doi:  10.3878/j.issn.1006-9895.1808.18149
近期气候预测系统IAP-DecPreS对印度洋偶极子的回报技巧: 全场同化和异常场同化的比较

Predictive Skill of the Near-Term Climate Prediction System IAP-DecPreS for the Indian Ocean Dipole: A Comparison of Full-Field and Anomaly Initializations
摘要点击 491  全文点击 241  投稿时间:2018-04-12  
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基金:  公益性行业 气象国家自然科学基金项目41661144009、41675089、41775091,公益性行业(气象)科研专项GYHY201506012
中文关键词:  气候预测  印度洋偶极子  全场同化  异常场同化
英文关键词:  Climate prediction  Indian Ocean Dipole mode  Full-field initialization  Anomaly initialization
        
作者中文名作者英文名单位
胡帅HU Shuai中国科学院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;中国科学院大学,北京100049
吴波WU Bo中国科学院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
周天军and ZHOU Tianjun中国科学院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;中国科学院大学,北京100049
引用:胡帅,吴波,周天军.2019.近期气候预测系统IAP-DecPreS对印度洋偶极子的回报技巧: 全场同化和异常场同化的比较[J].大气科学,43(4):831-845,doi:10.3878/j.issn.1006-9895.1808.18149.
Citation:HU Shuai,WU Bo,and ZHOU Tianjun.2019.Predictive Skill of the Near-Term Climate Prediction System IAP-DecPreS for the Indian Ocean Dipole: A Comparison of Full-Field and Anomaly Initializations[J].Chinese Journal of Atmospheric Sciences (in Chinese),43(4):831-845,doi:10.3878/j.issn.1006-9895.1808.18149.
中文摘要:
      印度洋偶极子(IOD)是热带印度洋年际变率主导模态之一,对于区域乃至全球气候有重要影响。准确预报IOD对于短期气候预测具有重要意义。中国科学院大气物理研究所最近建立了近期气候预测系统IAP-DecPreS,其初始化方案采用“集合最优插值—分析增量更新”(EnOI-IAU)方案,能够同化观测的海洋次表层温度廓线资料。本文分析了IAP-DecPreS季节回报试验对IOD的回报技巧,重点比较了全场同化和异常场同化两种初始化策略下预测系统对IOD的回报技巧。分析表明,8月起报秋季IOD,无论从确定性预报还是概率性预报的角度,基于全场同化的回报试验技巧均高于异常场同化的回报试验。对于5月起报的秋季IOD,基于两种初始化策略的回报试验技巧相当。研究发现,全场同化策略相对于异常场的优势主要源于它提高了对伴随ENSO发生的IOD的预报技巧。ENSO遥强迫触发的热带东印度洋“风—蒸发—SST”正反馈过程是IOD发展和维持的关键。采用全场同化策略的回报结果能够更好地模拟出IOD发展过程中ENSO遥强迫产生的异常降水场和异常风场的空间分布特征;而采用异常场同化策略,模拟的异常降水场和风场偏差较大。导致两种初始化策略预测结果技巧差异的主要原因是,全场同化能够减小模式对热带印度洋气候平均态降水固有的模拟偏差,从而提升了热带印度洋对ENSO遥强迫响应的模拟能力。而异常场同化由于在同化过程中保持了模式固有的气候平均态,因此模拟的热带印度洋对ENSO遥强迫的响应存在与模式自由积分类似的模拟偏差。
Abstract:
      The Indian Ocean Dipole (IOD), which is one of the dominant interannual variability modes of SST (sea surface temperature) in the tropical Indian Ocean, has striking impacts on regional and global climate. Thus, finding ways to achieve accurate and short-term climate predictions of IOD is an important subject of research. Recently, a near-term climate prediction system called IAP-DecPreS was constructed by the Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences. It is based on a newly developed initialization scheme (EnOI-IAU), which can assimilate the observed ocean temperature profile records. In this paper, the authors compare the differences in skills of the IAP-DecPreS for the IOD in fall (September-November) between the following two distinct initialization approaches: anomaly and full-field initializations. The results indicate that, for predictions starting from August, the hindcast runs based on the full-field initialization are more skilled at both deterministic and probabilistic predictions compared with those based on the anomaly initialization. For predictions starting from May, the predictive skill of the hindcasts based on the two initialization approaches are similar. Compared with the anomaly initialization, the full-field initialization is superior because it improves the predictive skill for the IOD events occurring together with ENSO. The wind-evaporation-SST positive feedback over the tropical eastern Indian Ocean, which is excited by the ENSO remote forcing, is key for the development and maintenance of the IOD. The hindcasts based on the full-field initialization can reproduce the spatial distributions of precipitation and wind anomalies associated with the ENSO during the IOD development stage. In contrast, for the hindcasts based on anomaly initialization, the biases of precipitation and wind anomalies are much larger. Full-field initialization can reduce the initial errors in the climatological precipitation over the tropical Indian Ocean, thus improving the accuracies in simulating the response of precipitation and wind anomalies over the tropical Indian Ocean to the ENSO remote forcing. In comparison, the anomaly initialization nearly does not change the model inherent climatology. Thus, the ENSO-related precipitation and wind anomalies over the tropical Indian Ocean simulated by the hindcasts based on anomaly initialization show biases similar to those of the model’s historical runs.
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