Rapid declines in Arctic sea ice have captured attention and pose significant challenges to a variety of stakeholders. There is a rising demand for Arctic sea ice prediction at daily to seasonal time scales, which is ...Rapid declines in Arctic sea ice have captured attention and pose significant challenges to a variety of stakeholders. There is a rising demand for Arctic sea ice prediction at daily to seasonal time scales, which is partly a sea ice initial condition problem. Thus, a multivariate data assimilation that integrates sea ice observations to generate realistic and skillful model initialization is needed to improve predictive skill of Arctic sea ice. Sea ice data assimilation is a relatively new research area. In this review paper, we focus on two challenges for implementing multivariate data assimilation systems for sea ice forecast. First, to address the challenge of limited spatiotemporal coverage and large uncertainties of observations, we discuss sea ice parameters derived from satellite remote sensing that(1) have been utilized for improved model initialization, including concentration, thickness and drift, and(2) are currently under development with the potential for enhancing the predictability of Arctic sea ice, including melt ponds and sea ice leads. Second, to strive to generate the ‘‘best" estimate of sea ice initial conditions by combining model simulations/forecasts and observations, we review capabilities and limitations of different data assimilation techniques that have been developed and used to assimilate observed sea ice parameters in dynamical models.展开更多
As a member of the Chinese modeling groups,the coupled ocean-ice component of the Chinese Academy of Sciences’Earth System Model,version 2.0(CAS-ESM2.0),is taking part in the Ocean Model Intercomparison Project Phase...As a member of the Chinese modeling groups,the coupled ocean-ice component of the Chinese Academy of Sciences’Earth System Model,version 2.0(CAS-ESM2.0),is taking part in the Ocean Model Intercomparison Project Phase 1(OMIP1)experiment of phase 6 of the Coupled Model Intercomparison Project(CMIP6).The simulation was conducted,and monthly outputs have been published on the ESGF(Earth System Grid Federation)data server.In this paper,the experimental dataset is introduced,and the preliminary performances of the ocean model in simulating the global ocean temperature,salinity,sea surface temperature,sea surface salinity,sea surface height,sea ice,and Atlantic Meridional Overturning Circulation(AMOC)are evaluated.The results show that the model is at quasi-equilibrium during the integration of 372 years,and performances of the model are reasonable compared with observations.This dataset is ready to be downloaded and used by the community in related research,e.g.,multi-ocean-sea-ice model performance evaluation and interannual variation in oceans driven by prescribed atmospheric forcing.展开更多
This paper describes the datasets from the Scenario Model Intercomparison Project(ScenarioMIP)simulation experiments run with the Chinese Academy of Sciences Flexible Global Ocean–Atmosphere–Land System Model,GridPo...This paper describes the datasets from the Scenario Model Intercomparison Project(ScenarioMIP)simulation experiments run with the Chinese Academy of Sciences Flexible Global Ocean–Atmosphere–Land System Model,GridPoint version 3(CAS FGOALS-g3).FGOALS-g3 is driven by eight shared socioeconomic pathways(SSPs)with different sets of future emission,concentration,and land-use scenarios.All Tier 1 and 2 experiments were carried out and were initialized using historical runs.A branch run method was used for the ensemble simulations.Model outputs were three-hourly,six-hourly,daily,and/or monthly mean values for the primary variables of the four component models.An evaluation and analysis of the simulations is also presented.The present results are expected to aid research into future climate change and socio-economic development.展开更多
The second version of the Chinese Academy of Sciences Earth System Model(CAS-ESM2.0)is participating in the Flux-Anomaly-Forced Model Intercomparison Project(FAFMIP)experiments in phase 6 of the Coupled Model Intercom...The second version of the Chinese Academy of Sciences Earth System Model(CAS-ESM2.0)is participating in the Flux-Anomaly-Forced Model Intercomparison Project(FAFMIP)experiments in phase 6 of the Coupled Model Intercomparison Project(CMIP6).The purpose of FAFMIP is to understand and reduce the uncertainty of ocean climate changes in response to increased CO2 forcing in atmosphere-ocean general circulation models(AOGCMs),including the simulations of ocean heat content(OHC)change,ocean circulation change,and sea level rise due to thermal expansion.FAFMIP experiments(including faf-heat,faf-stress,faf-water,faf-all,faf-passiveheat,faf-heat-NA50pct and faf-heat-NA0pct)have been conducted.All of the experiments were integrated over a 70-year period and the corresponding data have been uploaded to the Earth System Grid Federation data server for CMIP6 users to download.This paper describes the experimental design and model datasets and evaluates the preliminary results of CAS-ESM2.0 simulations of ocean climate changes in the FAFMIP experiments.The simulations of the changes in global ocean temperature,Atlantic Meridional Overturning Circulation(AMOC),OHC,and dynamic sea level(DSL),are all reasonably reproduced.展开更多
Two versions of the Chinese Academy of Sciences Flexible Global Ocean-Atmosphere-Land System model(CASFGOALS),version f3-L and g3,are used to simulate the two interglacial epochs of the mid-Holocene and the Last Inter...Two versions of the Chinese Academy of Sciences Flexible Global Ocean-Atmosphere-Land System model(CASFGOALS),version f3-L and g3,are used to simulate the two interglacial epochs of the mid-Holocene and the Last Interglacial in phase 4 of the Paleoclimate Modelling Intercomparison Project(PMIP4),which aims to study the impact of changes in orbital parameters on the Earth’s climate.Following the PMIP4 experimental protocols,four simulations for the mid-Holocene and two simulations for the Last Interglacial have been completed,and all the data,including monthly and daily outputs for the atmospheric,oceanic,land and sea-ice components,have been released on the Earth System Grid Federation(ESGF)node.These datasets contribute to PMIP4 and CMIP6(phase 6 of the Coupled Model Intercomparison Project)by providing the variables necessary for the two interglacial periods.In this paper,the basic information of the CAS-FGOALS models and the protocols for the two interglacials are briefly described,and the datasets are validated using proxy records.Results suggest that the CAS-FGOALS models capture the large-scale changes in the climate system in response to changes in solar insolation during the interglacial epochs,including warming in mid-to-high latitudes,changes in the hydrological cycle,the seasonal variation in the extent of sea ice,and the damping of interannual variabilities in the tropical Pacific.Meanwhile,disagreements within and between the models and the proxy data are also presented.These datasets will help the modeling and the proxy data communities with a better understanding of model performance and biases in paleoclimate simulations.展开更多
On 15 September 2020,the Arctic sea-ice extent(SIE)reached its annual minimum,which,based on data from the National Snow and Ice Data Center(NSIDC,2020a),was about 3.74 million km^(2)(1.44 million square miles).This v...On 15 September 2020,the Arctic sea-ice extent(SIE)reached its annual minimum,which,based on data from the National Snow and Ice Data Center(NSIDC,2020a),was about 3.74 million km^(2)(1.44 million square miles).This value was about 40%less than the climate average(~6.27 million km^(2))during 1980–2010.It was second only to the record low(3.34 million km^(2))set on 16 September 2012,but significantly smaller than the previous second-lowest(4.145 million km^(2),set on 7 September 2016)and third-lowest(4.147 million km^(2),set on 14 September 2007)values,making 2020 the second-lowest SIE year of the satellite era(42 years of data).展开更多
基金supported by the National Key R&D Program of China (2018YFA0605901)the NOAA Climate Program Office (NA15OAR4310163)+1 种基金the National Natural Science Foundation of China (41676185)and the Key Research Program of Frontier Sciences of Chinese Academy of Sciences (QYZDY-SSW-DQC021)
文摘Rapid declines in Arctic sea ice have captured attention and pose significant challenges to a variety of stakeholders. There is a rising demand for Arctic sea ice prediction at daily to seasonal time scales, which is partly a sea ice initial condition problem. Thus, a multivariate data assimilation that integrates sea ice observations to generate realistic and skillful model initialization is needed to improve predictive skill of Arctic sea ice. Sea ice data assimilation is a relatively new research area. In this review paper, we focus on two challenges for implementing multivariate data assimilation systems for sea ice forecast. First, to address the challenge of limited spatiotemporal coverage and large uncertainties of observations, we discuss sea ice parameters derived from satellite remote sensing that(1) have been utilized for improved model initialization, including concentration, thickness and drift, and(2) are currently under development with the potential for enhancing the predictability of Arctic sea ice, including melt ponds and sea ice leads. Second, to strive to generate the ‘‘best" estimate of sea ice initial conditions by combining model simulations/forecasts and observations, we review capabilities and limitations of different data assimilation techniques that have been developed and used to assimilate observed sea ice parameters in dynamical models.
基金supported by the National Natural Science Foundation of China(Grant Nos.41706036 and 41706028)the Key Research Program of Frontier Sciences,Chinese Academy of Sciences(Grant No.QYZDY-SSWDQC002)+2 种基金the National Key R&D Program for Developing Basic Sciences(Grant Nos.2016YFC14014012016YFC1401601 and 2016YFB0200804)the National Key Scientific and Technological Infrastructure project entitled“Earth System Science Numerical Simulator Facility”(Earth Lab)key operation construction projects of Chongqing Meteorological Bureau-“Construction of chongqing short-term climate numerical prediction platform”。
文摘As a member of the Chinese modeling groups,the coupled ocean-ice component of the Chinese Academy of Sciences’Earth System Model,version 2.0(CAS-ESM2.0),is taking part in the Ocean Model Intercomparison Project Phase 1(OMIP1)experiment of phase 6 of the Coupled Model Intercomparison Project(CMIP6).The simulation was conducted,and monthly outputs have been published on the ESGF(Earth System Grid Federation)data server.In this paper,the experimental dataset is introduced,and the preliminary performances of the ocean model in simulating the global ocean temperature,salinity,sea surface temperature,sea surface salinity,sea surface height,sea ice,and Atlantic Meridional Overturning Circulation(AMOC)are evaluated.The results show that the model is at quasi-equilibrium during the integration of 372 years,and performances of the model are reasonable compared with observations.This dataset is ready to be downloaded and used by the community in related research,e.g.,multi-ocean-sea-ice model performance evaluation and interannual variation in oceans driven by prescribed atmospheric forcing.
基金This study was supported by the National Key Research and Development Program of China(Grant Nos.2017YFA0603903,2017YFA0603901,and 2017YFA0603902)the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDB42010404)the National Basic Research(973)Program of China(Grant Nos.2015CB954102).
文摘This paper describes the datasets from the Scenario Model Intercomparison Project(ScenarioMIP)simulation experiments run with the Chinese Academy of Sciences Flexible Global Ocean–Atmosphere–Land System Model,GridPoint version 3(CAS FGOALS-g3).FGOALS-g3 is driven by eight shared socioeconomic pathways(SSPs)with different sets of future emission,concentration,and land-use scenarios.All Tier 1 and 2 experiments were carried out and were initialized using historical runs.A branch run method was used for the ensemble simulations.Model outputs were three-hourly,six-hourly,daily,and/or monthly mean values for the primary variables of the four component models.An evaluation and analysis of the simulations is also presented.The present results are expected to aid research into future climate change and socio-economic development.
基金supported by the National Major Research High Performance Computing Program of China(Grant No.2016YFB0200804)the National Natural Science Foundation of China(Grant Nos.41706036,41706028,41975129 and 41630530)+2 种基金the open fund of State Key Laboratory of Satellite Ocean Environment Dynamics,Second Institute of Oceanography(Grant No.QNHX2017)the National Key Scientific and Technological Infrastructure project entitled“Earth System Science Numerical Simulator Facility”(Earth Lab)key operation construction projects of Chongqing Meteorological Bureau"Construction of chongqing short-term climate numerical predic tion platform"。
文摘The second version of the Chinese Academy of Sciences Earth System Model(CAS-ESM2.0)is participating in the Flux-Anomaly-Forced Model Intercomparison Project(FAFMIP)experiments in phase 6 of the Coupled Model Intercomparison Project(CMIP6).The purpose of FAFMIP is to understand and reduce the uncertainty of ocean climate changes in response to increased CO2 forcing in atmosphere-ocean general circulation models(AOGCMs),including the simulations of ocean heat content(OHC)change,ocean circulation change,and sea level rise due to thermal expansion.FAFMIP experiments(including faf-heat,faf-stress,faf-water,faf-all,faf-passiveheat,faf-heat-NA50pct and faf-heat-NA0pct)have been conducted.All of the experiments were integrated over a 70-year period and the corresponding data have been uploaded to the Earth System Grid Federation data server for CMIP6 users to download.This paper describes the experimental design and model datasets and evaluates the preliminary results of CAS-ESM2.0 simulations of ocean climate changes in the FAFMIP experiments.The simulations of the changes in global ocean temperature,Atlantic Meridional Overturning Circulation(AMOC),OHC,and dynamic sea level(DSL),are all reasonably reproduced.
基金This study was supported by the National Key R&D Program for Developing Basic Sciences(Grant Nos.2016YFC1401401 and 2016YFC1401601)the Strategic Priority Research Program of Chinese Academy of Sciences(Grant Nos.XDA19060102 and XDB42000000)the National Natural Science Foundation of China(Grants Nos.91958201,41530426,41576025,41576026,41776030,41931183,41976026 and 41376002).
文摘Two versions of the Chinese Academy of Sciences Flexible Global Ocean-Atmosphere-Land System model(CASFGOALS),version f3-L and g3,are used to simulate the two interglacial epochs of the mid-Holocene and the Last Interglacial in phase 4 of the Paleoclimate Modelling Intercomparison Project(PMIP4),which aims to study the impact of changes in orbital parameters on the Earth’s climate.Following the PMIP4 experimental protocols,four simulations for the mid-Holocene and two simulations for the Last Interglacial have been completed,and all the data,including monthly and daily outputs for the atmospheric,oceanic,land and sea-ice components,have been released on the Earth System Grid Federation(ESGF)node.These datasets contribute to PMIP4 and CMIP6(phase 6 of the Coupled Model Intercomparison Project)by providing the variables necessary for the two interglacial periods.In this paper,the basic information of the CAS-FGOALS models and the protocols for the two interglacials are briefly described,and the datasets are validated using proxy records.Results suggest that the CAS-FGOALS models capture the large-scale changes in the climate system in response to changes in solar insolation during the interglacial epochs,including warming in mid-to-high latitudes,changes in the hydrological cycle,the seasonal variation in the extent of sea ice,and the damping of interannual variabilities in the tropical Pacific.Meanwhile,disagreements within and between the models and the proxy data are also presented.These datasets will help the modeling and the proxy data communities with a better understanding of model performance and biases in paleoclimate simulations.
基金This work was supported by the National Key R&D Program of China[grant number 2018YFA0605901]the National Natural Science Foundation of China[grant numbers 41861144016 and 42011530082].
文摘On 15 September 2020,the Arctic sea-ice extent(SIE)reached its annual minimum,which,based on data from the National Snow and Ice Data Center(NSIDC,2020a),was about 3.74 million km^(2)(1.44 million square miles).This value was about 40%less than the climate average(~6.27 million km^(2))during 1980–2010.It was second only to the record low(3.34 million km^(2))set on 16 September 2012,but significantly smaller than the previous second-lowest(4.145 million km^(2),set on 7 September 2016)and third-lowest(4.147 million km^(2),set on 14 September 2007)values,making 2020 the second-lowest SIE year of the satellite era(42 years of data).
