oceans are investigated. The MBLH detection method is based on the wavelet covariance transform (WCT) algorithm, while the distinctness (DT) parameter, which reflects the significance of the maximum WCT function values, is introduced. For the refractivity profiles with indistinct maximum WCT function values, the available surrounding RO-derived MBLHs are used as auxiliary information, which helps to improve the objectiveness of the inversion process. The RO-derived MBLHs are validated with the MBLHs derived from the collocated high-vertical-resolution radiosonde observations, and the seasonal distributions of the RO-derived MBLHs are presented. Further comparisons of the magnitudes and the distributions of the RO-derived MBLHs with those derived from two model datasets, the European Centre for Medium-Range Weather Forecasts (ECMWF) analyses and the National Centers for Environmental Prediction (NCEP) Aviation (AVN) 12-hour forecast data, reveal that although high correlations exist between the RO-derived and the model-derived MBLHs, the model-derived ones are generally lower than the RO-derived ones in most parts of the tropics and sub-tropic ocean areas during different seasons, which should be partially attributed to the limited vertical resolutions of the model datasets. The correlation analyses between the MBLHs and near-surface wind speeds demonstrate that over the Pacific Ocean, near-surface wind speed is an important factor that influences the variations of the MBLHs.
2023, 40(6): 1073-1088.
doi: 10.1007/s00376-022-2230-z
Abstract:
Human activities have notably affected the Earth’s climate through greenhouse gases (GHG), aerosol, and land use/land cover change (LULCC). To investigate the impact of forest changes on regional climate under different shared socioeconomic pathways (SSPs), changes in surface air temperature and precipitation over China under low and medium/high radiative forcing scenarios from 2021 to 2099 are analyzed using multimodel climate simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Results show that the climate responses to forest changes are more significant under the low radiative forcing scenario. Deforestation would increase the mean, interannual variability, and the trend of surface air temperature under the low radiative forcing scenario, but it would decrease those indices under the medium/high radiative forcing scenario. The changes in temperature show significant spatial heterogeneity. For precipitation, under the low radiative forcing scenario, deforestation would lead to a significant increase in northern China and a significant decrease in southern China, and the effects are persistent in the near term (2021–40), middle term (2041–70), and long term (2071–99). In contrast, under the medium/high radiative forcing scenario, precipitation increases in the near term and long term over most parts of China, but it decreases in the middle term, especially in southern, northern, and northeast China. The magnitude of precipitation response to deforestation remains comparatively small.
Human activities have notably affected the Earth’s climate through greenhouse gases (GHG), aerosol, and land use/land cover change (LULCC). To investigate the impact of forest changes on regional climate under different shared socioeconomic pathways (SSPs), changes in surface air temperature and precipitation over China under low and medium/high radiative forcing scenarios from 2021 to 2099 are analyzed using multimodel climate simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Results show that the climate responses to forest changes are more significant under the low radiative forcing scenario. Deforestation would increase the mean, interannual variability, and the trend of surface air temperature under the low radiative forcing scenario, but it would decrease those indices under the medium/high radiative forcing scenario. The changes in temperature show significant spatial heterogeneity. For precipitation, under the low radiative forcing scenario, deforestation would lead to a significant increase in northern China and a significant decrease in southern China, and the effects are persistent in the near term (2021–40), middle term (2041–70), and long term (2071–99). In contrast, under the medium/high radiative forcing scenario, precipitation increases in the near term and long term over most parts of China, but it decreases in the middle term, especially in southern, northern, and northeast China. The magnitude of precipitation response to deforestation remains comparatively small.
2023, 40(6): 1089-1103.
doi: 10.1007/s00376-022-2198-8
Abstract:
Turbulence in the nocturnal boundary layer (NBL) is still not well characterized, especially over complex underlying surfaces. Herein, gradient tower data and eddy covariance data collected by the Beijing 325-m tower were used to better understand the differentiating characteristics of turbulence regimes and vertical turbulence structure of urban the NBL. As for heights above the urban canopy layer (UCL), the relationship between turbulence velocity scale (VTKE) and wind speed (V) was consistent with the “HOckey-Stick” (HOST) theory proposed for a relatively flat area. Four regimes have been identified according to urban nocturnal stable boundary layer. Regime 1 occurs where local shear plays a leading role for weak turbulence under the constraint that the wind speed V<VT (threshold wind speed). Regime 2 is determined by the existence of strong turbulence that occurs when V>VT and is mainly driven by bulk shear. Regime 3 is identified by the existence of moderate turbulence when upside-down turbulence sporadic bursts occur in the presence of otherwise weak turbulence. Regime 4 is identified as buoyancy turbulence, when V>VT, and the turbulence regime is affected by a combination of local wind shear, bulk shear and buoyancy turbulence. The turbulence activities demonstrated a weak thermal stratification dependency in regime 1, for which within the UCL, the turbulence intensity was strongly affected by local wind shear when V<VT. This study further showed typical examples of different stable boundary layers and the variations between turbulence regimes by analyzing the evolution of wind vectors. Partly because of the influence of large-scale motions, the power spectral density of vertical velocity for upside-down structure showed an increase at low frequencies. The upside-down structures were also characterized by the highest frequency of the stable stratifications in the higher layer.
Turbulence in the nocturnal boundary layer (NBL) is still not well characterized, especially over complex underlying surfaces. Herein, gradient tower data and eddy covariance data collected by the Beijing 325-m tower were used to better understand the differentiating characteristics of turbulence regimes and vertical turbulence structure of urban the NBL. As for heights above the urban canopy layer (UCL), the relationship between turbulence velocity scale (VTKE) and wind speed (V) was consistent with the “HOckey-Stick” (HOST) theory proposed for a relatively flat area. Four regimes have been identified according to urban nocturnal stable boundary layer. Regime 1 occurs where local shear plays a leading role for weak turbulence under the constraint that the wind speed V<VT (threshold wind speed). Regime 2 is determined by the existence of strong turbulence that occurs when V>VT and is mainly driven by bulk shear. Regime 3 is identified by the existence of moderate turbulence when upside-down turbulence sporadic bursts occur in the presence of otherwise weak turbulence. Regime 4 is identified as buoyancy turbulence, when V>VT, and the turbulence regime is affected by a combination of local wind shear, bulk shear and buoyancy turbulence. The turbulence activities demonstrated a weak thermal stratification dependency in regime 1, for which within the UCL, the turbulence intensity was strongly affected by local wind shear when V<VT. This study further showed typical examples of different stable boundary layers and the variations between turbulence regimes by analyzing the evolution of wind vectors. Partly because of the influence of large-scale motions, the power spectral density of vertical velocity for upside-down structure showed an increase at low frequencies. The upside-down structures were also characterized by the highest frequency of the stable stratifications in the higher layer.
2023, 40(6): 1104-1116.
doi: 10.1007/s00376-022-2093-3
Abstract:
The ultraviolet aerosol index (UVAI) is essential for monitoring the absorbing aerosols during aerosol events. UVAI depends on the absorbing aerosol concentration, the viewing geometry, and the temporal drift of radiometric sensitivity. To efficiently detect absorbing aerosols with the highest precision and to improve the accuracy of long-term UVAI estimates, the background UVAI must be examined through the UVAI retrieval. This study presents a statistical method that calculates the background value of UVAI using TROPOspheric Monitoring Instrument (TROPOMI) observation data over the Pacific Ocean under clear-sky scenes. Radiative transfer calculations were performed to simulate the dependence of UVAI on aerosol type and viewing geometry. We firstly applied the background UVAI to reducing the effects of viewing geometry and the degradation of the TROPOMI irradiance measurements on the UVAI. The temporal variability of the background UVAI under the same viewing geometry and aerosol concentration was identified. Radiative transfer calculations were performed to study the changes in background UVAI using Aerosol Optical Depth from the Moderate Resolution Imaging Spectroradiometer (MODIS) and reflectance measurements from TROPOMI as input. The trends of the temporal variations in the background UVAI agreed with the simulations. Alterations in the background UVAI expressed the reflectance variations driven by the changes in satellite state. Decreasing trends in solar irradiance at 340 and 380 nm due to instrument degradation were identified. Our findings are valuable because they can be applied to future retrievals of UVAI from the Environmental Trace Gases Monitoring Instrument (EMI) onboard the Chinese GaoFen-5 satellite.
The ultraviolet aerosol index (UVAI) is essential for monitoring the absorbing aerosols during aerosol events. UVAI depends on the absorbing aerosol concentration, the viewing geometry, and the temporal drift of radiometric sensitivity. To efficiently detect absorbing aerosols with the highest precision and to improve the accuracy of long-term UVAI estimates, the background UVAI must be examined through the UVAI retrieval. This study presents a statistical method that calculates the background value of UVAI using TROPOspheric Monitoring Instrument (TROPOMI) observation data over the Pacific Ocean under clear-sky scenes. Radiative transfer calculations were performed to simulate the dependence of UVAI on aerosol type and viewing geometry. We firstly applied the background UVAI to reducing the effects of viewing geometry and the degradation of the TROPOMI irradiance measurements on the UVAI. The temporal variability of the background UVAI under the same viewing geometry and aerosol concentration was identified. Radiative transfer calculations were performed to study the changes in background UVAI using Aerosol Optical Depth from the Moderate Resolution Imaging Spectroradiometer (MODIS) and reflectance measurements from TROPOMI as input. The trends of the temporal variations in the background UVAI agreed with the simulations. Alterations in the background UVAI expressed the reflectance variations driven by the changes in satellite state. Decreasing trends in solar irradiance at 340 and 380 nm due to instrument degradation were identified. Our findings are valuable because they can be applied to future retrievals of UVAI from the Environmental Trace Gases Monitoring Instrument (EMI) onboard the Chinese GaoFen-5 satellite.
2023, 40(6): 1117-1131.
doi: 10.1007/s00376-022-2119-x
Abstract:
Convolutional neural networks (CNNs) have been widely studied and found to obtain favorable results in statistical downscaling to derive high-resolution climate variables from large-scale coarse general circulation models (GCMs). However, there is a lack of research exploring the predictor selection for CNN modeling. This paper presents an effective and efficient greedy elimination algorithm to address this problem. The algorithm has three main steps: predictor importance attribution, predictor removal, and CNN retraining, which are performed sequentially and iteratively. The importance of individual predictors is measured by a gradient-based importance metric computed by a CNN backpropagation technique, which was initially proposed for CNN interpretation. The algorithm is tested on the CNN-based statistical downscaling of monthly precipitation with 20 candidate predictors and compared with a correlation analysis-based approach. Linear models are implemented as benchmarks. The experiments illustrate that the predictor selection solution can reduce the number of input predictors by more than half, improve the accuracy of both linear and CNN models, and outperform the correlation analysis method. Although the RMSE (root-mean-square error) is reduced by only 0.8%, only 9 out of 20 predictors are used to build the CNN, and the FLOPs (Floating Point Operations) decrease by 20.4%. The results imply that the algorithm can find subset predictors that correlate more to the monthly precipitation of the target area and seasons in a nonlinear way. It is worth mentioning that the algorithm is compatible with other CNN models with stacked variables as input and has the potential for nonlinear correlation predictor selection.
Convolutional neural networks (CNNs) have been widely studied and found to obtain favorable results in statistical downscaling to derive high-resolution climate variables from large-scale coarse general circulation models (GCMs). However, there is a lack of research exploring the predictor selection for CNN modeling. This paper presents an effective and efficient greedy elimination algorithm to address this problem. The algorithm has three main steps: predictor importance attribution, predictor removal, and CNN retraining, which are performed sequentially and iteratively. The importance of individual predictors is measured by a gradient-based importance metric computed by a CNN backpropagation technique, which was initially proposed for CNN interpretation. The algorithm is tested on the CNN-based statistical downscaling of monthly precipitation with 20 candidate predictors and compared with a correlation analysis-based approach. Linear models are implemented as benchmarks. The experiments illustrate that the predictor selection solution can reduce the number of input predictors by more than half, improve the accuracy of both linear and CNN models, and outperform the correlation analysis method. Although the RMSE (root-mean-square error) is reduced by only 0.8%, only 9 out of 20 predictors are used to build the CNN, and the FLOPs (Floating Point Operations) decrease by 20.4%. The results imply that the algorithm can find subset predictors that correlate more to the monthly precipitation of the target area and seasons in a nonlinear way. It is worth mentioning that the algorithm is compatible with other CNN models with stacked variables as input and has the potential for nonlinear correlation predictor selection.
2023, 40(6): 1132-1141.
doi: 10.1007/s00376-022-2205-0
Abstract:
Surface solar radiation (SSR) is a key component of the energy budget of the Earth’s surface, and it varies at different spatial and temporal scales. Considerable knowledge of how and why SSR varies is crucial to a better understanding of climate change, which surely requires long-term measurements of high quality. The objective of this study is to introduce a value-added SSR dataset from Oct 2004 to Oct 2019 based on measurements taken at Xianghe, a suburban site in the North China Plain; two value-added products based on the 1-minute SSR measurements are developed. The first is clear sky detection by using a machine learning model. The second is cloud fraction estimation derived from an effective semi-empirical method. A “brightening” of global horizontal irradiance (GHI) was revealed and found to occur under both clear and cloudy conditions. This could likely be attributed to a reduction in aerosol loading and cloud fraction. This dataset could not only improve our knowledge of the variability and trend of SSR in the North China Plain, but also be beneficial for solar energy assessment and forecasting.
Surface solar radiation (SSR) is a key component of the energy budget of the Earth’s surface, and it varies at different spatial and temporal scales. Considerable knowledge of how and why SSR varies is crucial to a better understanding of climate change, which surely requires long-term measurements of high quality. The objective of this study is to introduce a value-added SSR dataset from Oct 2004 to Oct 2019 based on measurements taken at Xianghe, a suburban site in the North China Plain; two value-added products based on the 1-minute SSR measurements are developed. The first is clear sky detection by using a machine learning model. The second is cloud fraction estimation derived from an effective semi-empirical method. A “brightening” of global horizontal irradiance (GHI) was revealed and found to occur under both clear and cloudy conditions. This could likely be attributed to a reduction in aerosol loading and cloud fraction. This dataset could not only improve our knowledge of the variability and trend of SSR in the North China Plain, but also be beneficial for solar energy assessment and forecasting.
2023, 40(6): 1142-1157.
doi: 10.1007/s00376-022-2118-y
Abstract:
Thousands of lakes on the Tibetan Plateau (TP) play a critical role in the regional water cycle, weather, and climate. In recent years, the areas of TP lakes underwent drastic changes and have become a research hotspot. However, the characteristics of the lake-atmosphere interaction over the high-altitude lakes are still unclear, which inhibits model development and the accurate simulation of lake climate effects. The source region of the Yellow River (SRYR) has the largest outflow lake and freshwater lake on the TP and is one of the most densely distributed lakes on the TP. Since 2011, three observation sites have been set up in the Ngoring Lake basin in the SRYR to monitor the lake-atmosphere interaction and the differences among water-heat exchanges over the land and lake surfaces. This study presents an eight-year (2012–19), half-hourly, observation-based dataset related to lake–atmosphere interactions composed of three sites. The three sites represent the lake surface, the lakeside, and the land. The observations contain the basic meteorological elements, surface radiation, eddy covariance system, soil temperature, and moisture (for land). Information related to the sites and instruments, the continuity and completeness of data, and the differences among the observational results at different sites are described in this study. These data have been used in the previous study to reveal a few energy and water exchange characteristics of TP lakes and to validate and improve the lake and land surface model. The dataset is available at National Cryosphere Desert Data Center and Science Data Bank.
Thousands of lakes on the Tibetan Plateau (TP) play a critical role in the regional water cycle, weather, and climate. In recent years, the areas of TP lakes underwent drastic changes and have become a research hotspot. However, the characteristics of the lake-atmosphere interaction over the high-altitude lakes are still unclear, which inhibits model development and the accurate simulation of lake climate effects. The source region of the Yellow River (SRYR) has the largest outflow lake and freshwater lake on the TP and is one of the most densely distributed lakes on the TP. Since 2011, three observation sites have been set up in the Ngoring Lake basin in the SRYR to monitor the lake-atmosphere interaction and the differences among water-heat exchanges over the land and lake surfaces. This study presents an eight-year (2012–19), half-hourly, observation-based dataset related to lake–atmosphere interactions composed of three sites. The three sites represent the lake surface, the lakeside, and the land. The observations contain the basic meteorological elements, surface radiation, eddy covariance system, soil temperature, and moisture (for land). Information related to the sites and instruments, the continuity and completeness of data, and the differences among the observational results at different sites are described in this study. These data have been used in the previous study to reveal a few energy and water exchange characteristics of TP lakes and to validate and improve the lake and land surface model. The dataset is available at National Cryosphere Desert Data Center and Science Data Bank.
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