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An alternative approach for quantitatively estimating climate variability over China under the effects of ENSO events
Atmospheric Research ( IF 5.5 ) Pub Date : 2020-07-01 , DOI: 10.1016/j.atmosres.2020.104897 Ping Zhou , Zhiyong Liu , Linyin Cheng
Atmospheric Research ( IF 5.5 ) Pub Date : 2020-07-01 , DOI: 10.1016/j.atmosres.2020.104897 Ping Zhou , Zhiyong Liu , Linyin Cheng
Abstract Previous studies generally use the traditional composite analysis to diagnose the physical interrelationships between the El Nino-Southern Oscillation (ENSO) and climate variables such as temperature and precipitation. This study presents a simple probabilistic tool for quantifying changes in precipitation and temperature in the wet season over China during either developing or decaying phases of El Nino and La Nina events with a particular focus on the extreme conditions. We first construct the joint dependence structure between each climate variable (e.g. precipitation and temperature) and ENSO using a variety of bivariate copulas. We then examine variations of climate variables related to ENSO conditions through the conditioning sets of bivariate copulas. This approach allows a quantitative estimation of precipitation and temperature anomalies and a delineation of their spatial pattern across the country under individual effects of the developing and decaying phases of ENSO events. Comparison of results produced by the conditional probabilistic approach with those by conventional composite analysis reveals large similarity, highlighting the robustness of the presented approach in examining the response of climate variations to ENSO phases and its potential for a broader application in other regional/global diagnoses. Of particular importance is that this approach offers a way to yield probabilistic predictive information on extreme climate anomalies conditioned by ENSO signals. Despite only ENSO's effect considered in the current study, the presented approach could also be used to detect the effect of other large-scale climate signals on regional or global climate variations.
更新日期：2020-07-01
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