• 气候变化 •

### 近500 年南极涛动指数重建及其变率分析

1. 1. 北京师范大学减灾与应急管理研究院，地表过程与资源生态国家重点实验室，北京100875；
2. 陕西省气候中心， 西安710014
• 收稿日期:2009-11-30 修回日期:2010-01-17 出版日期:2010-03-30 发布日期:2010-03-30
• 作者简介:张自银(1981-), 男, 博士, 中国地理学会会员(S110007591A), 主要从事气候变化研究。E-mail: zzy@ires.cn
• 基金资助:

国家自然科学基金项目(40675035); 公益性行业科研专项项目(GYHY200806010); 国家科技支撑计划 (2007BAC29B02)

### Antarctic Oscillation Index Reconstruction since 1500 AD and Its Variability

ZHANG Zi-yin1, GONG Dao-yi1, HE Xue-zhao1, LEI Yang-na2, FENG Sheng-hui1

1. 1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Academy of Disaster Reduction and Emergency Management, Beijing Normal University, Beijing 100875, China;
2. Shaanxi provincial climate center， Xi'an 710014，China
• Received:2009-11-30 Revised:2010-01-17 Online:2010-03-30 Published:2010-03-30
• Supported by:

National Natural Science Foundation of China, No.40675035; R&D Special Fund for Public Welfare Industry (Meteorology), No.GYHY200806010; National Key Science and Technology Project, No.2007bac29b02]

Abstract:

The Antarctic Oscillation (AAO) is the dominant mode of atmospheric circulation variability over the southern hemisphere. It could not only play important roles in climate changes over southern hemisphere, but also exert lots of influences in some regions in northern hemisphere. Due to the lack of widespread instrumental records during historical periods, the understanding of the natural variability of AAO is limited. The purpose of this paper is to reconstruct an austral summer Antarctic Oscillation index (DJF-AAO) focusing on interannual-decadal variability since 1500 AD based on multiple proxies, such as tree-rings, corals, and ice-cores. A Marshall-AAO index derived from 12-station sea level pressure records since 1957 are selected as observational series for calibration. There are 263 variables retained after a series of screening criteria for proxies, to refine the major signatures contained in the proxies by applying principal component analysis, and then a series of screening criteria implemented again for the time coefficient (PC) corresponding to each eigenvector. After that, by applying multivariate regression method the observational AAO-PC relations were calibrated and cross-validated based on the period of 1957-1989, then regressions were employed to compute the DJF-AAO index in 1500-1956. In verification procedure we checked the explained variance (r2), reduction of error (RE), and the standard error (SE). The cross-validation was performed by applying a leave-one-out validation method. During the reconstruction period of 1500-1956, the mean of r2, RE, and SE are 59.9% , 0.47 and 0.67, respectively. These statistical data indicate that DJF-AAO reconstruction is relatively reasonable for the last 460 years approximately. The reconstruction is compared favorably with several existing shorter AAO indexes derived from station SLP records both on the interannual and decadal time scales. The leading periods of the DJF-AAO index are ~2.4, ~2.6, ~6.3, ~24.1, ~37.6 years during the last 500 years, which are all significant at the 95% level.