基于统计方法的SST年际和年代际可预报性研究

IOCAS-IR > 海洋环流与波动重点实验室

	基于统计方法的SST年际和年代际可预报性研究
	孟佳佳1,2
学位类型	硕士
导师	王法明
	2015-05-24
学位授予单位	中国科学院大学
学位授予地点	北京
学位专业	物理海洋学
关键词	Sst 年际年代际可预报性
摘要	气候系统是一个复杂的非线性系统，（不）可预报性是其固有的属性。研究表明，气候系统年际到年代际的可预报性主要来自海洋。海洋作为气候系统的重要组成部分，其海表面温度（SST）是衡量气候平均和变率的一个重要因子。因此，研究全球海表面温度SST的年际和年代际可预报性具有重要意义，可以为预测未来的气候变化提供依据。气候变量的可预报性定义为可预报分量的方差与总方差的比值。与经验正交函数（EOF）分解类似，根据可预报性最大的原则，可以将气候变量分解为可预报成分和空间结构的线性组合。本文分别利用NOAA扩展重建的ERSST资料和GFDL模式CM3工业化革命前试验模拟结果研究SST的年际、年代际可预报性和可预报成分，从而寻找海洋中存在年际和年代际可预报性的主要区域。通过对观测的月平均SST进行分析可知，月平均全球SST的可预报性为3个月，第一可预报成分的可预报性为2年以上，空间上表现为北太平洋和北大西洋的异常增暖，表征了与AMO相似的SST气候态的长期波动特征，第二三可预报成分的可预报性为6个月左右。年际可预报性主要集中在热带太平洋，热带太平洋SST的可预报性为4个月，可预报成分具有与ENSO类似的结构，均呈现热带中东太平洋的异常增暖，其中第二可预报成分与Nino3指数相关较高。因此，热带太平洋SST的可预报性来自ENSO。通过对CM3模式模拟的工业化革命以前的年平均SST进行分析可知，全球SST在前置时间为1年时，预报技巧为0.55。SST的年代际可预报性主要集中在中高纬度。北太平洋、北大西洋SST的可预报成分具有5年以上的可预报性，并呈现明显的年代际变率，北太平洋SST第二可预报成分与太平洋年代际振荡PDO有一定的相关，北大西洋SST第二可预报成分与大西洋多年代际振荡AMO相关较好。综上所述，SST的年际可预报性主要在热带，并且与ENSO有一定的联系，而SST的年代际可预报性主要在中高纬度，如北太平洋、北大西洋，年代际可预报性与太平洋年代际振荡PDO以及大西洋多年代际振荡AMO有一定的相关。
其他摘要	The climate system is complex and nonlinear, and predictability（unpredictability） is its inherent property. Studies have shown that interannual and decadal predictability of the climate system mainly comes from oceans. As an important component of the climate system, sea surface temperature (SST) of ocean is a significant factor of measuring the mean and variability of climate. Therefore, it is important to study the interannual and decadal predictability of SST, which can provide a reference for predicting the future climate change. Predictability of climatic variables is definited as the ratio between variance of predictable parts and total variance. Similar to empirical orthogonal function (EOF) decomposition, climatic variables could be decomposed into a linear combination of predictable components and spatial structure according to the principle of maximum predictability. In this paper, in order to investigate the areas where exist interannual and decadal predictability, interannual predictable component and predictability of SST is studied by using the extended reconstructed sea surface temperature（ERSST）data from the National Oceanic and Atmospheric Administration (NOAA), and decadal predictable components and predictability of SST is analyzed by using the output of pre-industrial experiment of GFDL model – CM3. By analyzing the observed monthly SST，it is informed that predictability of global SST is 3 months. Predictability of the first predictable component is more than 2 years and the spatial pattern is shown as abnormal warming in North Pacific and North Atlantic，which characters a long period fluctuation similar to AMO of SST climatology. The predictability of the second and third predictable component is about 6 months. Interannual predictability of SST is mainly in the tropical Pacific Ocean.It is shown that predictability of the tropical Pacific SST is 4 months and predictable components have similar spatial pattern of ENSO, which presents as abnormal warming of equatorial Pacific SST. Besides, the second predictable component is related better to Nino3 index. In a word, predictability of tropical Pacific SST comes from ENSO. By analyzing the pre-industrial yearly SST, it is informed that forecast skill of the global SST is maintained at about 0.55 when the lead time is 1 year. It is found that the decadal predictability is mainly in the middle and high latitudes. The predictable components of the North Pacific and North Atlantic have predictability of more than 5 years and show significant decadal variability. Furthermore, the second predictable component of North Pacific is related to the Pacific Decadal Osciallion（PDO）and the second predictable component of North Atlantic is related to the Atlantic Multi-decadal Osciallion（AMO）. In summary, the interannual predictability of SST is mainly in tropical Pacific ocean , which is related to ENSO, while the decadal predictability of SST is mainly in the middle and high latitudes, such as the North Pacific, North Atlantic. Decadal predictability has relation to the Pacific Decadal Oscillation (PDO) and the Atlantic Multi-decadal Oscillation (AMO).
学科领域	海洋环流与波动
语种	中文
文献类型	学位论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/22768
专题	海洋环流与波动重点实验室
作者单位	1.中国科学院海洋研究所 2.中国科学院大学
第一作者单位	中国科学院海洋研究所
推荐引用方式 GB/T 7714	孟佳佳. 基于统计方法的SST年际和年代际可预报性研究[D]. 北京. 中国科学院大学,2015.

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