Please wait a minute...
img img
高级检索
地球科学进展  2017, Vol. 32 Issue (4): 382-395    DOI: 10.11867/j.issn.1001-8166.2017.04.0382
论文     
耦合模式中北太平洋和北大西洋海表面温度年代际可预报性和预报技巧的季节依赖性
容新尧1, 刘征宇2, 3, 段晚锁4
1.中国气象科学研究院灾害天气国家重点实验室,北京 100081;
2.Department of Atmospheric and Oceanic Sciences & Center for Climatic Research, University of Wisconsin-Madison,Madison Wisconsin,USA;
3.北京大学物理学院大气与海洋科学系,北京 100871;
4.中国科学研大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室,北京 100029
Seasonal Dependence of the North Pacific and North Atlantic SST Predictability and Forecast Skill
Rong Xinyao1, Liu Zhengyu2, 3, Liu Yun2, Duan Wansuo4
1.State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China;
2.Department of Atmospheric and Oceanic Sciences & Center for Climatic Research, University of Wisconsin-Madison,Madison Wisconsin, USA;
3.Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University,Beijing 100871,China;
4.State Key Laboratory of Numerical Modeling for Ateospheric Sciences and GeophysicalFluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
 全文: PDF(1268 KB)   HTML
摘要:

利用一个全球耦合环流模式在理想模式框架下进行了3组动力预报试验,研究了北太平洋和北大西洋海表面温度异常(SSTA)的年代际可预报性和预报技巧。结果表明北太平洋年平均SSTA在年代际尺度上可预报性和预报技巧表现较低,明显弱于北大西洋。通过分析不同季节平均SSTA的可预报性与预报技巧,发现北太平洋中西部区域冬季平均SSTA的年代际可预报性和预报技巧显著高于其他季节,其量值和北大西洋相当,表现为明显的季节依赖性;北大西洋SSTA的可预报性和预报技巧也显示了随季节变化的特征。进一步分析表明,北太平洋SSTA年代际可预报性和预报技巧的季节依赖性归因于北太平洋冬季SSTA的年与年之间再现机制,这一再现机制源于北太平洋混合层显著的季节变化;而北大西洋SSTA的可预报性和预报技巧的季节依赖性则可能与其他过程(如大西洋年代际涛动)的季节变化有关。研究结果表明,对于年代际气候预报,如果考虑所关注指标的季节平均,则可能在某些季节获得比年平均更高的预报技巧。

关键词: 年代际预测耦合模式北大西洋北太平洋季节依赖性    
Abstract:

In this paper, the decadal predictability and forecast skill of the Sea Surface Temperature Anomalies (SSTA) in the North Pacific and North Atlantic Ocean were investigated by conducting three sets of perfect model forecast experiments using a global coupled general circulation model. The results show that the annual mean SSTA in the North Pacific is less predictable on decadal time scale, with the forecast skill notably weaker than that of the North Atlantic. By analyzing the predictability and forecast skill of seasonal mean SSTA, it is found that the decadal predictability and forecast skill of the winter mean (JFM) SSTA in the central and western North Pacific are significantly higher than those of other seasons, and the magnitude is comparable with that of the North Atlantic. The predictability and forecast skill of the North Atlantic SSTA also show seasonal variations. Further analysis indicates that the seasonal dependence of the SSTA decadal predictability and forecast skill in the North Pacific is due to the winter-to-winter reemergence mechanism of SSTA in the North Pacific, which results from the seasonal variation of the mixed layer depth of the North Pacific Ocean. While the seasonal dependence of the North Atlantic SSTA predictability and forecast skill might be related to seasonal variations of other processes, such as the Atlantic Decadal Oscillation. The results of this paper suggest that for decadal climate prediction, if the forecast skill of the seasonal mean is taken into account, we might obtain higher than annual mean forecast skill for some seasons.

Key words: Coupled GCM.    North Atlantic    Decadal prediction    North Pacific    Seasonal dependence
收稿日期: 2016-10-19 出版日期: 2017-04-20
ZTFLH:  P467  
基金资助:

公益性行业(气象)科研专项项目“基于FGOALS-s、CMA和CESM气候系统模式的年代际集合预测系统的建立与研究”(编号:GYHY201506012); 科技部全球变化研究项目(编号:2012CB955201)资助

作者简介: 容新尧(1979-),男,海南三亚人,副研究员,主要从事气候数值模拟及预测研究.E-mail:rongur@camscma.cn
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  

引用本文:

容新尧, 刘征宇, 段晚锁. 耦合模式中北太平洋和北大西洋海表面温度年代际可预报性和预报技巧的季节依赖性[J]. 地球科学进展, 2017, 32(4): 382-395.

Rong Xinyao, Liu Zhengyu, Liu Yun, Duan Wansuo. Seasonal Dependence of the North Pacific and North Atlantic SST Predictability and Forecast Skill. Advances in Earth Science, 2017, 32(4): 382-395.

链接本文:

http://www.adearth.ac.cn/CN/10.11867/j.issn.1001-8166.2017.04.0382        http://www.adearth.ac.cn/CN/Y2017/V32/I4/382

[1] Meehl G A, Goddard L, Murphy J, et al . Decadal prediction: Can it be skillful?[J]. Bulletin of the American Meteorological Society , 2009, 90(10): 1 467-1 485.
[2] Mantua N J, Hare S R, Zhang Y, et al . A Pacific interdecadal climate oscillation with impacts on salmon production[J]. Bulletin of the American Meteorological Society , 1997, 78(6): 1 069-1 079.
[3] Power S, Casey T, Folland C, et al . Interdecadal modulation of the impact ofENSO on Australia[J]. Climate Dynamics , 1999, 15(5): 319-324.
[4] Kushnir Y. Interdecadal variations in North Atlantic sea surface temperature and associated atmospheric conditions[J]. Journal of Climate , 1994, 7(1): 141-157.
[5] Delworth T L, Mann M E. Observed and simulated multidecadal variability in the Northern Hemisphere[J]. Climate Dynamics , 2000, 16(9): 661-676.
[6] Boer G. A study of atmosphere-ocean predictability on long time scales[J]. Climate Dynamics ,2000, 16(6): 469-477.
[7] Boer G. Long time-scale potential predictability in an ensemble of coupled climate models[J]. Climate Dynamics , 2004, 23(1): 29-44.
[8] Griffies S M, Bryan K. A predictability study of simulated North Atlantic multidecadal variability[J]. Climate Dynamics , 1997, 13(7/8): 459-487.
[9] Collins M. Climate predictability on interannual to decadal time scales: The initial value problem[J]. Climate Dynamics , 2002, 1(8): 671-692.
[10] Kumar A, Peng P, Chen M. Is there a relationship between potential and actual skill?[J]. Monthly Weather Review , 2014, 142(6): 2 220-2 227.
[11] Smith D, Cusack S, Colman A, et al . Improved surface temperature prediction for the coming decade from a global circulation model[J]. Science , 2007, 317(5 839): 796-799.
[12] Keenlyside N, Latif M, Jungclaus J, et al . Advancing decadal-scale climate prediction in the North Atlantic sector[J]. Nature , 2008, 453(7 191): 84-88.
[13] Newman M. An empirical benchmark for decadal forecasts of global surface temperature anomalies[J]. Journal of Climate , 2013, 26(14): 5 260-5 269.
[14] Torrence C, Webster P J. The annual cycle of persistence in the El Niño/Southern Oscillation[J]. Quarterly Journal of the Royal Meteorological Society , 1998, 124(550): 1 985-2 004.
[15] Mu M, Duan W S, Wang B. Season-dependent dynamics of nonlinear optimal error growth and El Niño-Southern Oscillation predictability in a theoretical model[J]. Journal of Geophysical Research ,2007,112:D10113,doi:10.1029/2005JD006981.
[16] Duan W S, Wei C. The “spring predictability barrier” for ENSO predictions and its possible mechanism: Results from a fully coupled model[J]. International Journal of Climatology , 2012, 33(5): 1 280-1 292.
[17] Namias J, Born R M. Temporal coherence in North Pacific sea surface temperature patterns[J]. Journal of Geophysical Research , 1970, 75(30): 5 952-5 955.
[18] Namias J, Born R M. Further studies of temporal coherence in North Pacific sea surface temperature patterns[J]. Journal of Geophysical Research , 1974, 79(6): 797-798.
[19] Deser C, Phillips A S, Hurrell J W. Pacific interdecadal climate variability: Linkages between the tropics and North Pacific in boreal winter since 1990[J]. Journal of Climate , 2004, 17(16): 3 109-3 124.
[20] An S I, Wang B. The forced and intrinsic low-frequency modes in the North Pacific[J]. Journal of Climate , 2005, 18(6): 876-885, doi:10.1175/JCLI-3298.1.
[21] Ding R Q, Li J P. Decadal and seasonal dependence of North Pacific SST persistence[J]. Journal of Geophysical Research , 2009, 114:D01105, doi:10.1029/2008JD010723.
[22] Watanabe M, Kimoto M. On the persistence of decadal SST anomalies in the North Atlantic[J]. Journal of Climate , 2000, 13(16): 3 017-3 028.
[23] Timlin M S, Alexander M A, Deser C. On the reemergence of North Atlantic SST anomalies[J]. Journal of Climate , 2002, 15(181): 2 707-2 712.
[24] Alexander M A, Deser C. A mechanism for the recurrence of wintertime midlatitude SST anomalies[J]. Journal of Physical Oceanography , 1995, 25(1): 122-137.
[25] Wen C H, Xue Y, Kumar A. Seasonal Prediction of North Pacific SSTs and PDO in the NCEP CFS Hindcasts[J]. Journal of Climate , 2012, 25(171): 5 689-5 710.
[26] Duan W S, Wu Y J. Season-dependent predictability and error growth dynamics of Pacific Decadal Oscillation-related sea surface temperature anomalies[J]. Climate Dynamics , 2015, 44(314): 1 053-1 072,doi:10.1007/s00382-014-2364-5.
[27] Wu Y, Duan W, Rong X. Seasonal predictability of sea surface temperature anomalies over the Kuroshio-Oyashio extension: Low in summer and high in winter[J]. Journal of Geophysical Research , 2016, 121(9), doi:10.1002/2016JC011887.
[28] Jacob R. Low Frequency Variability in A Simulated Atmosphere ocean System[D]. Wisconsin,USA:University of Wisconsin, 1997.
[29] Tobis M, Schafer C, Foster I, et al . FOAM: Expanding the horizons of climate modeling[C]∥Supercomputing, ACM/IEEE 1997 Conference, Supercomputing, ACM/IEEE 1997 Conference.1997.
[30] Liu Z, Kutzbach J, Wu L. Modeling climate shift of El Niño variability in the Holocene[J]. Geophysical Research Letters , 2000, 27(15): 2 265-2 268.
[31] Wu L, Liu Z, Gallimore R, et al . Pacific decadal variability: The tropical mode and the North Pacific mode[J]. Jouranl of Climate , 2003, 16(8): 1 101-1 120.
[32] Liu Z, Liu Y, Wu L, et al . Seasonal and long-term atmospheric responses to reemerging North Pacific Ocean variability: A combined dynamical and statistical assessment[J]. Jouranl of Climate , 2007, 20(6): 955-980.
[33] Liu Y, Liu Z, Zhang S, et al . Ensemble-based parameter estimation in a coupled GCMusing the adaptive spatial average method[J]. Jouranl of Climate , 2014, 27(11): 4 002-4 014.
[34] Lu F, Liu Z, Liu Y, et al . Understanding the control of extratropical atmospheric variability on ENSO using a coupled data assimilation approach[J]. Climate Dynamics , 2016, doi:10.1007/s00382-016-3256-7.
[35] Anderson J L. An ensemble adjustment Kalman filter for data assimilation[J]. Monthly Weather Review , 2001, 129(12): 2 884-2 903.
[36] Anderson J L. A local least squares framework for ensemble filtering[J]. Monthly Weather Review , 2003, 131(4): 634-642.
[37] White W B. Design of a global observing system for gyrescale upper ocean temperature variability[J]. Progress in Oceanography , 1995, 36(3): 169-217.
[38] Carton J A, Giese B S. A reanalysis of ocean climate using Simple Ocean Data Assimilation (SODA)[J]. Monthly Weather Review , 2008, 136(8): 2 999-3 017.

[1] 安俊岭, 陈勇, 屈玉, 陈琦, 庄炳亮, 张平文, 吴其重, 徐勤武, 曹乐, 姜海梅, 陈学舜, 郑捷. 全耦合空气质量预报模式系统[J]. 地球科学进展, 2018, 33(5): 445-454.
[2] 尹碧文, 任福民, 李国平. 1951—2014年西北太平洋双台风活动气候特征研究[J]. 地球科学进展, 2017, 32(6): 643-650.
[3] 周天军, 吴波. 年代际气候预测问题:科学前沿与挑战[J]. 地球科学进展, 2017, 32(4): 331-341.
[4] 满文敏, 周天军. IAP年代际预测试验中火山活动对太平洋海温预测技巧的影响[J]. 地球科学进展, 2017, 32(4): 353-361.
[5] 张丽霞, 张文霞, 周天军, 吴波. ENSEMBLES耦合模式对全球陆地季风区夏季降水的年代际预测能力评估[J]. 地球科学进展, 2017, 32(4): 409-419.
[6] 吴波, 周天军, 孙倩. 海洋模式初始化同化方案对IAP近期气候预测系统回报试验技巧的影响[J]. 地球科学进展, 2017, 32(4): 342-352.
[7] 韩振宇, 吴波, 辛晓歌. BCC_CSM1.1气候模式对全球海表温度年代际变化的回报能力评估[J]. 地球科学进展, 2017, 32(4): 396-408.
[8] 史文奇, 赵进平. 北欧海溢流的水文特征和变化机理综述[J]. 地球科学进展, 2017, 32(3): 245-261.
[9] 姚遥, 罗德海. 北大西洋涛动—欧洲阻塞及其对极端暴雪影响的研究进展[J]. 地球科学进展, 2016, 31(6): 581-594.
[10] 韩钦臣, 康建成, 王国栋, 朱炯. 基于海洋分析资料的吕宋海峡水交换的月际变化特征[J]. 地球科学进展, 2015, 30(5): 609-619.
[11] 李晓峰. 环状模概念[J]. 地球科学进展, 2015, 30(3): 1-.
[12] 邹立维,周天军. 区域海气耦合模式研究进展[J]. 地球科学进展, 2012, 27(8): 857-865.
[13] 张立凤, 吕庆平, 张永垂. 北太平洋涡旋振荡研究进展[J]. 地球科学进展, 2011, 26(11): 1143-1149.
[14] 陆日宇,富元海. 夏季东亚和西北太平洋地区的气候变异及其机理[J]. 地球科学进展, 2009, 24(2): 123-131.
[15] 张永垂,张立凤. 北太平洋Rossby波研究进展[J]. 地球科学进展, 2009, 24(11): 1219-1228.