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
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
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
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。
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(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.
