| 动力季节预测中预报误差与物理因子的关系 |
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| 引用本文： | 任宏利.动力季节预测中预报误差与物理因子的关系[J].应用气象学报,2008,19(3):276-286. |
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| 作者姓名： | 任宏利 |
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| 作者单位： | 国家气候中心 中国气象局气候研究开放实验室, 北京 100081 |
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| 基金项目： | 国家科技支撑计划
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国家自然科学基金 |
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| 摘 要： | 为了在动力季节预测中更好地运用统计经验来改进预报, 从研究气候系统物理因子影响模式预报误差的角度入手, 探讨了与气候模式有关的统计经验获取问题, 并利用国家气候中心海-气耦合模式的历史回报数据, 考察了动力季节预测中夏季环流和降水的预报误差与主要物理因子, 包括Ni?o3区海温指数、太平洋年代际振荡指数、南北半球环状模指数以及北大西洋涛动指数相关关系。分析结果显示:上述物理因子与模式预报的夏季环流和降水误差之间存在前期或同期的某种显著相关关系, 并且显著关系分布随因子的不同而表现出明显不同的区域性特征, 这为发展一种基于预报因子的误差订正新方法提供了新思路。
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| 关 键 词： | 动力季节预测 预报误差 物理因子 |
| 收稿时间： | 2007-07-03 |
Relationships Between Prediction Errors and Physical Predictors in Dynamical Seasonal Prediction |
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| Ren Hongli.Relationships Between Prediction Errors and Physical Predictors in Dynamical Seasonal Prediction[J].Quarterly Journal of Applied Meteorology,2008,19(3):276-286. |
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| Authors: | Ren Hongli |
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| Affiliation: | Laboratory for Climate Studies, National Climate Center, China Meteorological Administration, Beijing 100081 |
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| Abstract: | In order to better improve dynamical seasonal prediction by utilizing statistical experiences, the problem of capturing statistical experiences associated with climate model is discussed from the point of view of studying the impacts of physical predictors in climate system on model prediction errors. As model errors objectively exist in climate model and vary with the sate of climate system, the prediction errors of model change with state. Thus, deeply examining the characteristics that how the prediction errors of model are influenced by climate system state has important values to capture the statistical experiences associated with model and improve dynamical prediction in terms of the combination of dynamical and statistical methods. Problems and their meanings related with above characteristics are primarily proposed.Based on hindcast data of air-ocean coupled general circulation model in National Climate Center, China Meteorological Administration, the relationship between the prediction errors of summer mean circulation and total precipitation in dynamical seasonal prediction, and some primary physical predictors are comprehensively examined. These physical predictors selected include the sea surface temperature index in Ni?o3 region, the Pacific decadal oscillation index, the southern hemispheric annular mode index, the northern hemispheric annular mode index, and the North Atlantic oscillation index, which reflects the prevailing modes of interannual variability in climate system over tropics and extratropics.Results of correlation analyses show that there are some significant early or simultaneous relationships between the above-mentioned physical predictors and the model prediction errors of summer circulation and precipitation. In the five selected physical predictors, the sea surface temperature index in Ni?o3 region mainly correlates with the prediction errors of low-latitude circulation and precipitation. The correlationship between the Pacific decadal oscillation index and prediction errors is mostly characterized by simultaneous correlations and is significant not only over some low-latitude but also mid-and high-latitude areas. The southern and northern hemispheric annular mode indices standing for the leading modes over two extratropics are not corresponding to the evident early correlation with only some significant correlations over partial extratropics, whereas the simultaneous correlations are relatively evident and characterized by the distribution patterns similar to spatial modes of the two extratropical indices. The correlation situations between the North Atlantic oscillation index and circulation errors are very analogical to those corresponding to the northern hemispheric annular mode index, whereas the early and simultaneous correlations between it and precipitation errors are much better than those corresponding to circulation errors.By examining the physical processes that physical predictor influences the distribution of model prediction errors, investigating the relationship between predictor and prediction errors will not only help to evaluate the performance of model and provide reference for improving model, but also benefit the development of the new prediction strategy and methodology for correcting prediction errors. Thereby, physical basis and practical reference are provided to develop new method of predictor-based error correction, which will be studied and verified in further work. |
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| Keywords: | dynamical seasonal prediction prediction error physical predictor |
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