4.6 Article

Coupled impacts of ENSO AMO and PDO on temperature and precipitation in the Alabama-Coosa-Tallapoosa and Apalachicola-Chattahoochee-Flint river basins

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INTERNATIONAL JOURNAL OF CLIMATOLOGY
卷 38, 期 -, 页码 E717-E728

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WILEY
DOI: 10.1002/joc.5401

关键词

ENSO; AMO; PDO; teleconnection; seasonal climate; local scale

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Large-scale patterns of ocean surface temperature can influence weather across the globe and understanding their interaction with the local climate can improve seasonal forecasting of local temperature and precipitation. Here we focus on the combined interactions of the El Nino-Southern Oscillation (ENSO), the Atlantic Multidecadal Oscillation (AMO) and the Pacific Decadal Oscillation (PDO) in the Alabama-Coosa-Tallapoosa (ACT) and Apalachicola-Chattahoochee-Flint (ACF) river basins of the southeastern United States. Nonparametric ranks-sum tests of individual and coupled impacts of these teleconnections on the annual study area climate (1895-2009) found significant impacts. A positive AMO phase was associated with decreased precipitation and increased mean temperature while the negative AMO phase was associated with increased precipitation and decreased temperature. While an El Nino event generally increases regional precipitation, El Nino during a positive AMO or PDO phase resulted in precipitation below the long-term average in our study area. Because of many instances of El Nino being shared between AMO and PDO phase, the effects of the PDO and AMO on El Nino could not be distinguished. La Nina was associated with negative precipitation and increased temperature. The effects of La Nina on the temperature and precipitation anomaly were significantly increased during positive AMO and PDO phases. The coupled impacts of the aforementioned teleconnections demonstrate the necessity of including the effects of the AMO and the PDO when using ENSO-based forecasts. The significant shifts on the effects of teleconnections on area climate from AMO negative phase to AMO positive phase cast doubt on seasonal prediction for the study area based on the recent history (i.e. the use of the period 1950-2000 to predict seasonal climate since 2000).

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