Predictions of Nino3.4 SST in CFSv1 and CFSv2: a diagnostic comparison

被引:51
作者
Barnston, Anthony G. [1 ]
Tippett, Michael K. [1 ,2 ]
机构
[1] Columbia Univ, Int Res Inst Climate & Soc, Earth Inst, Palisades, NY 10964 USA
[2] King Abdulaziz Univ, Dept Meteorol, Ctr Excellence Climate Change Res, Jeddah, Saudi Arabia
基金
美国海洋和大气管理局;
关键词
Coupled ocean-atmosphere models; NOAA CFSv1 and CFSv2; ENSO prediction; Skill diagnosis; Model hindcasts; Nino3.4 SST index; Target month slippage; Statistical field significance; CLIMATE FORECAST SYSTEM; SEA-SURFACE TEMPERATURE; SOUTHERN-OSCILLATION; ENSO; SKILL; ANOMALIES; PACIFIC; OCEAN;
D O I
10.1007/s00382-013-1845-2
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
Diagnostic evaluations of the relative performances of CFSv1 and CFSv2 in prediction of monthly anomalies of the ENSO-related Nino3.4 SST index are conducted using the common hindcast period of 1982-2009 for lead times of up to 9 months. CFSv2 outperforms CFSv1 in temporal correlation skill for predictions at moderate to long lead times that traverse the northern spring ENSO predictability barrier (e.g., a forecast for July made in February). However, for predictions during less challenging times of the year (e.g., a forecast for January made in August), CFSv1 has higher correlations than CFSv2. This seeming retrogression is caused by a cold bias in CFSv2 predictions for Nino3.4 SST during 1982-1998, and a warm bias during 1999-2009. Work by others has related this time-conditional bias to changes in the observing system in late 1998 that affected the ocean reanalysis serving as initial conditions for CFSv2. A posteriori correction of these differing biases, and of a similar (but lesser) situation affecting CFSv1, allows for a more realistic evaluation of the relative performances of the two CFS versions. After the dual bias corrections, CFSv2 has slightly better correlation skill than CFSv1 for most months and lead times, with approximately equal skills for forecasts not traversing the ENSO predictability barrier and better skills for most (particularly long-lead) predictions traversing the barrier. The overall difference in correlation skill is not statistically field significant. However, CFSv2 has statistically significantly improved amplitude bias, and visibly better probabilistic reliability, and lacks target month slippage as compared with CFSv1. Together, all of the above improvements result in a highly significantly reduced overall RMSE-the metric most indicative of final accuracy.
引用
收藏
页码:1615 / 1633
页数:19
相关论文
共 45 条
[1]  
Barnston AG, 1997, ATMOS OCEAN, V35, P367
[2]   SKILL OF REAL-TIME SEASONAL ENSO MODEL PREDICTIONS DURING 2002-11 Is Our Capability Increasing? [J].
Barnston, Anthony G. ;
Tippett, Michael K. ;
L'Heureux, Michelle L. ;
Li, Shuhua ;
DeWitt, David G. .
BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY, 2012, 93 (05) :631-651
[3]  
Behringer D. W., 2004, AMS 84 ANN M WASH ST
[4]   Evaluating the tropospheric variability in National Centers for Environmental Prediction's climate forecast system reanalysis [J].
Chelliah, Muthuvel ;
Ebisuzaki, Wesley ;
Weaver, Scott ;
Kumar, Arun .
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2011, 116
[5]  
CHEN WY, 1982, MON WEATHER REV, V110, P808, DOI 10.1175/1520-0493(1982)110<0808:FINHMH>2.0.CO
[6]  
2
[7]  
DAVIS RE, 1976, J PHYS OCEANOGR, V6, P249, DOI 10.1175/1520-0485(1976)006<0249:POSSTA>2.0.CO
[8]  
2
[9]   Twentieth century tropical sea surface temperature trends revisited [J].
Deser, Clara ;
Phillips, Adam S. ;
Alexander, Michael A. .
GEOPHYSICAL RESEARCH LETTERS, 2010, 37
[10]  
Hayes W.L., 1973, Statistics for social sciences