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
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
This study investigated the characteristics and mechanisms of summer regional persistent extreme precipitation events(RPEPEs)over South China(SC)modulated by distinct intensity regimes of 10-30-day intraseasonal oscil...This study investigated the characteristics and mechanisms of summer regional persistent extreme precipitation events(RPEPEs)over South China(SC)modulated by distinct intensity regimes of 10-30-day intraseasonal oscillation(ISO).Diagnostic analyses revealed that the spatiotemporal evolution of RPEPEs exhibits robust phase-locking with the 10-30-day intraseasonal precipitation.By classifying RPEPEs into strong-ISO(SRPEPE)and weak-ISO(WRPEPE)composites based on the amplitude of 10-30-day filtered precipitation,we demonstrate a 14.6%enhancement in peak precipitation intensity during SRPEPEs compared to WRPEPEs.These distinct precipitation regimes are governed by fundamentally different Rossby wave teleconnection patterns over Eurasia.During SRPEPEs,a robust southeastward-propagating 10-30-day Rossby wave train originating from the Barents Sea traverses midlatitude Eurasia,effectively perturbing the northwestern Pacific upper-level circulation and establishing a favorable dynamic environment over SC.In contrast,WRPEPEs are associated with weaker eastward-propagating wave trains constrained along the subtropical jet stream.The horizontal convergence of background moisture driven by 10-30-day winds significantly amplifies lower-tropospheric humidity during SRPEPEs.The thermal advection of background temperature by 10-30-day geostrophic winds enhances baroclinic instability and vertical motion,intensifying precipitation under these moisture conditions.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
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
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
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
Deep learning(DL)has become a crucial technique for predicting the El Niño-Southern Oscillation(ENSO)and evaluating its predictability.While various DL-based models have been developed for ENSO predictions,many f...Deep learning(DL)has become a crucial technique for predicting the El Niño-Southern Oscillation(ENSO)and evaluating its predictability.While various DL-based models have been developed for ENSO predictions,many fail to capture the coherent multivariate evolution within the coupled ocean-atmosphere system of the tropical Pacific.To address this three-dimensional(3D)limitation and represent ENSO-related ocean-atmosphere interactions more accurately,a novel this 3D multivariate prediction model was proposed based on a Transformer architecture,which incorporates a spatiotemporal self-attention mechanism.This model,named 3D-Geoformer,offers several advantages,enabling accurate ENSO predictions up to one and a half years in advance.Furthermore,an integrated gradient method was introduced into the model to identify the sources of predictability for sea surface temperature(SST)variability in the eastern equatorial Pacific.Results reveal that the 3D-Geoformer effectively captures ENSO-related precursors during the evolution of ENSO events,particularly the thermocline feedback processes and ocean temperature anomaly pathways on and off the equator.By extending DL-based ENSO predictions from one-dimensional Niño time series to 3D multivariate fields,the 3D-Geoformer represents a significant advancement in ENSO prediction.This study provides details in the model formulation,analysis procedures,sensitivity experiments,and illustrative examples,offering practical guidance for the application of the model in ENSO research.展开更多
Persistent(5-day or longer)extreme cold events(ECEs)over northeastern China during the boreal winter of 1979–2020 are investigated using daily minimum temperature(Tmin)from the China Meteorological Data Network.The e...Persistent(5-day or longer)extreme cold events(ECEs)over northeastern China during the boreal winter of 1979–2020 are investigated using daily minimum temperature(Tmin)from the China Meteorological Data Network.The extreme cooling area and intensity indices associated with the ECEs exhibit a dominant 10–40-day periodicity,indicating a close link with atmospheric intraseasonal oscillations(ISOs).The ECEs are categorized into W-and N-type.In the former,the low-frequency cooling associated with the ISO first penetrates into the western boundary of the northeastern China domain and later occupies the entire domain at its peak phase.The upper-tropospheric circulation associated with this type is characterized by a northwest–southeast-oriented Rossby wave train,expanding from the Ural Mountains to the western Pacific Ocean.In the latter,the cooling invades the northern boundary first and then penetrates into the entire domain.The upper tropospheric precursory signal associated with this type is a zonally oriented negative geopotential height anomaly,which slowly moves southward.A downward-propagating signal is observed in the stratospheric potential vorticity field prior to the peak cooling,implying a possible stratospheric impact.In addition to the W-and N-types,ECEs can also occur in a localized region near either at the northern or southern boundary of the domain.展开更多
The “3·31” severe squall line event in eastern China was notable for its exceptional intensity and persistence,posing significant challenges to forecast accuracy. This study analyzed the maintenance stage of th...The “3·31” severe squall line event in eastern China was notable for its exceptional intensity and persistence,posing significant challenges to forecast accuracy. This study analyzed the maintenance stage of this event using highresolution convection-permitting numerical simulations, with a focus on vorticity budgets of the environmental flow, multiscale synoptic diagnostics, and Rotunno-Klemp-Weisman(RKW) theory. These analyses aimed to elucidate the mechanisms governing the morphological transition, the generation of associated convective gales, and the prolonged maintenance of the squall line event. The results show that the numerical simulation accurately reproduced the development and evolution of the squall line, particularly its location, with surface wind errors remaining within a reasonable range. The development of a mesoscale vortex modulated the dynamic and water vapor fields, providing favorable mesoscale environmental conditions for the organization and maintenance of the squall line. Vorticity budget analysis indicates that the divergence and tilting terms were the primary contributors to vorticity tendency. After the squall line entered Jiangxi Province, it exhibited a sharper leading edge and enhanced upward motion. Dry intrusion from the mid-toupper troposphere led to rapid downward momentum transfer at the meso-γ scale, thereby generating convective gales. In addition, the enhancement of the rear-inflow jet(RIJ) was related to the pressure difference between the interior and exterior of system, which resulted from the phase change of condensate within tilted updrafts. The RIJ was orthogonal to the squall line, causing it to transform from a linear into a bowing shape. Diagnosis based on the RKW theory underscore the important roles in both low-level and deep vertical wind shear in maintenaning the squall line. The ratios of the cold pool propagation velocity to the vertical wind shear were close to 1, which balanced with the ambient horizontal vorticity that allowed the convection to remain upright, thus sustaining the squall line's intensity for an extended period. In summary, the squall line event was sustained by a favorable environment created by the environmental vortex. The dry intrusion from the mid-to-upper troposphere and intensified RIJ resulted in the severe convective winds, while the balance between cold pool and ambient vertical wind shear promoted the system's prolonged maintenance. These findings provide an effective reference for the short-range forecasting of squall lines throughout their lifecycle.展开更多
Plastic pollution and microplastics in sediments are a growing concern for marine ecosystems worldwide.We examined the vertical distribution and properties of microplastics in beach sediments of Xuwen Coral Reef Natio...Plastic pollution and microplastics in sediments are a growing concern for marine ecosystems worldwide.We examined the vertical distribution and properties of microplastics in beach sediments of Xuwen Coral Reef National Nature Reserve,in Leizhou Peninsula,Zhanjiang,China.Sediment samples were taken in seven locations at 5-cm intervals from the surface to a depth of 30 cm.The vertical distribution of microplastic particles ranged from 0 to 1340 particles per kg on average of 119.05particles per kg.The most prevalent material was fibers(76%),followed by film(12%),fragments(11.2%),and foam(0.8%).The microplastics in size of 1-2 mm constituted the largest percentage(40%)of the total,followed by those in size of<1 mm(26.4%),2-3 mm(21.2%),3-4 mm(9.6%),and 4-5 mm(2.81%).Site S1 observed maximum sizes between 1 and 2 mm,S2 reported higher availability of microplastics with sizes ranging from 0.3 to 1 mm.Six different types of polymers were identified in the investigation,and mostly were polyethylene(PE)and polypropylene(PP).In general,the observation of microplastics in deeper sediments indicates that they have the ability to last for prolonged periods in the marine environment,which may present long-term hazards to benthic creatures.In conclusion,the discovery of microplastics in deep layers of coastal sediments highlights the necessity of minimizing plastic waste and enhancing management strategies to safeguard marine environments.展开更多
The discrepancy in the trends of the global zonal mean(GZM)intensity of the Hadley circulation(HCI)between reanalysis data and model simulations has been a problem for understanding the changes in HCI and the influenc...The discrepancy in the trends of the global zonal mean(GZM)intensity of the Hadley circulation(HCI)between reanalysis data and model simulations has been a problem for understanding the changes in HCI and the influence of external forcings.To understand the reason for this discrepancy,this study investigates the trends of intensity of regional HCI of the Northern Hemisphere over the eastern Pacific(EPA),western Pacific(WPA),Atlantic(ATL),Africa(AFR),the Indian Ocean(IDO),and residual area(RA),based on six reanalysis datasets and 13 CMIP6 models.In reanalysis data,the trends in regional HCI over EPA and ATL(WPA and AFR)contribute to(partially offset)the increasing trend in GZM HCI,while the trends in regional HCI over IDO are different in different reanalysis data.The CMIP6 models skillfully reproduce the trends in regional HCI over EPA,ATL,WPA,and AFR,but simulate notable decreasing trends in regional HCI over IDO,which is a key reason for the opposite trends in GZM HCI between reanalysis data and models.The discrepancy in IDO can be attributed to differences in the simulation of diabatic heating and zonal friction between reanalysis data and models.Optimal fingerprint analysis indicates that anthropogenic(ANT)and non-greenhouse gas(NOGHG)forcings are the dominant drivers of the HCI trends in the EPA and ATL regions.In the WPA(AFR)region,NOGHG(ANT)forcing serves as the primary driver.The findings contribute to improving the representation of regional HCI trends in models and improving the attribution of external forcings.展开更多
This study aims to enhance the extended-range prediction of midsummer(July) maximum temperature(Tmax)through a dynamical downscaling method. We compare the prediction skills of July Tmax over southern China between th...This study aims to enhance the extended-range prediction of midsummer(July) maximum temperature(Tmax)through a dynamical downscaling method. We compare the prediction skills of July Tmax over southern China between the NCEP Climate Forecast System version 2(CFSv2) and a high-resolution Weather Research and Forecasting(WRF) model,using gridded Tmax observation data and ERA5 reanalysis data as benchmarks. The WRF model is driven by CFSv2 multi-member ensemble hindcast and forecast data. Results indicate that the WRF model improves Tmax prediction across China, with particularly significant enhancement over the southern region of the middle and lower reaches of the Yangtze River, although a systematic cold bias remains. By applying bias correction to the daily Tmax simulations from both models, we find that the corrected WRF predictions exhibit marked improvement for both the annual and extended-range Tmax. Furthermore, this study explores the physical mechanisms contributing to the improved predictability in the regional model. The WRF model, with its refined physical parameterization schemes, better simulates middle to lower tropospheric geopotential height fields, as well as surface sensible and latent heat fluxes. These results demonstrate that the dynamical downscaling approach can significantly improve the temperature prediction in southern China, highlighting the potential applicational value of this method for extended-range high-temperature forecasting.展开更多
Land–atmosphere coupling and sea surface temperature(SST)anomalies both have essential impacts on weather and climate extremes.Based on the ERA5 reanalysis dataset and the CESM1.2.2 model,this study investigates the ...Land–atmosphere coupling and sea surface temperature(SST)anomalies both have essential impacts on weather and climate extremes.Based on the ERA5 reanalysis dataset and the CESM1.2.2 model,this study investigates the influence of land–atmosphere coupling on summer extreme hot-humid events(EHHE)over southern Eurasia under different SST backgrounds.The results suggest that coupling causes near-surface air temperature increases that exceed 0.5℃.From 1961 to 2020,the frequency of EHHE has continuously increased,and is closely related to soil moisture anomalies in the northern Indian Peninsula(IDP)and the middle and lower reaches of the Yangtze River(YRB).Numerical simulations further demonstrate that land–atmosphere coupling raises the risk of EHHE by 25.4%.In a typical El Niño SST background state,intensified land–atmosphere coupling tends to produce notable increases in the frequency of EHHE.The dominant processes that land–atmosphere coupling affects the EHHE variations are evidently different between these two regions.Land surface thermal anomalies predominate in the IDP,while moisture conditions are more critical in the YRB.When warm SST anomalies exist,dry soil anomalies in the IDP are prominent,and evaporation is constrained,increasing sensible heat flux.Positive geopotential height anomalies are significant,combined with adiabatic warming induced by descending motion and a noticeable warm center in the near-surface atmosphere.The southward shift of the westerly jet enhances divergence over YRB.The anticyclonic circulation anomalies over the western Pacific are conducive to guiding moisture transport to the YRB,providing a favorable circulation background for the development of summer EHHE.展开更多
Since the mid-20th century,the Mongolian Plateau(MP)has experienced decadal droughts coupled with extreme heatwaves,severely affecting regional ecology and social development.However,the mechanisms behind these decada...Since the mid-20th century,the Mongolian Plateau(MP)has experienced decadal droughts coupled with extreme heatwaves,severely affecting regional ecology and social development.However,the mechanisms behind these decadalscale compound heatwavedrought events remain debated.Here,using reconstructions and simulations from the Community Earth System Model Last Millennium Ensemble,we demonstrate that,over the last millennium,decadal droughts on the MP occurred under both warm and cold conditions,differing from recent compound heatwavedrought events.We found that by examining temperature changes during these drought periods,the distinct influences of external forcings and internal variability can be simply and effectively distinguished.Specifically,colddry events were primarily driven by volcanic eruptions that weakened the East Asian summer monsoon and midlatitude westerlies,reducing moisture transport to the MP.In contrast,warmdry events were predominantly induced by internal variability,notably the negative phase of the Atlantic Multidecadal Oscillation and the expansion of the Barents Sea ice extent.The recent extreme compound event was probably influenced by the combined effects of anthropogenic forcings and internal variability.These findings deepen our understanding of how external forcings and internal variability affect decadal drought events on the MP and highlight that recent compound events are unprecedented in the context of the last millennium.展开更多
Using in-situ microstructure observations from 2010 to 2018,this study investigates the performance and generalization of machine learning models in parameterizing turbulent mixing in the northwestern South China Sea....Using in-situ microstructure observations from 2010 to 2018,this study investigates the performance and generalization of machine learning models in parameterizing turbulent mixing in the northwestern South China Sea.The results show that the data-driven extreme gradient boosting(XGBoost)performs better than the other four models,i.e.,random forest,neural network,linear regression and support vector machine regression.In order to further improve the generalization of machine learning-based parameterization method,we propose a physics-informed machine learning(PIML)that couples the MacKinnon-Gregg model(known as the MG model)and Osborn’s formula to the XGBoost model.The correlation coefficient(r)and root mean square error(RMSE)between the estimated and observed 1g(ε)(whereεdenotes the turbulent kinetic energy dissipation rate)from the PIML are improved by 14%and 16%,respectively.The results also show that PIML effectively improves the generalization of the XGBoost-based parameterization method,enhancing r and RMSE by 35%and 75%,respectively.展开更多
The uncertainty of ocean turbulent mixing parameterization comprises a significant challenge in ocean and climate models. A depth-dependent deep learning ocean turbulent mixing parameterization scheme was proposed wit...The uncertainty of ocean turbulent mixing parameterization comprises a significant challenge in ocean and climate models. A depth-dependent deep learning ocean turbulent mixing parameterization scheme was proposed with the hydrological and microstructure observations conducted in summer 2012 in the shelf sea east of Hainan Island, in South China Sea(SCS). The deep neural network model is used and incorporates the Richardson number Ri, the normalized depth D, the horizontal velocity speed U, the shear S^(2), the stratification N^(2), and the density ρ as input parameters. Comparing to the scheme without parameter D and region division, the depth-dependent scheme improves the prediction of the turbulent kinetic energy dissipation rate ε. The correlation coefficient(r) between predicted and observed lgε increases from 0.49 to 0.62, and the root mean square error decreases from 0.56 to 0.48. Comparing to the traditional physics-driven parameterization schemes, such as the G89 and MG03, the data-driven approach achieves higher accuracy and generalization. The SHapley Additive Explanations(SHAP) framework analysis reveals the importance descending order of the input parameters as: ρ, D, U, N^(2), S^(2), and Ri in the whole depth, while D is most important in the upper and bottom boundary layers(D≤0.3&D≥0.65) and least important in middle layer(0.3<D<0.65). The research shows applicability of constructing deep learning-based ocean turbulent mixing parameterization schemes using limited observational data and well-established physical processes.展开更多
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the comments of three reviewers which substantially enhanced the quality of this manuscript.This research is jointly sponsored by the National Natural Science Foundation of China(grant 42575038)the National Key Research and Development Program of China(grant 2022YFF0801702)the National Natural Science Foundation of China(grant 42206257 and 42205163).
文摘This study investigated the characteristics and mechanisms of summer regional persistent extreme precipitation events(RPEPEs)over South China(SC)modulated by distinct intensity regimes of 10-30-day intraseasonal oscillation(ISO).Diagnostic analyses revealed that the spatiotemporal evolution of RPEPEs exhibits robust phase-locking with the 10-30-day intraseasonal precipitation.By classifying RPEPEs into strong-ISO(SRPEPE)and weak-ISO(WRPEPE)composites based on the amplitude of 10-30-day filtered precipitation,we demonstrate a 14.6%enhancement in peak precipitation intensity during SRPEPEs compared to WRPEPEs.These distinct precipitation regimes are governed by fundamentally different Rossby wave teleconnection patterns over Eurasia.During SRPEPEs,a robust southeastward-propagating 10-30-day Rossby wave train originating from the Barents Sea traverses midlatitude Eurasia,effectively perturbing the northwestern Pacific upper-level circulation and establishing a favorable dynamic environment over SC.In contrast,WRPEPEs are associated with weaker eastward-propagating wave trains constrained along the subtropical jet stream.The horizontal convergence of background moisture driven by 10-30-day winds significantly amplifies lower-tropospheric humidity during SRPEPEs.The thermal advection of background temperature by 10-30-day geostrophic winds enhances baroclinic instability and vertical motion,intensifying precipitation under these moisture conditions.
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 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Supported by the Laoshan Laboratory(No.LSKJ202202402)the National Natural Science Foundation of China(No.42030410)+2 种基金the Startup Foundation for Introducing Talent of Nanjing University of Information Science&Technology,and Jiangsu Innovation Research Group(No.JSSCTD 202346)supported by the China National Postdoctoral Program for Innovative Talents(No.BX20240169)the China Postdoctoral Science Foundation(No.2141062400101)。
文摘Deep learning(DL)has become a crucial technique for predicting the El Niño-Southern Oscillation(ENSO)and evaluating its predictability.While various DL-based models have been developed for ENSO predictions,many fail to capture the coherent multivariate evolution within the coupled ocean-atmosphere system of the tropical Pacific.To address this three-dimensional(3D)limitation and represent ENSO-related ocean-atmosphere interactions more accurately,a novel this 3D multivariate prediction model was proposed based on a Transformer architecture,which incorporates a spatiotemporal self-attention mechanism.This model,named 3D-Geoformer,offers several advantages,enabling accurate ENSO predictions up to one and a half years in advance.Furthermore,an integrated gradient method was introduced into the model to identify the sources of predictability for sea surface temperature(SST)variability in the eastern equatorial Pacific.Results reveal that the 3D-Geoformer effectively captures ENSO-related precursors during the evolution of ENSO events,particularly the thermocline feedback processes and ocean temperature anomaly pathways on and off the equator.By extending DL-based ENSO predictions from one-dimensional Niño time series to 3D multivariate fields,the 3D-Geoformer represents a significant advancement in ENSO prediction.This study provides details in the model formulation,analysis procedures,sensitivity experiments,and illustrative examples,offering practical guidance for the application of the model in ENSO research.
基金supported by the National Natural Science Foundation of China(Grant Nos.42088101 and 42075032).
文摘Persistent(5-day or longer)extreme cold events(ECEs)over northeastern China during the boreal winter of 1979–2020 are investigated using daily minimum temperature(Tmin)from the China Meteorological Data Network.The extreme cooling area and intensity indices associated with the ECEs exhibit a dominant 10–40-day periodicity,indicating a close link with atmospheric intraseasonal oscillations(ISOs).The ECEs are categorized into W-and N-type.In the former,the low-frequency cooling associated with the ISO first penetrates into the western boundary of the northeastern China domain and later occupies the entire domain at its peak phase.The upper-tropospheric circulation associated with this type is characterized by a northwest–southeast-oriented Rossby wave train,expanding from the Ural Mountains to the western Pacific Ocean.In the latter,the cooling invades the northern boundary first and then penetrates into the entire domain.The upper tropospheric precursory signal associated with this type is a zonally oriented negative geopotential height anomaly,which slowly moves southward.A downward-propagating signal is observed in the stratospheric potential vorticity field prior to the peak cooling,implying a possible stratospheric impact.In addition to the W-and N-types,ECEs can also occur in a localized region near either at the northern or southern boundary of the domain.
基金Jiangxi Meteorological Bureau Project (JXCX202304,JX2024Y01)Geological Disaster Prevention and Control Project of Jiangxi Provincial Department of Natural Resources(B360000030004)+1 种基金Key Research and Development Project of Jiangxi Province (20243BBH81005)Weather Review Project of China Meteorological Administration (FPZJ2025-066)。
文摘The “3·31” severe squall line event in eastern China was notable for its exceptional intensity and persistence,posing significant challenges to forecast accuracy. This study analyzed the maintenance stage of this event using highresolution convection-permitting numerical simulations, with a focus on vorticity budgets of the environmental flow, multiscale synoptic diagnostics, and Rotunno-Klemp-Weisman(RKW) theory. These analyses aimed to elucidate the mechanisms governing the morphological transition, the generation of associated convective gales, and the prolonged maintenance of the squall line event. The results show that the numerical simulation accurately reproduced the development and evolution of the squall line, particularly its location, with surface wind errors remaining within a reasonable range. The development of a mesoscale vortex modulated the dynamic and water vapor fields, providing favorable mesoscale environmental conditions for the organization and maintenance of the squall line. Vorticity budget analysis indicates that the divergence and tilting terms were the primary contributors to vorticity tendency. After the squall line entered Jiangxi Province, it exhibited a sharper leading edge and enhanced upward motion. Dry intrusion from the mid-toupper troposphere led to rapid downward momentum transfer at the meso-γ scale, thereby generating convective gales. In addition, the enhancement of the rear-inflow jet(RIJ) was related to the pressure difference between the interior and exterior of system, which resulted from the phase change of condensate within tilted updrafts. The RIJ was orthogonal to the squall line, causing it to transform from a linear into a bowing shape. Diagnosis based on the RKW theory underscore the important roles in both low-level and deep vertical wind shear in maintenaning the squall line. The ratios of the cold pool propagation velocity to the vertical wind shear were close to 1, which balanced with the ambient horizontal vorticity that allowed the convection to remain upright, thus sustaining the squall line's intensity for an extended period. In summary, the squall line event was sustained by a favorable environment created by the environmental vortex. The dry intrusion from the mid-to-upper troposphere and intensified RIJ resulted in the severe convective winds, while the balance between cold pool and ambient vertical wind shear promoted the system's prolonged maintenance. These findings provide an effective reference for the short-range forecasting of squall lines throughout their lifecycle.
基金Supported by the Southern Marine Science and Engineering Guangdong Laboratory、Zhanjiang(No.ZJW-2019-08)APN、CRRP2019-09MYOnodera、Shinichi Onodera、and the SCS Scholar Grant(No.002029002008/2019)。
文摘Plastic pollution and microplastics in sediments are a growing concern for marine ecosystems worldwide.We examined the vertical distribution and properties of microplastics in beach sediments of Xuwen Coral Reef National Nature Reserve,in Leizhou Peninsula,Zhanjiang,China.Sediment samples were taken in seven locations at 5-cm intervals from the surface to a depth of 30 cm.The vertical distribution of microplastic particles ranged from 0 to 1340 particles per kg on average of 119.05particles per kg.The most prevalent material was fibers(76%),followed by film(12%),fragments(11.2%),and foam(0.8%).The microplastics in size of 1-2 mm constituted the largest percentage(40%)of the total,followed by those in size of<1 mm(26.4%),2-3 mm(21.2%),3-4 mm(9.6%),and 4-5 mm(2.81%).Site S1 observed maximum sizes between 1 and 2 mm,S2 reported higher availability of microplastics with sizes ranging from 0.3 to 1 mm.Six different types of polymers were identified in the investigation,and mostly were polyethylene(PE)and polypropylene(PP).In general,the observation of microplastics in deeper sediments indicates that they have the ability to last for prolonged periods in the marine environment,which may present long-term hazards to benthic creatures.In conclusion,the discovery of microplastics in deep layers of coastal sediments highlights the necessity of minimizing plastic waste and enhancing management strategies to safeguard marine environments.
基金the National Key Research and Development Program of China[grant number 2022YFF0801704].
文摘The discrepancy in the trends of the global zonal mean(GZM)intensity of the Hadley circulation(HCI)between reanalysis data and model simulations has been a problem for understanding the changes in HCI and the influence of external forcings.To understand the reason for this discrepancy,this study investigates the trends of intensity of regional HCI of the Northern Hemisphere over the eastern Pacific(EPA),western Pacific(WPA),Atlantic(ATL),Africa(AFR),the Indian Ocean(IDO),and residual area(RA),based on six reanalysis datasets and 13 CMIP6 models.In reanalysis data,the trends in regional HCI over EPA and ATL(WPA and AFR)contribute to(partially offset)the increasing trend in GZM HCI,while the trends in regional HCI over IDO are different in different reanalysis data.The CMIP6 models skillfully reproduce the trends in regional HCI over EPA,ATL,WPA,and AFR,but simulate notable decreasing trends in regional HCI over IDO,which is a key reason for the opposite trends in GZM HCI between reanalysis data and models.The discrepancy in IDO can be attributed to differences in the simulation of diabatic heating and zonal friction between reanalysis data and models.Optimal fingerprint analysis indicates that anthropogenic(ANT)and non-greenhouse gas(NOGHG)forcings are the dominant drivers of the HCI trends in the EPA and ATL regions.In the WPA(AFR)region,NOGHG(ANT)forcing serves as the primary driver.The findings contribute to improving the representation of regional HCI trends in models and improving the attribution of external forcings.
基金National Natural Science Foundation of China(42275030, U2242206, 41730964)Joint Research Project for Meteorological Capacity Improvement (22NLTSZ002)+4 种基金National Key Research and Development Program (2018YFC1506006)China Meteorological Administration Project for Innovation and Development (CXFZ2022J009, CXFZ2022J031)Key Innovation Team of Climate Prediction of China Meteorological Ministration (CMA2023ZD03)Shandong Provincial Natural Science Foundation (ZR2023QD086)UK-China Research & Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund。
文摘This study aims to enhance the extended-range prediction of midsummer(July) maximum temperature(Tmax)through a dynamical downscaling method. We compare the prediction skills of July Tmax over southern China between the NCEP Climate Forecast System version 2(CFSv2) and a high-resolution Weather Research and Forecasting(WRF) model,using gridded Tmax observation data and ERA5 reanalysis data as benchmarks. The WRF model is driven by CFSv2 multi-member ensemble hindcast and forecast data. Results indicate that the WRF model improves Tmax prediction across China, with particularly significant enhancement over the southern region of the middle and lower reaches of the Yangtze River, although a systematic cold bias remains. By applying bias correction to the daily Tmax simulations from both models, we find that the corrected WRF predictions exhibit marked improvement for both the annual and extended-range Tmax. Furthermore, this study explores the physical mechanisms contributing to the improved predictability in the regional model. The WRF model, with its refined physical parameterization schemes, better simulates middle to lower tropospheric geopotential height fields, as well as surface sensible and latent heat fluxes. These results demonstrate that the dynamical downscaling approach can significantly improve the temperature prediction in southern China, highlighting the potential applicational value of this method for extended-range high-temperature forecasting.
基金supported by the National Science Foundation of China(Grant Nos.42088101 and 42275172).
文摘Land–atmosphere coupling and sea surface temperature(SST)anomalies both have essential impacts on weather and climate extremes.Based on the ERA5 reanalysis dataset and the CESM1.2.2 model,this study investigates the influence of land–atmosphere coupling on summer extreme hot-humid events(EHHE)over southern Eurasia under different SST backgrounds.The results suggest that coupling causes near-surface air temperature increases that exceed 0.5℃.From 1961 to 2020,the frequency of EHHE has continuously increased,and is closely related to soil moisture anomalies in the northern Indian Peninsula(IDP)and the middle and lower reaches of the Yangtze River(YRB).Numerical simulations further demonstrate that land–atmosphere coupling raises the risk of EHHE by 25.4%.In a typical El Niño SST background state,intensified land–atmosphere coupling tends to produce notable increases in the frequency of EHHE.The dominant processes that land–atmosphere coupling affects the EHHE variations are evidently different between these two regions.Land surface thermal anomalies predominate in the IDP,while moisture conditions are more critical in the YRB.When warm SST anomalies exist,dry soil anomalies in the IDP are prominent,and evaporation is constrained,increasing sensible heat flux.Positive geopotential height anomalies are significant,combined with adiabatic warming induced by descending motion and a noticeable warm center in the near-surface atmosphere.The southward shift of the westerly jet enhances divergence over YRB.The anticyclonic circulation anomalies over the western Pacific are conducive to guiding moisture transport to the YRB,providing a favorable circulation background for the development of summer EHHE.
基金supported by the National Natural Science Foundation of China(Grant Nos.42130604)the National Key Research and Development Program of China(Grant No.2023YFF0804704)+2 种基金the National Natural Science Foundation of China(Grant Nos.42105044)Swedish STINT(Grant No.CH2019-8377)the Priority Academic Program Development of Jiangsu Higher Education Institutions(Grant No.164320H116)。
文摘Since the mid-20th century,the Mongolian Plateau(MP)has experienced decadal droughts coupled with extreme heatwaves,severely affecting regional ecology and social development.However,the mechanisms behind these decadalscale compound heatwavedrought events remain debated.Here,using reconstructions and simulations from the Community Earth System Model Last Millennium Ensemble,we demonstrate that,over the last millennium,decadal droughts on the MP occurred under both warm and cold conditions,differing from recent compound heatwavedrought events.We found that by examining temperature changes during these drought periods,the distinct influences of external forcings and internal variability can be simply and effectively distinguished.Specifically,colddry events were primarily driven by volcanic eruptions that weakened the East Asian summer monsoon and midlatitude westerlies,reducing moisture transport to the MP.In contrast,warmdry events were predominantly induced by internal variability,notably the negative phase of the Atlantic Multidecadal Oscillation and the expansion of the Barents Sea ice extent.The recent extreme compound event was probably influenced by the combined effects of anthropogenic forcings and internal variability.These findings deepen our understanding of how external forcings and internal variability affect decadal drought events on the MP and highlight that recent compound events are unprecedented in the context of the last millennium.
基金The National Science and Technology Major Project under contract No.2024YFC2817003the National Natural Science Foundation of China under contract Nos 42276019 and 42249911the Guangdong Ordinary University Innovation Team Project under contract No.2023KCXTD015.
文摘Using in-situ microstructure observations from 2010 to 2018,this study investigates the performance and generalization of machine learning models in parameterizing turbulent mixing in the northwestern South China Sea.The results show that the data-driven extreme gradient boosting(XGBoost)performs better than the other four models,i.e.,random forest,neural network,linear regression and support vector machine regression.In order to further improve the generalization of machine learning-based parameterization method,we propose a physics-informed machine learning(PIML)that couples the MacKinnon-Gregg model(known as the MG model)and Osborn’s formula to the XGBoost model.The correlation coefficient(r)and root mean square error(RMSE)between the estimated and observed 1g(ε)(whereεdenotes the turbulent kinetic energy dissipation rate)from the PIML are improved by 14%and 16%,respectively.The results also show that PIML effectively improves the generalization of the XGBoost-based parameterization method,enhancing r and RMSE by 35%and 75%,respectively.
基金Supported by the National Natural Science Foundation of China(No.42276019)the Guangdong Provincial Observation and Research Station for Tropical Ocean Environment in Western Coastal Waters(No.GSTOEW)。
文摘The uncertainty of ocean turbulent mixing parameterization comprises a significant challenge in ocean and climate models. A depth-dependent deep learning ocean turbulent mixing parameterization scheme was proposed with the hydrological and microstructure observations conducted in summer 2012 in the shelf sea east of Hainan Island, in South China Sea(SCS). The deep neural network model is used and incorporates the Richardson number Ri, the normalized depth D, the horizontal velocity speed U, the shear S^(2), the stratification N^(2), and the density ρ as input parameters. Comparing to the scheme without parameter D and region division, the depth-dependent scheme improves the prediction of the turbulent kinetic energy dissipation rate ε. The correlation coefficient(r) between predicted and observed lgε increases from 0.49 to 0.62, and the root mean square error decreases from 0.56 to 0.48. Comparing to the traditional physics-driven parameterization schemes, such as the G89 and MG03, the data-driven approach achieves higher accuracy and generalization. The SHapley Additive Explanations(SHAP) framework analysis reveals the importance descending order of the input parameters as: ρ, D, U, N^(2), S^(2), and Ri in the whole depth, while D is most important in the upper and bottom boundary layers(D≤0.3&D≥0.65) and least important in middle layer(0.3<D<0.65). The research shows applicability of constructing deep learning-based ocean turbulent mixing parameterization schemes using limited observational data and well-established physical processes.
