Using the historical simulations from 27 models in phase 5 of the Coupled Model Intercomparison Project(CMIP5)and 27 models in phase 6(CMIP6),the authors evaluated the differences between CMIP5 and CMIP6 models in sim...Using the historical simulations from 27 models in phase 5 of the Coupled Model Intercomparison Project(CMIP5)and 27 models in phase 6(CMIP6),the authors evaluated the differences between CMIP5 and CMIP6 models in simulating the climate mean of extreme temperature over China through comparison with observations during 1979–2005.The CMIP6 models reproduce well the spatial distribution of annual maxima of daily maximum temperature(TXx),annual minima of daily minimum temperature(TNn),and frost days(FD).The model spread in CMIP6 is reduced relative to CMIP5 for some temperature indices,such as TXx,warm spell duration index(WSDI),and warm days(TX90 p).The multimodel median ensembles also capture the observed trend of extreme temperature.However,the CMIP6 models still have low skill in capturing TX90 p and cold nights(TN10 p)and have obvious cold biases or warm biases over the Tibetan Plateau.The ability of individual models varies for different indices,although some models outperform the others in terms of the average of all indices considered for different models.By comparing different version models from the same organization,the updated CMIP6 models show no significant difference from their counterparts from CMIP5 for some models.Compared with individual models,the median ensembles show better agreement with the observations for temperature indices and their means.展开更多
This study explores the model performance of the Coupled Model Intercomparison Project Phase 6(CMIP6)in simulating precipitation extremes over the mid–high latitudes of Asia,as compared with predecessor models in the...This study explores the model performance of the Coupled Model Intercomparison Project Phase 6(CMIP6)in simulating precipitation extremes over the mid–high latitudes of Asia,as compared with predecessor models in the previous phase,CMIP5.Results show that the multimodel ensemble median generally outperforms the individual models in simulating the climate means of precipitation extremes.The CMIP6 models possess a relatively higher capability in this respect than the CMIP5 models.However,discrepancies also exist between models and observation,insofar as most of the simulated indices are positively biased to varying degrees.With respect to the temporal performance of indices,the majority are overestimated at most time points,along with large uncertainty.Therefore,the capacity to simulate the interannual variability needs to be further improved.Furthermore,pairwise and multimodel ensemble comparisons were performed for 12 models to evaluate the performance of individual models,revealing that most of the new-version models are better than their predecessors,albeit with some variance in the metrics amongst models and indices.展开更多
This research evaluated the ability of different coupled climate models to simulate the historical variability of potential evapotranspiration(PET)for the time period 1979–2017 in phases 5 and 6 of the Coupled Model ...This research evaluated the ability of different coupled climate models to simulate the historical variability of potential evapotranspiration(PET)for the time period 1979–2017 in phases 5 and 6 of the Coupled Model Intercomparison Project(CMIP5 and CMIP6,respectively).Their projected future changes of PET under two emission scenarios for the 21st century were also compared.Results show that PET has an increasing trend of 0.2–0.6 mm d-1/50 yr over most land surfaces and that there are clear regional differences.The future value of PET is higher in the CMIP6 multi-model simulations than in the CMIP5 ones under the same emissions scenario,possibly because CMIP6 models simulate stronger warming for a given forcing or scenario.The contributions of each individual climate driver to future changes in PET were examined and revealed that the surface vapor pressure deficit makes a major contribution to changes in PET.Shortwave radiation increases PET in most terrestrial regions,except for northern Africa,East Asia,South Asia,and Australia;the effect of longwave radiation is the opposite to that of shortwave radiation.The contribution of surface wind speed to PET is small,but results in a slight reduction.展开更多
Extreme high temperature(EHT)events are among the most impact-related consequences related to climate change,especially for China,a nation with a large population that is vulnerable to the climate warming.Based on the...Extreme high temperature(EHT)events are among the most impact-related consequences related to climate change,especially for China,a nation with a large population that is vulnerable to the climate warming.Based on the latest Coupled Model Intercomparison Project Phase 6(CMIP6),this study assesses future EHT changes across China at five specific global warming thresholds(1.5℃-5℃).The results indicate that global mean temperature will increase by 1.5℃/2℃ before 2030/2050 relative to pre-industrial levels(1861-1900)under three future scenarios(SSP1-2.6,SSP2-4.5,and SSP5-8.5),and warming will occur faster under SSP5-8.5 compared to SSP1-2.6 and SSP2-4.5.Under SSP5-8.5,global warming will eventually exceed 5℃ by 2100,while under SSP1-2.6,it will stabilize around 2℃ after 2050.In China,most of the areas where warming exceeds global average levels will be located in Tibet and northern China(Northwest China,North China and Northeast China),covering 50%-70%of the country.Furthermore,about 0.19-0.44 billion people(accounting for 16%-41%of the national population)will experience warming above the global average.Compared to present-day(1995-2014),the warmest day(TXx)will increase most notably in northern China,while the number of warm days(TX90p)and warm spell duration indicator(WSDI)will increase most profoundly in southern China.For example,relative to the present-day,TXx will increase by 1℃-5℃ in northern China,and TX90p(WSDI)will increase by 25-150(10-80)days in southern China at 1.5℃-5℃ global warming.Compared to 2℃-5℃,limiting global warming to 1.5℃ will help avoid about 36%-87%of the EHT increases in China.展开更多
Under the ongoing global warming,the sea surface temperature(SST)over the entire Indian Ocean(IO)has been warming saliently at a rate of 0.014°C yr-1since the 1950s,which is larger than that in other regions of t...Under the ongoing global warming,the sea surface temperature(SST)over the entire Indian Ocean(IO)has been warming saliently at a rate of 0.014°C yr-1since the 1950s,which is larger than that in other regions of the globe.The salient IO warming reflects the synergistic effect of global warming and the internal variability of the climate system,and the warming could lead to climate anomalies in peripheral regions.The simulation performance of the sustained IO warming was evaluated by comparing 37 CMIP5 and 37 CMIP6 models with observed data.The results show that the warming in the IO can be captured by nearly all the CMIP models,but most tend to underestimate the magnitude of IO warming trends.There is no qualitative improvement in the simulation of the salient IO warming from CMIP5 to CMIP6.In addition,six metrics were used to investigate the performance of all models.Concerning the spatial pattern of warming trends,the CMIP5 models reveal a better simulation performance than those in CMIP6 models.Only nine best models(seven CMIP5 models and two CMIP6 models)can simulate a high warming trend in the IO region of0.014±0.001°C yr-1during 1950–2005,but these nine models still have some disadvantages among other metrics.The overall evaluation here provides necessary information for future investigation about the mechanism of the sustained IO warming based on the climate models with better performances.展开更多
The sea surface temperature(SST)seasonal cycle in the eastern equatorial Pacific(EEP)plays an important role in the El Nino–Southern Oscillation(ENSO)phenomenon.However,the reasonable simulation of SST seasonal cycle...The sea surface temperature(SST)seasonal cycle in the eastern equatorial Pacific(EEP)plays an important role in the El Nino–Southern Oscillation(ENSO)phenomenon.However,the reasonable simulation of SST seasonal cycle in the EEP is still a challenge for climate models.In this paper,we evaluated the performance of 17 CMIP6 climate models in simulating the seasonal cycle in the EEP and compared them with 43 CMIP5 climate models.In general,only CESM2 and SAM0-UNICON are able to successfully capture the annual mean SST characteristics,and the results showed that CMIP6 models have no fundamental improvement in the model annual mean bias.For the seasonal cycle,14 out of 17 climate models are able to represent the major characteristics of the observed SST annual evolution.In spring,12 models capture the 1–2 months leading the eastern equatorial Pacific region 1(EP1;5°S–5°N,110°–85°W)against the eastern equatorial Pacific region 2(EP2;5°S–5°N,140°–110°W).In autumn,only two models,GISS-E2-G and SAM0-UNICON,correctly show that the EP1 and EP2 SSTs vary in phase.For the CMIP6 MME SST simulation in EP1,both the cold bias along the equator in the warm phase and the warm bias in the cold phase lead to a weaker annual SST cycle in the CGCMs,which is similar to the CMIP5 results.However,both the seasonal cold bias and warm bias are considerably decreased for CMIP6,which leads the annual SST cycle to more closely reflect the observation.For the CMIP6 MME SST simulation in EP2,the amplitude is similar to the observed value due to the quasi-constant cold bias throughout the year,although the cold bias is clearly improved after August compared with CMIP5 models.Overall,although SAM0-UNICON successfully captured the seasonal cycle characteristics in the EEP and the improvement from CMIP5 to CMIP6 in simulating EEP SST is clear,the fundamental climate models simulated biases still exist.展开更多
This paper includes a comprehensive assessment of 40 models from the Coupled Model Intercomparison Project phase 5(CMIP5)and 33 models from the CMIP phase 6(CMIP6)to determine the climatological and seasonal variation...This paper includes a comprehensive assessment of 40 models from the Coupled Model Intercomparison Project phase 5(CMIP5)and 33 models from the CMIP phase 6(CMIP6)to determine the climatological and seasonal variation of ocean salinity from the surface to 2000 m.The general pattern of the ocean salinity climatology can be simulated by both the CMIP5 and CMIP6 models from the surface to 2000-m depth.However,this study shows an increased fresh bias in the surface and subsurface salinity in the CMIP6 multimodel mean,with a global average of−0.44 g kg^(−1) for the sea surface salinity(SSS)and−0.26 g kg^(−1) for the 0-1000-m averaged salinity(S1000)compared with the CMIP5 multimodel mean(−0.25 g kg^(−1) for the SSS and−0.07 g kg^(−1) for the S1000).In terms of the seasonal variation,both CMIP6 and CMIP5 models show positive(negative)anomalies in the first(second)half of the year in the global average SSS and S1000.The model-simulated variation in SSS is consistent with the observations,but not for S1000,suggesting a substantial uncertainty in simulating and understanding the seasonal variation in subsurface salinity.The CMIP5 and CMIP6 models overestimate the magnitude of the seasonal variation of the SSS in the tropics in the region 20°S-20°N but underestimate the magnitude of the seasonal change in S1000 in the Atlantic and Indian oceans.These assessments show new features of the model errors in simulating ocean salinity and support further studies of the global hydrological cycle.展开更多
基于43套CMIP6模式资料和Ocean Reanalysis System 5(ORAS5)同化数据,本文探讨了北冰洋加拿大海盆波弗特流涡在历史时期(1979—2014年)和未来SSP1-2.6和SSP5-8.5两种温室气体排放情景下的变化特征。研究结果表明,历史时期CMIP6的多个模...基于43套CMIP6模式资料和Ocean Reanalysis System 5(ORAS5)同化数据,本文探讨了北冰洋加拿大海盆波弗特流涡在历史时期(1979—2014年)和未来SSP1-2.6和SSP5-8.5两种温室气体排放情景下的变化特征。研究结果表明,历史时期CMIP6的多个模式及多模式平均都低估了流涡的强度,且对流涡强度变化趋势的模拟存在较大差异;其中35套CMIP6模式资料中有45%的模式资料能够较好地再现ORAS5同化数据所显示的上升趋势。在未来SSP1-2.6和SSP5-8.5两种排放情景下,波弗特流涡强度都将呈上升趋势,但在21世纪后期,后者的强度会低于前者且这种差异将随时间增大;海冰密集度和海平面气压呈显著正相关、均持续下降,但后者的下降趋势较前者更明显。对流涡增强的成因分析表明,SSP1-2.6排放情景下,流涡的增强主要与海冰密集度的下降有关,而SSP5-8.5排放情景下,流涡的弱增强则主要与海冰显著减少导致的波弗特高压减弱有关。展开更多
The South Pacific Quadrupole(SPQ) is the extratropical South Pacific’s second principal sea surface temperature mode.Previous observational studies have shown that the SPQ promotes the onset of the El Nino-Southern O...The South Pacific Quadrupole(SPQ) is the extratropical South Pacific’s second principal sea surface temperature mode.Previous observational studies have shown that the SPQ promotes the onset of the El Nino-Southern Oscillation(ENSO).The present study evaluates and compares simulations of the SPQ-ENSO relationship by 20 climate models from CMIP6 and their corresponding 20 previous models from CMIP5.It is found that 16 of the20 pairs of models are able to consistently reproduce the spatial pattern of the SPQ.In terms of simulating the SPQ-ENSO relationship,9 of the 16 CMIP6 models show significant improvement over their previous CMIP5 models.The multi-model ensemble(MME) of these 16 CMIP6 models simulates the SPQ-ENSO connection more realistically than the CMIP5 MME.Further analysis shows that the performance of the model simulations in reproducing the SPQ-ENSO relationship is strongly dependent on their ability to simulate the SPQ-related surface air-sea coupling processes over the southwestern and southeastern South Pacific,as well as the response of the SPQ-related equatorial subsurface ocean temperature anomalies.The improvement of the CMIP6 models in simulating these two processes is responsible for the improved performance of the CMIP6 models over their CMIP5 counterparts in simulating the SPQ-ENSO relationship.展开更多
This work evaluates the performances of climate models in simulating the Southern Ocean(SO)sea surface temperature(SST)by a large ensemble from phases 5 and 6 of the Coupled Model Intercomparison Project(CMIP5 and CMI...This work evaluates the performances of climate models in simulating the Southern Ocean(SO)sea surface temperature(SST)by a large ensemble from phases 5 and 6 of the Coupled Model Intercomparison Project(CMIP5 and CMIP6).By combining models from the same community sharing highly similar SO SST biases and eliminating the effect of global-mean biases on local SST biases,the results reveal that the ensemble-mean SO SST bias at 70°-30°S decreases from 0.38℃ in CMIP5 to 0.28℃ in CMIP6,together with increased intermodel consistency.The dominant mode of the intermodel variations in the zonal-mean SST biases is characterized as a meridional uniform warm bias pattern,explaining 79.1% of the intermodel variance and exhibiting positive principal values for most models.The ocean mixed layer heat budget further demonstrates that the SST biases at 70°-50°S primarily result from the excessive summertime heating effect from surface net heat flux.The biases in surface net heat flux south of 50°S are largely impacted by surface shortwave radiation from cloud and clear sky components at different latitudes.North of 50°S,the underestimated westerlies reduce the northward Ekman transport and hence northward cold advection in models,leading to warm SST biases year-round.In addition,the westerly biases are primarily traced back to the atmosphere-alone model simulations forced by the observed SST and sea ice.These results disclose the thermal origin at the high latitude and dynamical origin at the low latitude of the SO SST biases and underscore the significance of the deficiencies of atmospheric models in producing the SO SST biases.展开更多
Based on climate extreme indices calculated from a high-resolution daily observational dataset in China during 1961–2005,the performance of 12 climate models from phase 6 of the Coupled Model Intercomparison Project(...Based on climate extreme indices calculated from a high-resolution daily observational dataset in China during 1961–2005,the performance of 12 climate models from phase 6 of the Coupled Model Intercomparison Project(CMIP6),and 30 models from phase 5 of CMIP(CMIP5),are assessed in terms of spatial distribution and interannual variability.The CMIP6 multi-model ensemble mean(CMIP6-MME)can simulate well the spatial pattern of annual mean temperature,maximum daily maximum temperature,and minimum daily minimum temperature.However,CMIP6-MME has difficulties in reproducing cold nights and warm days,and has large cold biases over the Tibetan Plateau.Its performance in simulating extreme precipitation indices is generally lower than in simulating temperature indices.Compared to CMIP5,CMIP6 models show improvements in the simulation of climate indices over China.This is particularly true for precipitation indices for both the climatological pattern and the interannual variation,except for the consecutive dry days.The arealmean bias for total precipitation has been reduced from 127%(CMIP5-MME)to 79%(CMIP6-MME).The most striking feature is that the dry biases in southern China,very persistent and general in CMIP5-MME,are largely reduced in CMIP6-MME.Stronger ascent together with more abundant moisture can explain this reduction in dry biases.Wet biases for total precipitation,heavy precipitation,and precipitation intensity in the eastern Tibetan Plateau are still present in CMIP6-MME,but smaller,compared to CMIP5-MME.展开更多
The tropical Indian Ocean is an important region that affects local and remote climate systems,and the simulation of longterm trends in sea surface temperature(SST)is a major focus of climate research.This study prese...The tropical Indian Ocean is an important region that affects local and remote climate systems,and the simulation of longterm trends in sea surface temperature(SST)is a major focus of climate research.This study presents a preliminary assessment of multiple model simulations of tropical Indian Ocean SST warming from 1950 to 1999 based on outputs from the 20 Coupled Model Intercomparison Project(CMIP)Phase 5(CMIP5)models and the 36 CMIP 6(CMIP6)models to analyze and compare the warming patterns in historical simulations.Results indicate large discrepancies in the simulations of tropical Indian Ocean SST warming,especially for the eastern equatorial Indian Ocean.The multimodel ensemble mean and most of the individual models generally perform well in reproducing basin-wide SST warming.However,the strength of the SST warming trends simulated by the CMIP5 and CMIP6 models are weaker than those observed,especially for the CMIP6 models.In addition to the general warming trend analysis,decadal trends are also assessed,and a statistical method is introduced to measure the near-term variability in an SST time series.The simulations indicate large decadal variability over the entire tropical Indian Ocean,differing from observations in which significant decadal trend variability is observed only in the southeastern Indian Ocean.In the CMIP model simulations,maximum decadal variability occurs in boreal autumn,but the observations display the minimum and maximum variability in boreal autumn and spring,respectively.展开更多
This paper presents projections of climate extremes over China under global warming of 1.5,2,and 3℃ above pre-industrial(1861–1900),based on the latest Coupled Model Intercomparison Project phase 6(CMIP6)simulations...This paper presents projections of climate extremes over China under global warming of 1.5,2,and 3℃ above pre-industrial(1861–1900),based on the latest Coupled Model Intercomparison Project phase 6(CMIP6)simulations.Results are compared with what produced by the precedent phase of the project,CMIP5.Model evaluation for the reference period(1985–2005)indicates that CMIP6 models outperform their predecessors in CMIP5,especially in simulating precipitation extremes.Areal averages for changes of most indices are found larger in CMIP6 than in CMIP5.The emblematic annual mean temperature,when averaged over the whole of China in CMIP6,increases by 1.49,2.21,and 3.53℃(relative to1985–2005)for 1.5,2,and 3℃ above-preindustrial global warming levels,while the counterpart in CMIP5 is 1.20,1.93 and 3.39℃ respectively.Similarly,total precipitation increases by 5.3%,8.6%,and16.3%in CMIP6 and by 4.4%,7.0%and 12.8%in CMIP5,respectively.The spatial distribution of changes for extreme indices is generally consistent in both CMIP5 and CMIP6,but with significantly higher increases in CMIP6 over Northeast and Northwest China for the hottest day temperature,and South China for the coldest night temperature.In the south bank of the Yangtze River,and most regions around40°N,CMIP6 shows higher increases for both total precipitation and heavy precipitation.The projected difference between CMIP6 and CMIP5 is mainly attributable to the physical upgrading of climate models and largely independent from their emission scenarios.展开更多
基金supported by the National Key Research and Development Program of China grant number 2018YFC1509002the Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) grant number GML2019ZD0601。
文摘Using the historical simulations from 27 models in phase 5 of the Coupled Model Intercomparison Project(CMIP5)and 27 models in phase 6(CMIP6),the authors evaluated the differences between CMIP5 and CMIP6 models in simulating the climate mean of extreme temperature over China through comparison with observations during 1979–2005.The CMIP6 models reproduce well the spatial distribution of annual maxima of daily maximum temperature(TXx),annual minima of daily minimum temperature(TNn),and frost days(FD).The model spread in CMIP6 is reduced relative to CMIP5 for some temperature indices,such as TXx,warm spell duration index(WSDI),and warm days(TX90 p).The multimodel median ensembles also capture the observed trend of extreme temperature.However,the CMIP6 models still have low skill in capturing TX90 p and cold nights(TN10 p)and have obvious cold biases or warm biases over the Tibetan Plateau.The ability of individual models varies for different indices,although some models outperform the others in terms of the average of all indices considered for different models.By comparing different version models from the same organization,the updated CMIP6 models show no significant difference from their counterparts from CMIP5 for some models.Compared with individual models,the median ensembles show better agreement with the observations for temperature indices and their means.
基金jointly supported by the National Natural Science Foundation of China grant numbers 41991284 and41922034the Strategic Priority Research Program of the Chinese Academy of Sciences grant number XDA23090102the National Key Research and Development Program of China grant number 2016YFA0602401。
文摘This study explores the model performance of the Coupled Model Intercomparison Project Phase 6(CMIP6)in simulating precipitation extremes over the mid–high latitudes of Asia,as compared with predecessor models in the previous phase,CMIP5.Results show that the multimodel ensemble median generally outperforms the individual models in simulating the climate means of precipitation extremes.The CMIP6 models possess a relatively higher capability in this respect than the CMIP5 models.However,discrepancies also exist between models and observation,insofar as most of the simulated indices are positively biased to varying degrees.With respect to the temporal performance of indices,the majority are overestimated at most time points,along with large uncertainty.Therefore,the capacity to simulate the interannual variability needs to be further improved.Furthermore,pairwise and multimodel ensemble comparisons were performed for 12 models to evaluate the performance of individual models,revealing that most of the new-version models are better than their predecessors,albeit with some variance in the metrics amongst models and indices.
基金supported by the National Key Research and Development Program of China grant number 2018YFC1507704the National Natural Science Foundation of China grant numbers 41675094 and 41975115。
文摘This research evaluated the ability of different coupled climate models to simulate the historical variability of potential evapotranspiration(PET)for the time period 1979–2017 in phases 5 and 6 of the Coupled Model Intercomparison Project(CMIP5 and CMIP6,respectively).Their projected future changes of PET under two emission scenarios for the 21st century were also compared.Results show that PET has an increasing trend of 0.2–0.6 mm d-1/50 yr over most land surfaces and that there are clear regional differences.The future value of PET is higher in the CMIP6 multi-model simulations than in the CMIP5 ones under the same emissions scenario,possibly because CMIP6 models simulate stronger warming for a given forcing or scenario.The contributions of each individual climate driver to future changes in PET were examined and revealed that the surface vapor pressure deficit makes a major contribution to changes in PET.Shortwave radiation increases PET in most terrestrial regions,except for northern Africa,East Asia,South Asia,and Australia;the effect of longwave radiation is the opposite to that of shortwave radiation.The contribution of surface wind speed to PET is small,but results in a slight reduction.
基金supported by the National Key Research and Development Program of China(2017YFA0603804)the National Natural Science Foundation of China(41831174 and 41430528)+1 种基金the Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX19_1026)Guwei ZHANG was supported by the China Scholarship Council(NO.201908320503)。
文摘Extreme high temperature(EHT)events are among the most impact-related consequences related to climate change,especially for China,a nation with a large population that is vulnerable to the climate warming.Based on the latest Coupled Model Intercomparison Project Phase 6(CMIP6),this study assesses future EHT changes across China at five specific global warming thresholds(1.5℃-5℃).The results indicate that global mean temperature will increase by 1.5℃/2℃ before 2030/2050 relative to pre-industrial levels(1861-1900)under three future scenarios(SSP1-2.6,SSP2-4.5,and SSP5-8.5),and warming will occur faster under SSP5-8.5 compared to SSP1-2.6 and SSP2-4.5.Under SSP5-8.5,global warming will eventually exceed 5℃ by 2100,while under SSP1-2.6,it will stabilize around 2℃ after 2050.In China,most of the areas where warming exceeds global average levels will be located in Tibet and northern China(Northwest China,North China and Northeast China),covering 50%-70%of the country.Furthermore,about 0.19-0.44 billion people(accounting for 16%-41%of the national population)will experience warming above the global average.Compared to present-day(1995-2014),the warmest day(TXx)will increase most notably in northern China,while the number of warm days(TX90p)and warm spell duration indicator(WSDI)will increase most profoundly in southern China.For example,relative to the present-day,TXx will increase by 1℃-5℃ in northern China,and TX90p(WSDI)will increase by 25-150(10-80)days in southern China at 1.5℃-5℃ global warming.Compared to 2℃-5℃,limiting global warming to 1.5℃ will help avoid about 36%-87%of the EHT increases in China.
基金supported by the National Key R&D Program of China grant numbers 2016YFA0600602 and 2019YFC1510002the National Natural Science Foundation of China grant number 41776039。
文摘Under the ongoing global warming,the sea surface temperature(SST)over the entire Indian Ocean(IO)has been warming saliently at a rate of 0.014°C yr-1since the 1950s,which is larger than that in other regions of the globe.The salient IO warming reflects the synergistic effect of global warming and the internal variability of the climate system,and the warming could lead to climate anomalies in peripheral regions.The simulation performance of the sustained IO warming was evaluated by comparing 37 CMIP5 and 37 CMIP6 models with observed data.The results show that the warming in the IO can be captured by nearly all the CMIP models,but most tend to underestimate the magnitude of IO warming trends.There is no qualitative improvement in the simulation of the salient IO warming from CMIP5 to CMIP6.In addition,six metrics were used to investigate the performance of all models.Concerning the spatial pattern of warming trends,the CMIP5 models reveal a better simulation performance than those in CMIP6 models.Only nine best models(seven CMIP5 models and two CMIP6 models)can simulate a high warming trend in the IO region of0.014±0.001°C yr-1during 1950–2005,but these nine models still have some disadvantages among other metrics.The overall evaluation here provides necessary information for future investigation about the mechanism of the sustained IO warming based on the climate models with better performances.
基金The National Key R&D Program of China under contract No.2016YFA0602200the Basic Scientific Fund for National Public Research Institute of China under contract No.2016S03+3 种基金the grant of Qingdao National Laboratory for Marine Science and Technology under contract Nos 2017ASTCP-ES04 and QNLM20160RP0101the National Natural Science Foundation of China under contract No.41776019the Shanghai Natural Science Foundation under contract No.16ZR1416200the China-Korea Cooperation Project on Northwestern Pacific Climate Change and its Prediction。
文摘The sea surface temperature(SST)seasonal cycle in the eastern equatorial Pacific(EEP)plays an important role in the El Nino–Southern Oscillation(ENSO)phenomenon.However,the reasonable simulation of SST seasonal cycle in the EEP is still a challenge for climate models.In this paper,we evaluated the performance of 17 CMIP6 climate models in simulating the seasonal cycle in the EEP and compared them with 43 CMIP5 climate models.In general,only CESM2 and SAM0-UNICON are able to successfully capture the annual mean SST characteristics,and the results showed that CMIP6 models have no fundamental improvement in the model annual mean bias.For the seasonal cycle,14 out of 17 climate models are able to represent the major characteristics of the observed SST annual evolution.In spring,12 models capture the 1–2 months leading the eastern equatorial Pacific region 1(EP1;5°S–5°N,110°–85°W)against the eastern equatorial Pacific region 2(EP2;5°S–5°N,140°–110°W).In autumn,only two models,GISS-E2-G and SAM0-UNICON,correctly show that the EP1 and EP2 SSTs vary in phase.For the CMIP6 MME SST simulation in EP1,both the cold bias along the equator in the warm phase and the warm bias in the cold phase lead to a weaker annual SST cycle in the CGCMs,which is similar to the CMIP5 results.However,both the seasonal cold bias and warm bias are considerably decreased for CMIP6,which leads the annual SST cycle to more closely reflect the observation.For the CMIP6 MME SST simulation in EP2,the amplitude is similar to the observed value due to the quasi-constant cold bias throughout the year,although the cold bias is clearly improved after August compared with CMIP5 models.Overall,although SAM0-UNICON successfully captured the seasonal cycle characteristics in the EEP and the improvement from CMIP5 to CMIP6 in simulating EEP SST is clear,the fundamental climate models simulated biases still exist.
基金supported by the National Natural Science Foundation of China(Grant No.42076202)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB42040402).
文摘This paper includes a comprehensive assessment of 40 models from the Coupled Model Intercomparison Project phase 5(CMIP5)and 33 models from the CMIP phase 6(CMIP6)to determine the climatological and seasonal variation of ocean salinity from the surface to 2000 m.The general pattern of the ocean salinity climatology can be simulated by both the CMIP5 and CMIP6 models from the surface to 2000-m depth.However,this study shows an increased fresh bias in the surface and subsurface salinity in the CMIP6 multimodel mean,with a global average of−0.44 g kg^(−1) for the sea surface salinity(SSS)and−0.26 g kg^(−1) for the 0-1000-m averaged salinity(S1000)compared with the CMIP5 multimodel mean(−0.25 g kg^(−1) for the SSS and−0.07 g kg^(−1) for the S1000).In terms of the seasonal variation,both CMIP6 and CMIP5 models show positive(negative)anomalies in the first(second)half of the year in the global average SSS and S1000.The model-simulated variation in SSS is consistent with the observations,but not for S1000,suggesting a substantial uncertainty in simulating and understanding the seasonal variation in subsurface salinity.The CMIP5 and CMIP6 models overestimate the magnitude of the seasonal variation of the SSS in the tropics in the region 20°S-20°N but underestimate the magnitude of the seasonal change in S1000 in the Atlantic and Indian oceans.These assessments show new features of the model errors in simulating ocean salinity and support further studies of the global hydrological cycle.
文摘基于43套CMIP6模式资料和Ocean Reanalysis System 5(ORAS5)同化数据,本文探讨了北冰洋加拿大海盆波弗特流涡在历史时期(1979—2014年)和未来SSP1-2.6和SSP5-8.5两种温室气体排放情景下的变化特征。研究结果表明,历史时期CMIP6的多个模式及多模式平均都低估了流涡的强度,且对流涡强度变化趋势的模拟存在较大差异;其中35套CMIP6模式资料中有45%的模式资料能够较好地再现ORAS5同化数据所显示的上升趋势。在未来SSP1-2.6和SSP5-8.5两种排放情景下,波弗特流涡强度都将呈上升趋势,但在21世纪后期,后者的强度会低于前者且这种差异将随时间增大;海冰密集度和海平面气压呈显著正相关、均持续下降,但后者的下降趋势较前者更明显。对流涡增强的成因分析表明,SSP1-2.6排放情景下,流涡的增强主要与海冰密集度的下降有关,而SSP5-8.5排放情景下,流涡的弱增强则主要与海冰显著减少导致的波弗特高压减弱有关。
基金This research was jointly supported by the National Natural Science Foundation of China[Grant number 41975070]the State Key Laboratory of Tropical Oceanography,South China Sea Institute of Oceanology,Chinese Academy of Sciences[Project number LTO1901].
文摘The South Pacific Quadrupole(SPQ) is the extratropical South Pacific’s second principal sea surface temperature mode.Previous observational studies have shown that the SPQ promotes the onset of the El Nino-Southern Oscillation(ENSO).The present study evaluates and compares simulations of the SPQ-ENSO relationship by 20 climate models from CMIP6 and their corresponding 20 previous models from CMIP5.It is found that 16 of the20 pairs of models are able to consistently reproduce the spatial pattern of the SPQ.In terms of simulating the SPQ-ENSO relationship,9 of the 16 CMIP6 models show significant improvement over their previous CMIP5 models.The multi-model ensemble(MME) of these 16 CMIP6 models simulates the SPQ-ENSO connection more realistically than the CMIP5 MME.Further analysis shows that the performance of the model simulations in reproducing the SPQ-ENSO relationship is strongly dependent on their ability to simulate the SPQ-related surface air-sea coupling processes over the southwestern and southeastern South Pacific,as well as the response of the SPQ-related equatorial subsurface ocean temperature anomalies.The improvement of the CMIP6 models in simulating these two processes is responsible for the improved performance of the CMIP6 models over their CMIP5 counterparts in simulating the SPQ-ENSO relationship.
基金supported by the National Natural Science Foundation of China(Nos.42076208,42141019,41831175 and 41706026)the National Key Research and Development Program of China(No.2017YFA0604600)+1 种基金the Natural Science Foundation of Jiangsu Province(No.BK20211209)the Fundamental Research Funds for the Central Universities(Nos.B210202135 and B210201015).
文摘This work evaluates the performances of climate models in simulating the Southern Ocean(SO)sea surface temperature(SST)by a large ensemble from phases 5 and 6 of the Coupled Model Intercomparison Project(CMIP5 and CMIP6).By combining models from the same community sharing highly similar SO SST biases and eliminating the effect of global-mean biases on local SST biases,the results reveal that the ensemble-mean SO SST bias at 70°-30°S decreases from 0.38℃ in CMIP5 to 0.28℃ in CMIP6,together with increased intermodel consistency.The dominant mode of the intermodel variations in the zonal-mean SST biases is characterized as a meridional uniform warm bias pattern,explaining 79.1% of the intermodel variance and exhibiting positive principal values for most models.The ocean mixed layer heat budget further demonstrates that the SST biases at 70°-50°S primarily result from the excessive summertime heating effect from surface net heat flux.The biases in surface net heat flux south of 50°S are largely impacted by surface shortwave radiation from cloud and clear sky components at different latitudes.North of 50°S,the underestimated westerlies reduce the northward Ekman transport and hence northward cold advection in models,leading to warm SST biases year-round.In addition,the westerly biases are primarily traced back to the atmosphere-alone model simulations forced by the observed SST and sea ice.These results disclose the thermal origin at the high latitude and dynamical origin at the low latitude of the SO SST biases and underscore the significance of the deficiencies of atmospheric models in producing the SO SST biases.
基金This research was supported by the National Key Research and Development Program of China(Grant Nos.2017YFA0603804 and 2018YFC1507704)the Natural Science Foundation of China(Grant No.41805048).
文摘Based on climate extreme indices calculated from a high-resolution daily observational dataset in China during 1961–2005,the performance of 12 climate models from phase 6 of the Coupled Model Intercomparison Project(CMIP6),and 30 models from phase 5 of CMIP(CMIP5),are assessed in terms of spatial distribution and interannual variability.The CMIP6 multi-model ensemble mean(CMIP6-MME)can simulate well the spatial pattern of annual mean temperature,maximum daily maximum temperature,and minimum daily minimum temperature.However,CMIP6-MME has difficulties in reproducing cold nights and warm days,and has large cold biases over the Tibetan Plateau.Its performance in simulating extreme precipitation indices is generally lower than in simulating temperature indices.Compared to CMIP5,CMIP6 models show improvements in the simulation of climate indices over China.This is particularly true for precipitation indices for both the climatological pattern and the interannual variation,except for the consecutive dry days.The arealmean bias for total precipitation has been reduced from 127%(CMIP5-MME)to 79%(CMIP6-MME).The most striking feature is that the dry biases in southern China,very persistent and general in CMIP5-MME,are largely reduced in CMIP6-MME.Stronger ascent together with more abundant moisture can explain this reduction in dry biases.Wet biases for total precipitation,heavy precipitation,and precipitation intensity in the eastern Tibetan Plateau are still present in CMIP6-MME,but smaller,compared to CMIP5-MME.
基金supported by the Taishan Scholars Programs of Shandong Province(No.tsqn201909165)the Global Change and Air-Sea Interaction Program(Nos.GASI-04-QYQH-03,GASI-01-WIND-STwin)+1 种基金the Natural Science Foundation of China Grants(No.41876028)the Taishan Scholars Programs of Shandong Province(No.20190963).
文摘The tropical Indian Ocean is an important region that affects local and remote climate systems,and the simulation of longterm trends in sea surface temperature(SST)is a major focus of climate research.This study presents a preliminary assessment of multiple model simulations of tropical Indian Ocean SST warming from 1950 to 1999 based on outputs from the 20 Coupled Model Intercomparison Project(CMIP)Phase 5(CMIP5)models and the 36 CMIP 6(CMIP6)models to analyze and compare the warming patterns in historical simulations.Results indicate large discrepancies in the simulations of tropical Indian Ocean SST warming,especially for the eastern equatorial Indian Ocean.The multimodel ensemble mean and most of the individual models generally perform well in reproducing basin-wide SST warming.However,the strength of the SST warming trends simulated by the CMIP5 and CMIP6 models are weaker than those observed,especially for the CMIP6 models.In addition to the general warming trend analysis,decadal trends are also assessed,and a statistical method is introduced to measure the near-term variability in an SST time series.The simulations indicate large decadal variability over the entire tropical Indian Ocean,differing from observations in which significant decadal trend variability is observed only in the southeastern Indian Ocean.In the CMIP model simulations,maximum decadal variability occurs in boreal autumn,but the observations display the minimum and maximum variability in boreal autumn and spring,respectively.
基金supported by the National Key Research and Development Program of China(2017YFA0603804,2016YFA0600402,and 2018YFC1507704)。
文摘This paper presents projections of climate extremes over China under global warming of 1.5,2,and 3℃ above pre-industrial(1861–1900),based on the latest Coupled Model Intercomparison Project phase 6(CMIP6)simulations.Results are compared with what produced by the precedent phase of the project,CMIP5.Model evaluation for the reference period(1985–2005)indicates that CMIP6 models outperform their predecessors in CMIP5,especially in simulating precipitation extremes.Areal averages for changes of most indices are found larger in CMIP6 than in CMIP5.The emblematic annual mean temperature,when averaged over the whole of China in CMIP6,increases by 1.49,2.21,and 3.53℃(relative to1985–2005)for 1.5,2,and 3℃ above-preindustrial global warming levels,while the counterpart in CMIP5 is 1.20,1.93 and 3.39℃ respectively.Similarly,total precipitation increases by 5.3%,8.6%,and16.3%in CMIP6 and by 4.4%,7.0%and 12.8%in CMIP5,respectively.The spatial distribution of changes for extreme indices is generally consistent in both CMIP5 and CMIP6,but with significantly higher increases in CMIP6 over Northeast and Northwest China for the hottest day temperature,and South China for the coldest night temperature.In the south bank of the Yangtze River,and most regions around40°N,CMIP6 shows higher increases for both total precipitation and heavy precipitation.The projected difference between CMIP6 and CMIP5 is mainly attributable to the physical upgrading of climate models and largely independent from their emission scenarios.
