Precise forecasts of wildfire danger are crucial for proactive fuel management and emergency responses,yet they pose a challenge at the subseasonal scale due to limitations in prediction capabilities and a gap between...Precise forecasts of wildfire danger are crucial for proactive fuel management and emergency responses,yet they pose a challenge at the subseasonal scale due to limitations in prediction capabilities and a gap between forecast outputs and the needs of decision-makers.This study introduces an innovative hybrid modeling framework that integrates artificial intelligence(AI)with climate dynamic prediction systems to accurately forecast High Fire-Danger Days(HFDDs)for the following month.These HFDDs are derived from historical satellite fire data and the optimum fire danger index,with a particular focus on Southwest China as a case study.The AI module,based on the ResNet-18 neural network model,integrates observational and physically constrained analysis to establish links between HFDDs and optimal predictors of atmospheric circulation from both the concurrent and preceding months.Leveraging climate dynamical forecasting,this hybrid model provides more reliable deterministic predictions for monthly HFDDs than conventional methods that rely solely on terrestrial variables such as precipitation.More importantly,the integration of dynamical ensemble prediction enhances the model’s capability for skillful probabilistic predictions of HFDDs,facilitating the creation of customized fire danger outlooks and emergency action maps tailored to stakeholders’needs.The model’s added economic value was also evaluated,demonstrating its potential to improve decision-making in disaster management and bridge the“last-mile gap”in climate service delivery.This work contributes to the Seamless Prediction and Services for Sustainable Natural and Built Environment(SEPRESS)Program(2025–32),under the United Nations Educational Scientific and Cultural Organization(UNESCO)International Decade of Sciences for Sustainable Development(2024–33).展开更多
Predicting monsoon climate is one of the major endeavors in climate science and is becoming increasingly challenging due to global warming. The accuracy of monsoon seasonal predictions significantly impacts the lives ...Predicting monsoon climate is one of the major endeavors in climate science and is becoming increasingly challenging due to global warming. The accuracy of monsoon seasonal predictions significantly impacts the lives of billions who depend on or are affected by monsoons, as it is essential for the water cycle, food security, ecology, disaster prevention, and the economy of monsoon regions. Given the extensive literature on Asian monsoon climate prediction, we limit our focus to reviewing the seasonal prediction and predictability of the Asian Summer Monsoon (ASM). However, much of this review is also relevant to monsoon predictions in other seasons and regions. Over the past two decades, considerable progress has been made in the seasonal forecasting of the ASM, driven by an enhanced understanding of the sources of predictability and the dynamics of seasonal variability, along with advanced development in sophisticated models and technologies. This review centers on advances in understanding the physical foundation for monsoon climate prediction (section 2), significant findings and insights into the primary and regional sources of predictability arising from feedback processes among various climate components (sections 3 and 4), the effects of global warming and external forcings on predictability (section 5), developments in seasonal prediction models and techniques (section 6), the challenges and limitations of monsoon climate prediction (section 7), and emerging research trends with suggestions for future directions (section 8). We hope this review will stimulate creative activities to enhance monsoon climate prediction.展开更多
In the context of global warming, the increasing wildfire frequency has become a critical climate research focus in North America. This study used the Community Earth System Model(CESM 1.2) to investigate the impacts ...In the context of global warming, the increasing wildfire frequency has become a critical climate research focus in North America. This study used the Community Earth System Model(CESM 1.2) to investigate the impacts of 20thcentury wildfires on North American climate and hydrology. Summer represents the peak wildfire season in North America, with the Gulf of Mexico and Midwest regions experiencing the most severe effects. Wildfires not only damage vegetation during the fire season but also extend prolonged impacts into non-fire periods, showing distinct seasonal variations. In spring, wildfires increase surface albedo, triggering a cooling effect through enhanced snow cover and delayed snowmelt. Conversely, summer and autumn surface warming stems primarily from wildfire-suppressed vegetation transpiration. Warming near the Gulf of Mexico enhances moisture transport and precipitation, particularly in summer and autumn. Reduced evaporation and increased precipitation from the Gulf of Mexico significantly altered the hydrological cycle across North America, leading to increased runoff continent-wide.展开更多
In the summer of 2024, following a strong El Ni?o event in the preceding winter, the tropical Indian Ocean and tropical North Atlantic recorded their highest SSTs since 1961, along with a significant westward shift an...In the summer of 2024, following a strong El Ni?o event in the preceding winter, the tropical Indian Ocean and tropical North Atlantic recorded their highest SSTs since 1961, along with a significant westward shift and intensification of the western Pacific subtropical high(WPSH). Under these conditions, China experienced its hottest summer since 1961,and was hit by a series of high-impact extreme weather and climate events. From 9 June to 2 July, southern China experienced an unprecedented extreme precipitation event that exceeded the well-known 1998 summer precipitation event in both duration and impact scope, resulting in devastating floods in the Yangtze River basin. Subsequently, in early to midJuly, the Huanghe-Huaihe Basin suffered from a severe drought–flood abrupt alternation event, heavily affecting Henan and Shandong. Meanwhile, southern China underwent a widespread heatwave event lasting 74 days, ranking as the second most intense since 1961. From late July to the end of August, northern China faced unusually frequent heavy precipitation events, with cumulative precipitation reaching the second highest for the same period since 1961, causing floods in many rivers of northern China. This study provides a timely summary and assessment of the characteristics and impacts of these extreme events. It serves as a reference for climate change research, including mechanism analysis, numerical simulation,and climate event attribution, and also offers valuable insights for improving meteorological disaster prevention and mitigation strategies.展开更多
In research on the legendary Xia Dynasty of ancient China,the famous archaeological site of Erlitou and its culture are the most debated topics.A key question is whether this ancient culture is truly related to the Xi...In research on the legendary Xia Dynasty of ancient China,the famous archaeological site of Erlitou and its culture are the most debated topics.A key question is whether this ancient culture is truly related to the Xia Dynasty.This study combines traditional literature(Xia Xiao Zheng),archaeological evidence(on alligators),and climate simulation(of autumn rains)to demonstrate that the ancient Chinese phenological calendar,Xia Xiao Zheng,likely originated in the same region as the Erlitou culture.A logical explanation of these findings is that both Xia Xiao Zheng and the Erlitou culture are indeed closely related to the Xia Dynasty.展开更多
Global warming induced by increased CO_(2) has caused marked changes in the ocean.Previous estimates of ocean salinity change in response to global warming have considerable ambiguity,largely attributable to the diver...Global warming induced by increased CO_(2) has caused marked changes in the ocean.Previous estimates of ocean salinity change in response to global warming have considerable ambiguity,largely attributable to the diverse sensitivities of surface fluxes.This study utilizes data from the Flux-Anomaly-Forced Model Intercomparison Project to investigate how ocean salinity responds to perturbations of surface fluxes.The findings indicate the emergence of a sea surface salinity(SSS)dipole pattern predominantly in the North Atlantic and Pacific fresh pools,driven by surface flux perturbations.This results in an intensification of the“salty gets saltier and fresh gets fresher”SSS pattern across the global ocean.The spatial pattern amplification(PA)of SSS under global warming is estimated to be approximately 11.5%,with surface water flux perturbations being the most significant contributor to salinity PA,accounting for 8.1% of the change after 70 years in experiments since pre-industrial control(piControl).Notably,the zonal-depth distribution of salinity in the upper ocean exhibits lighter seawater above the denser water,with bowed isopycnals in the upper 400 m.This stable stratification inhibits vertical mixing of salinity and temperature.In response to the flux perturbations,there is a strong positive feedback due to consequent freshening.It is hypothesized that under global warming,an SSS amplification of 7.2%/℃ and a mixed-layer depth amplification of 12.5%/℃ will occur in the global ocean.It suggests that the salinity effect can exert a more stable ocean to hinder the downward transfer of heat,which provides positive feedback to future global warming.展开更多
Previous modeling studies have made significant contributions to understanding the climatic effects of historical land use and land cover change(LULCC).However,the absence of transient land cover simulations may lead ...Previous modeling studies have made significant contributions to understanding the climatic effects of historical land use and land cover change(LULCC).However,the absence of transient land cover simulations may lead to uncertainties or inaccuracies in assessing their impacts.Further investigation of differences between fixed and transient LULCC simulations is needed.Here,we employ the Community Earth System Model(CESM)to analyze contrasting responses of mean and extreme near-surface air temperature to historical land cover change.Our results show that forest cover in Europe generally follows a linear upward trend,while East Asia experiences deforestation processes during the historical period.It is found that temperature changes do not exhibit similar seasonal variation and have regional dependence,with Europe showing more pronounced seasonal variability.It is also demonstrated that using fixed land cover simulations exaggerates the temperature responses,leading to an overestimation of temperatures.In Europe,the overestimation of mean and extreme near-surface air temperature is approximately 0.2℃ and 0.3℃,respectively.However,the overestimation is about 0.1℃ in East Asia.Besides,we further disentangle the local and nonlocal effects in the temperature changes and show that nonlocal atmospheric feedbacks dominate the temperature responses in Europe,while local and nonlocal effects exhibit similar temperature variations in East Asia.Further efforts to explore the nonlocal effects of realistic land cover change could help enhance our understanding of climatic effects of land cover change at midlatitudes.展开更多
Global land monsoon precipitation(GLMP)is highly sensitive to changes in interhemispheric thermal contrast(ITC).Amplified interhemispheric asymmetries of GLMP due to enhanced ITC driven by high-level anthropogenic emi...Global land monsoon precipitation(GLMP)is highly sensitive to changes in interhemispheric thermal contrast(ITC).Amplified interhemispheric asymmetries of GLMP due to enhanced ITC driven by high-level anthropogenic emissions are expected to simultaneously increase the probability of regional floods and droughts,threatening ecosystems within global terrestrial monsoon regions and the freshwater supply for billions of residents in these areas.In this study,the responses of GLMP to the evolution of ITC toward the carbon neutrality goal are assessed using multimodel outputs from a new model intercomparison project(CovidMIP).The results show that the Northern Hemisphere-Southern Hemisphere(NH-SH)asymmetry of GLMP in boreal summer weakens during the 2040s,as a persistent reduction in well-mixed greenhouse gas(WMGHG)emissions leads to a downward trend in the ITC after 2040.At the same time,the reduction in WMGHG emissions dampens the Eastern Hemisphere-Western Hemisphere(EH-WH)asymmetry of GLMP by inducing La Niña-like cooling and enhancing moisture transport to Inner America.The resulting increases in land monsoon precipitation(LMP)may alleviate drought under the global warming scenario by about 19%-25%and 7%-9%in the WH and SH monsoon regions,respectively.However,a persistent reduction in aerosol emissions in Asia will dominate the increases in LMP in this region until the mid-21st century,and these increases may be approximately 23%-60%of the growth under the global warming scenario.Our results highlight the different rates of response of aerosol and WMGHG concentrations to the carbon neutrality goal,leading to various changes in LMP at global and regional scales.展开更多
High spatiotemporal resolution infrared radiances from FY-4A/AGRI(Advanced Geostationary Radiation Imager)can provide crucial information for rapidly developing severe convective weather.This study established a symme...High spatiotemporal resolution infrared radiances from FY-4A/AGRI(Advanced Geostationary Radiation Imager)can provide crucial information for rapidly developing severe convective weather.This study established a symmetric observation error model that differentiates between land and sea for FY-4A/AGRI all-sky assimilation,developed an all-sky assimilation scheme for FY-4A/AGRI based on hydrometeor control variables,and investigated the impacts of all-sky FY-4A/AGRI water vapor channels at different altitudes and rapid-update assimilation at different frequencies on the assimilation and forecasting of a severe convective weather event.Results show that simultaneous assimilation of two water vapor channels can enhance precipitation forecasts compared to single-channel assimilation,which is mainly attributable to a more accurate analysis of water vapor and hydrometeor information.Experiments with different assimilation frequencies demonstrate that the hourly assimilation frequency,compared to other frequencies,incorporates the high-frequency information from AGRI while reducing the impact of spurious oscillations caused by excessively high-frequency assimilation.This hourly assimilation frequency reduces the incoordination among thermal,dynamical,and water vapor conditions caused by excessively fast or slow assimilation frequencies,thus improving the forecast accuracy compared to other frequencies.展开更多
Since the initiation of the subseasonal-to-seasonal prediction project by the World Meteorological Organization,the accuracy of model forecasts has improved notably.However,substantial discrepancies have been observed...Since the initiation of the subseasonal-to-seasonal prediction project by the World Meteorological Organization,the accuracy of model forecasts has improved notably.However,substantial discrepancies have been observed among forecast results produced by different ensemble members when applied to South China.To enhance the accuracy of sub-seasonal forecasts in this region,it is essential to develop new methods that can effectively leverage multiple predictive models.This study introduces a weighted ensemble forecasting method based on online learning to improve forecast accuracy.We utilized ensemble forecasts from three models:the Integrated Forecasting System model from the European Centre for Medium-Range Weather Forecasts,the Climate Forecast System Version 2 model from the National Centers for Environmental Prediction,and the Beijing Climate Center-Climate Prediction System version 3 model from the China Meteorological Administration.The ensemble weights are trained using an online learning approach.The results indicate that the forecasts obtained through online learning outperform those of the original dynamical models.Compared to the simple ensemble results of the three models,the weighted ensemble model showed a stronger capability to capture temperature and precipitation patterns in South China.Therefore,this method has the potential to improve the accuracy of sub-seasonal forecasts in this region.展开更多
Northeast China serves as an important crop production region.Accurately forecasting summer precipitation in Northeast China(NEC-PR)has been a challenge due to its wide range of time scales influenced by varying clima...Northeast China serves as an important crop production region.Accurately forecasting summer precipitation in Northeast China(NEC-PR)has been a challenge due to its wide range of time scales influenced by varying climatic conditions.This study presents a scale separation hybrid statistical model with recurrent neural network(SS-RNN)to predict the summer monthly NEC-PR.The SS-RNN model decomposes the multiple scales of the NEC-PR into several spatiotemporal intrinsic mode functions covering annual to decadal time scales.This strategy provides a way to derive appropriate predictors and establish predictive models for the primary spatial modes of the NEC-PR at various time scales.Our results demonstrate substantial improvements by the SS-RNN model in predicting the summer monthly NEC-PR as compared with dynamic models,particularly in predicting the spatial pattern of the NEC-PR.In this paper we take August,the month of the highest NEC-PR,to assess our model skill.Independent forecasts of the August NEC-PR over the period 2021–24 achieve significant spatial anomaly correlation coefficients,reaching a maximum value of 0.83.Additional verifications by station observations show that the model hits most station anomalies,achieving a mean predictive skill score of 90.展开更多
During the winter of 2023/24,three distinct snowfall events occurred in eastern China,significantly impacting agriculture and transportation.The ability to provide subseasonal predictions with lead times beyond the we...During the winter of 2023/24,three distinct snowfall events occurred in eastern China,significantly impacting agriculture and transportation.The ability to provide subseasonal predictions with lead times beyond the weather timescale(longer than one week)is essential for effective disaster prevention and mitigation.Here,we assess the prediction skills of three subseasonal to seasonal(S2S)models from the S2S Prediction project regarding the three snowfall processes during the 2023/24 winter season,and identify the key sources of predictability for such events occurring over eastern China.The surface air temperature(SAT)and precipitation distribution for the three snowfall processes were successfully reproduced up to a lead time of 10–15 and 10 days,respectively.Since the skill in predicting snowfall is reliant on both SAT and precipitation predictions,all three S2S models therefore failed to predict the three snowfall processes beyond the weather timescale.The capacity in capturing Eurasian midlatitude transient Rossby waves and tropical convection anomalies determines the ability of the models to predict snowfall;inaccuracies in modeling these circulation systems result in an underestimation of SAT and precipitation anomalies beyond 15 and 10 days,respectively.Singular value decomposition analysis based on winter seasons from 1991/92 to 2023/24 further identified the coupling modes that exist between Eurasian midlatitude Rossby waves and SAT over eastern China,as well as between tropical convection and precipitation over the same region.These findings suggest that the configurations of tropical and extratropical signals provide universal subseasonal predictability sources for winter snowfall over eastern China.展开更多
This study investigates the width of the secondary eyewall(SE)immediately following its formation in tropical cyclones with surface environmental winds aligned and counter-aligned with environmental vertical wind shea...This study investigates the width of the secondary eyewall(SE)immediately following its formation in tropical cyclones with surface environmental winds aligned and counter-aligned with environmental vertical wind shear(VWS),using idealized numerical experiments.Results reveal that the SE develops greater radial extent when surface winds align with VWS compared to counter-aligned conditions.In alignment configurations,shear-enhanced surface winds on the right flank amplify surface enthalpy fluxes,thereby elevating boundary-layer entropy within the downshear outer-core region.Subsequently,more vigorous outer rainbands develop,inducing marked acceleration of tangential winds in the outer core preceding SE formation.The resultant radial expansion of supergradient winds near the boundary-layer top triggers widespread convective activity immediately beyond the inner core.Progressive axisymmetrization of this convective forcing ultimately generates an expansive SE structure.展开更多
The Zebiak–Cane(ZC) model, renowned as a coupled ocean-atmosphere model specifically designed to simulate and predict El Ni??o-Southern Oscillation(ENSO), is an indispensable tool for ENSO studies. However, the origi...The Zebiak–Cane(ZC) model, renowned as a coupled ocean-atmosphere model specifically designed to simulate and predict El Ni??o-Southern Oscillation(ENSO), is an indispensable tool for ENSO studies. However, the original ZC model exhibits certain biases in reproducing the ENSO–related sea surface temperature anomalies and heating anomalies, limiting its broader applicability. To improve the accuracy of ENSO simulation, we propose a modified ZC model based on Xie et al.(2015), named the MZC_XJH model, through refining the heating parameterization scheme. The performance in simulating the nonlinear SST–precipitation relationship in the MZC_XJH model is firstly elaborated. Then, we investigate the impacts of three key atmospheric parameters on ENSO simulation by conducting experiments with the MZC_XJH model. Through assessing the performance in simulating five fundamental ENSO metrics(amplitude, periodicity,seasonality, diversity, and skewness), we uncover that the sensitivities of simulated ENSO behaviors to different parameters are distinct. Moreover, we explain why a particular parameter greatly affects some simulated ENSO behaviors while others exert minor influence. We also reveal that the nonlinear effect due to the covariation of multi-parameters on ENSO simulation warrants careful consideration when tuning multi-parameters synchronously. Lastly, we present an updated version of the MZC_XJH model, in which some biases have been mitigated but some remain obvious. Although there are no universally optimal parameters that would ensure flawless performance in simulating every aspect of ENSO, this study provides a valuable reference for tuning atmospheric parameters in the MZC_XJH model, rendering the MZC_XJH model applicable to some research objectives.展开更多
The global monsoon system,encompassing the Asian-Australian,African,and American monsoons,sustains two-thirds of the world’s population by regulating water resources and agriculture.Monsoon anomalies pose severe risk...The global monsoon system,encompassing the Asian-Australian,African,and American monsoons,sustains two-thirds of the world’s population by regulating water resources and agriculture.Monsoon anomalies pose severe risks,including floods and droughts.Recent research associated with the implementation of the Global Monsoons Model Intercomparison Project under the umbrella of CMIP6 has advanced our understanding of its historical variability and driving mechanisms.Observational data reveal a 20th-century shift:increased rainfall pre-1950s,followed by aridification and partial recovery post-1980s,driven by both internal variability(e.g.,Atlantic Multidecadal Oscillation)and external forcings(greenhouse gases,aerosols),while ENSO drives interannual variability through ocean-atmosphere interactions.Future projections under greenhouse forcing suggest long-term monsoon intensification,though regional disparities and model uncertainties persist.Models indicate robust trends but struggle to quantify extremes,where thermodynamic effects(warming-induced moisture rise)uniformly boost heavy rainfall,while dynamical shifts(circulation changes)create spatial heterogeneity.Volcanic eruptions and proposed solar radiation modification(SRM)further complicate predictions:tropical eruptions suppress monsoons,whereas high-latitude events alter cross-equatorial flows,highlighting unresolved feedbacks.The emergent constraint approach is booming in terms of correcting future projections and reducing uncertainty with respect to the global monsoons.Critical challenges remain.Model biases and sparse 20th-century observational data hinder accurate attribution.The interplay between natural variability and anthropogenic forcings,along with nonlinear extreme precipitation risks under warming,demands deeper mechanistic insights.Additionally,SRM’s regional impacts and hemispheric monsoon interactions require systematic evaluation.Addressing these gaps necessitates enhanced observational networks,refined climate models,and interdisciplinary efforts to disentangle multiscale drivers,ultimately improving resilience strategies for monsoon-dependent regions.展开更多
Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiote...Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiotemporal distribution of Arctic SIC is more challenging than predicting its total extent.In this study,spatiotemporal prediction models for monthly Arctic SIC at 1-to 3-month leads are developed based on U-Net-an effective convolutional deep-learning approach.Based on explicit Arctic sea-ice-atmosphere interactions,11 variables associated with Arctic sea-ice variations are selected as predictors,including observed Arctic SIC,atmospheric,oceanic,and heat flux variables at 1-to 3-month leads.The prediction skills for the monthly Arctic SIC of the test set(from January 2018 to December 2022)are evaluated by examining the mean absolute error(MAE)and binary accuracy(BA).Results showed that the U-Net model had lower MAE and higher BA for Arctic SIC compared to two dynamic climate prediction systems(CFSv2 and NorCPM).By analyzing the relative importance of each predictor,the prediction accuracy relies more on the SIC at the 1-month lead,but on the surface net solar radiation flux at 2-to 3-month leads.However,dynamic models show limited prediction skills for surface net solar radiation flux and other physical processes,especially in autumn.Therefore,the U-Net model can be used to capture the connections among these key physical processes associated with Arctic sea ice and thus offers a significant advantage in predicting Arctic SIC.展开更多
AdshtT Marine stratocumulus clouds profoundly affect Earth's energy budget by reflecting solar radiation over extensive oceanic areas.Yet,after using a large-eddy simulation(LES)and a Lagrangian microphysics schem...AdshtT Marine stratocumulus clouds profoundly affect Earth's energy budget by reflecting solar radiation over extensive oceanic areas.Yet,after using a large-eddy simulation(LES)and a Lagrangian microphysics scheme(Super-Droplet Method,SDM)for entrainment-mixing studies,uncertainty remains in the grid resolution and super-droplet number concentration(SDNC)required for accurate homogeneity capture.This study analyzes the homogeneous mixing degree(HMD)and the Damkohler numbe(Da)in stratocumulus using an LES with SDM,from microphysical and dynamical perspectives,respectively.Results show that HMD and Da both display a top-to-base gradient,with more intense inhomogeneity near the cloud top and relatively homogeneous conditions toward the base,although the upper region i more complex.Even at a fine horizontal resolution of 12.5 m and vertical resolution of 2.5 m,HMD remains sensitive and does not converge,whereas Da converges at coarser grid spacings(up to horizontal and vertical spacings of 25 m anc 10 m,respectively)in the mid-cloud region.Similarly,HMD requires an SDNC well above 128 per cell for near-complete convergence,while Da converges once SDNC exceeds about I6 per cell.This difference arises because HMD depends on microphysical details,thereby demanding a high SDNC to capture local droplet inhomogeneities,whereas Da reflects turbulence-evaporation timescales that converge more readily once extreme droplet gradients are resolved.We further find that HMD and Da exhibit a significant negative correlation,with stronger anti-correlations emerging under finer spatial resolutions,reinforcing their complementary roles in diagnosing mixing regimes.Overall,these findings provide guidelines for selecting numerical configurations in entrainment-mixing simulations,ensuring that both turbulence-driven and microphysical processes are adequately resolved,.展开更多
Clouds play an important role in global atmospheric energy and water vapor budgets, and the low cloud simulations suffer from large biases in many atmospheric general circulation models. In this study, cloud microphys...Clouds play an important role in global atmospheric energy and water vapor budgets, and the low cloud simulations suffer from large biases in many atmospheric general circulation models. In this study, cloud microphysical processes such as raindrop evaporation and cloud water accretion in a double-moment six-class cloud microphysics scheme were revised to enhance the simulation of low clouds using the Global-Regional Integrated Forecast System(GRIST)model. The validation of the revised scheme using a single-column version of the GRIST demonstrated a reasonable reduction in liquid water biases. The revised parameterization simulated medium-and low-level cloud fractions that were in better agreement with the observations than the original scheme. Long-term global simulations indicate the mitigation of the originally overestimated low-level cloud fraction and cloud-water mixing ratio in mid-to high-latitude regions,primarily owing to enhanced accretion processes and weakened raindrop evaporation. The reduced low clouds with the revised scheme showed better consistency with satellite observations, particularly at mid-and high-latitudes. Further improvements can be observed in the simulated cloud shortwave radiative forcing and vertical distribution of total cloud cover. Annual precipitation in mid-latitude regions has also improved, particularly over the oceans, with significantly increased large-scale and decreased convective precipitation.展开更多
Precipitable water vapor(PWV)is a key component of the Earth’s climate system,playing a vital role in weather,climate,and hydrological cycling.Passive microwave remote sensing offers a promising approach to measure a...Precipitable water vapor(PWV)is a key component of the Earth’s climate system,playing a vital role in weather,climate,and hydrological cycling.Passive microwave remote sensing offers a promising approach to measure all-sky PWV,though it remains challenging over land.Building on our previous development of a machine learning algorithm,we have created a global terrestrial PWV dataset using measurements from the MicroWave Radiation Imager(MWRI)aboard three FY-3 satellite series(FY-3B,FY-3C and FY-3D).The dataset spans from 2012 to 2020 at a spatial resolution of 0.25°×0.25°.It was validated against SuomiNet GPS and IGRA2(Integrated Global Radiosonde Archive Version 2)PWV products,achieving root-mean-square errors(RMSEs)of 4.47 and 3.89 mm,respectively,with RMSE values ranging from 2.90 to 5.49 mm across diverse surface conditions.As an all-weather PWV product with high-precision,the MWRI PWV dataset addresses gaps in global passive microwave-based terrestrial PWV observations,offering significant value for atmospheric research,climate modeling,hydrological studies,and beyond.展开更多
Ground-based radar is the primary means by which severe storms are monitored and tracked;however, due to limited coverage, important data is often missed over ocean and mountainous areas. On the other hand, geostation...Ground-based radar is the primary means by which severe storms are monitored and tracked;however, due to limited coverage, important data is often missed over ocean and mountainous areas. On the other hand, geostationary(GEO)weather satellites provide continuous observations with seamless coverage with advanced imager, despite their limited capability to penetrate clouds. Combining satellite and ground-radar observations could exploit the advantages of both techniques, providing tracking capability close to that of ground radar while maintaining full spatial coverage. This study presents a novel method called Multi-dimensional satellite Observation information for Radar Estimation(MORE) to reconstruct radar composite reflectivity(CREF). Deep learning techniques are important components of MORE for estimating CREF from China's Fengyun-4B(FY-4B) GEO satellite observations. Two models are developed: an infraredonly(IR-Single) model available for all times, and a visible-infrared(VIS+IR) model for daytime applications. These models incorporate multi-dimensional satellite observation information, including temporal, spatial, spectral, and viewing angle information, to enhance the accuracy of radar echo reconstruction. Results demonstrate that the VIS+IR model outperforms the IR-Single model, and both models achieves a root-mean-square error(RMSE) of less than 6 dBZ and a coefficient of determination(R~2) of greater than 0.7. The models effectively reconstruct radar echoes, including strong echoes exceeding 50 dBZ, and show good agreement with precipitation data in radar-blind areas. This study offers a valuable solution for severe weather monitoring and tracking in regions lacking ground-based radar observations, and provides a potential tool for enhanced data assimilation in numerical weather prediction(NWP) models.展开更多
基金J.YANG was supported by funding from the National Natural Science Foundation of China(Grant Nos.42475022,42261144671)the National Key R&D Program of China(Project No.2024YFC3013100)+2 种基金the Fundamental Research Funds for the Central UniversitiesM.LU was supported by the Otto Poon Centre of Climate Resilience and Sustainability at HKUST and the Hong Kong Research Grant Committee(Project No.16300424)Data processing and storage were supported by the National Key Scientific and Technological Infrastructure project“Earth System Numerical Simulation Facility”(EarthLab).
文摘Precise forecasts of wildfire danger are crucial for proactive fuel management and emergency responses,yet they pose a challenge at the subseasonal scale due to limitations in prediction capabilities and a gap between forecast outputs and the needs of decision-makers.This study introduces an innovative hybrid modeling framework that integrates artificial intelligence(AI)with climate dynamic prediction systems to accurately forecast High Fire-Danger Days(HFDDs)for the following month.These HFDDs are derived from historical satellite fire data and the optimum fire danger index,with a particular focus on Southwest China as a case study.The AI module,based on the ResNet-18 neural network model,integrates observational and physically constrained analysis to establish links between HFDDs and optimal predictors of atmospheric circulation from both the concurrent and preceding months.Leveraging climate dynamical forecasting,this hybrid model provides more reliable deterministic predictions for monthly HFDDs than conventional methods that rely solely on terrestrial variables such as precipitation.More importantly,the integration of dynamical ensemble prediction enhances the model’s capability for skillful probabilistic predictions of HFDDs,facilitating the creation of customized fire danger outlooks and emergency action maps tailored to stakeholders’needs.The model’s added economic value was also evaluated,demonstrating its potential to improve decision-making in disaster management and bridge the“last-mile gap”in climate service delivery.This work contributes to the Seamless Prediction and Services for Sustainable Natural and Built Environment(SEPRESS)Program(2025–32),under the United Nations Educational Scientific and Cultural Organization(UNESCO)International Decade of Sciences for Sustainable Development(2024–33).
基金supported by the National Natural Science Foundation of China(Grant No.U2342208)support from NSF/Climate Dynamics Award#2025057。
文摘Predicting monsoon climate is one of the major endeavors in climate science and is becoming increasingly challenging due to global warming. The accuracy of monsoon seasonal predictions significantly impacts the lives of billions who depend on or are affected by monsoons, as it is essential for the water cycle, food security, ecology, disaster prevention, and the economy of monsoon regions. Given the extensive literature on Asian monsoon climate prediction, we limit our focus to reviewing the seasonal prediction and predictability of the Asian Summer Monsoon (ASM). However, much of this review is also relevant to monsoon predictions in other seasons and regions. Over the past two decades, considerable progress has been made in the seasonal forecasting of the ASM, driven by an enhanced understanding of the sources of predictability and the dynamics of seasonal variability, along with advanced development in sophisticated models and technologies. This review centers on advances in understanding the physical foundation for monsoon climate prediction (section 2), significant findings and insights into the primary and regional sources of predictability arising from feedback processes among various climate components (sections 3 and 4), the effects of global warming and external forcings on predictability (section 5), developments in seasonal prediction models and techniques (section 6), the challenges and limitations of monsoon climate prediction (section 7), and emerging research trends with suggestions for future directions (section 8). We hope this review will stimulate creative activities to enhance monsoon climate prediction.
基金National Natural Science Foundation of China(42175022)。
文摘In the context of global warming, the increasing wildfire frequency has become a critical climate research focus in North America. This study used the Community Earth System Model(CESM 1.2) to investigate the impacts of 20thcentury wildfires on North American climate and hydrology. Summer represents the peak wildfire season in North America, with the Gulf of Mexico and Midwest regions experiencing the most severe effects. Wildfires not only damage vegetation during the fire season but also extend prolonged impacts into non-fire periods, showing distinct seasonal variations. In spring, wildfires increase surface albedo, triggering a cooling effect through enhanced snow cover and delayed snowmelt. Conversely, summer and autumn surface warming stems primarily from wildfire-suppressed vegetation transpiration. Warming near the Gulf of Mexico enhances moisture transport and precipitation, particularly in summer and autumn. Reduced evaporation and increased precipitation from the Gulf of Mexico significantly altered the hydrological cycle across North America, leading to increased runoff continent-wide.
基金supported by the National Natural Science Foundation of China (Grant Nos.42005029 and 41701103)the China Meteorological Administration Special Foundation for Innovation and Development (Grant No.CXFZ2024Q007)。
文摘In the summer of 2024, following a strong El Ni?o event in the preceding winter, the tropical Indian Ocean and tropical North Atlantic recorded their highest SSTs since 1961, along with a significant westward shift and intensification of the western Pacific subtropical high(WPSH). Under these conditions, China experienced its hottest summer since 1961,and was hit by a series of high-impact extreme weather and climate events. From 9 June to 2 July, southern China experienced an unprecedented extreme precipitation event that exceeded the well-known 1998 summer precipitation event in both duration and impact scope, resulting in devastating floods in the Yangtze River basin. Subsequently, in early to midJuly, the Huanghe-Huaihe Basin suffered from a severe drought–flood abrupt alternation event, heavily affecting Henan and Shandong. Meanwhile, southern China underwent a widespread heatwave event lasting 74 days, ranking as the second most intense since 1961. From late July to the end of August, northern China faced unusually frequent heavy precipitation events, with cumulative precipitation reaching the second highest for the same period since 1961, causing floods in many rivers of northern China. This study provides a timely summary and assessment of the characteristics and impacts of these extreme events. It serves as a reference for climate change research, including mechanism analysis, numerical simulation,and climate event attribution, and also offers valuable insights for improving meteorological disaster prevention and mitigation strategies.
基金National Key Research and Development Program of China,No.2023YFF0804704National Natural Science Foundation of China,No.42130604,No.42105044+1 种基金Major Projects of the Ministry of Education's Key Research Bases of Humanities and Social Sciences,No.22JJD770020Social Scienceof Northwest University,No.21XNFH007。
文摘In research on the legendary Xia Dynasty of ancient China,the famous archaeological site of Erlitou and its culture are the most debated topics.A key question is whether this ancient culture is truly related to the Xia Dynasty.This study combines traditional literature(Xia Xiao Zheng),archaeological evidence(on alligators),and climate simulation(of autumn rains)to demonstrate that the ancient Chinese phenological calendar,Xia Xiao Zheng,likely originated in the same region as the Erlitou culture.A logical explanation of these findings is that both Xia Xiao Zheng and the Erlitou culture are indeed closely related to the Xia Dynasty.
基金supported by the Laoshan Laboratory[grant number LSKJ202202403]the National Natural Science Foundation of China[grant number 42030410]+1 种基金additionally supported by the Startup Foundation for Introducing Talent of NUISTJiangsu Innovation Research Group[grant number JSSCTD202346]。
文摘Global warming induced by increased CO_(2) has caused marked changes in the ocean.Previous estimates of ocean salinity change in response to global warming have considerable ambiguity,largely attributable to the diverse sensitivities of surface fluxes.This study utilizes data from the Flux-Anomaly-Forced Model Intercomparison Project to investigate how ocean salinity responds to perturbations of surface fluxes.The findings indicate the emergence of a sea surface salinity(SSS)dipole pattern predominantly in the North Atlantic and Pacific fresh pools,driven by surface flux perturbations.This results in an intensification of the“salty gets saltier and fresh gets fresher”SSS pattern across the global ocean.The spatial pattern amplification(PA)of SSS under global warming is estimated to be approximately 11.5%,with surface water flux perturbations being the most significant contributor to salinity PA,accounting for 8.1% of the change after 70 years in experiments since pre-industrial control(piControl).Notably,the zonal-depth distribution of salinity in the upper ocean exhibits lighter seawater above the denser water,with bowed isopycnals in the upper 400 m.This stable stratification inhibits vertical mixing of salinity and temperature.In response to the flux perturbations,there is a strong positive feedback due to consequent freshening.It is hypothesized that under global warming,an SSS amplification of 7.2%/℃ and a mixed-layer depth amplification of 12.5%/℃ will occur in the global ocean.It suggests that the salinity effect can exert a more stable ocean to hinder the downward transfer of heat,which provides positive feedback to future global warming.
基金supported by the National Key R&D Program of China(Grant No.2022YFF0801601).
文摘Previous modeling studies have made significant contributions to understanding the climatic effects of historical land use and land cover change(LULCC).However,the absence of transient land cover simulations may lead to uncertainties or inaccuracies in assessing their impacts.Further investigation of differences between fixed and transient LULCC simulations is needed.Here,we employ the Community Earth System Model(CESM)to analyze contrasting responses of mean and extreme near-surface air temperature to historical land cover change.Our results show that forest cover in Europe generally follows a linear upward trend,while East Asia experiences deforestation processes during the historical period.It is found that temperature changes do not exhibit similar seasonal variation and have regional dependence,with Europe showing more pronounced seasonal variability.It is also demonstrated that using fixed land cover simulations exaggerates the temperature responses,leading to an overestimation of temperatures.In Europe,the overestimation of mean and extreme near-surface air temperature is approximately 0.2℃ and 0.3℃,respectively.However,the overestimation is about 0.1℃ in East Asia.Besides,we further disentangle the local and nonlocal effects in the temperature changes and show that nonlocal atmospheric feedbacks dominate the temperature responses in Europe,while local and nonlocal effects exhibit similar temperature variations in East Asia.Further efforts to explore the nonlocal effects of realistic land cover change could help enhance our understanding of climatic effects of land cover change at midlatitudes.
基金funded by the National Natural Science Foundation of China(Grant No.42275039)the Meteorological Joint Fund by NSF and CMA(Grant No.U2342224)+1 种基金the National Key R&D Program of China(Grant No.2022YFC3701202)the S&T Development Fund of CAMS(Grant No.2024KJ019)。
文摘Global land monsoon precipitation(GLMP)is highly sensitive to changes in interhemispheric thermal contrast(ITC).Amplified interhemispheric asymmetries of GLMP due to enhanced ITC driven by high-level anthropogenic emissions are expected to simultaneously increase the probability of regional floods and droughts,threatening ecosystems within global terrestrial monsoon regions and the freshwater supply for billions of residents in these areas.In this study,the responses of GLMP to the evolution of ITC toward the carbon neutrality goal are assessed using multimodel outputs from a new model intercomparison project(CovidMIP).The results show that the Northern Hemisphere-Southern Hemisphere(NH-SH)asymmetry of GLMP in boreal summer weakens during the 2040s,as a persistent reduction in well-mixed greenhouse gas(WMGHG)emissions leads to a downward trend in the ITC after 2040.At the same time,the reduction in WMGHG emissions dampens the Eastern Hemisphere-Western Hemisphere(EH-WH)asymmetry of GLMP by inducing La Niña-like cooling and enhancing moisture transport to Inner America.The resulting increases in land monsoon precipitation(LMP)may alleviate drought under the global warming scenario by about 19%-25%and 7%-9%in the WH and SH monsoon regions,respectively.However,a persistent reduction in aerosol emissions in Asia will dominate the increases in LMP in this region until the mid-21st century,and these increases may be approximately 23%-60%of the growth under the global warming scenario.Our results highlight the different rates of response of aerosol and WMGHG concentrations to the carbon neutrality goal,leading to various changes in LMP at global and regional scales.
基金supported by the National Key R&D Program of China(Grant No.2022YFC3080500)the National Natural Science Foundation of China(Grant Nos.U2142208,42475158,and 42105149)the High-Performance Computing Center of Nanjing University of Information Science&Technology for supporting this work。
文摘High spatiotemporal resolution infrared radiances from FY-4A/AGRI(Advanced Geostationary Radiation Imager)can provide crucial information for rapidly developing severe convective weather.This study established a symmetric observation error model that differentiates between land and sea for FY-4A/AGRI all-sky assimilation,developed an all-sky assimilation scheme for FY-4A/AGRI based on hydrometeor control variables,and investigated the impacts of all-sky FY-4A/AGRI water vapor channels at different altitudes and rapid-update assimilation at different frequencies on the assimilation and forecasting of a severe convective weather event.Results show that simultaneous assimilation of two water vapor channels can enhance precipitation forecasts compared to single-channel assimilation,which is mainly attributable to a more accurate analysis of water vapor and hydrometeor information.Experiments with different assimilation frequencies demonstrate that the hourly assimilation frequency,compared to other frequencies,incorporates the high-frequency information from AGRI while reducing the impact of spurious oscillations caused by excessively high-frequency assimilation.This hourly assimilation frequency reduces the incoordination among thermal,dynamical,and water vapor conditions caused by excessively fast or slow assimilation frequencies,thus improving the forecast accuracy compared to other frequencies.
基金Science and Technology Development Program of the“Taihu Light”(K20231023)CMA Numerical Weather Prediction R&D Project(TCYF2024QH007)+1 种基金“Qing Lan”Project of Jiangsu Province for C.H.LUWuxi University Research Start-up Fund for Introduced Talents(2023r037)。
文摘Since the initiation of the subseasonal-to-seasonal prediction project by the World Meteorological Organization,the accuracy of model forecasts has improved notably.However,substantial discrepancies have been observed among forecast results produced by different ensemble members when applied to South China.To enhance the accuracy of sub-seasonal forecasts in this region,it is essential to develop new methods that can effectively leverage multiple predictive models.This study introduces a weighted ensemble forecasting method based on online learning to improve forecast accuracy.We utilized ensemble forecasts from three models:the Integrated Forecasting System model from the European Centre for Medium-Range Weather Forecasts,the Climate Forecast System Version 2 model from the National Centers for Environmental Prediction,and the Beijing Climate Center-Climate Prediction System version 3 model from the China Meteorological Administration.The ensemble weights are trained using an online learning approach.The results indicate that the forecasts obtained through online learning outperform those of the original dynamical models.Compared to the simple ensemble results of the three models,the weighted ensemble model showed a stronger capability to capture temperature and precipitation patterns in South China.Therefore,this method has the potential to improve the accuracy of sub-seasonal forecasts in this region.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFC3002803)the National Key Research and Development Program of China(Grant No.2024YFF0808402)the National Natural Science Foundation of China(Grant No.42375169)。
文摘Northeast China serves as an important crop production region.Accurately forecasting summer precipitation in Northeast China(NEC-PR)has been a challenge due to its wide range of time scales influenced by varying climatic conditions.This study presents a scale separation hybrid statistical model with recurrent neural network(SS-RNN)to predict the summer monthly NEC-PR.The SS-RNN model decomposes the multiple scales of the NEC-PR into several spatiotemporal intrinsic mode functions covering annual to decadal time scales.This strategy provides a way to derive appropriate predictors and establish predictive models for the primary spatial modes of the NEC-PR at various time scales.Our results demonstrate substantial improvements by the SS-RNN model in predicting the summer monthly NEC-PR as compared with dynamic models,particularly in predicting the spatial pattern of the NEC-PR.In this paper we take August,the month of the highest NEC-PR,to assess our model skill.Independent forecasts of the August NEC-PR over the period 2021–24 achieve significant spatial anomaly correlation coefficients,reaching a maximum value of 0.83.Additional verifications by station observations show that the model hits most station anomalies,achieving a mean predictive skill score of 90.
基金supported by the National Key R&D Program of China(Grant No.2022YFF0801702)the Natural Science Foundation of Jiangsu Province(Grant No.BK20250045&BK20231110)the China Meteorological Administration Innovation and Development Project(Grant No.CXFZ2025Q007).
文摘During the winter of 2023/24,three distinct snowfall events occurred in eastern China,significantly impacting agriculture and transportation.The ability to provide subseasonal predictions with lead times beyond the weather timescale(longer than one week)is essential for effective disaster prevention and mitigation.Here,we assess the prediction skills of three subseasonal to seasonal(S2S)models from the S2S Prediction project regarding the three snowfall processes during the 2023/24 winter season,and identify the key sources of predictability for such events occurring over eastern China.The surface air temperature(SAT)and precipitation distribution for the three snowfall processes were successfully reproduced up to a lead time of 10–15 and 10 days,respectively.Since the skill in predicting snowfall is reliant on both SAT and precipitation predictions,all three S2S models therefore failed to predict the three snowfall processes beyond the weather timescale.The capacity in capturing Eurasian midlatitude transient Rossby waves and tropical convection anomalies determines the ability of the models to predict snowfall;inaccuracies in modeling these circulation systems result in an underestimation of SAT and precipitation anomalies beyond 15 and 10 days,respectively.Singular value decomposition analysis based on winter seasons from 1991/92 to 2023/24 further identified the coupling modes that exist between Eurasian midlatitude Rossby waves and SAT over eastern China,as well as between tropical convection and precipitation over the same region.These findings suggest that the configurations of tropical and extratropical signals provide universal subseasonal predictability sources for winter snowfall over eastern China.
基金jointly supported by the National Natural Science Foundation of China[grant numbers U2342202,42175005,and 42175016]the Qing Lan Project[grant number R2023Q06]。
文摘This study investigates the width of the secondary eyewall(SE)immediately following its formation in tropical cyclones with surface environmental winds aligned and counter-aligned with environmental vertical wind shear(VWS),using idealized numerical experiments.Results reveal that the SE develops greater radial extent when surface winds align with VWS compared to counter-aligned conditions.In alignment configurations,shear-enhanced surface winds on the right flank amplify surface enthalpy fluxes,thereby elevating boundary-layer entropy within the downshear outer-core region.Subsequently,more vigorous outer rainbands develop,inducing marked acceleration of tangential winds in the outer core preceding SE formation.The resultant radial expansion of supergradient winds near the boundary-layer top triggers widespread convective activity immediately beyond the inner core.Progressive axisymmetrization of this convective forcing ultimately generates an expansive SE structure.
基金supported by the National Key Research and Development Program of China (Grant No.2022YFF0802004)the Excellent Youth Natural Science Foundation of Jiangsu Province (BK20230061)+1 种基金the Joint Open Project of KLME&CIC-FEMD (Grant No.KLME202501)Jiangsu Innovation Research Group (Grant No.JSSCTD 202346)。
文摘The Zebiak–Cane(ZC) model, renowned as a coupled ocean-atmosphere model specifically designed to simulate and predict El Ni??o-Southern Oscillation(ENSO), is an indispensable tool for ENSO studies. However, the original ZC model exhibits certain biases in reproducing the ENSO–related sea surface temperature anomalies and heating anomalies, limiting its broader applicability. To improve the accuracy of ENSO simulation, we propose a modified ZC model based on Xie et al.(2015), named the MZC_XJH model, through refining the heating parameterization scheme. The performance in simulating the nonlinear SST–precipitation relationship in the MZC_XJH model is firstly elaborated. Then, we investigate the impacts of three key atmospheric parameters on ENSO simulation by conducting experiments with the MZC_XJH model. Through assessing the performance in simulating five fundamental ENSO metrics(amplitude, periodicity,seasonality, diversity, and skewness), we uncover that the sensitivities of simulated ENSO behaviors to different parameters are distinct. Moreover, we explain why a particular parameter greatly affects some simulated ENSO behaviors while others exert minor influence. We also reveal that the nonlinear effect due to the covariation of multi-parameters on ENSO simulation warrants careful consideration when tuning multi-parameters synchronously. Lastly, we present an updated version of the MZC_XJH model, in which some biases have been mitigated but some remain obvious. Although there are no universally optimal parameters that would ensure flawless performance in simulating every aspect of ENSO, this study provides a valuable reference for tuning atmospheric parameters in the MZC_XJH model, rendering the MZC_XJH model applicable to some research objectives.
基金supported by the National Key Research and Development Program of China(Grant No.2020YFA0608904)the International Partnership Program of the Chinese Academy of Sciences(Grant Nos.060GJHZ2023079GC and 134111KYSB20160031)+1 种基金supported by the Office of Science,U.S.Department of Energy(DOE)Biological and Environmental Research as part of the Regional and Global Model Analysis program area through the Water Cycle and Climate Extremes Modeling(WACCEM)scientific focus areaoperated for DOE by Battelle Memorial Institute under contract DE-AC05-76RL01830。
文摘The global monsoon system,encompassing the Asian-Australian,African,and American monsoons,sustains two-thirds of the world’s population by regulating water resources and agriculture.Monsoon anomalies pose severe risks,including floods and droughts.Recent research associated with the implementation of the Global Monsoons Model Intercomparison Project under the umbrella of CMIP6 has advanced our understanding of its historical variability and driving mechanisms.Observational data reveal a 20th-century shift:increased rainfall pre-1950s,followed by aridification and partial recovery post-1980s,driven by both internal variability(e.g.,Atlantic Multidecadal Oscillation)and external forcings(greenhouse gases,aerosols),while ENSO drives interannual variability through ocean-atmosphere interactions.Future projections under greenhouse forcing suggest long-term monsoon intensification,though regional disparities and model uncertainties persist.Models indicate robust trends but struggle to quantify extremes,where thermodynamic effects(warming-induced moisture rise)uniformly boost heavy rainfall,while dynamical shifts(circulation changes)create spatial heterogeneity.Volcanic eruptions and proposed solar radiation modification(SRM)further complicate predictions:tropical eruptions suppress monsoons,whereas high-latitude events alter cross-equatorial flows,highlighting unresolved feedbacks.The emergent constraint approach is booming in terms of correcting future projections and reducing uncertainty with respect to the global monsoons.Critical challenges remain.Model biases and sparse 20th-century observational data hinder accurate attribution.The interplay between natural variability and anthropogenic forcings,along with nonlinear extreme precipitation risks under warming,demands deeper mechanistic insights.Additionally,SRM’s regional impacts and hemispheric monsoon interactions require systematic evaluation.Addressing these gaps necessitates enhanced observational networks,refined climate models,and interdisciplinary efforts to disentangle multiscale drivers,ultimately improving resilience strategies for monsoon-dependent regions.
基金supported by the National Key Research and Development Program of China[grant number 2022YFE0106800]an Innovation Group Project of the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number 311024001]+3 种基金a project supported by the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number SML2023SP209]a Research Council of Norway funded project(MAPARC)[grant number 328943]a Nansen Center´s basic institutional funding[grant number 342624]the high-performance computing support from the School of Atmospheric Science at Sun Yat-sen University。
文摘Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiotemporal distribution of Arctic SIC is more challenging than predicting its total extent.In this study,spatiotemporal prediction models for monthly Arctic SIC at 1-to 3-month leads are developed based on U-Net-an effective convolutional deep-learning approach.Based on explicit Arctic sea-ice-atmosphere interactions,11 variables associated with Arctic sea-ice variations are selected as predictors,including observed Arctic SIC,atmospheric,oceanic,and heat flux variables at 1-to 3-month leads.The prediction skills for the monthly Arctic SIC of the test set(from January 2018 to December 2022)are evaluated by examining the mean absolute error(MAE)and binary accuracy(BA).Results showed that the U-Net model had lower MAE and higher BA for Arctic SIC compared to two dynamic climate prediction systems(CFSv2 and NorCPM).By analyzing the relative importance of each predictor,the prediction accuracy relies more on the SIC at the 1-month lead,but on the surface net solar radiation flux at 2-to 3-month leads.However,dynamic models show limited prediction skills for surface net solar radiation flux and other physical processes,especially in autumn.Therefore,the U-Net model can be used to capture the connections among these key physical processes associated with Arctic sea ice and thus offers a significant advantage in predicting Arctic SIC.
基金supported by the National Natural Science Foundation of China(Grant Nos.42325503 and 42230604)The appointment of Chunsong Lu at Nanjing University of Information Science&Technology is partially supported by the Jiangsu Specially-Appointed Professor(Grant No.R2024T01).This research partly used the computational resources of Hokkaido University through the HPCI System Research Project(project IDs:hp200078,hp210059,hp220062,hp230166,and hp240151)and the computer facilities of the Center for Cooperative Work on Data Science and Computational Science,University of Hyogo.Shin-ichiro SHIMA was supported by JSPS KAKENHI,Grant 20H00225 and 23H00149and JST(Moonshot R and D)(Grant JPMJMS2286).We acknowledge the High-Performance Computing Center of the Nanjing University of Information Science and Technology for their support of this work.This study was also supported by the National Key Scientific and Technological Infrastructure project“Earth System Science Numerical Simulator Facility”(EarthLab,2024-EL-PT-000615).Chongzhi YIN would like to thank Lei Zhu for his generous support and informative discussions.
文摘AdshtT Marine stratocumulus clouds profoundly affect Earth's energy budget by reflecting solar radiation over extensive oceanic areas.Yet,after using a large-eddy simulation(LES)and a Lagrangian microphysics scheme(Super-Droplet Method,SDM)for entrainment-mixing studies,uncertainty remains in the grid resolution and super-droplet number concentration(SDNC)required for accurate homogeneity capture.This study analyzes the homogeneous mixing degree(HMD)and the Damkohler numbe(Da)in stratocumulus using an LES with SDM,from microphysical and dynamical perspectives,respectively.Results show that HMD and Da both display a top-to-base gradient,with more intense inhomogeneity near the cloud top and relatively homogeneous conditions toward the base,although the upper region i more complex.Even at a fine horizontal resolution of 12.5 m and vertical resolution of 2.5 m,HMD remains sensitive and does not converge,whereas Da converges at coarser grid spacings(up to horizontal and vertical spacings of 25 m anc 10 m,respectively)in the mid-cloud region.Similarly,HMD requires an SDNC well above 128 per cell for near-complete convergence,while Da converges once SDNC exceeds about I6 per cell.This difference arises because HMD depends on microphysical details,thereby demanding a high SDNC to capture local droplet inhomogeneities,whereas Da reflects turbulence-evaporation timescales that converge more readily once extreme droplet gradients are resolved.We further find that HMD and Da exhibit a significant negative correlation,with stronger anti-correlations emerging under finer spatial resolutions,reinforcing their complementary roles in diagnosing mixing regimes.Overall,these findings provide guidelines for selecting numerical configurations in entrainment-mixing simulations,ensuring that both turbulence-driven and microphysical processes are adequately resolved,.
基金National Natural Science Foundation of China(42375153,42105153,42205157)Development of Science and Technology at Chinese Academy of Meteorological Sciences(2023KJ038)。
文摘Clouds play an important role in global atmospheric energy and water vapor budgets, and the low cloud simulations suffer from large biases in many atmospheric general circulation models. In this study, cloud microphysical processes such as raindrop evaporation and cloud water accretion in a double-moment six-class cloud microphysics scheme were revised to enhance the simulation of low clouds using the Global-Regional Integrated Forecast System(GRIST)model. The validation of the revised scheme using a single-column version of the GRIST demonstrated a reasonable reduction in liquid water biases. The revised parameterization simulated medium-and low-level cloud fractions that were in better agreement with the observations than the original scheme. Long-term global simulations indicate the mitigation of the originally overestimated low-level cloud fraction and cloud-water mixing ratio in mid-to high-latitude regions,primarily owing to enhanced accretion processes and weakened raindrop evaporation. The reduced low clouds with the revised scheme showed better consistency with satellite observations, particularly at mid-and high-latitudes. Further improvements can be observed in the simulated cloud shortwave radiative forcing and vertical distribution of total cloud cover. Annual precipitation in mid-latitude regions has also improved, particularly over the oceans, with significantly increased large-scale and decreased convective precipitation.
基金supported by the National Natural Science Foundation of China(Grant Nos.42075079 and U2442214).
文摘Precipitable water vapor(PWV)is a key component of the Earth’s climate system,playing a vital role in weather,climate,and hydrological cycling.Passive microwave remote sensing offers a promising approach to measure all-sky PWV,though it remains challenging over land.Building on our previous development of a machine learning algorithm,we have created a global terrestrial PWV dataset using measurements from the MicroWave Radiation Imager(MWRI)aboard three FY-3 satellite series(FY-3B,FY-3C and FY-3D).The dataset spans from 2012 to 2020 at a spatial resolution of 0.25°×0.25°.It was validated against SuomiNet GPS and IGRA2(Integrated Global Radiosonde Archive Version 2)PWV products,achieving root-mean-square errors(RMSEs)of 4.47 and 3.89 mm,respectively,with RMSE values ranging from 2.90 to 5.49 mm across diverse surface conditions.As an all-weather PWV product with high-precision,the MWRI PWV dataset addresses gaps in global passive microwave-based terrestrial PWV observations,offering significant value for atmospheric research,climate modeling,hydrological studies,and beyond.
基金supported by the National Natural Science Foundation of China (NSFC) (Grant No.42205044)Feng Yun Application Pioneering Project (FY-APP) Innovation Center for Feng Yun Meteorological Satellite (FYSIC) Special Project (FY-APP-XC-2023.04)the Wuxi University Research Start-up Fund for Recruited Talent。
文摘Ground-based radar is the primary means by which severe storms are monitored and tracked;however, due to limited coverage, important data is often missed over ocean and mountainous areas. On the other hand, geostationary(GEO)weather satellites provide continuous observations with seamless coverage with advanced imager, despite their limited capability to penetrate clouds. Combining satellite and ground-radar observations could exploit the advantages of both techniques, providing tracking capability close to that of ground radar while maintaining full spatial coverage. This study presents a novel method called Multi-dimensional satellite Observation information for Radar Estimation(MORE) to reconstruct radar composite reflectivity(CREF). Deep learning techniques are important components of MORE for estimating CREF from China's Fengyun-4B(FY-4B) GEO satellite observations. Two models are developed: an infraredonly(IR-Single) model available for all times, and a visible-infrared(VIS+IR) model for daytime applications. These models incorporate multi-dimensional satellite observation information, including temporal, spatial, spectral, and viewing angle information, to enhance the accuracy of radar echo reconstruction. Results demonstrate that the VIS+IR model outperforms the IR-Single model, and both models achieves a root-mean-square error(RMSE) of less than 6 dBZ and a coefficient of determination(R~2) of greater than 0.7. The models effectively reconstruct radar echoes, including strong echoes exceeding 50 dBZ, and show good agreement with precipitation data in radar-blind areas. This study offers a valuable solution for severe weather monitoring and tracking in regions lacking ground-based radar observations, and provides a potential tool for enhanced data assimilation in numerical weather prediction(NWP) models.
