Large-ensemble simulations of the atmosphere-only time-slice experiments for the Polar Amplification Model Intercomparison Project(PAMIP)were carried out by the model group of the Chinese Academy of Sciences(CAS)Flexi...Large-ensemble simulations of the atmosphere-only time-slice experiments for the Polar Amplification Model Intercomparison Project(PAMIP)were carried out by the model group of the Chinese Academy of Sciences(CAS)Flexible Global Ocean-Atmosphere-Land System(FGOALS-f3-L).Eight groups of experiments forced by different combinations of the sea surface temperature(SST)and sea ice concentration(SIC)for pre-industrial,present-day,and future conditions were performed and published.The time-lag method was used to generate the 100 ensemble members,with each member integrating from 1 April 2000 to 30 June 2001 and the first two months as the spin-up period.The basic model responses of the surface air temperature(SAT)and precipitation were documented.The results indicate that Arctic amplification is mainly caused by Arctic SIC forcing changes.The SAT responses to the Arctic SIC decrease alone show an obvious increase over high latitudes,which is similar to the results from the combined forcing of SST and SIC.However,the change in global precipitation is dominated by the changes in the global SST rather than SIC,partly because tropical precipitation is mainly driven by local SST changes.The uncertainty of the model responses was also investigated through the analysis of the large-ensemble members.The relative roles of SST and SIC,together with their combined influence on Arctic amplification,are also discussed.All of these model datasets will contribute to PAMIP multi-model analysis and improve the understanding of polar amplification.展开更多
The outputs of the Chinese Academy of Sciences(CAS) Flexible Global Ocean–Atmosphere–Land System(FGOALS-f3-L) model for the baseline experiment of the Atmospheric Model Intercomparison Project simulation in the Diag...The outputs of the Chinese Academy of Sciences(CAS) Flexible Global Ocean–Atmosphere–Land System(FGOALS-f3-L) model for the baseline experiment of the Atmospheric Model Intercomparison Project simulation in the Diagnostic,Evaluation and Characterization of Klima common experiments of phase 6 of the Coupled Model Intercomparison Project(CMIP6) are described in this paper. The CAS FGOALS-f3-L model, experiment settings, and outputs are all given. In total,there are three ensemble experiments over the period 1979–2014, which are performed with different initial states. The model outputs contain a total of 37 variables and include the required three-hourly mean, six-hourly transient, daily and monthly mean datasets. The baseline performances of the model are validated at different time scales. The preliminary evaluation suggests that the CAS FGOALS-f3-L model can capture the basic patterns of atmospheric circulation and precipitation well, including the propagation of the Madden–Julian Oscillation, activities of tropical cyclones, and the characterization of extreme precipitation. These datasets contribute to the benchmark of current model behaviors for the desired continuity of CMIP.展开更多
The Chinese Academy of Sciences(CAS)Flexible Global Ocean Atmosphere Land System(FGOALS-f3-L)model datasets prepared for the sixth phase of the Coupled Model Intercomparison Project(CMIP6)Global Monsoons Model Interco...The Chinese Academy of Sciences(CAS)Flexible Global Ocean Atmosphere Land System(FGOALS-f3-L)model datasets prepared for the sixth phase of the Coupled Model Intercomparison Project(CMIP6)Global Monsoons Model Intercomparison Project(GMMIP)Tier-1 and Tier-3 experiments are introduced in this paper,and the model descriptions,experimental design and model outputs are demonstrated.There are three simulations in Tier-1,with different initial states,and five simulations in Tier-3,with different topographies or surface thermal status.Specifically,Tier-3 contains four orographic perturbation experiments that remove the Tibetan Iranian Plateau,East African and Arabian Peninsula highlands,Sierra Madre,and Andes,and one thermal perturbation experiment that removes the surface sensible heating over the Tibetan Iranian Plateau and surrounding regions at altitudes above 500 m.These datasets will contribute to CMIP6’s value as a benchmark to evaluate the importance of long-term and short-term trends of the sea surface temperature in monsoon circulations and precipitation,and to a better understanding of the orographic impact on the global monsoon system over highlands.展开更多
Following the High-Resolution Model Intercomparison Project(HighResMIP)Tier 2 protocol under the Coupled Model Intercomparison Project Phase 6(CMIP6),three numerical experiments are conducted with the Chinese Academy ...Following the High-Resolution Model Intercomparison Project(HighResMIP)Tier 2 protocol under the Coupled Model Intercomparison Project Phase 6(CMIP6),three numerical experiments are conducted with the Chinese Academy of Sciences Flexible Global Ocean-Atmosphere-Land System Model,version f3-H(CAS FGOALS-f3-H),and a 101-year(1950–2050)global high-resolution simulation dataset is presented in this study.The basic configuration of the FGOALSf3-H model and numerical experiments design are briefly described,and then the historical simulation is validated.Forced by observed radiative agents from 1950 to 2014,the coupled model essentially reproduces the observed long-term trends of temperature,precipitation,and sea ice extent,as well as the large-scale pattern of temperature and precipitation.With an approximate 0.25°horizontal resolution in the atmosphere and 0.1°in the ocean,the coupled models also simulate energetic western boundary currents and the Antarctic Circulation Current(ACC),reasonable characteristics of extreme precipitation,and realistic frontal scale air-sea interaction.The dataset and supporting detailed information have been published in the Earth System Grid Federation.展开更多
Cloud dominates influence factors of atmospheric radiation, while aerosol–cloud interactions are of vital importance in its spatiotemporal distribution. In this study, a two-moment(mass and number) cloud microphysics...Cloud dominates influence factors of atmospheric radiation, while aerosol–cloud interactions are of vital importance in its spatiotemporal distribution. In this study, a two-moment(mass and number) cloud microphysics scheme, which significantly improved the treatment of the coupled processes of aerosols and clouds, was incorporated into version 1.1 of the IAP/LASG global Finite-volume Atmospheric Model(FAMIL1.1). For illustrative purposes, the characteristics of the energy balance and cloud radiative forcing(CRF) in an AMIP-type simulation with prescribed aerosols were compared with those in observational/reanalysis data. Even within the constraints of the prescribed aerosol mass, the model simulated global mean energy balance at the top of the atmosphere(TOA) and at the Earth’s surface, as well as their seasonal variation, are in good agreement with the observational data. The maximum deviation terms lie in the surface downwelling longwave radiation and surface latent heat flux, which are 3.5 W m-2(1%) and 3 W m-2(3.5%), individually. The spatial correlations of the annual TOA net radiation flux and the net CRF between simulation and observation were around 0.97 and 0.90, respectively. A major weakness is that FAMIL1.1 predicts more liquid water content and less ice water content over most oceans. Detailed comparisons are presented for a number of regions, with a focus on the Asian monsoon region(AMR). The results indicate that FAMIL1.1 well reproduces the summer–winter contrast for both the geographical distribution of the longwave CRF and shortwave CRF over the AMR. Finally, the model bias and possible solutions, as well as further works to develop FAMIL1.1 are discussed.展开更多
Seasonal prediction of summer rainfall is crucial to reduction of regional disasters,but currently it has a low prediction skill.We developed a dynamical and machine learning hybrid(MLD)seasonal prediction method for ...Seasonal prediction of summer rainfall is crucial to reduction of regional disasters,but currently it has a low prediction skill.We developed a dynamical and machine learning hybrid(MLD)seasonal prediction method for summer rainfall in China based on circulation fields from the Chinese Academy of Sciences(CAS)Flexible Global Ocean-Atmosphere-Land System Model finite volume version 2(FGOALS-f2)operational dynamical prediction model.Through selecting optimum hyperparameters for three machine learning methods to obtain the best fit and least overfitting,an ensemble mean of the random forest and gradient boosting regression tree methods was shown to have the highest prediction skill measured by the anomalous correlation coefficient.The skill has an average value of 0.34 in the historical cross-validation period(1981-2010)and 0.20 in the 10-yr period(2011-2020)of independent prediction,which significantly improves the dynamical prediction skill by 400%.Both reducing overfitting and using the best dynamical prediction are important in applications of the MLD method and in-depth analysis of these warrants a further investigation.展开更多
基金the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA19070404)the National Natural Science Foundation of China(Grant Nos.42030602,91837101 and 91937302).
文摘Large-ensemble simulations of the atmosphere-only time-slice experiments for the Polar Amplification Model Intercomparison Project(PAMIP)were carried out by the model group of the Chinese Academy of Sciences(CAS)Flexible Global Ocean-Atmosphere-Land System(FGOALS-f3-L).Eight groups of experiments forced by different combinations of the sea surface temperature(SST)and sea ice concentration(SIC)for pre-industrial,present-day,and future conditions were performed and published.The time-lag method was used to generate the 100 ensemble members,with each member integrating from 1 April 2000 to 30 June 2001 and the first two months as the spin-up period.The basic model responses of the surface air temperature(SAT)and precipitation were documented.The results indicate that Arctic amplification is mainly caused by Arctic SIC forcing changes.The SAT responses to the Arctic SIC decrease alone show an obvious increase over high latitudes,which is similar to the results from the combined forcing of SST and SIC.However,the change in global precipitation is dominated by the changes in the global SST rather than SIC,partly because tropical precipitation is mainly driven by local SST changes.The uncertainty of the model responses was also investigated through the analysis of the large-ensemble members.The relative roles of SST and SIC,together with their combined influence on Arctic amplification,are also discussed.All of these model datasets will contribute to PAMIP multi-model analysis and improve the understanding of polar amplification.
基金funded by the National Key Research and development Program of China (Grant No. 2017YFA0604004)the National Natural Science Foundation of China (Grant Nos. 91737306, U1811464, 41530426, 91837101, 41730963, and 91637312)
文摘The outputs of the Chinese Academy of Sciences(CAS) Flexible Global Ocean–Atmosphere–Land System(FGOALS-f3-L) model for the baseline experiment of the Atmospheric Model Intercomparison Project simulation in the Diagnostic,Evaluation and Characterization of Klima common experiments of phase 6 of the Coupled Model Intercomparison Project(CMIP6) are described in this paper. The CAS FGOALS-f3-L model, experiment settings, and outputs are all given. In total,there are three ensemble experiments over the period 1979–2014, which are performed with different initial states. The model outputs contain a total of 37 variables and include the required three-hourly mean, six-hourly transient, daily and monthly mean datasets. The baseline performances of the model are validated at different time scales. The preliminary evaluation suggests that the CAS FGOALS-f3-L model can capture the basic patterns of atmospheric circulation and precipitation well, including the propagation of the Madden–Julian Oscillation, activities of tropical cyclones, and the characterization of extreme precipitation. These datasets contribute to the benchmark of current model behaviors for the desired continuity of CMIP.
基金funded by the National Natural Science Foundation of China (Grant Nos. 91737306, 91637312, 41730963, 91837101, 91637208, 41530426)the Key Research Program of Frontier Sciences, Chinese Academy of Sciences (Grant QYZDY-SSW-DQC018)
文摘The Chinese Academy of Sciences(CAS)Flexible Global Ocean Atmosphere Land System(FGOALS-f3-L)model datasets prepared for the sixth phase of the Coupled Model Intercomparison Project(CMIP6)Global Monsoons Model Intercomparison Project(GMMIP)Tier-1 and Tier-3 experiments are introduced in this paper,and the model descriptions,experimental design and model outputs are demonstrated.There are three simulations in Tier-1,with different initial states,and five simulations in Tier-3,with different topographies or surface thermal status.Specifically,Tier-3 contains four orographic perturbation experiments that remove the Tibetan Iranian Plateau,East African and Arabian Peninsula highlands,Sierra Madre,and Andes,and one thermal perturbation experiment that removes the surface sensible heating over the Tibetan Iranian Plateau and surrounding regions at altitudes above 500 m.These datasets will contribute to CMIP6’s value as a benchmark to evaluate the importance of long-term and short-term trends of the sea surface temperature in monsoon circulations and precipitation,and to a better understanding of the orographic impact on the global monsoon system over highlands.
基金jointly supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant Nos.XDA19060102 and XDB42000000)National Natural Science Foundation of China(Grant Nos.91958201 and 42130608)+1 种基金the National Key Research and Development Program of China(Grant No.2020YFA0608800)supported by the National Key Scientific and Technological Infrastructure project“Earth System Numerical Simulation Facility”(EarthLab)。
文摘Following the High-Resolution Model Intercomparison Project(HighResMIP)Tier 2 protocol under the Coupled Model Intercomparison Project Phase 6(CMIP6),three numerical experiments are conducted with the Chinese Academy of Sciences Flexible Global Ocean-Atmosphere-Land System Model,version f3-H(CAS FGOALS-f3-H),and a 101-year(1950–2050)global high-resolution simulation dataset is presented in this study.The basic configuration of the FGOALSf3-H model and numerical experiments design are briefly described,and then the historical simulation is validated.Forced by observed radiative agents from 1950 to 2014,the coupled model essentially reproduces the observed long-term trends of temperature,precipitation,and sea ice extent,as well as the large-scale pattern of temperature and precipitation.With an approximate 0.25°horizontal resolution in the atmosphere and 0.1°in the ocean,the coupled models also simulate energetic western boundary currents and the Antarctic Circulation Current(ACC),reasonable characteristics of extreme precipitation,and realistic frontal scale air-sea interaction.The dataset and supporting detailed information have been published in the Earth System Grid Federation.
基金supported by the National Key Research and Development Program of China[grant number 2018YFC1505706]the National Natural Science Foundation of China[grant numbers 91937302,91737306,41975109]the Strategic Priority Research Program of the Chinese Academy of Sciences[grant number XDA17010105]。
基金funded by the National Natural Science Foundation of China (Grants 41675100, 91737306, and U1811464)
文摘Cloud dominates influence factors of atmospheric radiation, while aerosol–cloud interactions are of vital importance in its spatiotemporal distribution. In this study, a two-moment(mass and number) cloud microphysics scheme, which significantly improved the treatment of the coupled processes of aerosols and clouds, was incorporated into version 1.1 of the IAP/LASG global Finite-volume Atmospheric Model(FAMIL1.1). For illustrative purposes, the characteristics of the energy balance and cloud radiative forcing(CRF) in an AMIP-type simulation with prescribed aerosols were compared with those in observational/reanalysis data. Even within the constraints of the prescribed aerosol mass, the model simulated global mean energy balance at the top of the atmosphere(TOA) and at the Earth’s surface, as well as their seasonal variation, are in good agreement with the observational data. The maximum deviation terms lie in the surface downwelling longwave radiation and surface latent heat flux, which are 3.5 W m-2(1%) and 3 W m-2(3.5%), individually. The spatial correlations of the annual TOA net radiation flux and the net CRF between simulation and observation were around 0.97 and 0.90, respectively. A major weakness is that FAMIL1.1 predicts more liquid water content and less ice water content over most oceans. Detailed comparisons are presented for a number of regions, with a focus on the Asian monsoon region(AMR). The results indicate that FAMIL1.1 well reproduces the summer–winter contrast for both the geographical distribution of the longwave CRF and shortwave CRF over the AMR. Finally, the model bias and possible solutions, as well as further works to develop FAMIL1.1 are discussed.
基金funded by the Na-tional Natural Science Foundation of China[grant number 42005117]the Strategic Priority Research Program of the Chinese Academy of Sciences[grant number XDB40030205]the Key Special Project for the Introducing Talents Team of the Southern Marine Science and Engineering Guangdong Laboratory(Guangdong)[grant number GML2019ZD0601]。
基金Supported by the National Natural Science Foundation of China(42022034,41775071,and U1811464)China National Key Research and Development Program[Early Warning and Prevention of Major Natural Disaster(2018YFC1506005)]。
文摘Seasonal prediction of summer rainfall is crucial to reduction of regional disasters,but currently it has a low prediction skill.We developed a dynamical and machine learning hybrid(MLD)seasonal prediction method for summer rainfall in China based on circulation fields from the Chinese Academy of Sciences(CAS)Flexible Global Ocean-Atmosphere-Land System Model finite volume version 2(FGOALS-f2)operational dynamical prediction model.Through selecting optimum hyperparameters for three machine learning methods to obtain the best fit and least overfitting,an ensemble mean of the random forest and gradient boosting regression tree methods was shown to have the highest prediction skill measured by the anomalous correlation coefficient.The skill has an average value of 0.34 in the historical cross-validation period(1981-2010)and 0.20 in the 10-yr period(2011-2020)of independent prediction,which significantly improves the dynamical prediction skill by 400%.Both reducing overfitting and using the best dynamical prediction are important in applications of the MLD method and in-depth analysis of these warrants a further investigation.
