BCC＿CSM1.1气候模式年代际试验对北极涛动季节回报能力的初步评估
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摘要
本文基于国家气候中心气候系统模式BCC_CSM1.1自1960—2004年每年起报的年代际预测试验结果,初步评估了该模式对北极涛动(AO)的预报技巧。同时,把该模式年代际预测结果与历史试验模拟比较,分析了气候模式初始化对年代际试验预测季节尺度AO及其年际变化的贡献。结果表明,年代际试验和历史试验均能反映出AO模态是北半球中高纬大气变率第一模态的特征,其中年代际预测试验回报的AO模态与观测的空间相关系数高于历史试验。两组试验基本能再现AO指数冬季最强、夏季最弱的特征。与历史试验相比,年代际预测试验回报月和冬季AO指数与观测的相关系数更高,特别是年代际试验与观测的月AO指数相关系数达到了0.1的显著性水平。年代际试验回报月、春季AO指数的变化周期更接近观测结果。因此,年代际试验中初始状态使用海温资料进行初始化,在一定程度上可以提高AO的回报能力。
This study assesses projection skill of Arctic Oscillation(AO) in initialized decadal experimentwith the Beijing Climate Center Climate System Model(BCC_CSM1.1). As compared with the observationsand uninitialized historical experiment, the contribution of climate model initialization to predict the seasonalscale AO and its interannual variations is estimated. Results show that the spatial correlation coefficient of AOmode, which depicts the dominant mode of the extra-tropical atmospheric variability, simulated by the decadalexperiment is higher than that in the historical experiment. The two groups of experiments can basically reproducethe characteristics of the strongest winter AO index and the weakest summer index. Compared with historicalexperiment, the correlation coefficient of the monthly and winter AO index is higher in the decadal experiment. Inparticular, the correlation coefficient of the monthly AO index between the decadal simulations and the observationreached 0.1 significant level. Furthermore, the periodicity of the monthly and spring AO index are achieved onlyin the decadal experiment. Hence, the hindcast skill of AO is robust when the initial state is initialized by seasurface temperature data.
This study assesses projection skill of Arctic Oscillation(AO) in initialized decadal experimentwith the Beijing Climate Center Climate System Model(BCC_CSM1.1). As compared with the observationsand uninitialized historical experiment, the contribution of climate model initialization to predict the seasonalscale AO and its interannual variations is estimated. Results show that the spatial correlation coefficient of AOmode, which depicts the dominant mode of the extra-tropical atmospheric variability, simulated by the decadalexperiment is higher than that in the historical experiment. The two groups of experiments can basically reproducethe characteristics of the strongest winter AO index and the weakest summer index. Compared with historicalexperiment, the correlation coefficient of the monthly and winter AO index is higher in the decadal experiment. Inparticular, the correlation coefficient of the monthly AO index between the decadal simulations and the observationreached 0.1 significant level. Furthermore, the periodicity of the monthly and spring AO index are achieved onlyin the decadal experiment. Hence, the hindcast skill of AO is robust when the initial state is initialized by seasurface temperature data.
引文
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[11] Cattiaux J, Cassou C. Opposite CMIP3/CMIP5 trends in the wintertimeNorthern Annular Mode explained by combined local sea ice and remote tropical influences[J]. Geophysical Research Letters, 2013, 40(14):3682-3687
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[13] Gong H N, Wang L, Chen W, et al. Biases of the wintertime Arctic Oscillation in CMIP5 models[J]. Environmental Research Letters,2017, 12,014001
[14] Sun J, Ahn J. Dynamical seasonal predictability of the Arctic Oscillation using a CGCM[J]. International Journal of Climatology,2015,35(7):1342-1353
[15] Stockdale T N, Molteni F, Ferranti L. Atmospheric initial conditions and the predictability of the Arctic Oscillation[J]. Geophysical Research Letters, 2015, 42(4):1173-1179
[16] Dunstone N, Smith D, Scaife A, et al. Skilful predictions of the winter North Atlantic Oscillation one year ahead[J]. Nature Geoscience, 2016,9(11):809-814
[17] Ambaum M, Hoskins B J, Stephenson D B. Arctic Oscillation or North Atlantic Oscillation?[J]. Journal of Climate, 2001, 14(16):3495-3507
[18] Taylor K E, Stouffer R J, Meehl G A. An overview of CMIP5 and the experiment design[J]. Bulletin of the American Meteorological Society, 2012, 93(4):485-498
[19] Wu T W, Yu R C, Zhang F, et al. The Beijing Climate Center atmospheric general circulation model:description and its performance for the present day climate[J]. Climate Dynamics, 2010, 34(1):123-147
[20] LI Q Q, Tan J, Wang L N, et al. Simulation of the natural distribution of carbon and nutrients in the ocean based on the global ocean carbon cycle model MOM4_L40[J]. Chinese Journal of Geophysics, 2015, 58(1):1-19
[21] Wu T W, Li W P, Ji J J, et al. Global carbon budgets simulated by the Beijing Climate Center climate system model for the last century[J].Journal of Geophysical Research Atmospheres, 2013, 118(10):4326-4347
[22]辛晓歌,吴统文,张洁.BCC气候系统模式开展的CMIP5试验介绍[J].气候变化研究进展,2012, 8(5):378-382
[23] Carton J A, Giese B S. A reanalysis of ocean climate using Simple Ocean Data Assimilation(SODA)[J]. Monthly Weather Review, 2008,136:2999-3017
[24] Allan R, Ansell T. A new globally complete monthly historical gridded mean sea level pressure dataset(HadSLP2):1850-2004[J]. Journal of Climate, 2006, 19:5816-5842
[25] Thompson D W J, Wallace J M. Annular modes in the extratropical circulation. Part I month-to-month variability[J]. Journal of Climate,2000, 13:1000-1016
[26] Garcfa-Serrano J, Doblas-Reyes F J. On the assessment of near-surface global temperature and North Atlantic multi-decadal variability in the ensembles decadal hindcast[J]. Climate Dynamics, 2012, 39:2025-2040
[27] Goddard L, Kumar A, Solomon A, et al. A verification framework for interannual-to-decadal predictions experiments[J]. Climate Dynamics,2013.40:245-272
[28] Wei M, Li Q Q, Xin X G, et al. Improved decadal climate prediction in the North Atlantic using EnOI-assimilated initial condition[J]. Science Bulletin, 2017, 62:1142-1147
[29] Xin X G, Gao F, Wei M, et al. Decadal prediction skill of BCCCSM1.1 model in East Asia[J]. International journal of Climatology,2018.38:584-592
[30]朱献,董文杰,郭彦.CMIP3及CMIP5模式对冬季和春季北极涛动变率模拟的比较[J].气候变化研究进展,2013, 9(3):165-172
[31]左金清,李维京,任宏利.CMIP5模式对北极涛动的模拟评估[J].气候变化研究进展,2013, 9(3):157-164
[32] Jadin E A. Arctic Oscillation and interannual variations of the sea surface temperature in the Atlantic and Pacific[J]. Russian Meteorology and Hydrology,2001,18:28-40
[2] Thompson D W J, Wallace J M. The Arctic Oscillation signature in the wintertime geopotential height and temperature fields[J]. Geophysical Research Letters, 1998, 25(9):1297-1300
[3] Thompson D W, Wallace J M. Regional climate impacts of the Northern Hemisphere annular mode[J]. Science, 2001, 293:85-89
[4]龚道溢,王绍武.近百年北极涛动对中国冬季气候的影响[J].地理学报,2003, 58(4):559-568
[5]梁苏洁,丁一汇,赵南,等.近50年中国大陆冬季气温和区域环流的年代际变化研究[J].大气科学,2014, 38(5):974-992
[6]所玲玲,黄嘉佑,谭本馗.北极涛动对我国冬季同期极端气温的影响研究[J].热带气象学报,2008, 24(2):163-168
[7]琚建华,吕俊梅,任菊章.北极涛动年代际变化对华北地区干旱化的影响[J].高原气象,2006, 25(1):74-81
[8] He S P, Gao Y Q, Li F, et al. Impact of Arctic Oscillation on the East Asian climate:a review[J]. Earth-Science Reviews, 2017, 164:48-62
[9] Miller R L, Schmidt G A, Shindell D T. Forced annular variations in the 20th century Intergovernmental Panel on Climate Change Fourth Assessment Report models[J]. Journal of Geophysical Research Atmospheres,2006, 111,D18101
[10]辛晓歌,周天军,宇如聪.气候系统模式对北极涛动的模拟[J].地球物理学报,2008, 51(2):337-351
[11] Cattiaux J, Cassou C. Opposite CMIP3/CMIP5 trends in the wintertimeNorthern Annular Mode explained by combined local sea ice and remote tropical influences[J]. Geophysical Research Letters, 2013, 40(14):3682-3687
[12] Zuo J Q, Li W J, Ren H L. Representation of the Arctic Oscillation in the CMIP5 models[J]. Advances in Climate Change Research, 2013, 4(4):242-249
[13] Gong H N, Wang L, Chen W, et al. Biases of the wintertime Arctic Oscillation in CMIP5 models[J]. Environmental Research Letters,2017, 12,014001
[14] Sun J, Ahn J. Dynamical seasonal predictability of the Arctic Oscillation using a CGCM[J]. International Journal of Climatology,2015,35(7):1342-1353
[15] Stockdale T N, Molteni F, Ferranti L. Atmospheric initial conditions and the predictability of the Arctic Oscillation[J]. Geophysical Research Letters, 2015, 42(4):1173-1179
[16] Dunstone N, Smith D, Scaife A, et al. Skilful predictions of the winter North Atlantic Oscillation one year ahead[J]. Nature Geoscience, 2016,9(11):809-814
[17] Ambaum M, Hoskins B J, Stephenson D B. Arctic Oscillation or North Atlantic Oscillation?[J]. Journal of Climate, 2001, 14(16):3495-3507
[18] Taylor K E, Stouffer R J, Meehl G A. An overview of CMIP5 and the experiment design[J]. Bulletin of the American Meteorological Society, 2012, 93(4):485-498
[19] Wu T W, Yu R C, Zhang F, et al. The Beijing Climate Center atmospheric general circulation model:description and its performance for the present day climate[J]. Climate Dynamics, 2010, 34(1):123-147
[20] LI Q Q, Tan J, Wang L N, et al. Simulation of the natural distribution of carbon and nutrients in the ocean based on the global ocean carbon cycle model MOM4_L40[J]. Chinese Journal of Geophysics, 2015, 58(1):1-19
[21] Wu T W, Li W P, Ji J J, et al. Global carbon budgets simulated by the Beijing Climate Center climate system model for the last century[J].Journal of Geophysical Research Atmospheres, 2013, 118(10):4326-4347
[22]辛晓歌,吴统文,张洁.BCC气候系统模式开展的CMIP5试验介绍[J].气候变化研究进展,2012, 8(5):378-382
[23] Carton J A, Giese B S. A reanalysis of ocean climate using Simple Ocean Data Assimilation(SODA)[J]. Monthly Weather Review, 2008,136:2999-3017
[24] Allan R, Ansell T. A new globally complete monthly historical gridded mean sea level pressure dataset(HadSLP2):1850-2004[J]. Journal of Climate, 2006, 19:5816-5842
[25] Thompson D W J, Wallace J M. Annular modes in the extratropical circulation. Part I month-to-month variability[J]. Journal of Climate,2000, 13:1000-1016
[26] Garcfa-Serrano J, Doblas-Reyes F J. On the assessment of near-surface global temperature and North Atlantic multi-decadal variability in the ensembles decadal hindcast[J]. Climate Dynamics, 2012, 39:2025-2040
[27] Goddard L, Kumar A, Solomon A, et al. A verification framework for interannual-to-decadal predictions experiments[J]. Climate Dynamics,2013.40:245-272
[28] Wei M, Li Q Q, Xin X G, et al. Improved decadal climate prediction in the North Atlantic using EnOI-assimilated initial condition[J]. Science Bulletin, 2017, 62:1142-1147
[29] Xin X G, Gao F, Wei M, et al. Decadal prediction skill of BCCCSM1.1 model in East Asia[J]. International journal of Climatology,2018.38:584-592
[30]朱献,董文杰,郭彦.CMIP3及CMIP5模式对冬季和春季北极涛动变率模拟的比较[J].气候变化研究进展,2013, 9(3):165-172
[31]左金清,李维京,任宏利.CMIP5模式对北极涛动的模拟评估[J].气候变化研究进展,2013, 9(3):157-164
[32] Jadin E A. Arctic Oscillation and interannual variations of the sea surface temperature in the Atlantic and Pacific[J]. Russian Meteorology and Hydrology,2001,18:28-40
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