Improved decadal climate prediction in the North Atlantic using EnOI-assimilated initial condition  

作　　者：Min Wei Qingquan Li Xiaoge Xin Wei Zhou Zhenyu Han Yong Luo Zongci Zhao 

机构地区：[1]Ministry of Education Key Laboratory for Earth System Modeling,Department of Earth System Science,Tsinghua University,Beijing 100084,China [2]Joint Center for Global Change Studies,Beijing 100875,China [3]National Meteorological Information Center,China Meteorological Administration,Beijing 100081,China [4]Laboratory for Climate Studies,National Climate Center,China Meteorological Administration,Beijing 100081,China [5]Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters,Nanjing University of Information Science and Technology,Nanjing 210044,China [6]State Key Laboratory of Tropical Oceanography,South China Sea Institute of Oceanology,Chinese Academy of Sciences,Guangzhou 510301,China

出　　处：《Science Bulletin》2017年第16期1142-1147,共6页科学通报（英文版）

基　　金：supported by the National Program on Key Basic Research Project of China(2012CB955203,2016YFA0602100,2013CB430202,2016YFA0602200 and 2016YFE0102404);Tsinghua University Initiative Scientific Research Program(20131089357)

摘　　要：Decadal prediction experiments of Beijing Climate Center climate system model version 1.1 （BCC- CSM1.1） participated in Coupled Model Intercomparison Project Phase 5 （CMIPS） had poor skill in extratropics of the North Atlantic, the initialization of which was done by relaxing modeled ocean temperature to the Simple Ocean Data Assimilation （SODA） reanalysis data. This study aims to improve the prediction skill of this model by using the assimilation technique in the initialization. New ocean data are firstly generated by assimilating the sea surface temperature （SST） of the Hadley Centre Sea Ice and Sea Surface Temperature （HadlSST） dataset to the ocean model of BCC-CSM1.1 via Ensemble Optimum Interpolation （EnOI）. Then a suite of decadal reforecasts launched annually over the period 1961- 2005 is carried out with simulated ocean temperature restored to the assimilated ocean data. Comparisons between the re-forecasts and previous CMIP5 forecasts show that the re-forecasts are more skillful in mid-to-high latitude SST of the North Atlantic. Improved prediction skill is also found for the Atlantic multi-decadal oscillation （AMO）, which is consistent with the better skill of Atlantic meridional overturning circulation （AMOC） predicted by the re-forecasts. We conclude that the EnOl assimilation generates better ocean data than the SODA reanalysis for initializing decadal climate prediction of BCC-CSM1.1 model.Decadal prediction experiments of Beijing Climate Center climate system model version 1.1(BCCCSM1.1) participated in Coupled Model Intercomparison Project Phase 5(CMIP5) had poor skill in extratropics of the North Atlantic, the initialization of which was done by relaxing modeled ocean temperature to the Simple Ocean Data Assimilation(SODA) reanalysis data. This study aims to improve the prediction skill of this model by using the assimilation technique in the initialization. New ocean data are firstly generated by assimilating the sea surface temperature(SST) of the Hadley Centre Sea Ice and Sea Surface Temperature(Had ISST) dataset to the ocean model of BCC-CSM1.1 via Ensemble Optimum Interpolation(EnOI). Then a suite of decadal re-forecasts launched annually over the period 1961–2005 is carried out with simulated ocean temperature restored to the assimilated ocean data.Comparisons between the re-forecasts and previous CMIP5 forecasts show that the re-forecasts are more skillful in mid-to-high latitude SST of the North Atlantic. Improved prediction skill is also found for the Atlantic multi-decadal oscillation(AMO), which is consistent with the better skill of Atlantic meridional overturning circulation(AMOC) predicted by the re-forecasts. We conclude that the EnOI assimilation generates better ocean data than the SODA reanalysis for initializing decadal climate prediction of BCC-CSM1.1 model.

关 键 词：Decadal prediction EnOl Initialization AMO BCC-CSM 

分 类 号：P456[天文地球—大气科学及气象学]