The Effect of the North Atlantic Oscillation on Monthly Precipitation in Selected European Locations: A Non-Linear Time Series Approach

IF 1.5 3区 环境科学与生态学 Q4 ENVIRONMENTAL SCIENCES
Environmetrics Pub Date : 2025-02-24 DOI:10.1002/env.2896
Changli He, Jian Kang, Annastiina Silvennoinen, Timo Teräsvirta
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引用次数: 0

Abstract

In this article, the relationship between the monthly precipitation in 30 European cities and towns, and two Algerian ones, and the North Atlantic Oscillation (NAO) index is characterized using the Vector Seasonal Shifting Mean and Covariance Autoregressive model, extended to contain exogenous variables. The results, based on monthly time series from 1851 up until 2020, include shifting monthly means for the rainfall series and the estimated coefficients of the exogenous NAO variable. They suggest that in the north and the west, the amount of rain in the boreal winter months has increased or stayed the same during the observation period, whereas in the Mediterranean area, there have been systematic decreases. Results on the North Atlantic Oscillation indicate that the NAO has its strongest effect on precipitation during the winter months. The (negative) effect is particularly strong in Western Europe, Lisbon, and the Mediterranean rim. In contrast, the effect in northern locations is positive for the winter months. The constancy of error variances and correlations is tested and, if rejected, the time-varying alternative is estimated. A spatial relationship between the error correlations and the distance between the corresponding pairs of cities is estimated.

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来源期刊
Environmetrics
Environmetrics 环境科学-环境科学
CiteScore
2.90
自引率
17.60%
发文量
67
审稿时长
18-36 weeks
期刊介绍: Environmetrics, the official journal of The International Environmetrics Society (TIES), an Association of the International Statistical Institute, is devoted to the dissemination of high-quality quantitative research in the environmental sciences. The journal welcomes pertinent and innovative submissions from quantitative disciplines developing new statistical and mathematical techniques, methods, and theories that solve modern environmental problems. Articles must proffer substantive, new statistical or mathematical advances to answer important scientific questions in the environmental sciences, or must develop novel or enhanced statistical methodology with clear applications to environmental science. New methods should be illustrated with recent environmental data.
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