This study uses the coupled atmosphere–surface climate feedback–response analysis method（CFRAM） to analyze the surface temperature biases in the Flexible Global Ocean–Atmosphere–Land System model, spectral versi...This study uses the coupled atmosphere–surface climate feedback–response analysis method（CFRAM） to analyze the surface temperature biases in the Flexible Global Ocean–Atmosphere–Land System model, spectral version 2（FGOALS-s2）in January and July. The process-based decomposition of the surface temperature biases, defined as the difference between the model and ERA-Interim during 1979–2005, enables us to attribute the model surface temperature biases to individual radiative processes including ozone, water vapor, cloud, and surface albedo; and non-radiative processes including surface sensible and latent heat fluxes, and dynamic processes at the surface and in the atmosphere. The results show that significant model surface temperature biases are almost globally present, are generally larger over land than over oceans, and are relatively larger in summer than in winter. Relative to the model biases in non-radiative processes, which tend to dominate the surface temperature biases in most parts of the world, biases in radiative processes are much smaller, except in the sub-polar Antarctic region where the cold biases from the much overestimated surface albedo are compensated for by the warm biases from nonradiative processes. The larger biases in non-radiative processes mainly lie in surface heat fluxes and in surface dynamics,which are twice as large in the Southern Hemisphere as in the Northern Hemisphere and always tend to compensate for each other. In particular, the upward/downward heat fluxes are systematically underestimated/overestimated in most parts of the world, and are mainly compensated for by surface dynamic processes including the increased heat storage in deep oceans across the globe.展开更多
The East Asian Summer Monsoon(EASM)provides the majority of annual rainfall to countries in East Asia.Although state-of-the-art models broadly project increased EASM rainfall,the spread of projections is large and sim...The East Asian Summer Monsoon(EASM)provides the majority of annual rainfall to countries in East Asia.Although state-of-the-art models broadly project increased EASM rainfall,the spread of projections is large and simulations of present-day rainfall show significant climatological biases.Systematic evapotranspiration biases occur locally over East Asia,and globally over land,in simulations both with and without a coupled ocean.This study explores the relationship between evapotranspiration and EASM precipitation biases.First,idealized model simulations are presented in which the parameterization of land evaporation is modified,while sea surface temperature is fixed.The results suggest a feedback whereby excessive evapotranspiration over East Asia results in cooling of land,a weakened monsoon low,and a shift of rainfall from the Philippine Sea to China,further fueling evapotranspiration.Cross-model regressions against evapotranspiration over China indicate a similar pattern of behavior in Atmospheric Model Intercomparison Project(AMIP)simulations.Possible causes of this pattern are investigated.The feedback is not explained by an overly intense global hydrological cycle or by differences in radiative processes.Analysis of land-only simulations indicates that evapotranspiration biases are present even when models are forced with prescribed rainfall.These are strengthened when coupled to the atmosphere,suggesting a role for land-model errors in driving atmospheric biases.Coupled atmosphere-ocean models are shown to have similar evapotranspiration biases to those in AMIP over China,but different precipitation biases,including a northward shift in the ITCZ over the Pacific and Atlantic Oceans.展开更多
This study presents a simplified multivariate bias correction scheme that is sequentially implemented in the GEOS5 data assimilation system and compared against a control experiment without model bias correction. The ...This study presents a simplified multivariate bias correction scheme that is sequentially implemented in the GEOS5 data assimilation system and compared against a control experiment without model bias correction. The results show considerable improvement in terms of the mean biases of rawinsonde observation-minus-background （OmB） residuals for observed water vapor, wind and temperature variables. The time series spectral analysis shows whitening of bias-corrected OmB residuals, and mean biases for rawinsonde observation-minus-analysis （OmA） are also improved. Some wind and temperature biases in the control experiment near the equatorial tropopause nearly vanish from the bias-corrected experiment. Despite the analysis improvement, the bias correction scheme has only a moderate impact on forecast skill. Significant interaction is also found among quality-control, satellite observation bias correction, and background bias correction, and the latter positively impacts satellite bias correction.展开更多
Cloud diurnal variation is crucial for regulating cloud radiative effects and atmospheric dynamics.However,it is often overlooked in the evaluation and development of climate models.Thus,this study aims to investigate...Cloud diurnal variation is crucial for regulating cloud radiative effects and atmospheric dynamics.However,it is often overlooked in the evaluation and development of climate models.Thus,this study aims to investigate the daily mean(CFR)and diurnal variation(CDV)of cloud fraction across high-,middle-,low-level,and total clouds in the FGOALS-f3-L general circulation model.The bias of total CDV is decomposed into the model biases in CFRs and CDVs of clouds at all three levels.Results indicate that the model generally underestimates low-level cloud fraction during the daytime and high-/middle-level cloud fraction at nighttime.The simulation biases of low clouds,especially their CDV biases,dominate the bias of total CDV.Compensation effects exist among the bias decompositions,where the negative contributions of underestimated daytime low-level cloud fraction are partially offset by the opposing contributions from biases in high-/middle-level clouds.Meanwhile,the bias contributions have notable land–ocean differences and region-dependent characteristics,consistent with the model biases in these variables.Additionally,the study estimates the influences of CFR and CDV biases on the bias of shortwave cloud radiative effects.It reveals that the impacts of CDV biases can reach half of those from CFR biases,highlighting the importance of accurate CDV representation in climate models.展开更多
In this paper, the performance of existing biased estimators (Ridge Estimator (RE), Almost Unbiased Ridge Estimator (AURE), Liu Estimator (LE), Almost Unbiased Liu Estimator (AULE), Principal Component Regression Esti...In this paper, the performance of existing biased estimators (Ridge Estimator (RE), Almost Unbiased Ridge Estimator (AURE), Liu Estimator (LE), Almost Unbiased Liu Estimator (AULE), Principal Component Regression Estimator (PCRE), r-k class estimator and r-d class estimator) and the respective predictors were considered in a misspecified linear regression model when there exists multicollinearity among explanatory variables. A generalized form was used to compare these estimators and predictors in the mean square error sense. Further, theoretical findings were established using mean square error matrix and scalar mean square error. Finally, a numerical example and a Monte Carlo simulation study were done to illustrate the theoretical findings. The simulation study revealed that LE and RE outperform the other estimators when weak multicollinearity exists, and RE, r-k class and r-d class estimators outperform the other estimators when moderated and high multicollinearity exist for certain values of shrinkage parameters, respectively. The predictors based on the LE and RE are always superior to the other predictors for certain values of shrinkage parameters.展开更多
An analytical variational method for the ground state of the biased quantum Rabi model in the ultra-strong coupling regime is presented. This analytical variational method can be obtained by a unitary transformation o...An analytical variational method for the ground state of the biased quantum Rabi model in the ultra-strong coupling regime is presented. This analytical variational method can be obtained by a unitary transformation or alternatively by assuming the form of the ground state wave function. The key of the method is to introduce a variational parameter λ,which can be determined by minimizing the energy functional. Using this method, we calculate the physical observables with high accuracy in comparison with the numerical exact ones. Our method evidently improves over the widely used general rotating-wave approximation（GRWA） in both qualitative and quantitative aspects.展开更多
The datasets of two Ocean Model Intercomparison Project(OMIP)simulation experiments from the LASG/IAP Climate Ocean Model,version 3(LICOM3),forced by two different sets of atmospheric surface data,are described in thi...The datasets of two Ocean Model Intercomparison Project(OMIP)simulation experiments from the LASG/IAP Climate Ocean Model,version 3(LICOM3),forced by two different sets of atmospheric surface data,are described in this paper.The experiment forced by CORE-II(Co-ordinated Ocean–Ice Reference Experiments,Phase II)data(1948–2009)is called OMIP1,and that forced by JRA55-do(surface dataset for driving ocean–sea-ice models based on Japanese 55-year atmospheric reanalysis)data(1958–2018)is called OMIP2.First,the improvement of LICOM from CMIP5 to CMIP6 and the configurations of the two experiments are described.Second,the basic performances of the two experiments are validated using the climatological-mean and interannual time scales from observation.We find that the mean states,interannual variabilities,and long-term linear trends can be reproduced well by the two experiments.The differences between the two datasets are also discussed.Finally,the usage of these data is described.These datasets are helpful toward understanding the origin system bias of the fully coupled model.展开更多
El Nino-Southern Oscillation(ENSO) is the strongest interannual signal that is producedby basinscale processes in the tropical Pacific,with significant effects on weather and climate worldwide.In the past,extensive an...El Nino-Southern Oscillation(ENSO) is the strongest interannual signal that is producedby basinscale processes in the tropical Pacific,with significant effects on weather and climate worldwide.In the past,extensive and intensive international efforts have been devoted to coupled model developments for ENSO studies.A hierarchy of coupled ocean-atmo sphere models has been formulated;in terms of their complexity,they can be categorized into intermediate coupled models(ICMs),hybrid coupled models(HCMs),and fully coupled general circulation models(CGCMs).ENSO modeling has made significant progress over the past decades,reaching a stage where coupled models can now be used to successfully predict ENSO events 6 months to one year in advance.Meanwhile,ENSO exhibits great diversity and complexity as observed in nature,which still cannot be adequately captured by current state-of-the-art coupled models,presenting a challenge to ENSO modeling.We primarily reviewed the long-term efforts in ENSO modeling continually and steadily made at different institutions in China;some selected representative examples are presented here to review the current status of ENSO model developments and applications,which have been actively pursued with noticeable progress being made recently.As ENSO simulations are very sensitive to model formulations and process representations etc.,dedicated efforts have been devoted to ENSO model developments and improvements.Now,different ocean-atmosphere coupled models have been available in China,which exhibit good model performances and have already had a variety of applications to climate modeling,including the Coupled Model Intercomparison Project Phase 6(CMIP6).Nevertheless,large biases and uncertainties still exist in ENSO simulations and predictions,and there are clear rooms for their improvements,which are still an active area of researches and applications.Here,model performances of ENSO simulations are assessed in terms of advantages and disadvantages with these differently formulated coupled models,pinpointing to the areas where they need to be further improved for ENSO studies.These analyses provide valuable guidance for future improvements in ENSO simulations and predictions.展开更多
Climate models are vital for understanding and projecting global climate change and its associated impacts.However,these models suffer from biases that limit their accuracy in historical simulations and the trustworth...Climate models are vital for understanding and projecting global climate change and its associated impacts.However,these models suffer from biases that limit their accuracy in historical simulations and the trustworthiness of future projections.Addressing these challenges requires addressing internal variability,hindering the direct alignment between model simulations and observations,and thwarting conventional supervised learning methods.Here,we employ an unsupervised Cycle-consistent Generative Adversarial Network(CycleGAN),to correct daily Sea Surface Temperature(SST)simulations from the Community Earth System Model 2(CESM2).Our results reveal that the CycleGAN not only corrects climatological biases but also improves the simulation of major dynamic modes including the El Niño-Southern Oscillation(ENSO)and the Indian Ocean Dipole mode,as well as SST extremes.Notably,it substantially corrects climatological SST biases,decreasing the globally averaged Root-Mean-Square Error(RMSE)by 58%.Intriguingly,the CycleGAN effectively addresses the well-known excessive westward bias in ENSO SST anomalies,a common issue in climate models that traditional methods,like quantile mapping,struggle to rectify.Additionally,it substantially improves the simulation of SST extremes,raising the pattern correlation coefficient(PCC)from 0.56 to 0.88 and lowering the RMSE from 0.5 to 0.32.This enhancement is attributed to better representations of interannual,intraseasonal,and synoptic scales variabilities.Our study offers a novel approach to correct global SST simulations and underscores its effectiveness across different time scales and primary dynamical modes.展开更多
The simulation and prediction of the climatology and interannual variability of the East Asia winter monsoon(EAWM),as well as the associated atmospheric circulation,was investigated using the hindcast data from Global...The simulation and prediction of the climatology and interannual variability of the East Asia winter monsoon(EAWM),as well as the associated atmospheric circulation,was investigated using the hindcast data from Global Seasonal Forecast System version 5(GloSea5),with a focus on the evolution of model bias among different forecast lead times.While GloSea5 reproduces the climatological means of large-scale circulation systems related to the EAWM well,systematic biases exist,including a cold bias for most of China’s mainland,especially for North and Northeast China.GloSea5 shows robust skill in predicting the EAWM intensity index two months ahead,which can be attributed to the performance in representing the leading modes of surface air temperature and associated background circulation.GloSea5 realistically reproduces the synergistic effect of El Niño–Southern Oscillation(ENSO)and the Arctic Oscillation(AO)on the EAWM,especially for the western North Pacific anticyclone(WNPAC).Compared with the North Pacific and North America,the representation of circulation anomalies over Eurasia is poor,especially for sea level pressure(SLP),which limits the prediction skill for surface air temperature over East Asia.The representation of SLP anomalies might be associated with the model performance in simulating the interaction between atmospheric circulations and underlying surface conditions.展开更多
Recent work has shown the dominance of the Himalaya in supporting the Indian summer monsoon （ISM）, perhaps by surface sensible heating along its southern slope and by mechanical blocking acting to separate moist tro...Recent work has shown the dominance of the Himalaya in supporting the Indian summer monsoon （ISM）, perhaps by surface sensible heating along its southern slope and by mechanical blocking acting to separate moist tropical flow from drier midlatitnde air. Previous studies have also shown that Indian snmmer rainfall is largely unaffected in sensitivity experiments that remove only the Tibetan Plateau. However, given the large biases in simulating the monsoon in CMIP5 models, such results may be model dependent. This study investigates the impact of orographic forcing from the Tibetan Plateau, Himalaya and Iranian Plateau on the ISM and East Asian snmmer monsoon （EASM） in the UK Met Office＇s HadGEM3-GA6 and China＇s Institute of Atmospheric Physics FGOALS-FAMIL global climate models. The models chosen featnre oppositesigned biases in their simulation of the ISM rainfall and circulation climatology. The changes to ISM and EASM circulation across the sensitivity experiments are similar in both models and consistent with previous studies. However, considerable differences exist in the rainfall responses over India and China, and in the detailed aspects such as onset and retreat dates. In particular, the models show opposing changes in Indian monsoon rainfall when the Himalaya and Tibetan Plateau orography are removed. Our results show that a multi-model approach, as suggested in the forthcoming Global Monsoon Model Intercomparison Project （GMMIP） associated with CMIP6, is needed to clarify the impact of orographic forcing on the Asian monsoon and to fully understand the implications of model systematic error.展开更多
Extreme precipitation is one of the most severe weather of high socio-economic impact.In the warming climate,extreme precipitation has been shown to increase in both intensity and frequency over China and over the wor...Extreme precipitation is one of the most severe weather of high socio-economic impact.In the warming climate,extreme precipitation has been shown to increase in both intensity and frequency over China and over the world[[1],[2],[3]].On 20 July 2021,an extreme precipitation event hit Zhengzhou,a highly urbanized megacity with a dense population of 12.8 million in North China.The hourly rainfall rate was up to 201.9 mm.展开更多
The spread of an advantageous mutation through a population is of fundamental interest in population genetics. While the classical Moran model is formulated for a well-mixed population, it has long been recognized tha...The spread of an advantageous mutation through a population is of fundamental interest in population genetics. While the classical Moran model is formulated for a well-mixed population, it has long been recognized that in real-world applications, the population usually has an explicit spatial structure which can significantly influence the dynamics. In the context of cancer initiation in epithelial tissue, several recent works have analyzed the dynamics of advantageous mutant spread on integer lattices, using the biased voter model from particle systems theory. In this spatial version of the Moran model, individuals first reproduce according to their fitness and then replace a neighboring individual. From a biological standpoint, the opposite dynamics, where individuals first die and are then replaced by a neighboring individual according to its fitness, are equally relevant. Here, we investigate this death-birth analogue of the biased voter model. We construct the process mathematically, derive the associated dual process, establish bounds on the survival probability of a single mutant, and prove that the process has an asymptotic shape. We also briefly discuss alternative birth-death and death-birth dynamics, depending on how the mutant fitness advantage affects the dynamics. We show that birth-death and death-birth formulations of the biased voter model are equivalent when fitness affects the former event of each update of the model, whereas the birth-death model is fundamentally different from the death-birth model when fitness affects the latter event.展开更多
基金jointly supported by projects XDA11010402 GYHY201406001the National Basic Key Project (973) 2010CB428603 and 2010CB950400
文摘This study uses the coupled atmosphere–surface climate feedback–response analysis method（CFRAM） to analyze the surface temperature biases in the Flexible Global Ocean–Atmosphere–Land System model, spectral version 2（FGOALS-s2）in January and July. The process-based decomposition of the surface temperature biases, defined as the difference between the model and ERA-Interim during 1979–2005, enables us to attribute the model surface temperature biases to individual radiative processes including ozone, water vapor, cloud, and surface albedo; and non-radiative processes including surface sensible and latent heat fluxes, and dynamic processes at the surface and in the atmosphere. The results show that significant model surface temperature biases are almost globally present, are generally larger over land than over oceans, and are relatively larger in summer than in winter. Relative to the model biases in non-radiative processes, which tend to dominate the surface temperature biases in most parts of the world, biases in radiative processes are much smaller, except in the sub-polar Antarctic region where the cold biases from the much overestimated surface albedo are compensated for by the warm biases from nonradiative processes. The larger biases in non-radiative processes mainly lie in surface heat fluxes and in surface dynamics,which are twice as large in the Southern Hemisphere as in the Northern Hemisphere and always tend to compensate for each other. In particular, the upward/downward heat fluxes are systematically underestimated/overestimated in most parts of the world, and are mainly compensated for by surface dynamic processes including the increased heat storage in deep oceans across the globe.
基金supported by the UK–China Research and Innovation Partnership Fund, through the Met Office Climate Science for Service Partnership (CSSP) China, as part of the Newton Fund
文摘The East Asian Summer Monsoon(EASM)provides the majority of annual rainfall to countries in East Asia.Although state-of-the-art models broadly project increased EASM rainfall,the spread of projections is large and simulations of present-day rainfall show significant climatological biases.Systematic evapotranspiration biases occur locally over East Asia,and globally over land,in simulations both with and without a coupled ocean.This study explores the relationship between evapotranspiration and EASM precipitation biases.First,idealized model simulations are presented in which the parameterization of land evaporation is modified,while sea surface temperature is fixed.The results suggest a feedback whereby excessive evapotranspiration over East Asia results in cooling of land,a weakened monsoon low,and a shift of rainfall from the Philippine Sea to China,further fueling evapotranspiration.Cross-model regressions against evapotranspiration over China indicate a similar pattern of behavior in Atmospheric Model Intercomparison Project(AMIP)simulations.Possible causes of this pattern are investigated.The feedback is not explained by an overly intense global hydrological cycle or by differences in radiative processes.Analysis of land-only simulations indicates that evapotranspiration biases are present even when models are forced with prescribed rainfall.These are strengthened when coupled to the atmosphere,suggesting a role for land-model errors in driving atmospheric biases.Coupled atmosphere-ocean models are shown to have similar evapotranspiration biases to those in AMIP over China,but different precipitation biases,including a northward shift in the ITCZ over the Pacific and Atlantic Oceans.
文摘This study presents a simplified multivariate bias correction scheme that is sequentially implemented in the GEOS5 data assimilation system and compared against a control experiment without model bias correction. The results show considerable improvement in terms of the mean biases of rawinsonde observation-minus-background （OmB） residuals for observed water vapor, wind and temperature variables. The time series spectral analysis shows whitening of bias-corrected OmB residuals, and mean biases for rawinsonde observation-minus-analysis （OmA） are also improved. Some wind and temperature biases in the control experiment near the equatorial tropopause nearly vanish from the bias-corrected experiment. Despite the analysis improvement, the bias correction scheme has only a moderate impact on forecast skill. Significant interaction is also found among quality-control, satellite observation bias correction, and background bias correction, and the latter positively impacts satellite bias correction.
基金supported by the National Natural Science Foundation of China[grant number 42275074].
文摘Cloud diurnal variation is crucial for regulating cloud radiative effects and atmospheric dynamics.However,it is often overlooked in the evaluation and development of climate models.Thus,this study aims to investigate the daily mean(CFR)and diurnal variation(CDV)of cloud fraction across high-,middle-,low-level,and total clouds in the FGOALS-f3-L general circulation model.The bias of total CDV is decomposed into the model biases in CFRs and CDVs of clouds at all three levels.Results indicate that the model generally underestimates low-level cloud fraction during the daytime and high-/middle-level cloud fraction at nighttime.The simulation biases of low clouds,especially their CDV biases,dominate the bias of total CDV.Compensation effects exist among the bias decompositions,where the negative contributions of underestimated daytime low-level cloud fraction are partially offset by the opposing contributions from biases in high-/middle-level clouds.Meanwhile,the bias contributions have notable land–ocean differences and region-dependent characteristics,consistent with the model biases in these variables.Additionally,the study estimates the influences of CFR and CDV biases on the bias of shortwave cloud radiative effects.It reveals that the impacts of CDV biases can reach half of those from CFR biases,highlighting the importance of accurate CDV representation in climate models.
文摘In this paper, the performance of existing biased estimators (Ridge Estimator (RE), Almost Unbiased Ridge Estimator (AURE), Liu Estimator (LE), Almost Unbiased Liu Estimator (AULE), Principal Component Regression Estimator (PCRE), r-k class estimator and r-d class estimator) and the respective predictors were considered in a misspecified linear regression model when there exists multicollinearity among explanatory variables. A generalized form was used to compare these estimators and predictors in the mean square error sense. Further, theoretical findings were established using mean square error matrix and scalar mean square error. Finally, a numerical example and a Monte Carlo simulation study were done to illustrate the theoretical findings. The simulation study revealed that LE and RE outperform the other estimators when weak multicollinearity exists, and RE, r-k class and r-d class estimators outperform the other estimators when moderated and high multicollinearity exist for certain values of shrinkage parameters, respectively. The predictors based on the LE and RE are always superior to the other predictors for certain values of shrinkage parameters.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11674139,11604009,and 11704025)the Program for Changjiang Scholars and Innovative Research Team in University,China(Grant No.IRT-16R35)+1 种基金the Fundamental Research Funds for the Central Universities,Chinathe financial support of the Future and Emerging Technologies(FET)programme within the Seventh Framework Programme for Research of the European Commission,under FET-Open Grant No.618083(CNTQC)
文摘An analytical variational method for the ground state of the biased quantum Rabi model in the ultra-strong coupling regime is presented. This analytical variational method can be obtained by a unitary transformation or alternatively by assuming the form of the ground state wave function. The key of the method is to introduce a variational parameter λ,which can be determined by minimizing the energy functional. Using this method, we calculate the physical observables with high accuracy in comparison with the numerical exact ones. Our method evidently improves over the widely used general rotating-wave approximation（GRWA） in both qualitative and quantitative aspects.
基金supported by the National Key R&D Program for Developing Basic Sciences (Grant Nos. 2016YFC1401401 and 2016YFC1401601)the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDC01000000)the National Natural Science Foundation of China (Grants Nos. 41576026, 41576025, 41776030, 41931183 and 41976026)
文摘The datasets of two Ocean Model Intercomparison Project(OMIP)simulation experiments from the LASG/IAP Climate Ocean Model,version 3(LICOM3),forced by two different sets of atmospheric surface data,are described in this paper.The experiment forced by CORE-II(Co-ordinated Ocean–Ice Reference Experiments,Phase II)data(1948–2009)is called OMIP1,and that forced by JRA55-do(surface dataset for driving ocean–sea-ice models based on Japanese 55-year atmospheric reanalysis)data(1958–2018)is called OMIP2.First,the improvement of LICOM from CMIP5 to CMIP6 and the configurations of the two experiments are described.Second,the basic performances of the two experiments are validated using the climatological-mean and interannual time scales from observation.We find that the mean states,interannual variabilities,and long-term linear trends can be reproduced well by the two experiments.The differences between the two datasets are also discussed.Finally,the usage of these data is described.These datasets are helpful toward understanding the origin system bias of the fully coupled model.
基金the National Key Research and Development Program of China (Nos.2017YFC1404102,2017YFC1404100)the Strategic Priority Research Program of Chinese Academy of Sciences (Nos.XDB 40000000,XDB 42000000)+4 种基金the National Natural Science Foundation of China (Nos.41690122(41690120),41705082,41421005)the Shandong Taishan Scholarship,the China Postdoctoral Science Foundation (Nos.2018M640659,2019M662453)YU Yongqiang is jointly supported by the Strategic Priority Research Program of Chinese Academy of Sciences (Nos.XDA 19060102.XDB 42000000)REN Hong-Li is jointly supported by the China National Science Foundation (No.41975094)the China National Key Research and Development Program on Monitoring,Early Warning and Prevention of Major Natural Disaster (No.2018YFC1506004)
文摘El Nino-Southern Oscillation(ENSO) is the strongest interannual signal that is producedby basinscale processes in the tropical Pacific,with significant effects on weather and climate worldwide.In the past,extensive and intensive international efforts have been devoted to coupled model developments for ENSO studies.A hierarchy of coupled ocean-atmo sphere models has been formulated;in terms of their complexity,they can be categorized into intermediate coupled models(ICMs),hybrid coupled models(HCMs),and fully coupled general circulation models(CGCMs).ENSO modeling has made significant progress over the past decades,reaching a stage where coupled models can now be used to successfully predict ENSO events 6 months to one year in advance.Meanwhile,ENSO exhibits great diversity and complexity as observed in nature,which still cannot be adequately captured by current state-of-the-art coupled models,presenting a challenge to ENSO modeling.We primarily reviewed the long-term efforts in ENSO modeling continually and steadily made at different institutions in China;some selected representative examples are presented here to review the current status of ENSO model developments and applications,which have been actively pursued with noticeable progress being made recently.As ENSO simulations are very sensitive to model formulations and process representations etc.,dedicated efforts have been devoted to ENSO model developments and improvements.Now,different ocean-atmosphere coupled models have been available in China,which exhibit good model performances and have already had a variety of applications to climate modeling,including the Coupled Model Intercomparison Project Phase 6(CMIP6).Nevertheless,large biases and uncertainties still exist in ENSO simulations and predictions,and there are clear rooms for their improvements,which are still an active area of researches and applications.Here,model performances of ENSO simulations are assessed in terms of advantages and disadvantages with these differently formulated coupled models,pinpointing to the areas where they need to be further improved for ENSO studies.These analyses provide valuable guidance for future improvements in ENSO simulations and predictions.
基金supported by the National Natural Science Foundation of China(Grant Nos.42141019 and 42261144687)the Second Tibetan Plateau Scientific Expedition and Research(STEP)program(Grant No.2019QZKK0102)+4 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB42010404)the National Natural Science Foundation of China(Grant No.42175049)the Guangdong Meteorological Service Science and Technology Research Project(Grant No.GRMC2021M01)the National Key Scientific and Technological Infrastructure project“Earth System Science Numerical Simulator Facility”(EarthLab)for computational support and Prof.Shiming XIANG for many useful discussionsNiklas BOERS acknowledges funding from the Volkswagen foundation.
文摘Climate models are vital for understanding and projecting global climate change and its associated impacts.However,these models suffer from biases that limit their accuracy in historical simulations and the trustworthiness of future projections.Addressing these challenges requires addressing internal variability,hindering the direct alignment between model simulations and observations,and thwarting conventional supervised learning methods.Here,we employ an unsupervised Cycle-consistent Generative Adversarial Network(CycleGAN),to correct daily Sea Surface Temperature(SST)simulations from the Community Earth System Model 2(CESM2).Our results reveal that the CycleGAN not only corrects climatological biases but also improves the simulation of major dynamic modes including the El Niño-Southern Oscillation(ENSO)and the Indian Ocean Dipole mode,as well as SST extremes.Notably,it substantially corrects climatological SST biases,decreasing the globally averaged Root-Mean-Square Error(RMSE)by 58%.Intriguingly,the CycleGAN effectively addresses the well-known excessive westward bias in ENSO SST anomalies,a common issue in climate models that traditional methods,like quantile mapping,struggle to rectify.Additionally,it substantially improves the simulation of SST extremes,raising the pattern correlation coefficient(PCC)from 0.56 to 0.88 and lowering the RMSE from 0.5 to 0.32.This enhancement is attributed to better representations of interannual,intraseasonal,and synoptic scales variabilities.Our study offers a novel approach to correct global SST simulations and underscores its effectiveness across different time scales and primary dynamical modes.
基金supported by the State Key Program of the National Natural Science of China(Grant No.41730964)the National Key Research and Development Program on Monitoring,Early Warning and Prevention of Major Natural Disaster(2018YFC1506000)+2 种基金the National Natural Science Foundation of China(Grant Nos.41975091 and 42175047)National Basic Research Program of China(2015CB453203)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The simulation and prediction of the climatology and interannual variability of the East Asia winter monsoon(EAWM),as well as the associated atmospheric circulation,was investigated using the hindcast data from Global Seasonal Forecast System version 5(GloSea5),with a focus on the evolution of model bias among different forecast lead times.While GloSea5 reproduces the climatological means of large-scale circulation systems related to the EAWM well,systematic biases exist,including a cold bias for most of China’s mainland,especially for North and Northeast China.GloSea5 shows robust skill in predicting the EAWM intensity index two months ahead,which can be attributed to the performance in representing the leading modes of surface air temperature and associated background circulation.GloSea5 realistically reproduces the synergistic effect of El Niño–Southern Oscillation(ENSO)and the Arctic Oscillation(AO)on the EAWM,especially for the western North Pacific anticyclone(WNPAC).Compared with the North Pacific and North America,the representation of circulation anomalies over Eurasia is poor,especially for sea level pressure(SLP),which limits the prediction skill for surface air temperature over East Asia.The representation of SLP anomalies might be associated with the model performance in simulating the interaction between atmospheric circulations and underlying surface conditions.
基金supported jointly by the UK-China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership(CSSP) Chinathe Major Research Plan of the National Natural Science Foundation of China(Grant Nos.91637312 and 91437219)
文摘Recent work has shown the dominance of the Himalaya in supporting the Indian summer monsoon （ISM）, perhaps by surface sensible heating along its southern slope and by mechanical blocking acting to separate moist tropical flow from drier midlatitnde air. Previous studies have also shown that Indian snmmer rainfall is largely unaffected in sensitivity experiments that remove only the Tibetan Plateau. However, given the large biases in simulating the monsoon in CMIP5 models, such results may be model dependent. This study investigates the impact of orographic forcing from the Tibetan Plateau, Himalaya and Iranian Plateau on the ISM and East Asian snmmer monsoon （EASM） in the UK Met Office＇s HadGEM3-GA6 and China＇s Institute of Atmospheric Physics FGOALS-FAMIL global climate models. The models chosen featnre oppositesigned biases in their simulation of the ISM rainfall and circulation climatology. The changes to ISM and EASM circulation across the sensitivity experiments are similar in both models and consistent with previous studies. However, considerable differences exist in the rainfall responses over India and China, and in the detailed aspects such as onset and retreat dates. In particular, the models show opposing changes in Indian monsoon rainfall when the Himalaya and Tibetan Plateau orography are removed. Our results show that a multi-model approach, as suggested in the forthcoming Global Monsoon Model Intercomparison Project （GMMIP） associated with CMIP6, is needed to clarify the impact of orographic forcing on the Asian monsoon and to fully understand the implications of model systematic error.
基金supported by the National Key Research and Development Program of China(2023YFC3007502)the National Natural Science Foundation of China(42122036)the Fundamental Research Funds for the Central Universities(14380222).
文摘Extreme precipitation is one of the most severe weather of high socio-economic impact.In the warming climate,extreme precipitation has been shown to increase in both intensity and frequency over China and over the world[[1],[2],[3]].On 20 July 2021,an extreme precipitation event hit Zhengzhou,a highly urbanized megacity with a dense population of 12.8 million in North China.The hourly rainfall rate was up to 201.9 mm.
基金supported in part by the NIH grant R01CA241134supported in part by the NSF grant CMMI-1552764+3 种基金supported in part by the NSF grants DMS-1349724 and DMS-2052465supported in part by the NSF grant CCF-1740761supported in part by the U.S.-Norway Fulbright Foundation and the Research Council of Norway R&D Grant 309273supported in part by the Norwegian Centennial Chair grant and the Doctoral Dissertation Fellowship from the University of Minnesota.
文摘The spread of an advantageous mutation through a population is of fundamental interest in population genetics. While the classical Moran model is formulated for a well-mixed population, it has long been recognized that in real-world applications, the population usually has an explicit spatial structure which can significantly influence the dynamics. In the context of cancer initiation in epithelial tissue, several recent works have analyzed the dynamics of advantageous mutant spread on integer lattices, using the biased voter model from particle systems theory. In this spatial version of the Moran model, individuals first reproduce according to their fitness and then replace a neighboring individual. From a biological standpoint, the opposite dynamics, where individuals first die and are then replaced by a neighboring individual according to its fitness, are equally relevant. Here, we investigate this death-birth analogue of the biased voter model. We construct the process mathematically, derive the associated dual process, establish bounds on the survival probability of a single mutant, and prove that the process has an asymptotic shape. We also briefly discuss alternative birth-death and death-birth dynamics, depending on how the mutant fitness advantage affects the dynamics. We show that birth-death and death-birth formulations of the biased voter model are equivalent when fitness affects the former event of each update of the model, whereas the birth-death model is fundamentally different from the death-birth model when fitness affects the latter event.
