本文评估了地球系统模式FIO-ESM(First Institute of Oceanography-Earth System Model)基于集合调整Kalman滤波同化实验对1992-2013年北极海冰的模拟能力。结果显示:尽管同化资料只包括了全球海表温度和全球海面高度异常两类数据,而并...本文评估了地球系统模式FIO-ESM(First Institute of Oceanography-Earth System Model)基于集合调整Kalman滤波同化实验对1992-2013年北极海冰的模拟能力。结果显示:尽管同化资料只包括了全球海表温度和全球海面高度异常两类数据,而并没有对海冰进行同化,但实验结果能很好地模拟出与观测相符的北极海冰基本态和长期变化趋势,卫星观测和FIO-ESM同化实验所得的北极海冰覆盖范围在1992-2013年间的线性变化趋势分别为-7.06×105和-6.44×105 km2/(10a),同化所得的逐月海冰覆盖范围异常和卫星观测之间的相关系数为0.78。与FIO-ESM参加CMIP5(Coupled Model Intercomparison Project Phase 5)实验结果相比,该同化结果所模拟的北极海冰覆盖范围的长期变化趋势和海冰密集度的空间变化趋势均与卫星观测更加吻合,这说明该同化可为利用FIO-ESM开展北极短期气候预测提供较好的预测初始场。展开更多
Recently atmospheric and oceanic observations indicate the tropical Pacific is at the El Ni？o condition. However,it＇s not clear whether this El Ni？o event of this year is comparable to the very strong one of 1997/9...Recently atmospheric and oceanic observations indicate the tropical Pacific is at the El Ni？o condition. However,it＇s not clear whether this El Ni？o event of this year is comparable to the very strong one of 1997/98 which brought huge influence on the whole world. In this study, based on the Ensemble Adjusted Kalman Filter（EAKF）assimilation scheme and First Institute of Oceanography-Earth System Model（FIO-ESM）, the assimilation system is setup, which can provide reasonable initial conditions for prediction. And the hindcast results suggest the skill of El Ni？o-Southern Oscillation（ENSO） prediction is comparable to other dynamical coupled models. Then the prediction for 2015/16 El Ni？o by using FIO-ESM is started from 1 November 2015. The ensemble results indicate that the 2015/16 El Ni？o will continue to be strong. By the end of 2015, the strongest strength is very like more than 2.0°C and the ensemble mean strength is 2.34°C, which indicates 2015/16 El Ni？o event will be very strong but slightly less than that of 1997/98 El Ni？o event（2.40°C） calculated relative a climatology based on the years1992–2014. The prediction results also suggest 2015/16 El Ni？o event will be a transition to ENSO-neutral level in the early spring（FMA） 2016, and then may transfer to La Ni？a in summer 2016.展开更多
To improve the Arctic sea ice forecast skill of the First Institute of Oceanography-Earth System Model(FIO-ESM)climate forecast system,satellite-derived sea ice concentration and sea ice thickness from the Pan-Arctic ...To improve the Arctic sea ice forecast skill of the First Institute of Oceanography-Earth System Model(FIO-ESM)climate forecast system,satellite-derived sea ice concentration and sea ice thickness from the Pan-Arctic IceOcean Modeling and Assimilation System(PIOMAS)are assimilated into this system,using the method of localized error subspace transform ensemble Kalman filter(LESTKF).Five-year(2014–2018)Arctic sea ice assimilation experiments and a 2-month near-real-time forecast in August 2018 were conducted to study the roles of ice data assimilation.Assimilation experiment results show that ice concentration assimilation can help to get better modeled ice concentration and ice extent.All the biases of ice concentration,ice cover,ice volume,and ice thickness can be reduced dramatically through ice concentration and thickness assimilation.The near-real-time forecast results indicate that ice data assimilation can improve the forecast skill significantly in the FIO-ESM climate forecast system.The forecasted Arctic integrated ice edge error is reduced by around 1/3 by sea ice data assimilation.Compared with the six near-real-time Arctic sea ice forecast results from the subseasonal-toseasonal(S2 S)Prediction Project,FIO-ESM climate forecast system with LESTKF ice data assimilation has relatively high Arctic sea ice forecast skill in 2018 summer sea ice forecast.Since sea ice thickness in the PIOMAS is updated in time,it is a good choice for data assimilation to improve sea ice prediction skills in the near-realtime Arctic sea ice seasonal prediction.展开更多
数值模拟方法在研究长时间的气候变化上扮演着重要角色。一直以来,数值模式模拟年代际气候变化如太平洋年代际震荡(PDO)的位相转换存在巨大挑战。本文利用自然资源部第一海洋研究所研发的地球系统模式(First Institute of Oceanography-...数值模拟方法在研究长时间的气候变化上扮演着重要角色。一直以来,数值模式模拟年代际气候变化如太平洋年代际震荡(PDO)的位相转换存在巨大挑战。本文利用自然资源部第一海洋研究所研发的地球系统模式(First Institute of Oceanography-Earth System Model Version 2,FIO-ESM v2.0)145年(1870–2014年)历史气候模拟试验结果,结合再分析资料和另外两个地球系统模式结果,分析评估了该模式对太平洋年代际振荡的模拟能力。研究发现,FIO-ESM v2.0能够再现历史时期PDO的空间模态分布特征,其PDO指数具有10~30年的周期变化特征,同时于1960年以后能刻画出与再分析数据结果相近的PDO位相转变特征。研究表明,FIO-ESM v2.0能够较为准确地模拟出PDO的位相转变特征。另外,本文还评估了该模式对大气环流模态的模拟能力及其与PDO之间的关系,以及该模式模拟PDO的可能机制。该模式的PDO与大气环流的阿留申低压模态相关。进一步的分析表明,平流作用和热通量是关键年代际海域海温异常振幅的主要因素,而罗斯贝波西传时间则可能是影响PDO位相转变的关键因素。展开更多
We introduced the Coupled Model Intercomparison Project Phase 6(CMIP6)Ocean Model Intercomparison Project CORE2-forced(OMIP-1)experiment by using the First Institute of Oceanography Earth System Model version 2.0(FIO-...We introduced the Coupled Model Intercomparison Project Phase 6(CMIP6)Ocean Model Intercomparison Project CORE2-forced(OMIP-1)experiment by using the First Institute of Oceanography Earth System Model version 2.0(FIO-ESM v2.0),and comprehensively evaluated the simulation results.Unlike other OMIP models,FIO-ESM v2.0 includes a coupled ocean surface wave component model that takes into account non-breaking surface wave-induced vertical mixing in the ocean and effect of surface wave Stokes drift on air-sea momentum and heat fluxes in the climate system.A sub-layer sea surface temperature(SST)diurnal cycle parameterization was also employed to take into account effect of SST diurnal cycle on air-sea heat fluxes to improve simulations of air-sea interactions.Evaluations show that mean values and long-term trends of significant wave height were adequately reproduced in the FIO-ESM v2.0 OMIP-1 simulations,and there is a reasonable fit between the SST diurnal cycle obtained from in situ observations and that parameterized by FIO-ESM v2.0.Evaluations of model drift,temperature,salinity,mixed layer depth,and the Atlantic Meridional Overturning Circulation show that the model performs well in the FIO-ESM v2.0 OMIP-1 simulation.However,the summer sea ice extent of the Arctic and Antarctic is underestimated.展开更多
The seasonal prediction of sea surface temperature(SST) and precipitation in the North Pacific based on the hindcast results of The First Institute of Oceanography Earth System Model(FIO-ESM) is assessed in this study...The seasonal prediction of sea surface temperature(SST) and precipitation in the North Pacific based on the hindcast results of The First Institute of Oceanography Earth System Model(FIO-ESM) is assessed in this study.The Ensemble Adjusted Kalman Filter assimilation scheme is used to generate initial conditions, which are shown to be reliable by comparison with the observations. Based on this comparison, we analyze the FIO-ESM 6-month hindcast results starting from each month of 1993–2013. The model exhibits high SST prediction skills over most of the North Pacific for two seasons in advance. Furthermore, it remains skillful at long lead times for midlatitudes. The reliable prediction of SST can transfer fairly well to precipitation prediction via air-sea interactions.The average skill of the North Pacific variability(NPV) index from 1 to 6 months lead is as high as 0.72(0.55) when El Ni?o-Southern Oscillation and NPV are in phase(out of phase) at initial conditions. The prediction skill of the NPV index of FIO-ESM is improved by 11.6%(23.6%) over the Climate Forecast System, Version 2. For seasonal dependence, the skill of FIO-ESM is higher than the skill of persistence prediction in the later period of prediction.展开更多
大西洋经向翻转环流(Atlantic meridional overturning circulation,AMOC)作为全球大洋的极向热量输送带,对大西洋附近区域的天气及全球气候变化都存在至关重要的影响。采用自然资源部第一海洋研究所研发的地球系统模式FIO-ESM v2.0(Fir...大西洋经向翻转环流(Atlantic meridional overturning circulation,AMOC)作为全球大洋的极向热量输送带,对大西洋附近区域的天气及全球气候变化都存在至关重要的影响。采用自然资源部第一海洋研究所研发的地球系统模式FIO-ESM v2.0(First Institute of Oceanography-earth system model version 2.0)分析了1850~2014年AMOC的空间分布特征及时间变化规律,并进一步讨论造成该变化的可能因素。研究结果表明:1850~2014年AMOC最大值出现在40°N、1000 m深度附近,其时间序列总体呈现-0.0791×10^(6)m^(3)/(s·a)的减弱趋势,该期间伴随着Labrador、Irminger海域冬季混合层深度的变浅。通过将模式计算的AMOC强度与RAPID(rapid climate change programme)和OSNAP(overturning in the subpolar North Atlantic program)观测资料进行对比,结合模式间并行比较结果显示该模式能较好地再现观测数据期间的AMOC变化规律。FIO-ESM v2.0模式模拟的AMOC具有55 a左右的年代际周期,Labrador、Irminger海域冬季混合层深度变化揭示的对流变化以及Labrador、GIN海域表层海水密度变化造成的海水下沉对AMOC强度的周期性振荡贡献较明显,其周期性变化与海表盐度(sea surface salinity,SSS)、海表温度(sea surface temperature,SST)、蒸发与降水的差值、北大西洋涛动(North Atlantic oscillation,NAO)等要素的变化密切相关。展开更多
Three tiers of experiments in the Global Monsoons Model Intercomparison Project(GMMIP),one of the endorsed model intercomparison projects of phase 6 of the Coupled Model Intercomparison Project(CMIP6),are implemented ...Three tiers of experiments in the Global Monsoons Model Intercomparison Project(GMMIP),one of the endorsed model intercomparison projects of phase 6 of the Coupled Model Intercomparison Project(CMIP6),are implemented by the First Institute of Oceanography Earth System Model version 2(FIO-ESM v2.0),following the GMMIP protocols.Evaluation of global mean surface air temperature from 1870 to 2014 and climatological precipitation(1979–2014)in tier-1 shows that the atmosphere model of FIO-ESM v2.0 can reproduce the basic observed atmospheric features.In tier-2,the internal variability is captured by the coupled model,with the SST restoring to the model climatology plus the observed anomalies in the tropical Pacific and North Atlantic.Simulation of the Northern Hemisphere summer monsoon circulation is significantly improved by the SST restoration in the North Atlantic.In tier-3,five orographic perturbation experiments are conducted covering the period 1979–2014 by modifying the surface elevation or vertical heating in the prescribed region.In particular,the strength of the South Asian summer monsoon is reduced by removing the topography or thermal forcing above 500 m over the Asian continent.Monthly and daily simulated outputs of FIO-ESM v2.0 are provided through the Earth System Grid Federation(ESGF)node to contribute to a better understanding of the global monsoon system.展开更多
The Arctic is one of Earth’s regions highly susceptible to climate change.However,in situ long-term observations used for climate research are relatively sparse in the Arctic Ocean,and current climate models exhibit ...The Arctic is one of Earth’s regions highly susceptible to climate change.However,in situ long-term observations used for climate research are relatively sparse in the Arctic Ocean,and current climate models exhibit notable biases in Arctic Ocean simulations.Here,we present an Arctic Ocean dynamical downscaling dataset,obtained from the global ocean-sea ice model FESOM2 with a regionally refined horizonal resolution of 4.5 km in the Arctic region,which is driven by bias-corrected surface forcings derived from a climate model.The dataset includes 115 years(1900-2014)of historical simulations and two 86-year future projection simulations(2015-2100)for the SSP2-4.5 and SSP5-8.5 scenarios.The historical simulations demonstrate substantially reduced biases in temperature,salinity and sea-ice thickness compared to CMIP6 climate models.Common biases in the representation of the Atlantic Water layer found in climate model simulations are also markedly reduced in the dataset.Serving as a crucial long-term data source for climate change assessments and scientific research for the Arctic Ocean,this dataset provides valuable information for the scientific community.展开更多
The present study evaluates a simulation of the global ocean mixed layer depth （MLD） using the First Institute of Oceanography-Earth System Model （FIO- ESM）. The seasonal variation of the global MLD from the FIO-E...The present study evaluates a simulation of the global ocean mixed layer depth （MLD） using the First Institute of Oceanography-Earth System Model （FIO- ESM）. The seasonal variation of the global MLD from the FIO-ESM simulation is compared to Argo observational data. The Argo data show that the global ocean MLD has a strong seasonal variation with a deep MLD in winter and a shallow MLD in summer, while the spring and fall seasons act as transitional periods. Overall, the FIO-ESM simula- tion accurately captures the seasonal variation in MLD in most areas. It exhibits a better performance during summer and fall than during winter and spring. The simulated MLD in the Southern Hemisphere is much closer to observations than that in the Northern Hemisphere. In general, the simulated MLD over the South Atlantic Ocean matches the observation best among the six areas. Additionally, the model slightly underestimates the MLD in parts of the North Atlantic Ocean, and slightly overestimates the MLD over the other ocean basins.展开更多
文摘本文评估了地球系统模式FIO-ESM(First Institute of Oceanography-Earth System Model)基于集合调整Kalman滤波同化实验对1992-2013年北极海冰的模拟能力。结果显示:尽管同化资料只包括了全球海表温度和全球海面高度异常两类数据,而并没有对海冰进行同化,但实验结果能很好地模拟出与观测相符的北极海冰基本态和长期变化趋势,卫星观测和FIO-ESM同化实验所得的北极海冰覆盖范围在1992-2013年间的线性变化趋势分别为-7.06×105和-6.44×105 km2/(10a),同化所得的逐月海冰覆盖范围异常和卫星观测之间的相关系数为0.78。与FIO-ESM参加CMIP5(Coupled Model Intercomparison Project Phase 5)实验结果相比,该同化结果所模拟的北极海冰覆盖范围的长期变化趋势和海冰密集度的空间变化趋势均与卫星观测更加吻合,这说明该同化可为利用FIO-ESM开展北极短期气候预测提供较好的预测初始场。
基金The National Natural Science Foundation of China-Shandong Joint Fund for Marine Science Research Centers under contract No.U1406404the Public Science and Technology Research Funds Projects of Ocean under contract Nos 201105019 and 201505013
文摘Recently atmospheric and oceanic observations indicate the tropical Pacific is at the El Ni？o condition. However,it＇s not clear whether this El Ni？o event of this year is comparable to the very strong one of 1997/98 which brought huge influence on the whole world. In this study, based on the Ensemble Adjusted Kalman Filter（EAKF）assimilation scheme and First Institute of Oceanography-Earth System Model（FIO-ESM）, the assimilation system is setup, which can provide reasonable initial conditions for prediction. And the hindcast results suggest the skill of El Ni？o-Southern Oscillation（ENSO） prediction is comparable to other dynamical coupled models. Then the prediction for 2015/16 El Ni？o by using FIO-ESM is started from 1 November 2015. The ensemble results indicate that the 2015/16 El Ni？o will continue to be strong. By the end of 2015, the strongest strength is very like more than 2.0°C and the ensemble mean strength is 2.34°C, which indicates 2015/16 El Ni？o event will be very strong but slightly less than that of 1997/98 El Ni？o event（2.40°C） calculated relative a climatology based on the years1992–2014. The prediction results also suggest 2015/16 El Ni？o event will be a transition to ENSO-neutral level in the early spring（FMA） 2016, and then may transfer to La Ni？a in summer 2016.
基金The National Key Research and Development Program of China under contract Nos 2018YFC1407205 and2018YFA0605901the Basic Scientific Fund for National Public Research Institute of China(ShuXingbei Young Talent Program)under contract No.2019S06+1 种基金the National Natural Science Foundation of China under contract Nos 41821004,42022042 and 41941012the China-Korea Cooperation Project on Northwestern Pacific Climate Change and its Prediction。
文摘To improve the Arctic sea ice forecast skill of the First Institute of Oceanography-Earth System Model(FIO-ESM)climate forecast system,satellite-derived sea ice concentration and sea ice thickness from the Pan-Arctic IceOcean Modeling and Assimilation System(PIOMAS)are assimilated into this system,using the method of localized error subspace transform ensemble Kalman filter(LESTKF).Five-year(2014–2018)Arctic sea ice assimilation experiments and a 2-month near-real-time forecast in August 2018 were conducted to study the roles of ice data assimilation.Assimilation experiment results show that ice concentration assimilation can help to get better modeled ice concentration and ice extent.All the biases of ice concentration,ice cover,ice volume,and ice thickness can be reduced dramatically through ice concentration and thickness assimilation.The near-real-time forecast results indicate that ice data assimilation can improve the forecast skill significantly in the FIO-ESM climate forecast system.The forecasted Arctic integrated ice edge error is reduced by around 1/3 by sea ice data assimilation.Compared with the six near-real-time Arctic sea ice forecast results from the subseasonal-toseasonal(S2 S)Prediction Project,FIO-ESM climate forecast system with LESTKF ice data assimilation has relatively high Arctic sea ice forecast skill in 2018 summer sea ice forecast.Since sea ice thickness in the PIOMAS is updated in time,it is a good choice for data assimilation to improve sea ice prediction skills in the near-realtime Arctic sea ice seasonal prediction.
文摘数值模拟方法在研究长时间的气候变化上扮演着重要角色。一直以来,数值模式模拟年代际气候变化如太平洋年代际震荡(PDO)的位相转换存在巨大挑战。本文利用自然资源部第一海洋研究所研发的地球系统模式(First Institute of Oceanography-Earth System Model Version 2,FIO-ESM v2.0)145年(1870–2014年)历史气候模拟试验结果,结合再分析资料和另外两个地球系统模式结果,分析评估了该模式对太平洋年代际振荡的模拟能力。研究发现,FIO-ESM v2.0能够再现历史时期PDO的空间模态分布特征,其PDO指数具有10~30年的周期变化特征,同时于1960年以后能刻画出与再分析数据结果相近的PDO位相转变特征。研究表明,FIO-ESM v2.0能够较为准确地模拟出PDO的位相转变特征。另外,本文还评估了该模式对大气环流模态的模拟能力及其与PDO之间的关系,以及该模式模拟PDO的可能机制。该模式的PDO与大气环流的阿留申低压模态相关。进一步的分析表明,平流作用和热通量是关键年代际海域海温异常振幅的主要因素,而罗斯贝波西传时间则可能是影响PDO位相转变的关键因素。
基金The National Key R&D Program of China under contract Nos 2018YFA0605701 and 2016YFB0201100the National Natural Science Foundation of China under contract Nos 41941012 and 41821004the Basic Scientific Fund for National Public Research Institute of China(Shu Xingbei Young Talent Program)under contract No.2019S06。
文摘We introduced the Coupled Model Intercomparison Project Phase 6(CMIP6)Ocean Model Intercomparison Project CORE2-forced(OMIP-1)experiment by using the First Institute of Oceanography Earth System Model version 2.0(FIO-ESM v2.0),and comprehensively evaluated the simulation results.Unlike other OMIP models,FIO-ESM v2.0 includes a coupled ocean surface wave component model that takes into account non-breaking surface wave-induced vertical mixing in the ocean and effect of surface wave Stokes drift on air-sea momentum and heat fluxes in the climate system.A sub-layer sea surface temperature(SST)diurnal cycle parameterization was also employed to take into account effect of SST diurnal cycle on air-sea heat fluxes to improve simulations of air-sea interactions.Evaluations show that mean values and long-term trends of significant wave height were adequately reproduced in the FIO-ESM v2.0 OMIP-1 simulations,and there is a reasonable fit between the SST diurnal cycle obtained from in situ observations and that parameterized by FIO-ESM v2.0.Evaluations of model drift,temperature,salinity,mixed layer depth,and the Atlantic Meridional Overturning Circulation show that the model performs well in the FIO-ESM v2.0 OMIP-1 simulation.However,the summer sea ice extent of the Arctic and Antarctic is underestimated.
基金The National Natural Science Foundation of China(NSFC)-Shandong Joint Fund for Marine Science Research Centers under contract No.U1606405the National Programme on Global Change and Air-Sea Interaction under contract Nos GASIIPOVAI-05 and GASI-IPOVAI-06+5 种基金the International Cooperation Project on the China-Australia Research Centre for Maritime Engineering of Ministry of Science and Technology,China under contract No.2016YFE0101400the Qingdao National Laboratory for Marine Science and Technology through the AoShan Talents Program under contract No.2015ASTPthe Transparency Program of Pacific Ocean-South China Sea-Indian Ocean under contract No.2015ASKJ01the Scientific and Technological Innovation Project of Qingdao National Laboratory for Marine Science and Technology under contract No.2016ASKJ16the Public Science and Technology Research Funds Projects of Ocean under contract No.201505013the China-Korea Cooperation Project on the Trend of North-West Pacific Climate Change
文摘The seasonal prediction of sea surface temperature(SST) and precipitation in the North Pacific based on the hindcast results of The First Institute of Oceanography Earth System Model(FIO-ESM) is assessed in this study.The Ensemble Adjusted Kalman Filter assimilation scheme is used to generate initial conditions, which are shown to be reliable by comparison with the observations. Based on this comparison, we analyze the FIO-ESM 6-month hindcast results starting from each month of 1993–2013. The model exhibits high SST prediction skills over most of the North Pacific for two seasons in advance. Furthermore, it remains skillful at long lead times for midlatitudes. The reliable prediction of SST can transfer fairly well to precipitation prediction via air-sea interactions.The average skill of the North Pacific variability(NPV) index from 1 to 6 months lead is as high as 0.72(0.55) when El Ni?o-Southern Oscillation and NPV are in phase(out of phase) at initial conditions. The prediction skill of the NPV index of FIO-ESM is improved by 11.6%(23.6%) over the Climate Forecast System, Version 2. For seasonal dependence, the skill of FIO-ESM is higher than the skill of persistence prediction in the later period of prediction.
文摘大西洋经向翻转环流(Atlantic meridional overturning circulation,AMOC)作为全球大洋的极向热量输送带,对大西洋附近区域的天气及全球气候变化都存在至关重要的影响。采用自然资源部第一海洋研究所研发的地球系统模式FIO-ESM v2.0(First Institute of Oceanography-earth system model version 2.0)分析了1850~2014年AMOC的空间分布特征及时间变化规律,并进一步讨论造成该变化的可能因素。研究结果表明:1850~2014年AMOC最大值出现在40°N、1000 m深度附近,其时间序列总体呈现-0.0791×10^(6)m^(3)/(s·a)的减弱趋势,该期间伴随着Labrador、Irminger海域冬季混合层深度的变浅。通过将模式计算的AMOC强度与RAPID(rapid climate change programme)和OSNAP(overturning in the subpolar North Atlantic program)观测资料进行对比,结合模式间并行比较结果显示该模式能较好地再现观测数据期间的AMOC变化规律。FIO-ESM v2.0模式模拟的AMOC具有55 a左右的年代际周期,Labrador、Irminger海域冬季混合层深度变化揭示的对流变化以及Labrador、GIN海域表层海水密度变化造成的海水下沉对AMOC强度的周期性振荡贡献较明显,其周期性变化与海表盐度(sea surface salinity,SSS)、海表温度(sea surface temperature,SST)、蒸发与降水的差值、北大西洋涛动(North Atlantic oscillation,NAO)等要素的变化密切相关。
基金This research was jointly supported by the National Key Research and Development Program of China(Grant No.2017YFC1404004)the Project of Indo-Pacific Ocean Environment Variation and Air-sea Interactions(Grant No.GASIIPOVAI-06)+5 种基金the Basic Scientific Fund of the National Public Research Institute of China(Grant No.2019S06)Ying BAO was supported by the National Key Research and Development Program of China(Grant No.2016YFA0602200)Zhenya SONG was supported by the National Natural Science Foundation of China(Grant No.41821004)the Basic Scientific Fund of the National Public Research Institute of China(Grant No.2016S03)the China–Korea Cooperation Project on Northwestern Pacific Climate Change and its PredictionAll numerical experiments were carried out at the Beijing Super Cloud Computing Center(BSCC).
文摘Three tiers of experiments in the Global Monsoons Model Intercomparison Project(GMMIP),one of the endorsed model intercomparison projects of phase 6 of the Coupled Model Intercomparison Project(CMIP6),are implemented by the First Institute of Oceanography Earth System Model version 2(FIO-ESM v2.0),following the GMMIP protocols.Evaluation of global mean surface air temperature from 1870 to 2014 and climatological precipitation(1979–2014)in tier-1 shows that the atmosphere model of FIO-ESM v2.0 can reproduce the basic observed atmospheric features.In tier-2,the internal variability is captured by the coupled model,with the SST restoring to the model climatology plus the observed anomalies in the tropical Pacific and North Atlantic.Simulation of the Northern Hemisphere summer monsoon circulation is significantly improved by the SST restoration in the North Atlantic.In tier-3,five orographic perturbation experiments are conducted covering the period 1979–2014 by modifying the surface elevation or vertical heating in the prescribed region.In particular,the strength of the South Asian summer monsoon is reduced by removing the topography or thermal forcing above 500 m over the Asian continent.Monthly and daily simulated outputs of FIO-ESM v2.0 are provided through the Earth System Grid Federation(ESGF)node to contribute to a better understanding of the global monsoon system.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFF0804600)the National Natural Science Foundation of China(Grant Nos.42276253 and 41821004)+1 种基金the Shandong Provincial Natural Science Foundation(Grant No.ZR2022JQ17)the Taishan Scholar Foundation of Shandong Province(Grant No.tsqn202211264).
文摘The Arctic is one of Earth’s regions highly susceptible to climate change.However,in situ long-term observations used for climate research are relatively sparse in the Arctic Ocean,and current climate models exhibit notable biases in Arctic Ocean simulations.Here,we present an Arctic Ocean dynamical downscaling dataset,obtained from the global ocean-sea ice model FESOM2 with a regionally refined horizonal resolution of 4.5 km in the Arctic region,which is driven by bias-corrected surface forcings derived from a climate model.The dataset includes 115 years(1900-2014)of historical simulations and two 86-year future projection simulations(2015-2100)for the SSP2-4.5 and SSP5-8.5 scenarios.The historical simulations demonstrate substantially reduced biases in temperature,salinity and sea-ice thickness compared to CMIP6 climate models.Common biases in the representation of the Atlantic Water layer found in climate model simulations are also markedly reduced in the dataset.Serving as a crucial long-term data source for climate change assessments and scientific research for the Arctic Ocean,this dataset provides valuable information for the scientific community.
基金The present study was supported by the National Natural Science Foundation of China （Grant Nos. 41476022 and 41490643）, the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology （2013r121, 2014r072）, the Program for Innovation Research and Entrepreneurship team in Jiangsu Province, and the National Programme on Global Change and Air-Sea Interaction （No. GASI- 03-IPOVAI-05）. Appreciation is extended to the anonymous reviewers and the editors for their valuable comments.
文摘The present study evaluates a simulation of the global ocean mixed layer depth （MLD） using the First Institute of Oceanography-Earth System Model （FIO- ESM）. The seasonal variation of the global MLD from the FIO-ESM simulation is compared to Argo observational data. The Argo data show that the global ocean MLD has a strong seasonal variation with a deep MLD in winter and a shallow MLD in summer, while the spring and fall seasons act as transitional periods. Overall, the FIO-ESM simula- tion accurately captures the seasonal variation in MLD in most areas. It exhibits a better performance during summer and fall than during winter and spring. The simulated MLD in the Southern Hemisphere is much closer to observations than that in the Northern Hemisphere. In general, the simulated MLD over the South Atlantic Ocean matches the observation best among the six areas. Additionally, the model slightly underestimates the MLD in parts of the North Atlantic Ocean, and slightly overestimates the MLD over the other ocean basins.
