收稿日期: 1996-12-30
修回日期: 1997-02-03
网络出版日期: 1998-02-01
基金资助
国家自然科学基金资助项目“气候预测的可预报性及不确定性研究”(项目编号:49475261) 资助.
PREDICTABILITY AND UNCERTAINTY IN SHORT-TERM CLIMATE PREDICTION
Received date: 1996-12-30
Revised date: 1997-02-03
Online published: 1998-02-01
分析总结了近年来国内外短期气候预测业务、预测试验研究及可预报性研究的成果。指出无论用大气环流模式（AGAM）还是用统计方法，月平均环流预报与观测实况的相关系数均在0.2～0.3之间。用统计方法所作的气温预报水平与之相当，降水预报水平还要略低一些。季度预报大多依靠统计方法。近年来我国汛期降水预报水平有明显提高，但也只相当于相关系数0.2～0.3。用耦合环流模式（CGCM）积分作季度预报仅仅才开始试验。用各种模式作ENSO预报时表现出一定技巧，预报时效可达半年以上，但仍有春季预报障碍等问题。短期内气候预测业务可能仍然以统计方法为主。但必须大力开展气候系统机理的研究，并建立相应的模式。不了解气候变率形成的物理机制, 短期气候预测水平不可能有显著提高。
王绍武 . 短期气候预测的可预报性与不确定性[J]. 地球科学进展, 1998 , 13(1) : 8 -14 . DOI: 10.11867/j.issn.1001-8166.1998.01.0008
Operational prediction works, experimental predictions and predictability studies on short-term(monthly to interannual scale) climate prediction during the last ten years or so were reviewed. It is indicated that correlation coefficients(C.C.) between predicted and observed monthly mean 500 hPa hight anomalies vary between 0. 2 and 0. 3 independently what model or statistical method was used. Temperature prediction shows similar skill, but it is much lower for precipitation prediction. Seasonal prediction was mainly based on statistical method. CGCM used in seasonal prediction is only being in the first stage of the development. Seasonal rainfall forecasting in China showed nearly the same skill(0.2～0.3 of C. C. ). Central task of seasonal prediction is of improving the ability to predict the changes in SST, ice-snow cover and land surface conditions. The most successful skill was found in ENSO predictions, C. C. was above 0. 6 with six or more months lead. But, it is still suffered from the predictability barrier in spring. Therefore, operational short-term climate predictions in the next a few years should be as usual based on the statistics rather than on the dynamics. However, great efforts should be made in studying the dynamic and thermodynamic mechanism of the climate system, and improving the GCM. Without these efforts, it is impossible to increase significantly the prediction skill.
Key words： Short-term climate prediction; Predictability; Uncertainty.
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