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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Original Paper
Application of Conditional Nonlinear Local Lyapunov Exponent to the Second Kind Predictability
Ming ZHANG, Ruiqiang Ding, Quanjia Zhong, Jianping Li, Deyu Lu
, Available online   , Manuscript accepted  18 February 2024, doi: 10.1007/s00376-024-3297-5
In order to quantify the influence of external forcings on the predictability limit using observational data, the author introduced an algorithm of the conditional nonlinear local Lyapunov exponent (CNLLE) method. The effectiveness of this algorithm is validated and compared with the nonlinear local Lyapunov exponent (NLLE) and signal-to-noise ratio (SNR) methods using a coupled Lorenz model. The results show that the CNLLE method is able to capture the slow error growth constrained by external forcings, as such, it can quantify the predictability limit induced by the external forcings. On this basis, a preliminary attempt was made to apply this method to measure the influence of ENSO on the predictability limit for both atmospheric and oceanic variable fields. The spatial distribution of predictability limit induced by ENSO is similar to that arising from the initial conditions calculated by the NLLE method. This similarity supports ENSO as the major predictable signal for weather and climate prediction. In addition, a ratio of predictability limit (RPL) calculated by the CNLLE method to that calculated by the NLLE method was proposed. The RPL larger than 1 indicates that the external forcings can significantly benefit the long-term predictability limit. For instance, ENSO can effectively extend the predictability limit arising from the initial conditions of sea surface temperature (SST) over the tropical Indian Ocean by approximately four months, as well as the predictability limit of sea level pressure (SLP) over the eastern and western Pacific Ocean. Moreover, the impact of ENSO on geopotential height (GHT) predictability limit is primarily confined to the troposphere.
Quantifying the role of the eddy transfer coefficient in simulating the response of the Southern Ocean Meridional Overturning Circulation to enhanced westerlies in a coarse-resolution model
Yiwen LI, Hailong LIU, Pengfei LIN, Eric Chassignet, Zipeng Yu, fanghua wu
, Available online   , Manuscript accepted  18 February 2024, doi: 10.1007/s00376-024-3278-8
This study assesses the capability of a coarse-resolution ocean model to replicate the Southern Ocean Meridional Overturning Circulation's (MOC) response to intensified westerlies, focusing on the role of the eddy transfer coefficient (κ). κ is a parameter commonly used to represent the velocities induced by unresolved eddies. Our findings reveal that a stratification-dependent κ, incorporating spatiotemporal variability, leads to the most robust eddy-induced MOC response, capturing 82% of the reference eddy-resolving simulation. Decomposing the eddy-induced velocity into its vertical variation (VV) and spatial structure (SS) components unveils that the enhanced eddy compensation response primarily stems from an augmented SS term, while the introduced VV term weakens the response. Furthermore, the temporal variability of the stratification-dependent κ emerges as a key factor in enhancing the eddy compensation response to intensified westerlies. The experiment with stratification-dependent κ exhibits a more potent eddy compensation response compared to the constant κ, attributed to the structure of κ and the vertical variation of the density slope. These results underscore the critical role of accurately representing κ in capturing the Southern Ocean MOC's response and emphasize the significance of the isopycnal slope in modulating the eddy compensation mechanism.
Synergistic Impacts of Indian Ocean SST and Indo-China Peninsula Soil Moisture on the 2020 Record-breaking Meiyu
Yinshuo Dong, Haishan Chen, Xuan Dong, Wenjian Hua, Wenjun Zhang
, Available online   , Manuscript accepted  05 February 2024, doi: 10.1007/s00376-024-3204-0
The Yangtze River basin (YRB) experienced a record-breaking Meiyu season in June‒July 2020. This unique long-lasting extreme event and its origin have attracted considerable attention. Previous studies have suggested that the Indian Ocean (IO) SST forcing and soil moisture anomaly over the Indo−China Peninsula (ICP) are responsible for this unexpected event. However, the relative contributions of IO SST and ICP soil moisture to the 2020 Meiyu rainfall event, especially their linkage with atmospheric circulation changes remain unclear. By using observations and numerical simulations, this study examines the synergistic impacts of IO SST and ICP soil moisture on the extreme Meiyu in 2020. Results show that the prolonged dry soil moisture leads to a warmer surface over the ICP in May under strong IO SST backgrounds. The intensification of warm condition further magnifies the land thermal effects, which in turn facilitates the westward extension of the western North Pacific subtropical high (WNPSH) in June‒July. The intensified WNPSH amplifies the water vapor convergence and ascending motion over the YRB, thereby contributing to the 2020 Meiyu. In contrast, the land thermal anomalies diminish during normal IO SST backgrounds due to the limited persistence of soil moisture. The roles of IO SST and ICP soil moisture are verified and quantified using the Community Earth System Model. The synergistic impacts of them yields a notable 32% increase in YRB precipitation. Our findings provide evidence for the combined influences of Indian SST forcing and soil moisture variability on the occurrence of the 2020 super Meiyu.
The Predictability Limit of Oceanic Mesoscale Eddy Tracks in the South China Sea
Hailong LIU, Pingxiang Chu, Yao Meng, Mengrong DING, Pengfei LIN, Ruiqiang Ding, Pengfei Wang, Weipeng ZHENG
, Available online   , Manuscript accepted  01 February 2024, doi: 10.1007/s00376-024-3250-7
This study uses the nonlinear local Lyapunov exponent (NLLE) method to quantitatively estimate the predictability limit of oceanic mesoscale eddy (OME) tracks using three eddy datasets for both annual and seasonal mean. The results show that the predictability limit of OME tracks is about 39 days for cyclonic eddies (CEs) and 44 days for anticyclonic eddies (AEs) in the South China Sea (SCS). The predictability limit is related to the OME properties and seasons. The long-lived, large-amplitude, and -radius OMEs tend to have a higher predictability limit. The predictability limit of AE (CE) tracks is highest in autumn (winter) with 52 (53) days and lowest in spring (summer) with 40 (30) days. The spatial distribution of the predictability limit of OME tracks also varies with seasons, and we found that the higher predictability limits area often overlaps with periodic OMEs. Additionally, the predictability limit of periodic OME tracks is about 49 days for both CEs and AEs, which is 5-10 days higher than the mean values. Usually, in the SCS, OMEs with high predictability limit values often show extender and smoother trajectories and often move along the northern slope of SCS.
Refining the factors affecting N2O emissions from upland soils with or without N fertilizer application at a global scale
Weniqan JIANG, Yong Li, Siqi LI, Meihui Wang, Bo Wang, Ji LIU, Jianlin Shen, Xunhua Zheng
, Available online   , Manuscript accepted  01 February 2024, doi: 10.1007/s00376-024-3234-7
Nitrous oxide (N2O), a long-lived greenhouse gas, is mainly attributed to agricultural soils, which attracted tremendous concentrations to investigate its sources, affecting factors and effective mitigation practices in recent decades. However, the hierarchy of factors influencing N2O emissions from agricultural soils at the global scale remained unclear. In this study, we carried out correlation and structural equation modeling analysis on a large global N2O emission dataset to explore the hierarchy of influencing factors affecting N2O emissions from the non-nitrogen (N) and N fertilized upland farming systems, in aspects of climatic factors, soil properties and agricultural practices. Our results showed that the average N2O emission intensity in the N fertilized soils (17.83 g N ha–1 day–1) was three times significantly greater than that in the non-N fertilized soils (5.34 g N ha−1 day−1) (p < 0.001). N2O emission intensity was significantly correlated with climatic factors, soil properties and agricultural practices. Climate factors and agricultural practices were the most important effect factors on N2O emission in non-N fertilized and fertilized upland soils, respectively. The variance partitioning analysis for the three major climatic zones indicated that soil properties and climate were the key influencing factors in non-N fertilized soils, and soil properties and agricultural practices were the key factors in N fertilizer soils. Deploying enhanced agricultural practices, such as reduced fertilizer N rate combined with the addition of nitrification and urease inhibitors can potentially mitigate N2O emissions by more than 60% in upland farming systems.
Correcting Climate Model Sea Surface Temperature Simulations with Generative Adversarial Networks: Climatology, Interannual Variability, and Extremes
Ya wang, Gang Huang, Baoxiang Pan, Pengfei LIN, Niklas Boers, weichen tao, Yutong Chen, Bo Liu, Haijie Li
, Available online   , Manuscript accepted  31 January 2024, doi: 10.1007/s00376-024-3288-6
Climate models are vital for understanding and projecting global climate change and associated impacts. However, these models suffer from biases that limit their accuracy in historical simulations and the trustworthiness of future projections. Addressing these challenges requires addressing internal variability, hindering direct alignment between model simulations and observations and thwarting conventional supervised learning methods. Here, we employ an unsupervised Cycle-consistent Generative Adversarial Network (CycleGAN), to correct daily Sea Surface Temperature (SST) simulations from the Community Earth System Model 2 (CESM2). Our results reveal that the CycleGAN not only corrects climatological biases but also improves the simulation of major dynamic modes including the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole mode, as well as SST extremes. Notably, it substantially corrects climatological SST biases, decreasing the globally averaged Root Mean Square Error (RMSE) by 58%. Intriguingly, the CycleGAN effectively addresses the well-known excessive westward bias in ENSO SST anomalies, a common issue in climate models that traditional methods, like quantile mapping, struggle to rectify. Additionally, it substantially improves the simulation of SST extremes, raising the pattern correlation coefficient (PCC) from 0.56 to 0.88 and lowering the RMSE from 0.5 to 0.32. This enhancement is attributed to better representations of interannual variability and variabilities at intraseasonal and synoptic scales. Our study offers a novel approach to correct global SST simulations, and underscores its effectiveness across different time scales and primary dynamical modes.
Machine Learning Analysis of Impact of Western US Fires on Central US Hailstorms
Xinming Lin, Jiwen Fan, Yuwei Zhang, Z. Jason Hou
, Available online   , Manuscript accepted  31 January 2024, doi: 10.1007/s00376-024-3198-7
Fires including wildfires harm air quality and essential public services like transportation, communication, and utilities. These fires can also influence atmospheric conditions, including temperature and aerosols, potentially affecting severe convective storms. Here we investigate the remote impacts of fires in the western United States (WUS) on the occurrence of large hail (size >=1 inch) in central US (CUS) over the 20-year period (2001- 2020) using machine learning (ML) methods, Random Forest (RF) and Extreme Gradient Boosting (XGB). The developed RF and XGB models demonstrate high accuracy (>90%) and F1-scores of up to 0.78 in predicting large hail occurrences when WUS fires and CUS hailstorms coincide, particularly in four states (i.e., WY, SD, NE, and KS). The key contributing variables identified from both ML models include the meteorological variables in the fire region (temperature and moisture), the westerly wind over the plume transport path, and the fire features (i.e., the maximum fire power and burned area). The results confirm a linkage between WUS fires and severe weather in the CUS, corroborating the findings of our previous modeling study conducted on the case simulations.
Microphysical Characteristics of Rainfall Based on Long-Term Observations with 2DVD in Yangbajing, Tibet
Ming LI, Yongheng BI, Yonghai SHEN, Yinan Wang, Ciren Nima, Tianlu CHEN, Daren Lu
, Available online   , Manuscript accepted  23 January 2024, doi: 10.1007/s00376-024-3299-3
Raindrop size distribution (DSD) plays a crucial role in enhancing the accuracy of radar quantitative precipitation estimation in the Tibetan Plateau (TP). However, there is a notable scarcity of long-term, high-resolution observations in this region. To address this issue, long-term observations from a two-dimensional video disdrometer (2DVD) were leveraged to refine the radar and satellite-based algorithms for quantifying precipitation in the hinterland of the TP. It was observed that weak precipitation (R < 1 mm h-1) accounts for 86% of the total precipitation time, while small raindrops (D < 2 mm) comprise 99% of the total raindrop count. Furthermore, the average spectral width of the DSD increases with increasing rain rate. The DSD characteristics of convective and stratiform precipitation were discussed across five different rain rates, revealing that convective precipitation in the Yangbajing (YBJ) exhibits characteristics similar to maritime-like precipitation. The constrained relations between the slope Λ and μ, and of gamma DSDs were derived. Additionally, we establish a correlation between the equivalent diameter and drop axis ratio found that raindrops on the TP are closer to spherical shapes. Lastly, the application of the rainfall retrieval algorithms of the dual-frequency precipitation radar in the TP is improved based on the statistical results of the DSD.
Detection of Turbulence Anomalies Using Symbol Classifier Algorithm in Airborne Quick Access Record (QAR) Data Analysis
Zibo Zhuang, Kunyun Lin, Hongying Zhang, P. W. Chan
, Available online   , Manuscript accepted  17 January 2024, doi: 10.1007/s00376-024-3195-x
As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry, it has become imperative to monitor and mitigate these threats to ensure civil aviation safety. The eddy dissipation rate (EDR) has been established as the standard metric for quantifying turbulence in civil aviation. This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder (QAR) data. The detection of atmospheric turbulence is approached as an anomaly detection problem. Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events. Moreover, comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available. In summary, the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data, comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms. This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards.
New Record Ocean Temperatures and Related Climate Indicators in 2023
Lijing CHENG, John ABRAHAM, Kevin E. TRENBERTH, Tim BOYER, Michael E. MANN, Jiang ZHU, Fan WANG, Fujiang YU, Ricardo LOCARNINI, John FASULLO, Fei ZHENG, Yuanlong LI, Bin ZHANG, Liying WAN, Xingrong CHEN, Dakui WANG, Licheng FENG, Xiangzhou SONG, Yulong LIU, Franco RESEGHETTI, Simona SIMONCELLI, Viktor GOURETSKI, Gengxin CHEN, Alexey MISHONOV, Jim REAGAN, Karina VON SCHUCKMANN, Yuying PAN, Zhetao TAN, Yujing ZHU, Wangxu WEI, Guancheng LI, Qiuping REN, Lijuan CAO, Yayang LU
, Available online   , Manuscript accepted  09 January 2024, doi: 10.1007/s00376-024-3378-5
The global physical and biogeochemical environment has been substantially altered in response to increased atmospheric greenhouse gases from human activities. In 2023, the sea surface temperature (SST) and upper 2000 m ocean heat content (OHC) reached record highs. The 0–2000 m OHC in 2023 exceeded that of 2022 by 15 ± 10 ZJ (1 Zetta Joules = 1021 Joules) (updated IAP/CAS data); 9 ± 5 ZJ (NCEI/NOAA data). The Tropical Atlantic Ocean, the Mediterranean Sea, and southern oceans recorded their highest OHC observed since the 1950s. Associated with the onset of a strong El Niño, the global SST reached its record high in 2023 with an annual mean of ~0.23°C higher than 2022 and an astounding > 0.3°C above 2022 values for the second half of 2023. The density stratification and spatial temperature inhomogeneity indexes reached their highest values in 2023.
A Deep Learning Approach for Forecasting Thunderstorm Gusts in the Beijing–Tianjin–Hebei Region
Yunqing LIU, Lu Yang, Chen Mingxuan, Linye Song, Lei Han, Jingfeng XU
, Available online   , Manuscript accepted  05 January 2024, doi: 10.1007/s00376-023-3255-7
Thunderstorm gusts are a common form of severe convective weather in the warm season in North China, and it is of great importance to correctly forecast them. At present, the forecasting of thunderstorm gusts is mainly based on traditional subjective methods, which fails to achieve high-resolution and high-frequency gridded forecasts based on multiple observation sources. In this paper, we propose a deep learning method called Thunderstorm Gusts TransU-net (TG-TransUnet) to forecast thunderstorm gusts in North China based on multi-source gridded product data from the Institute of Urban Meteorology (IUM) with a lead time of 1 to 6 h. To determine the specific range of thunderstorm gusts, we combine three meteorological variables: radar reflectivity factor, lightning location, and 1-h maximum instantaneous wind speed from automatic weather stations (AWSs), and obtain a reasonable ground truth of thunderstorm gusts. Then, we transform the forecasting problem into an image-to-image problem in deep learning under the TG-TransUnet architecture, which is based on convolutional neural networks and a transformer. The analysis and forecast data of the enriched multi-source gridded comprehensive forecasting system for the period 2021–2023 are then used as training, validation, and testing datasets. Finally, the performance of TG-TransUnet is compared with other methods. The results show that TG-TransUnet has the best prediction results at 1–6 h. The IUM is currently using this model to support thunderstorm gusts forecasting in North China.
Assessments of data-driven deep learning models of one-month prediction of pan-Arctic sea ice thickness
Chentao Song, Jiang Zhu, Xichen Li
, Available online   , Manuscript accepted  04 January 2024, doi: 10.1007/s00376-023-3259-3
In recent years, deep learning methods have been gradually applied to the prediction tasks of Arctic sea ice concentration, but relatively few works have been done for larger spatial and temporal scales, mainly due to the limited time coverage of observations and reanalysis data. Meanwhile, deep learning predictions of sea ice thickness (SIT) have still received little attention. In this study, based on the ConvLSTM and fully convolutional U-net (FC-Unet) algorithms, two data-driven deep learning models are built and trained using CMIP6 historical simulations for transfer learning and reanalysis/observations for fine-tuning. These enable monthly predictions of Arctic SIT without considering the complex physical processes involved. Through comprehensive assessments of prediction skills by season and region, the results suggest that using a broader set of CMIP6 data for transfer learning, as well as incorporating multiple climate variables as predictors, contribute to better prediction results. And both two DL models can effectively predict the spatiotemporal features of SIT anomalies. The predicted SIT anomalies of FC-Unet model, in particular, the spatial correlations with reanalysis reach an average level of 89% over all months, while temporal anomaly correlation coefficients are close to 1 in most cases. Besides, the models also show robust performance in predicting SIT and SIE during extreme events. The effectiveness and reliability of purposed deep transfer learning models in predicting Arctic SIT canfacilitate more accurate pan-Arctic predictions, climate change research and real-time business applications.
Different ENSO impacts on eastern China precipitation pattern in early and late winter associated with seasonally-varying Kuroshio anticyclonic anomalies
Jingrui YAN, Wenjun Zhang, Suqiong HU, Feng JIANG
, Available online   , Manuscript accepted  04 January 2024, doi: 10.1007/s00376-023-3196-1
Winter precipitation over eastern China displays remarkable interannual variability, which has been suggested to be closely related to El Niño–Southern Oscillation (ENSO). This study finds that ENSO impacts on eastern China precipitation pattern exhibit obvious differences in early (November–December) and late (January–February) winter. In early winter, precipitation anomalies associated with ENSO are characterized by a monopole spatial distribution over eastern China. In contrast, the precipitation anomaly pattern in late winter changes remarkably, manifesting as a dipole spatial distribution. The noteworthy change in precipitation responses from early to late winter can be largely attributed to the seasonally varying Kuroshio anticyclonic anomalies. In early winter of El Niño years, anticyclonic circulation anomalies appear both over the Philippine Sea and Kuroshio region, enhancing water vapor transport to the entire eastern China, and contributing to more precipitation there. In late winter of El Niño years, the anticyclone over the Philippine Sea is further strengthened, while the one over the Kuroshio dissipates, which could result in different water vapor transport between northern and southern parts of eastern China and thus a dipole precipitation distribution. Roughly opposite anomalies of circulation and precipitation are displayed during La Niña winters. Further analysis suggests that the seasonally-varying Kuroshio anticyclonic anomalies are possibly related to the enhancement of ENSO-related tropical central–eastern Pacific convection from early to late winter. These results have important implications for the seasonal-to-interannual predictability of winter precipitation over eastern China.
Future changes in various cold surges over China in CMIP6 projection
Li Ma, Zhigang Wei, Xianru Li, Shuting Wu
, Available online   , Manuscript accepted  03 January 2024, doi: 10.1007/s00376-023-3220-5
Cold surges (CSs) often occur in the mid-latitude regions of the Northern Hemisphere and have enormous effects on socioeconomic development. We report that the occurrences of CSs and persistent CSs (PCSs) have rebounded since the 1990s, but the frequencies of strong CSs (SCSs) and extreme CSs (ECSs) changed from increasing to decreasing trends after 2000. The highest-ranked model ensemble approach was used to project the occurrences of various CSs under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. The frequencies of the total CSs showed overall decreasing trends. However, under the SSP1-2.6 scenario, SCSs and ECSs showed slight increasing trends in China. The anomalous anticyclonic circulation with a significant positive anomaly of 500 hPa geopotential height (Z500) at high latitudes and significant negative anomalies in China were favourable for the intrusion of cold air into China. In addition, the frequencies of all CS types under the SPP5-8.5 scenario greatly decreased in the long term (2071-2100), which was related to the negative anomalies in the sea surface temperature (SST) in central and western North Pacific, differences in sea level pressure (SLP) between high- and mid-latitude regions, and a weaker East Asian trough. In terms of ECSs, the decreasing trends observed in the historical period were maintained until 2024 under the SSP1-2.6 scenario. Compared to the SSP1-2.6 scenario, Z500 showed trends of strengthening ridges over the Ural region and northern East Asia and weakening ridges over Siberia under the SSP2-4.5 and SSP5-8.5 scenarios, contributing to the shift to increasing trends of ECSs after 2014
The Global Energy and Water Exchanges Project in Central Asia; The Case for a Regional Hydroclimate Project
Michael Brody, Maksim Kulikov, Sagynbek Orumbaev, Peter van Oevelen
, Available online   , Manuscript accepted  02 January 2024, doi: 10.1007/s00376-023-3384-z
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.
Influence of Irregular Coastlines on a Tornadic Mesovortex in the Pearl River Delta during Monsoon Season. Part II: Numerical Experiments
Lanqiang Bai, Dan Yao, Zhiyong Meng, Yu Zhang, Xianxiang Huang, Zhaoming Li
, Available online   , Manuscript accepted  27 December 2023, doi: 10.1007/s00376-023-3096-4
As demonstrated in Part I of this study, wind-shift boundaries routinely form along the west coast of the Pearl River Delta due to the land–sea contrast of trumpet-shaped coastline in summer monsoon season. It is proposed that the unique topography has played essential roles in the modification of vorticity budget of mesovortex formation. Part II aims to examine the mesovortex genesis during the 1 June 2020 tornadic event and the roles of trumpet-shaped coastline through multiple numerical simulations. On mesoscale, the modeling reproduced two mesovortices that were in close proximity in time and space to the realistic mesovortices. In agreement with observations, finger-like echoes preceding hook echoes were also reproduced over the triple point. On storm scale, in addition to the modeled mesovortex over the triple point, another mesovortex originated from an enhanced discrete vortex along airmass boundary via shear instability. Results from sensitivity experiments suggest that simulation of rotating storms in this region is sensitive to local environmental details and storm dynamics. The strengths of cold pool surges from preexisting storms influence the wrap-up of finger-like echoes and the mesovortex formation. Although the simulations did not perfectly mimic the observed processes on storm scale, they provide an opportunity to better understand the genesis of rotating storms in this tornado hotspot. The findings suggest that the trumpet-shaped coastline is an important component for the mesovortex production during the monsoon active season. It is hoped that this study will increase the situational awareness for forecasters regarding the regional nonmesocyclone tornado environments.
How well do the downward shortwave radiation products from satellite and reanalysis over the transect of Zhongshan Station to Dome A, East Antarctica?
Jiajia Jia, Zhaoliang Zeng, Wenqian Zhang, Xiangdong Zheng, Yaqiang Wang, Minghu Ding
, Available online   , Manuscript accepted  27 December 2023, doi: 10.1007/s00376-023-3136-0
The downward shortwave radiation (DSR) is an important part of the Earth's energy balance, which drives the energy, water, and carbon cycles of Earth’s system. Due to the harsh environment, the accuracy of DSR derived from satellite and reanalysis has not been systematically evaluated over the transect of Zhongshan Station to Dome A, East Antarctica. Therefore, this study aims to evaluate DSR reanalysis products (ERA5-Land, ERA5, MERRA-2) and satellite (CERES and ICDR) products in the above areas. The results indicate that DSR exhibits obvious month and seasonal variations, with higher values in summer than in winter. All DSR products showed underestimation and ERA5_Land (ICDR) DSR product has the highest (lowest) accuracy, corresponding R was 0.987 (0.893), root mean square error was 23.91 (91.45) W·m-2, mean bias was -1.67 (-56.41) W·m-2 and mean absolute error was 13.37 (58.99) W·m-2. The RMSE values at seven stations, namely Zhongshan, Panda 100, Panda 300, Panda 400, Taishan, Panda 1100, and Kunlun, were 30.38, 29.4, 34.5, 29.1, 20.3, 17.26 and 15.46 W·m-2, respectively. Corresponding bias were 9.69, -12.1, -19.1, -15.5, -8.11, 6.29, and 3.59 W·m-2. Specifically, in terms of seasonality, ERA5-Land, ERA5, and MERRA-2 reanalysis products demonstrate higher accuracy during autumn and winter compared to summer. Conversely, satellite products exhibit greater accuracy during the warm season rather than the cold. Cloud cover, water vapor, total ozone, and severe weather are the main factors affecting DSR. The error of DSR products is greatest in coastal areas (particularly at Zhongshan Station) and decreases towards Antarctica inland.
Spatial variation in CO2 concentration improves simulated surface air temperature increase in the Northern Hemisphere
Jing Peng, Li Dan, xiba tang
, Available online   , Manuscript accepted  20 December 2023, doi: 10.1007/s00376-023-3249-5
Increasing concentration of atmospheric CO2 since the Industrial Revolution has affected surface air temperature. However, the impact of the spatial distribution of atmospheric CO2 concentration on surface air temperature biases remains highly unclear. By incorporating the spatial distribution of satellite-derived atmospheric CO2 concentration in the Beijing Normal University Earth System Model, this study investigated the increase in surface air temperature since the Industrial Revolution in the Northern Hemisphere under historical conditions from 1976–2005. In comparison with the increase in surface temperature simulated using a uniform distribution of CO2, simulation with a nonuniform distribution of CO2 produced better agreement with the Climatic Research Unit (CRU) data in the Northern Hemisphere under the historical condition relative to the baseline over the period 1901–1930. Hemispheric JJA surface air temperature increased by 1.28 ± 0.29 °C in simulations with a uniform distribution of CO2, by 1.00 ± 0.24 °C in simulations with a non-uniform distribution of CO2, and by 0.24 °C in the CRU data. The decrease in down shortwave radiation in the non-uniform CO2 simulation was primarily attributable to reduced warming in Eurasia, combined with feedbacks resulting from increased leaf area index and latent heat fluxes. These effects were more pronounced in the non-uniform CO2 simulation compared to the uniform CO2 simulation. Results indicate that consideration of the spatial distribution of CO2 concentration can reduce the overestimated increase in surface air temperature simulated by Earth system models.
Large eddy simulation of vertical structure and size distribution of deep layer clouds
Bangjun Cao, Xianyu Yang, JUN WEN, Qin Hu, Ziyuan Zhu
, Available online   , Manuscript accepted  11 December 2023, doi: 10.1007/s00376-023-3134-2
In a convective scheme featuring a discretized Cloud Size Distribution (CSD), the assumed lateral mixing rate is inversely proportional to the exponential coefficient of plume size. This follows a typical assumption of -1, but it has unveiled inherent uncertainties, especially for deep layer clouds. Addressing this knowledge gap, we conducted comprehensive large eddy simulations (LES) and conducted comparative analyses focused on terrestrial regions. Our investigation has illuminated that cloud formation adheres to the tenets of Bernoulli trials, illustrating power-law scaling that remains consistent regardless of the inherent deep layer cloud attributes existing between cloud size and the number of cloud. This scaling paradigm encompasses liquid, ice, and mixed-phase in deep layer clouds. The exponent characterizing the interplay between cloud scale and number in the deep layer cloud, specifically for liquid, ice, or mixed-phase clouds, resembles that of shallow convection, but converges closely to zero. This convergence signifies a propensity for diminished cloud numbers and sizes within deep layer clouds. Notably, the infusion of abundant moisture and the release of latent heat by condensation within the lower atmospheric strata make substantial contributions. However, this role on ice phase formation is limited. The emergence of liquid and ice phases in deep layer clouds is facilitated by the latent heat and influenced by the wind shear inherent in the mid-levels. These interrelationships hold potential applications in formulating parameterizations and post-processing model outcomes.
U-Net models for representing wind stress anomalies over the tropical Pacific and their integrations with an intermediate coupled model for ENSO studies
Shuangying Du, Rong-Hua Zhang
, Available online   , Manuscript accepted  08 December 2023, doi: 10.1007/s00376-023-3179-2
El Niño-Southern Oscillation (ENSO) is the strongest interannual climate mode influencing the coupled ocean-atmosphere system in the tropical Pacific, and numerous dynamic and statistical models have been developed to simulate and predict it. In some simplified coupled ocean-atmosphere models, the relationship between sea surface temperature (SST) anomalies and wind stress (τ) anomalies can be constructed by statistical methods, such as singular value decomposition (SVD). In recent years, the applications of artificial intelligence (AI) to climate modeling have shown promising prospects, and the integrations of AI-based models with dynamic models are active areas of research. This study constructs U-Net models for representing the relationship between SSTAs and τ anomalies in the tropical Pacific; the UNet-derived τ model, denoted as τ<sub>UNet</sub>, is then used to replace the original SVD-based τ model of an intermediate coupled model (ICM), forming a newly AI-integrated ICM, referred to as ICM-UNet. The simulation results obtained from the ICM-UNet demonstrate its ability in representing the spatiotemporal variability of oceanic and atmospheric anomaly fields in the equatorial Pacific. In the ocean-only case study, the τ<sub>UNet</sub>-derived wind stress anomaly fields are used to force the ocean component of the ICM, also indicating reasonable simulations of typical ENSO events. These results demonstrate the feasibility of integrating AI-derived model with physics-based dynamical model for ENSO modeling studies. Furthermore, the successful integration of the dynamical ocean models with the AI-based atmospheric wind model provides a novel approach to ocean-atmosphere interaction modeling studies.
Impacts of future changes in heavy precipitation and extreme drought on economy over South China and Indochina
Bin Tang, Wenting Hu, anmin Duan, Yimin Liu, Wen Bao, Yue Xin, Xianyi Yang
, Available online   , Manuscript accepted  06 December 2023, doi: 10.1007/s00376-023-3158-7
Heavy precipitation and extreme drought have caused severe economic losses over the South China and Indochina (INCSC) in recent decades. Given the areas with large gross domestic product (GDP) in the INCSC region are distributed along the coastline and greatly affected by global warming, understanding the possible economic impacts induced by future changes in the maximum consecutive 5-day precipitation (RX5day) and the maximum consecutive dry days (CDD) is critical for adaptation planning in this region. Based on latest data released by phase 6 of the Coupled Model Intercomparison Project (CMIP6), future projections of precipitation extremes with bias correction and their impacts on GDP over the INCSC region under fossil-fueled development way of Shared Socioeconomic Pathways (SSP5-8.5) are investigated. Results indicate that RX5day will intensify robustly throughout the INCSC region, while CDD will lengthen in most regions under global warming. The changes in climate consistently dominate the effect on GDP over the INCSC region, rather than the change of GDP. If only consider the effect of climate change on GDP, the changes in precipitation extremes bring a larger impact on economy in the future to the provinces of Hunan, Jiangxi, Fujian, Guangdong and Hainan in South China, as well as the Malay Peninsula and southern Cambodia in Indochina. Thus, timely regional adaptation strategies are urgent for these regions. Moreover, from the sub-regional average viewpoint, over two thirds of CMIP6 models agree that maintaining a lower global warming level will reduce the impacts on economy from heavy precipitation over the INCSC region.
Improving Short-Range Precipitation Forecast of Numerical Weather Prediction Through a Deep Learning-Based Mask Approach
Jiaqi Zheng, Qing Ling, Jia Li, Yerong Feng
, Available online   , Manuscript accepted  06 December 2023, doi: 10.1007/s00376-023-3085-7
Due to various technical issues, existing numerical weather prediction (NWP) models often perform poorly at forecasting rainfall in the first several hours. To correct the bias of an NWP model and improve the accuracy of short-range precipitation forecasting, we propose a deep learning-based approach called UNetMask, which combines the NWP forecasts with the output of a convolutional neural network called UNet. The UNetMask involves training the UNet on historical data from the NWP model and gridded rainfall observations for 6-hour precipitation forecasting. The overlap of the UNet output and the NWP forecasts at the same rainfall threshold yields a mask. UNetMask blends the UNet output and the NWP forecasts by taking the maximum between them and passing through the mask, which provides the corrected 6-hour rainfall forecasts. We evaluated UNetMask on a test set and in real-time verification. The results showed that UNetMask outperforms the NWP model in 6-hour precipitation prediction by reducing FAR and improving CSI scores. Sensitivity tests also showed that different small rainfall thresholds applied to the UNet and the NWP model have different effect on UNetMask's forecast performance. This study shows that UNetMask is a promising approach for improving rainfall forecasting of NWP models.
The circulation background and genesis mechanism of a cold vortex over the Tibetan Plateau during late April 2018
Duming Gao, Jiangyu Mao, Guoxiong Wu, Yimin Liu
, Available online   , Manuscript accepted  30 November 2023, doi: 10.1007/s00376-023-3124-4
A cold vortex occurred over the northeastern Tibetan Plateau (TP) on 27 April 2018 and subsequently brought excessive rainfall to the spring farming area in southern China when moving eastward. This study investigates the genesis mechanism of the cold TP vortex (TPV) by diagnosing reanalysis data and conducting numerical experiments. Results demonstrate that the cold TPV was generated in a highly baroclinic environment with significant contributions of positive potential vorticity (PV) forcing from the tropopause and diurnal thermodynamic impact from the surface. As a positive PV anomaly in the lower stratosphere moved towards the TP, the PV forcing at the tropopause pushed the tropospheric isentropic surfaces upward, forming isentropic displacement air ascent and reducing static stability over the TP. The descent of tropopause over the TP also produced a tropopause folding over the northeastern TP associated with a narrow high PV column intruding downwards over the TPV genesis site, resulting in air ascent in the free atmosphere. This, in conjunction with the air descent in the valley area during the night, produced air stretching just at the TPV genesis site. Because the surface cooling in night increased the surface static stability, the aforementioned vertical air-stretching thus converted the produced static stability to vertical vorticity. Consequently, the cold TPV was generated over the valley in night.
Evaluation and projection of population exposure to temperature extremes over the Beijing-Tianjin-Hebei region using a high-resolution regional climate model RegCM4 ensemble
Peihua Qin, Zhenghui Xie, Rui Han, Buchun Liu
, Available online   , Manuscript accepted  30 November 2023, doi: 10.1007/s00376-023-3123-5
Temperature extremes over fast urbanizing regions with high population density have been scrutinized due to their severe impacts on human safety and economics. In this study, we evaluated the performance of regional climate model RegCM4 with hydrostatic and non-hydrostatic dynamic core in simulating seasonal temperature and temperature extremes in the historical period of 1991-1999 at 12 km spatial resolution in China and at 3 km resolution over the Beijing-Tianjin-Hebei (JJJ) region, a typical urban agglomeration of China. Simulations of spatial distributions of temperature extremes over the JJJ region using RegCM4 with hydrostatic and non-hydrostatic core showed high spatial correlations of more than 0.8 with the observations. Under a warming climate, temperature extremes of annual maximum daily temperature (TXx), and summer days (SU) in China and JJJ region showed obvious increases at the end of the 21th century while frost days (FD) are generally reduced. The ensemble of RegCM4 with different land surface components was used to examine population exposure to temperature extremes over the JJJ region. Population exposure to temperature extremes was found to decrease in 2091−2099 relative to 1991−1999 over majority of JJJ region under the joint impacts of increases of temperature extremes over the JJJ, and population decrease over the JJJ region except downtown areas. Furthermore, changes in population exposure to temperature extremes were mainly dominated by population changes in the future. Finally, we quantified the change in exposure to temperature extremes over the JJJ region. This study helps to provide relative policies to face with future climate risks over JJJ region.
Improved Diurnal Cycle of Precipitation on Land in a Global Non-hydrostatic Model Using a Revised Deep NSAS Convective Scheme
Yifan Zhao, Xindong Peng, Xiaohan Li, Siyuan Chen
, Available online   , Manuscript accepted  30 November 2023, doi: 10.1007/s00376-023-3121-7
In relatively coarse-resolution atmospheric models, cumulus parameterization helps account for the effect of subgrid-scale convection, which produces supplemental rainfall to the grid-scale precipitation and impacts the diurnal cycle of precipitation. In this study, the diurnal cycle of precipitation was studied using the new simplified Arakawa-Schubert scheme in a global non-hydrostatic atmospheric model, i.e., the Yin-Yang-grid Unified Model for the Atmosphere. Two new diagnostic closures and a convective trigger function were suggested to emphasize the job of cloud work function corresponding to the free tropospheric large-scale forcing. Numerical results of the 0.25-degree model in 3-month batched real-case simulations revealed the improvement of diurnal precipitation variation by using the revised trigger function with an enhanced dynamical constraint on the convective initiation and a suitable threshold of the trigger. By reducing the occurrence of convection during peak solar radiation hours, the revised scheme was demonstrated to be effective in delaying the appearance of early-afternoon rainfall peaks over most land areas and accentuating the nocturnal peaks that were wrongly concealed by the more substantial afternoon peak. In addition, the revised scheme enhanced the simulation capability of the precipitation probability density function, such as increasing the extremely low- and high-intensity precipitation events and decreasing small and moderate rainfall events, which contributed to the reduction of precipitation bias over mid-latitude and tropical lands.
TP-PROFILE monitoring the thermodynamical structure of the troposphere over the Third Pole
Xuelong Chen, Yaoming Ma, Binbin Wang, Xu Xiangde, Xinghong Cheng, Yajing Liu, Xin Xu, Luhan Li, Weiqiang Ma
, Available online   , Manuscript accepted  24 November 2023, doi: 10.1007/s00376-023-3199-y
Ground-based microwave radiometers (MWRs) operating in the K- and V-bands (20–60 GHz) can help us obtain temperature and humidity profiles in the troposphere. Despite some soundings from local meteorological observatories, the tropospheric atmosphere over the Tibetan Plateau (TP) has never been continuously observed. As part of the Chinese Second Tibetan Plateau Scientific Expedition and Research Program (STEP), the Tibetan Plateau Atmospheric Profile (TP-PROFILE) project aims to construct a comprehensive MWR troposphere observation network to study the synoptic process and environmental changes on the TP. This initiative has collected three years data from the MWR network. This paper introduces the data information, the data quality, and data downloading. Some applications of the data obtained from these MWRs were also demonstrated. Our comparisons of MWR against nearest radiosonde observation demonstrate that the TP-PROFILE MWR system is adequate for monitoring the thermal and moisture variability of the troposphere over the TP. The continuous temperature and moisture profiles derived from the MWR data provide a unique perspective on the evolution of the thermodynamic structure associated with the heating of the TP. The TP-PROFILE discloses that the low-temporal resolution instruments may have a large uncertainty in their vapor estimation in the mountain valleys on the TP.
Non-crossing Quantile Regression Neural Network as a Calibration Tool for Ensemble Weather Forecasts
Mengmeng SONG, Dazhi YANG, Sebastian LERCH, Xiang’ao XIA, Gokhan Mert YAGLI, Jamie M. BRIGHT, Yanbo SHEN, Bai LIU, Xingli LIU, Martin János MAYER
, Available online   , Manuscript accepted  22 November 2023, doi: 10.1007/s00376-023-3184-5
Despite the maturity of ensemble numerical weather prediction (NWP), the resulting forecasts are still, more often than not, under-dispersed. As such, forecast calibration tools have become popular. Among those tools, quantile regression (QR) is highly competitive in terms of both flexibility and predictive performance. Nevertheless, a long-standing problem of QR is quantile crossing, which greatly limits the interpretability of QR-calibrated forecasts. On this point, this study proposes a non-crossing quantile regression neural network (NCQRNN), for calibrating ensemble NWP forecasts into a set of reliable quantile forecasts without crossing. The overarching design principle of NCQRNN is to add on top of the conventional QRNN structure another hidden layer, which imposes a non-decreasing mapping between the combined output from nodes of the last hidden layer to the nodes of the output layer, through a triangular weight matrix with positive entries. The empirical part of the work considers a solar irradiance case study, in which four years of ensemble irradiance forecasts at seven locations, issued by the European Centre for Medium-Range Weather Forecasts, are calibrated via NCQRNN, as well as via an eclectic mix of benchmarking models, ranging from the naïve climatology to the state-of-the-art deep-learning and other non-crossing models. Formal and stringent forecast verification suggests that the forecasts post-processed via NCQRNN attain the maximum sharpness subject to calibration, amongst all competitors. Furthermore, the proposed conception to resolve quantile crossing is remarkably simple yet general, and thus has broad applicability as it can be integrated with many shallow- and deep-learning-based neural networks.
Enhancing Deep Learning Soil Moisture Forecasting Models by Integrating Physics-based Models
Lu LI, Yongjiu DAI, Zhongwang WEI, Wei SHANGGUAN, Nan WEI, Yonggen ZHANG, Qingliang LI, Xian-Xiang LI
, Available online   , Manuscript accepted  16 November 2023, doi: 10.1007/s00376-023-3181-8
Accurate soil moisture (SM) prediction is critical for understanding hydrological processes. Physics-based (PB) models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient representation of land-surface processes. In addition to PB models, deep learning (DL) models have been widely used in SM predictions recently. However, few pure DL models have notably high success rates due to lacking physical information. Thus, we developed hybrid models to effectively integrate the outputs of PB models into DL models to improve SM predictions. To this end, we first developed a hybrid model based on the attention mechanism to take advantage of PB models at each forecast time scale ( attention model). We further built an ensemble model that combined the advantages of different hybrid schemes ( ensemble model). We utilized SM forecasts from the Global Forecast System to enhance the convolutional long short-term memory (ConvLSTM) model for 1–16 days of SM predictions. The performances of the proposed hybrid models were investigated and compared with two existing hybrid models. The results showed that the attention model could leverage benefits of PB models and achieved the best predictability of drought events among the different hybrid models. Moreover, the ensemble model performed best among all hybrid models at all forecast time scales and different soil conditions. It is highlighted that the ensemble model outperformed the pure DL model over 79.5% of in situ stations for 16-day predictions. These findings suggest that our proposed hybrid models can adequately exploit the benefits of PB model outputs to aid DL models in making SM predictions.
Influence of Irregular Coastlines on a Tornadic Mesovortex in the Pearl River Delta during Monsoon Season. Part I: Prestorm Environment and Storm Evolution
Lanqiang Bai, Zhiyong Meng, Dan Yao, Yu Zhang, Xianxiang Huang, Zhaoming Li, Xiaoding YU
, Available online   , Manuscript accepted  10 November 2023, doi: 10.1007/s00376-023-3095-5
The Pearl River Delta (PRD), a tornado hotspot, forms a distinct "trumpet" shape coastline that concaves toward the South China sea. During the summer monsoon season, low-level southwesterlies over the PRD sea surface tend to be turned toward the west coast, constituting a convergent wind field along with the land-side southwesterlies which influences regional convective weather. This two-part study explores the roles of this unique land–sea contrast of trumpet-shaped coastline in the formation of a tornadic mesovortex within monsoonal flows in this region. Part I primarily presents observational analyses of prestorm environments and storm evolutions. The rotating storm developed in a low-shear environment (not ideal for supercell) under the interactions of three airmasses in the influence of the land–sea contrast, monsoon and storm cold outflows. This intersection zone (“triple point”) is typically characterized by local enhancements of ambient vertical vorticity and convergence. Based on a rapid-scan X-band phased-array radar, finger-like echoes were recognized shortly after the gust front intruding the triple point. Developed over the triple point, they rapidly wrapped up with a well-defined low-level mesovortex. It is thus presumed that the triple point may have played roles in the mesovortex genesis, which will be demonstrated in Part II with multiple sensitivity numerical simulations. The findings also suggest that when storms pass over the boundary intersection zone in the PRD, relatively high possibility of rotating storm is expected even in a low-shear environment. Improved knowledge of such environments provides additional guidance to assess the regional tornado risk.
Distribution and formation causes of PM2.5 and O3 double high pollution events in China during 2013–2020
Zhixuan Tong, Yingying Yan, Shaofei Kong, J-T Lin, Nan Chen, Bo Zhu, Jing Ma, Tianliang Zhao, Shihua Qi
, Available online   , Manuscript accepted  09 November 2023, doi: 10.1007/s00376-023-3156-9
The PM2.5 and O3 double high pollution (DHP) events occur frequently over China in recent years, but the causes of DHP events are not completely clear. In this study, the spatiotemporal distribution of DHP events in China during 2013–2020 is analyzed. The synoptic types affecting the DHP events are identified with the Lamb–Jenkinson circulation classification method. The meteorological and chemical causes of DHP events controlled by main synoptic types are further investigated. Results show that the DHP events mainly occur over North China Plain, Yangtze River Delta, Pearl River Delta, Sichuan Basin, and Central China. Seasonally, DHP events show high occurrence frequency in spring and summer, and the frequency of occurrence in autumn and winter is still not negligible. The main circulation types of DHP events are cyclone and anticyclone types, accounting for over 40% of the total DHP events over five main polluted regions in China. Compared with the non–DHP events, the DHP events are characterized by static or weak wind, high temperature and low humidity. Although the formation of high concentrations of PM2.5 and ozone is dominated by different meteorological conditions, the diurnal cycles of meteorological conditions cause PM2.5 (03:00–12:00) and ozone (15:00–21:00) to exceed the standard at different periods of the DHP day. The indices of SOR, NOR, and OC/EC further indicate the rapid secondary conversions during DHP events. The results of the study can be informative for future decisions on the management of DHP events.
Track-pattern-based Characteristics of Extratropical Transitioning Tropical Cyclones in the Western North Pacific
Hong Huang, Dan Wu, Yuan Wang, Zhen Wang, Yu Liu
, Available online   , Manuscript accepted  06 November 2023, doi: 10.1007/s00376-023-2330-4
Based on the Regional Specialized Meteorological Center (RSMC) Tokyo-Typhoon Center best-track data and the NCEP-NCAR reanalysis dataset, the extratropical transitioning (ET) tropical cyclones (ETCs) over the western North Pacific (WNP) during 1951–2021 are classified into six clusters using fuzzy c-means clustering method (FCM) according to their track patterns. The characteristics of the six hard cluster-ETCs with the highest membership coefficient are shown. Most tropical cyclones (TCs) assigned to C2, C5 and C6 make landfall over the eastern Asian countries, which impose severe threats to these regions. 93.2% of landing TCs complete ET after landfall, wherein 39.8% ETCs complete the transitioning within one day. The ETCs over the WNP have experienced a decrease in the past four decades, wherein C5 has a significant decrease both in inter-annual and inter-decadal scales with the expansion and intensification of the western Pacific subtropical high (WPSH). The favorable large-scale circulation pattern makes C2 become the dominant track pattern and own the largest number of intensifying ETCs among the six clusters, the number of which increases nonsignificantly in the past four decades. The surface roughness variation and three-dimensional background circulation lead to the maximum number of landing TCs and minimum number of intensifying ETCs in C5. The results will facilitate better understanding of the spatio-temporal distributions of ET events and associated environment background fields, which will be beneficial to the effective monitoring of these events over the WNP.
The Roles of Upper-Level Descending Inflow in the Moat Formation in Simulated Tropical Cyclones with Secondary Eyewall Formation
Nannan Qin, Liguang Wu
, Available online   , Manuscript accepted  31 October 2023, doi: 10.1007/s00376-023-3075-9
This study investigated the effects of the upper-level descending inflow (ULDI) associated with the inner-eyewall convection on the formation of the moat in tropical cyclones (TCs) with the secondary eyewall formation (SEF). A clear moat with SEF occurs in the TC with a significant ULDI, while no SEF occurs in the TC without a significant ULDI in our numerical experiments. The eyewall convection develops more vigorously in the control run. An ULDI occurs outside of the inner-eyewall convection, where it is symmetrically unstable. The ULDI is initially triggered by the diabatic warming released by the inner eyewall and later enhanced by the cooling below the anvil cloud. The ULDI penetrates to the outer edge of the inner eyewall with relatively dry air and prevents excessive solid-phase hydrometeors from being advected further outward. It produces much sublimation cooling of falling hydrometeors between the eyewall and the outer convection. The sublimation cooling results in negative buoyancy and further induces strong subsidence between the eyewall and the outer convection. As a result, a clear moat is generated. The development of moat of an ongoing SEF prevents the outer rainband from moving farther inward, helping the outer rainband to symmetrize into an outer eyewall. For the sensitivity experiment, no significant ULDI forms since the eyewall convection is weaker, and the eyewall anvil develops relatively lower and thus is unfavorable for the formation of the moat and thus an outer eyewall. This study suggests that a better-represented simulation of inner-eyewall convective structures and the distribution of the solid-phase hydrometeors is important to the prediction of SEF.
Characteristics and Mechanisms of Persistent Wet-Cold Events with Different Cold-Air Paths in South China
Xiaojuan Sun, chen Li, Chuhan Lu, Panxing Wang
, Available online   , Manuscript accepted  24 October 2023, doi: 10.1007/s00376-023-3088-4
We investigate the characteristics and mechanisms of persistent wet-cold events (PWCEs) with different types of cold-air paths. Results show that the cumulative single-station frequency of the PWCEs in the western part of South China is higher than that in the eastern part. The pattern of single-station frequency of the PWCEs are Yangtze River (YR) uniform and east-west inverse. The YR uniform pattern is the dominant mode, so we focus on this pattern. The cold-air paths for PWCEs of YR uniform pattern are divided into three types, namely the west, northwest and north types, among which the west type accounts for the largest proportion. The differences in atmospheric circulation of the PWCEs under the three types of paths are obvious. The thermal inversion layer in the lower troposphere is favorable for precipitation during the PWCEs. The positive water vapor budget for the three types of the PWCEs mainly appears in the southern boundary.
Factors influencing the spatial variability of air temperature urban heat island intensity in Chinese cities
Heng Lyu, Wei Wang, Keer Zhang, Chang Cao, Wei Xiao, Xuhui Lee
, Available online   , Manuscript accepted  24 October 2023, doi: 10.1007/s00376-023-3012-y
Few studies have investigated spatial patterns of the air temperature urban heat island (AUHI) and its controlling factors. In this study, the data generated by an urban climate model was used to investigate the spatial variations of the AUHI across China and the underlying climate and ecological drivers. A total of 355 urban clusters were used. We performed an attribution analysis of the AUHI to elucidate the mechanisms underlying the AUHI formation. The results show that midday AUHI is negatively correlated with climate wetness (humid: 0.34 K; semi-humid: 0.50 K; semi-arid: 0.73 K). The annual mean midnight AUHI does not show discernible spatial patterns, but is generally stronger than the midday AUHI. The urban-rural difference in convection efficiency is the largest contributor to the midday AUHI in the humid (0.32 ± 0.09 K) and the semi-arid (0.36 ± 0.11 K) climate zones. The release of the anthropogenic heat in urban land is the dominant contributor to the midnight AUHI in all the three climate zones. The rural vegetation density is the most important driver of the daytime and nighttime AUHI spatial variations. A spatial covariance analysis reveals that this vegetation influence is manifested mainly through its regulation on heat storage in rural land.
Study on Quantitative Precipitation Estimation by Polarimetric Radar Using Deep Learning
Jiang HUANGFU, Zhiqun HU, Jiafeng ZHENG, Lirong WANG, Yongjie ZHU
, Available online   , Manuscript accepted  24 October 2023, doi: 10.1007/s00376-023-3039-0
Accurate radar quantitative precipitation estimation (QPE) plays an essential role in disaster prevention and mitigation. In this paper, two deep learning-based QPE networks including a single-parameter network and a multi-parameter network are designed. Meanwhile, a self-defined loss function (SLF) is proposed during modeling. The dataset includes Shijiazhuang S-band dual polarimetric radar (CINRAD/SAD) data and rain gauge data within the radar’s 100-km detection range during the flood season of 2021 in North China. Considering that the specific propagation phase shift (KDP) has a roughly linear relationship with the precipitation intensity, KDP is set to 0.5° km–1 as a threshold value to divide all the rain data (AR) into a heavy rain (HR) and light rain (LR) dataset. Subsequently, 12 deep learning-based QPE models are trained according to the input radar parameters, the precipitation datasets, and whether an SLF was adopted, respectively. The results suggest that the effects of QPE after distinguishing rainfall intensity are better than those without distinguishing, and the effects of using SLF are better than those that used MSE as a loss function. A Z-R relationship and a ZH-KDP-R synthesis method are compared with deep learning-based QPE. The mean relative errors (MRE) of AR models using SLF are improved by 61.90%, 51.21%, and 56.34% compared with the Z-R relational method, and by 38.63%, 42.55%, and 47.49% compared with the synthesis method. Finally, the models are further evaluated in three precipitation processes, which manifest that the deep learning-based models have significant advantages over the traditional empirical formula methods.
CAS-ESM2.0 dataset for the Carbon Dioxide Removal Model Intercomparison Project (CDRMIP)
Jiangbo Jin, Duoying Ji, Xiao Dong, Kece FEI, Run GUO, Juanxiong He, Yi Yu, Zhaoyang Chai, He Zhang, Dongling Zhang, Kangjun Chen, Qing-Cun Zeng
, Available online   , Manuscript accepted  20 October 2023, doi: 10.1007/s00376-023-3089-3
Understanding the response of the Earth system to varying concentrations of carbon dioxide (CO2) is critical for projecting possible future climate change, and for providing insight into mitigation and adaptation strategies in the near future. In this study, we provide a dataset by conducting an experiment involving carbon dioxide removal (CDR)—a potential way to suppress global warming—using the Chinese Academy of Sciences Earth System Model version 2.0 (CAS-ESM2.0). A preliminary evaluation is provided. The model is integrated from 200–340 years as a 1% yr−1 CO2 concentration increase experiment, and then to ~478 years as a carbon dioxide removal experiment, until CO2 returns to its original value. Finally, another 80 years is integrated in which CO2 is kept constant. Changes in 2m temperature, precipitation, sea surface temperature, ocean temperature, Atlantic meridional overturning circulation (AMOC), and sea surface height are all analyzed. In the ramp-up period, global mean 2m temperature and precipitation both increase while the AMOC weakens. Values of all the above variables change in the opposite direction in the ramp-down period, with delayed peak relative to the CO2 peak. After CO2 returns to its original value, global mean 2m temperature is still ~1K higher than in the original state, and precipitation is ~0.07 mm/d higher. At the end of the simulation, there is a ~0.5℃ increase in ocean temperature, and a 1 Sv weakening in AMOC. Our model simulation produces similar results to those of comparable experiments previously reported in the literature.
Seasonal characteristics of forecasting uncertainties in surface PM2.5 concentration associated with leading-time over the Beijing-Tianjin-Hebei region
Qiuyan Du, Chun Zhao, Jiawang Feng, Zining Yang, Jiamin Xu, Jun Gu, Mingshuai Zhang, Mingyue Xu, Shengfu Lin
, Available online   , Manuscript accepted  19 October 2023, doi: 10.1007/s00376-023-3060-3
Forecasting uncertainties in meteorological fields have long been recognized as the main limitation on the accuracy and predictability of air quality forecasts. However, the impacts of meteorological forecasting uncertainties on air quality forecasts in different seasons are still not well known. In this study, a series of forecasts with different leading-time for January, April, July, and October of 2018 are conducted over the Beijing-Tianjin-Hebei (BTH) region, and the impacts of meteorological forecasting uncertainties on surface PM2.5 concentration forecasts with each leading-time are investigated. With increasing leading-time, the forecasted PM2.5 concentrations change significantly and have obvious seasonal variation. In general, the forecasting uncertainties in monthly mean surface PM2.5 concentrations in the BTH region due to leading-time are the largest (80%) in spring, followed by autumn (~50%), summer (~40%), and winter (20%). In winter, the forecasting uncertainties in total surface PM2.5 mass due to leading-time are mainly due to the uncertainties in PBL heights and hence the PBL mixing of anthropogenic primary particles. In spring, the forecasting uncertainties are mainly from the impacts of leading-time on the northwesterly, thereby further enhancing the condensation production of anthropogenic secondary particles by long-range transported natural dust. In summer, the forecasting uncertainties result mainly from the decrease of dry and wet deposition rates, which are associated with the reduction of near-surface wind speed and precipitation rate. In autumn, the forecasting uncertainties are mainly from the change in the transport of remote natural dust and anthropogenic particles, which is associated with the change in large-scale circulation.
Summer Atmospheric Water Cycle under the Transition Influence of Westerly and Summer Monsoon over Yarlung Zangbo River Basin in the Southern Tibetan Plateau
Qianhui Ma, Chunyan Zhang, Donghai Wang, Zihao Pang
, Available online   , Manuscript accepted  17 October 2023, doi: 10.1007/s00376-023-3094-6
This study compares summer atmospheric water cycle including moisture sources and consumption in the upstream, midstream, and downstream regions of the Yarlung Zangbo River Basin in the southern Tibetan Plateau. Evolutions of the moisture properties under the westerly and summer southerly monsoon influence are examined using 5-yr multi-source measurements and ERA5 reanalysis data. Note that moisture consumption in this study is associated with clouds, precipitation, and diabatic heating. Compared to the midstream and downstream regions, the upstream region has less moisture, clouds, and precipitation, where the moisture is brought by the westerly. In early August, the vertical wet advection over this region becomes enhanced and generates more high clouds and precipitation. The midstream region has moisture carried by the westerly in June and by the southerly monsoon from July to August. The higher vertical wet advection maximum here forms more high clouds, with a precipitation peak in early July. The downstream region is mainly affected by the southerly-driven wet advection. The rich moisture and strong vertical wet advection here produce the most clouds and precipitation among the three regions, with a precipitation peak in late June. The height of the maximum moisture condensation is different between the midstream region (325 hPa) and the other two regions (375 hPa), due to the higher upward motion maximum in the midstream region. The diabatic heating structures show that stratiform clouds dominate the upstream region, stratiform clouds and deep convection co-exist in the midstream region, and deep convection systems characterize the downstream region.
Representation of the stratospheric circulation in CRA-40 reanalysis: the Arctic polar vortex and the quasi-biennial oscillation
Zixu Wang, Shirui Yan, Jinggao Hu, Jiechun Deng, RONGCAI REN, Jian Rao
, Available online   , Manuscript accepted  13 October 2023, doi: 10.1007/s00376-023-3127-1
The representation of the Arctic stratospheric circulation and the quasi-biennial oscillation (QBO) during the period 1981–2019 in a 40-yr China’s global reanalysis dataset (CRA-40) is evaluated by comparisons with the two widely used reanalysis datasets, ERA-5 and MERRA-2. CRA-40 has a comparable performance with ERA-5 and MERRA-2 in characterizing the winter and spring circulation in the lower and middle Arctic stratosphere. Specifically, differences in climatological polar-mean temperature and polar night jet among the three reanalyses are within ±0.5 K and ±0.5 m s–1, respectively. The onset dates of the stratospheric sudden warming and stratospheric final warming events at 10 hPa in CRA-40, together with the dynamics and circulation anomalies during the onset process of warming events, are nearly identical to the other two reanalyses with slight differences. By contrast, CRA-40 has deteriorated performance in describing the QBO below 10 hPa compared to the other two reanalysis products, as manifested by the larger easterly biases of the QBO index, the remarkably weaker amplitude of the QBO, and the weaker wavelet power of the QBO period. Such pronounced biases are mainly concentrated in the period 1981–1998 and largely reduced by at least 39% in 1999–2019. Thus, particular caution is needed in studying the QBO based on CRA-40. Compared to the lower and middle stratosphere, all the three reanalyses have greater disagreement in the upper stratosphere in both the polar region and the tropics.
Comparison of a Spectral Bin and Two Multi-Moment Bulk Microphysics Schemes for Supercell Simulation: Investigation into Key Processes Responsible for Hydrometeor Distributions and Precipitation
Marcus JOHNSON, Ming XUE, Youngsun JUNG
, Available online   , Manuscript accepted  09 October 2023, doi: 10.1007/s00376-023-3069-7
There are more uncertainties with ice hydrometeor representations and related processes than liquid hydrometeors within microphysics parameterization (MP) schemes because of their complicated geometries and physical properties. Idealized supercell simulations are produced using the WRF model coupled with “full” Hebrew University spectral bin MP (HU-SBM), and NSSL and Thompson bulk MP (BMP) schemes. HU-SBM downdrafts are typically weaker than those of the NSSL and Thompson simulations, accompanied by less rain evaporation. HU-SBM produces more cloud ice (plates), graupel, and hail than the BMPs, yet precipitates less at the surface. The limiting mass bins (and subsequently, particle size) of rimed ice in HU-SBM and slower rimed ice fall speeds lead to smaller melting-level net rimed ice fluxes than those of the BMPs. Aggregation from plates in HU-SBM, together with snow–graupel collisions, leads to a greater snow contribution to rain than those of the BMPs. Replacing HU-SBM’s fall speeds using the formulations of the BMPs after aggregating the discrete bin values to mass mixing ratios and total number concentrations increases net rain and rimed ice fluxes. Still, they are smaller in magnitude than bulk rain, NSSL hail, and Thompson graupel net fluxes near the surface. Conversely, the melting-layer net rimed ice fluxes are reduced when the fall speeds for the NSSL and Thompson simulations are calculated using HU-SBM fall speed formulations after discretizing the bulk particle size distributions (PSDs) into spectral bins. The results highlight precipitation sensitivity to storm dynamics, fall speed, hydrometeor evolution governed by process rates, and MP PSD design.
Westerlies affecting the seasonal variation of water vapor transport over the Tibetan Plateau induced by BOB TCs
Xiaoli Zhou, Wen Zhou, Dongxiao WANG, Qiang Xie, yang lei, Qihua Peng
, Available online   , Manuscript accepted  09 October 2023, doi: 10.1007/s00376-023-3093-7
This study investigates the activity of tropical cyclones (TCs) in the Bay of Bengal (BOB) from 1979 to 2018 to discover the mechanism affecting the contribution rate of the meridional moisture budget anomaly (MMBA) over the south boundary of the Tibetan Plateau (SBTP). May and October–December are the bimodal phases of BOB TC frequency, which decreases month by month from October to December and is relatively low in May. However, the contribution rate of MMBA is the highest in May. Seasonal variation in the meridional position of the westerlies is the key factor affecting the contribution rate. The relatively south (north) position of the westerlies in November and December (May) results in the lower (higher) contribution rate of MMBA. This mechanism is confirmed by the momentum equation. When water vapor enters the westerlies near the trough line, the direction of the resultant meridional force is north, the farther north the trough is, and the farther north the water vapor can be transported. When water vapor enters the westerlies from the area near the ridge line, for Type-T (Type-R) TCs [water vapor enters the westerlies in front of a trough (ridge)], the direction of the resultant meridional force is south and the direction of the resultant zonal force is east (west), which is not conducive to the northward transport of water vapor. Especially if the trough or ridge is relatively south, the water vapor may not cross the SBTP.
Local Torrential Rainfall Event within a Mei-Yu Season Mesoscale Convective System: Importance of Back-Building Processes
Honglei Zhang, Ming Xue, Hangfeng Shen, Xiaofan Li, Guoqing Zhai
, Available online   , Manuscript accepted  09 October 2023, doi: 10.1007/s00376-023-3033-6
An extreme rainfall event occurred over Hangzhou, China during the afternoon hours on 24 June 2013. This event occurred under suitable synoptic conditions and the maximum 4-hour cumulative rainfall amount was over 150 mm. This rainfall event had two major rainbands. One was caused by a quasi-stationary convective line, the other by a back-building convective line related to the interaction of outflow boundary from the first rainband and an existing low-level mesoscale convergence line associated with a Mei-Yu frontal system. The rainfall event lasted 4 hours while the back-building process occurred in 2 hours when the extreme rainfall center formed. So far, few studies have examined the back-building processes in the Mei-Yu season that are caused by the interaction of a mesoscale convergence line and a convective cold pool. The two rainbands are successfully reproduced by the Weather Research and Forecasting (WRF) model with 4-level two-way interactive nesting. In the model, new cells repeatedly occur at the west side of older cells, and the back-building process occurs in an environment with large CAPE, low LFC and plenty of water vapor. Outflows from older cells enhance the low-level convergence that forces new cells. High precipitation efficiency of the back-building training cells leads to accumulated precipitation of over 150 mm. Sensitivity experiments without evaporation of rainwater show that the convective cold pool plays an important role in the organization of the back-building process in the current extremely precipitation case.
Different El Niño Flavors and Associated Atmospheric Teleconnections as Simulated in a Hybrid Coupled Model
Junya HU, Hongna WANG, Chuan GAO, Rong-Hua ZHANG
, Available online   , Manuscript accepted  08 October 2023, doi: 10.1007/s00376-023-3082-x
A previously developed hybrid coupled model (HCM) is composed of an intermediate tropical Pacific Ocean model and a global atmospheric general circulation model (AGCM), denoted as HCMAGCM. In this study, different El Niño flavors, namely the Eastern-Pacific (EP) and Central-Pacific (CP) types, and the associated global atmospheric teleconnections are examined in a 1000-yr control simulation of the HCMAGCM. The HCMAGCM indicates profoundly different characteristics among EP and CP El Niño events in terms of related oceanic and atmospheric variables in the tropical Pacific, including the amplitude and spatial patterns of sea surface temperature (SST), zonal wind stress, and precipitation anomalies. An SST budget analysis indicates that the thermocline feedback and zonal advective feedback dominantly contribute to the growth of EP and CP El Niño events, respectively. Corresponding to the shifts in the tropical rainfall and deep convection during EP and CP El Niño events, the model also reproduces the differences in the extratropical atmospheric responses during the boreal winter. In particular, the EP El Niño tends to be dominant in exciting a poleward wave train pattern to the Northern Hemisphere, while the CP El Niño tends to preferably produce a wave train similar to the Pacific North American (PNA) pattern. As a result, different climatic impacts exist in North American regions, with a warm-north and cold-south pattern during an EP El Niño and a warm-northeast and cold-southwest pattern during a CP El Niño, respectively. This modeling result highlights the importance of internal natural processes within the tropical Pacific as they relate to the genesis of ENSO diversity because the active ocean–atmosphere coupling is allowed only in the tropical Pacific within the framework of the HCMAGCM.
Changes in Spring Snow Cover over the Eastern and Western Tibetan Plateau and Their Associated Mechanism
Fangchi LIU, Xiaojing JIA, Wei DONG
, Available online   , Manuscript accepted  07 October 2023, doi: 10.1007/s00376-023-3111-9
The spring snow cover (SC) over the western Tibetan Plateau (TP) (TPSC) (W_TPSC) and eastern TPSC (E_TPSC) have displayed remarkable decreasing and increasing trends, respectively, during 1985–2020. The current work investigates the possible mechanisms accounting for these distinct TPSC changes. Our results indicate that the decrease in W_TPSC is primarily attributed to rising temperatures, while the increase in E_TPSC is closely linked to enhanced precipitation. Local circulation analysis shows that the essential system responsible for the TPSC changes is a significant anticyclonic system centered over the northwestern TP. The anomalous descending motion and adiabatic heating linked to this anticyclone leads to warmer temperatures and consequent snowmelt over the western TP. Conversely, anomalous easterly winds along the southern flank of this anticyclone serve to transport additional moisture from the North Pacific, leading to an increase in snowfall over the eastern TP. Further analysis reveals that the anomalous anticyclone is associated with an atmospheric wave pattern that originates from upstream regions. Springtime warming of the subtropical North Atlantic (NA) sea surface temperature (SST) induces an atmospheric pattern resembling a wave train that travels eastward across the Eurasian continent before reaching the TP. Furthermore, the decline in winter sea ice (SIC) over the Barents Sea exerts a persistent warming influence on the atmosphere, inducing an anomalous atmospheric circulation that propagates southeastward and strengthens the northwest TP anticyclone in spring. Additionally, an enhancement of subtropical stationary waves has resulted in significant increases in easterly moisture fluxes over the coastal areas of East Asia, which further promotes more snowfall over eastern TP.
Assessing the Performance of a Dynamical Downscaling Simulation Driven by a Bias-Corrected CMIP6 Dataset for Asian Climate
Zhongfeng XU, Ying HAN, Meng-Zhuo ZHANG, Chi-Yung TAM, Zong-Liang YANG, Ahmed M. EL KENAWY, Congbin FU
, Available online   , Manuscript accepted  07 October 2023, doi: 10.1007/s00376-023-3101-y
In this study, we aim to assess dynamical downscaling simulations by utilizing a novel bias-corrected global climate model (GCM) data to drive a regional climate model (RCM) over the Asia-western North Pacific region. Three simulations were conducted with a 25-km grid spacing for the period 1980–2014. The first simulation (WRF_ERA5) was driven by the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) dataset and served as the validation dataset. The original GCM dataset (MPI-ESM1-2-HR model) was used to drive the second simulation (WRF_GCM), while the third simulation (WRF_GCMbc) was driven by the bias-corrected GCM dataset. The bias-corrected GCM data has an ERA5-based mean and interannual variance and long-term trends derived from the ensemble mean of 18 CMIP6 models. Results demonstrate that the WRF_GCMbc significantly reduced the root-mean-square errors (RMSEs) of the climatological mean of downscaled variables, including temperature, precipitation, snow, wind, relative humidity, and planetary boundary layer height by 50%–90% compared to the WRF_GCM. Similarly, the RMSEs of interannual-to-interdecadal variances of downscaled variables were reduced by 30%–60%. Furthermore, the WRF_GCMbc better captured the annual cycle of the monsoon circulation and intraseasonal and day-to-day variabilities. The leading empirical orthogonal function (EOF) shows a monopole precipitation mode in the WRF_GCM. In contrast, the WRF_GCMbc successfully reproduced the observed tri-pole mode of summer precipitation over eastern China. This improvement could be attributed to a better-simulated location of the western North Pacific subtropical high in the WRF_GCMbc after GCM bias correction.
Assimilating FY-4A AGRI Radiances with a Channel-Sensitive Cloud Detection Scheme for the Analysis and Forecasting of Multiple Typhoons
Feifei SHEN, Aiqing SHU, Zhiquan LIU, Hong LI, Lipeng JIANG, Tao ZHANG, Dongmei XU
, Available online   , Manuscript accepted  07 October 2023, doi: 10.1007/s00376-023-3072-z
This paper presents an attempt at assimilating clear-sky FY-4A Advanced Geosynchronous Radiation Imager (AGRI) radiances from two water vapor channels for the prediction of three landfalling typhoon events over the West Pacific Ocean using the 3DVar data assimilation (DA) method along with the WRF model. A channel-sensitive cloud detection scheme based on the particle filter (PF) algorithm is developed and examined against a cloud detection scheme using the multivariate and minimum residual (MMR) algorithm and another traditional cloud mask–dependent cloud detection scheme. Results show that both channel-sensitive cloud detection schemes are effective, while the PF scheme is able to reserve more pixels than the MMR scheme for the same channel. In general, the added value of AGRI radiances is confirmed when comparing with the control experiment without AGRI radiances. Moreover, it is found that the analysis fields of the PF experiment are mostly improved in terms of better depicting the typhoon, including the temperature, moisture, and dynamical conditions. The typhoon track forecast skill is improved with AGRI radiance DA, which could be explained by better simulating the upper trough. The impact of assimilating AGRI radiances on typhoon intensity forecasts is small. On the other hand, improved rainfall forecasts from AGRI DA experiments are found along with reduced errors for both the thermodynamic and moisture fields, albeit the improvements are limited.
Spatiotemporal Characteristics of Rainfall over Different Terrain Features in the Middle Reaches of the Yangtze River Basin during the Warm Seasons of 2016–20
Qian WEI, Jianhua SUN, Shenming FU, Yuanchun ZHANG, Xiaofang WANG
, Available online   , Manuscript accepted  25 September 2023, doi: 10.1007/s00376-023-3034-5
Based on hourly rain gauge data during May–September of 2016–20, we analyze the spatiotemporal distributions of total rainfall (TR) and short-duration heavy rainfall (SDHR; hourly rainfall ≥ 20 mm) and their diurnal variations over the middle reaches of the Yangtze River basin. For all three types of terrain (i.e., mountain, foothill, and plain), the amount of TR and SDHR both maximize in June/July, and the contribution of SDHR to TR (CST) peaks in August (amount: 23%; frequency: 1.74%). Foothill rainfall is characterized by a high TR amount and a high CST (in amount); mountain rainfall is characterized by a high TR frequency but a small CST (in amount); and plain rainfall shows a low TR amount and frequency, but a high CST (in amount). Overall, stations with high TR (amount and frequency) are mainly located over the mountains and in the foothills, while those with high SDHR (amount and frequency) are mainly concentrated in the foothills and plains close to mountainous areas. For all three types of terrain, the diurnal variations of both TR and SDHR exhibit a double peak (weak early morning and strong late afternoon) and a phase shift from the early-morning peak to the late-afternoon peak from May to August. Around the late-afternoon peak, the amount of TR and SDHR in the foothills is larger than over the mountains and plains. The TR intensity in the foothills increases significantly from midnight to afternoon, suggesting that thermal instability may play an important role in this process.
Projecting Spring Consecutive Rainfall Events in the Three Gorges Reservoir based on Triple-Nested Dynamical Downscaling
Yanxin ZHENG, Shuanglin LI, Noel KEENLYSIDE, Shengping HE, Lingling SUO
, Available online   , Manuscript accepted  07 September 2023, doi: 10.1007/s00376-023-3118-2
Spring consecutive rainfall events (CREs) are key triggers of geological hazards in the Three Gorges Reservoir area (TGR), China. However, previous projections of CREs based on the direct outputs of global climate models (GCMs) are subject to considerable uncertainties, largely caused by their coarse resolution. This study applies a triple-nested WRF (Weather Research and Forecasting) model dynamical downscaling, driven by a GCM, MIROC6 (Model for Interdisciplinary Research on Climate, version 6), to improve the historical simulation and reduce the uncertainties in the future projection of CREs in the TGR. Results indicate that WRF has better performances in reproducing the observed rainfall in terms of the daily probability distribution, monthly evolution and duration of rainfall events, demonstrating the ability of WRF in simulating CREs. Thus, the triple-nested WRF is applied to project the future changes of CREs under the middle-of-the-road and fossil-fueled development scenarios. It is indicated that light and moderate rainfall and the duration of continuous rainfall spells will decrease in the TGR, leading to a decrease in the frequency of CREs. Meanwhile, the duration, rainfall amount, and intensity of CREs is projected to increase in the central-west TGR. These results are inconsistent with the raw projection of MIROC6. Observational diagnosis implies that CREs are mainly contributed by the vertical moisture advection. Such a synoptic contribution is captured well by WRF, which is not the case in MIROC6, indicating larger uncertainties in the CREs projected by MIROC6.
Relative Impacts of Sea Ice Loss and Atmospheric Internal Variability on the Winter Arctic to East Asian Surface Air Temperature Based on Large-Ensemble Simulations with NorESM2
Shengping HE, Helge DRANGE, Tore FUREVIK, Huijun WANG, Ke FAN, Lise Seland GRAFF, Yvan J. ORSOLINI
, Available online   , Manuscript accepted  21 June 2023, doi: 10.1007/s00376-023-3006-9
To quantify the relative contributions of Arctic sea ice and unforced atmospheric internal variability to the “warm Arctic, cold East Asia” (WACE) teleconnection, this study analyses three sets of large-ensemble simulations carried out by the Norwegian Earth System Model with a coupled atmosphere–land surface model, forced by seasonal sea ice conditions from preindustrial, present-day, and future periods. Each ensemble member within the same set uses the same forcing but with small perturbations to the atmospheric initial state. Hence, the difference between the present-day (or future) ensemble mean and the preindustrial ensemble mean provides the ice-loss-induced response, while the difference of the individual members within the present-day (or future) set is the effect of atmospheric internal variability. Results indicate that both present-day and future sea ice loss can force a negative phase of the Arctic Oscillation with a WACE pattern in winter. The magnitude of ice-induced Arctic warming is over four (ten) times larger than the ice-induced East Asian cooling in the present-day (future) experiment; the latter having a magnitude that is about 30% of the observed cooling. Sea ice loss contributes about 60% (80%) to the Arctic winter warming in the present-day (future) experiment. Atmospheric internal variability can also induce a WACE pattern with comparable magnitudes between the Arctic and East Asia. Ice-loss-induced East Asian cooling can easily be masked by atmospheric internal variability effects because random atmospheric internal variability may induce a larger magnitude warming. The observed WACE pattern occurs as a result of both Arctic sea ice loss and atmospheric internal variability, with the former dominating Arctic warming and the latter dominating East Asian cooling.
The Role of Underlying Boundary Forcing in Shaping the Recent Decadal Change of Persistent Anomalous Activity over the Ural Mountains
Ting LEI, Shuanglin LI
, Available online   , Manuscript accepted  21 June 2023, doi: 10.1007/s00376-023-2365-6
Observational analyses demonstrate that the Ural persistent positive height anomaly event (PAE) experienced a decadal increase around the year 2000, exhibiting a southward displacement afterward. These decadal variations are related to a large-scale circulation shift over the Eurasian Continent. The effects of underlying sea ice and sea surface temperature (SST) anomalies on the Ural PAE and the related atmospheric circulation were explored by Atmospheric Model Intercomparison Project (AMIP) experiments from the Coupled Model Intercomparison Project Phase 6 and by sensitivity experiments using the Atmospheric General Circulation Model (AGCM). The AMIP experiment results suggest that the underlying sea ice and SST anomalies play important roles. The individual contributions of sea ice loss in the Barents-Kara Seas and the SST anomalies linked to the phase transition of the Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO) are further investigated by AGCM sensitivity experiments isolating the respective forcings. The sea ice decline in Barents-Kara Seas triggers an atmospheric wave train over the Eurasian mid-to-high latitudes with positive anomalies over the Urals, favoring the occurrence of Ural PAEs. The shift in the PDO to its negative phase triggers a wave train propagating downstream from the North Pacific. One positive anomaly lobe of the wave train is located over the Ural Mountains and increases the PAE there. The negative-to-positive transition of the AMO phase since the late-1990s causes positive 500-hPa height anomalies south of the Ural Mountains, which promote a southward shift of Ural PAE.
Time-lagged Effects of the Spring Atmospheric Heat Source over the Tibetan Plateau on Summer Precipitation in Northeast China during 1961–2020: Role of Soil Moisture
Yizhe HAN, Dabang JIANG, Dong SI, Yaoming MA, Weiqiang MA
, Available online   , Manuscript accepted  21 June 2023, doi: 10.1007/s00376-023-2363-8
The spring atmospheric heat source (AHS) over the Tibetan Plateau (TP) has been suggested to affect the Asian summer monsoon and summer precipitation over South China. However, its influence on the summer precipitation in Northeast China (NEC) remains unknown. The connection between spring TP AHS and subsequent summer precipitation over NEC from 1961 to 2020 is analyzed in this study. Results illustrate that stronger spring TP AHS can enhance subsequent summer NEC precipitation, and higher soil moisture in the Yellow River Valley‒North China region (YRVNC) acts as a bridge. During spring, the strong TP AHS could strengthen the transportation of water vapor to East China and lead to excessive rainfall in the YRVNC. Thus, soil moisture increases, which regulates local thermal conditions by decreasing local surface skin temperature and sensible heat. Owing to the memory of soil moisture, the lower spring sensible heat over the YRVNC can last until mid-summer, decrease the land–sea thermal contrast, and weaken the southerly winds over the East Asia–western Pacific region and convective activities over the South China Sea and tropical western Pacific. This modulates the East Asia–Pacific teleconnection pattern, which leads to a cyclonic anomaly and excessive summer precipitation over NEC.
Projecting Wintertime Newly Formed Arctic Sea Ice through Weighting CMIP6 Model Performance and Independence
Jiazhen ZHAO, Shengping HE, Ke FAN, Huijun WANG, Fei LI
, Available online   , Manuscript accepted  06 May 2023, doi: 10.1007/s00376-023-2393-2
Precipitous Arctic sea-ice decline and the corresponding increase in Arctic open-water areas in summer months give more space for sea-ice growth in the subsequent cold seasons. Compared to the decline of the entire Arctic multiyear sea ice, changes in newly formed sea ice indicate more thermodynamic and dynamic information on Arctic atmosphere–ocean–ice interaction and northern mid–high latitude atmospheric teleconnections. Here, we use a large multimodel ensemble from phase 6 of the Coupled Model Intercomparison Project (CMIP6) to investigate future changes in wintertime newly formed Arctic sea ice. The commonly used model-democracy approach that gives equal weight to each model essentially assumes that all models are independent and equally plausible, which contradicts with the fact that there are large interdependencies in the ensemble and discrepancies in models’ performances in reproducing observations. Therefore, instead of using the arithmetic mean of well-performing models or all available models for projections like in previous studies, we employ a newly developed model weighting scheme that weights all models in the ensemble with consideration of their performance and independence to provide more reliable projections. Model democracy leads to evident bias and large intermodel spread in CMIP6 projections of newly formed Arctic sea ice. However, we show that both the bias and the intermodel spread can be effectively reduced by the weighting scheme. Projections from the weighted models indicate that wintertime newly formed Arctic sea ice is likely to increase dramatically until the middle of this century regardless of the emissions scenario. Thereafter, it may decrease (or remain stable) if the Arctic warming crosses a threshold (or is extensively constrained).
The asymmetric connection of SST in the Tasman Sea with respect to the opposite phases of ENSO in austral summer
Xueqian Sun, Shuanglin Li, Stefan Liess
, Available online   , Manuscript accepted  11 February 2022, doi: 10.1007/s00376-022-0421-y
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.
Effect of the vertical diffusion of moisture in the planetary boundary layer on an idealized tropical cyclone
Hongxiong Xu, Dajun Zhao
, Available online   , Manuscript accepted  01 June 2021, doi:
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.
Estimations of Land Surface Characteristic Parameters and Turbulent Heat Fluxes over the Tibetan Plateau Based on FY-4A/AGRI Data
Nan GE, Lei ZHONG, Yaoming MA, Yunfei FU, Mijun ZOU, Meilin CHENG, Xian WANG, Ziyu HUANG
, Available online   , Manuscript accepted  10 November 2020, doi: 10.1007/s00376-020-0169-5
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.
News & Views
Deep Learning Shows Promise for Seasonal Prediction of Antarctic Sea Ice in a Rapid Decline Scenario
Xiaoran DONG, Yafei NIE, Jinfei WANG, Hao LUO, Yuchun GAO, Yun WANG, Jiping LIU, Dake CHEN, Qinghua YANG
, Available online   , Manuscript accepted  25 January 2024, doi: 10.1007/s00376-024-3380-y
The rapidly changing Antarctic sea ice has garnered significant interest. To enhance the prediction skill for sea ice and respond to the Sea Ice Prediction Network-South’s latest call, this study presents the reforecast results of Antarctic sea-ice area and extent from December to June of the coming year with a Convolutional Long Short-Term Memory (ConvLSTM) Network. The reforecast experiments demonstrate that ConvLSTM captures the interannual and interseasonal variability of Antarctic sea ice successfully, and performs better than the European Centre for Medium-Range Weather Forecasts. Based on this, we present the prediction from December 2023 to June 2024, indicating that the Antarctic sea ice will remain at lows, but may not create a new record low. This research highlights the promising application of deep learning in Antarctic sea-ice prediction.
The Global Energy and Water Exchanges (GEWEX) Project in Central Asia: The Case for a Regional Hydroclimate Project
Michael BRODY, Maksim KULIKOV, Sagynbek ORUNBAEV, Peter J. VAN OEVELEN
, Available online   , Manuscript accepted  02 January 2024, doi: 10.1007/s00376-023-3384-2
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 a shrinking snowpack, which 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. Projections of future precipitation show significant uncertainties in type, amount, and distribution. Regional Hydroclimate Projects (RHPs) are an approach to studying these issues. Initial steps to develop an 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 the Global Energy and Water Exchanges (GEWEX) project for the region include both observations and modeling, as well as development of better and additional precipitation observations, all of which are topics for the next workshop. A well-designed RHP should lead to reductions in critical climate uncertainties in policy-relevant timeframes that can influence decisions on necessary investments in climate adaptation.
El Niño and the AMO Sparked the Astonishingly Large Margin of Warming in the Global Mean Surface Temperature in 2023
Kexin LI, Fei ZHENG, Jiang ZHU, Qing-Cun ZENG
, Available online   , Manuscript accepted  28 December 2023, doi: 10.1007/s00376-023-3371-4
In 2023, the majority of the Earth witnessed its warmest boreal summer and autumn since 1850. Whether 2023 will indeed turn out to be the warmest year on record and what caused the astonishingly large margin of warming has become one of the hottest topics in the scientific community and is closely connected to the future development of human society. We analyzed the monthly varying global mean surface temperature (GMST) in 2023 and found that the globe, the land, and the oceans in 2023 all exhibit extraordinary warming, which is distinct from any previous year in recorded history. Based on the GMST statistical ensemble prediction model developed at the Institute of Atmospheric Physics, the GMST in 2023 is predicted to be 1.41°C ± 0.07°C, which will certainly surpass that in 2016 as the warmest year since 1850, and is approaching the 1.5°C global warming threshold. Compared to 2022, the GMST in 2023 will increase by 0.24°C, with 88% of the increment contributed by the annual variability as mostly affected by El Niño. Moreover, the multidecadal variability related to the Atlantic Multidecadal Oscillation (AMO) in 2023 also provided an important warming background for sparking the GMST rise. As a result, the GMST in 2023 is projected to be 1.15°C ± 0.07°C, with only a 0.02°C increment, if the effects of natural variability—including El Niño and the AMO—are eliminated and only the global warming trend is considered.
Scientific Advances and Weather Services of the China Meteorological Administration’s National Forecasting Systems during the Beijing 2022 Winter Olympics
Guo DENG, Xueshun SHEN, Jun DU, Jiandong GONG, Hua TONG, Liantang DENG, Zhifang XU, Jing CHEN, Jian SUN, Yong WANG, Jiangkai HU, Jianjie WANG, Mingxuan CHEN, Huiling YUAN, Yutao ZHANG, Hongqi LI, Yuanzhe WANG, Li GAO, Li SHENG, Da LI, Li LI, Hao WANG, Ying ZHAO, Yinglin LI, Zhili LIU, Wenhua GUO
, Available online   , Manuscript accepted  27 December 2023, doi: 10.1007/s00376-023-3206-3
Since the Beijing 2022 Winter Olympics was the first Winter Olympics in history held in continental winter monsoon climate conditions across complex terrain areas, there is a deficiency of relevant research, operational techniques, and experience. This made providing meteorological services for this event particularly challenging. The China Meteorological Administration (CMA) Earth System Modeling and Prediction Centre, achieved breakthroughs in research on short- and medium-term deterministic and ensemble numerical predictions. Several key technologies crucial for precise winter weather services during the Winter Olympics were developed. A comprehensive framework, known as the Operational System for High-Precision Weather Forecasting for the Winter Olympics, was established. Some of these advancements represent the highest level of capabilities currently available in China. The meteorological service provided to the Beijing 2022 Games also exceeded previous Winter Olympic Games in both variety and quality. This included achievements such as the “100-meter level, minute level” downscaled spatiotemporal resolution and forecasts spanning 1 to 15 days. Around 30 new technologies and over 60 kinds of products that align with the requirements of the Winter Olympics Organizing Committee were developed, and many of these techniques have since been integrated into the CMA’s operational national forecasting systems. These accomplishments were facilitated by a dedicated weather forecasting and research initiative, in conjunction with the preexisting real-time operational forecasting systems of the CMA. This program represents one of the five subprograms of the WMO’s high-impact weather forecasting demonstration project (SMART2022), and is also a part of their Regional Association (RA) II Research Development Project (Hangzhou RDP). Therefore, the research accomplishments and meteorological service experiences from this program will be carried forward into forthcoming high-impact weather forecasting activities. This article provides an overview and assessment of this program and the operational national forecasting systems.
Recent Ventures in Interdisciplinary Arctic Research: The ARCPATH Project
Astrid E. J. OGILVIE, Leslie A. KING, Noel KEENLYSIDE, François COUNILLON, Brynhildur DAVIðSDÓTTIR, Níels EINARSSON, Sergey GULEV, Ke FAN, Torben KOENIGK, James R. MCGOODWIN, Marianne H. RASMUSSON, Shuting YANG
, Available online   , Manuscript accepted  21 December 2023, doi: 10.1007/s00376-023-3333-x
This paper celebrates Professor Yongqi GAO’s significant achievements in the field of interdisciplinary studies within the content of his final research project Arctic Climate Predictions: Pathways to Resilient Sustainable Societies - ARCPATH ( The disciplines represented in the project are related to climatology, anthropology, marine biology, economics, and the broad spectrum of social-ecological studies. Team members were drawn from the Nordic countries, Russia, China, the United States, and Canada. The project was transdisciplinary as well as interdisciplinary as it included collaboration with local knowledge holders. ARCPATH made significant contributions to Arctic research through an improved understanding of the mechanisms that drive climate variability in the Arctic. In tandem with this research, a combination of historical investigations and social, economic, and marine biological fieldwork was carried out for the project study areas of Iceland, Greenland, Norway, and the surrounding seas, with a focus on the joint use of ocean and sea-ice data as well as social-ecological drivers, ARCPATH was able to provide an improved framework for predicting the near-term variation of Arctic climate on spatial scales relevant to society, as well as evaluating possible related changes in socioeconomic realms. In summary, through the integration of information from several different disciplines and research approaches, ARCPATH served to create new and valuable knowledge on crucial issues, thus providing new pathways to action for Arctic communities.
A Tutorial Review of the Solar Power Curve: Regressions, Model chains, and Their Hybridization and Probabilistic Extensions
Dazhi YANG, Xiang’ao XIA, Martin János MAYER
, Available online   , Manuscript accepted  23 January 2024, doi: 10.1007/s00376-024-3229-4
Owing to the persisting hype in pushing toward global carbon neutrality, the study scope of atmospheric science is rapidly expanding. Among numerous trending topics, energy meteorology has been attracting the most attention hitherto. One essential skill of solar energy meteorologists is solar power curve modeling, which seeks to map irradiance and auxiliary weather variables to solar power, by statistical and/or physical means. In this regard, this tutorial review aims to deliver a complete overview of those fundamental scientific and engineering principles pertaining to the solar power curve. Solar power curves can be modeled in two primary ways, one of regression and the other of model chain. Both classes of modeling approaches, alongside their hybridization and probabilistic extensions, which allow accuracy improvement and uncertainty quantification, are scrutinized and contrasted thoroughly in this review.