Relating CMIP5 Model Biases to Seasonal Forecast Skill in the Tropical Pacific
Relating CMIP5 Model Biases to Seasonal Forecast Skill in the Tropical Pacific
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DOI：
10.1029/2019gl086765
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发表时间：
2020-03-16
影响因子：
5.2
通讯作者：
Wittenberg, Andrew T.
中科院分区：
文献类型：
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作者：
Ding, Hui;Newman, Matthew;Wittenberg, Andrew T.
我们研究了热带太平洋平均状态偏差和厄尔尼诺/南方涛动预报技能之间的联系，使用模型模拟海表温度（SST; 1961-2015）和降水（1979-2015）的后报，在0-12个月的铅，从耦合模型相互比较项目（CMIP 5）的第五阶段产生的28个不同的模型。在给定模式下，模式模拟预报技术已被证明可以达到甚至超过传统的同化初始化预报技术。具有赤道太平洋SST、降水和10 m纬向风最真实的平均状态和年际变率的模型也能生成赤道太平洋中部最熟练的降水预报和6个月或更长时间的最佳SST预报。这些结果显示了模型气候偏差和季节预测误差之间的直接联系，表明模型模拟后报技能（即模型能够捕捉到热带太平洋异常现象的观测演变的程度）是一个信息丰富的厄尔尼诺/南方涛动指标用于气候模拟。
We examine links between tropical Pacific mean state biases and El Nino/Southern Oscillation forecast skill, using model-analog hindcasts of sea surface temperature (SST; 1961-2015) and precipitation (1979-2015) at leads of 0-12 months, generated by 28 different models from the fifth phase of the Coupled Model Intercomparison Project (CMIP5). Model-analog forecast skill has been demonstrated to match or even exceed traditional assimilation-initialized forecast skill in a given model. Models with the most realistic mean states and interannual variability for SST, precipitation, and 10-m zonal winds in the equatorial Pacific also generate the most skillful precipitation forecasts in the central equatorial Pacific and the best SST forecasts at 6-month or longer leads. These results show direct links between model climatological biases and seasonal forecast errors, demonstrating that model-analog hindcast skill-that is, how well a model can capture the observed evolution of tropical Pacific anomalies-is an informative El Nino/Southern Oscillation metric for climate simulations.