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El Niño–Southern Oscillation and Pacific Decadal Oscillation impacts on precipitation in the southern and central United States: Evaluation of spatial distribution and predictions
Author(s) -
Kurtzman Daniel,
Scanlon Bridget R.
Publication year - 2007
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2007wr005863
Subject(s) - pacific decadal oscillation , precipitation , climatology , el niño southern oscillation , environmental science , southern oscillation , geography , geology , meteorology
Understanding and predicting regional impacts of El Niño–Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) on winter (October–March) precipitation can provide valuable inputs to agricultural and water resources managers. Effects of ENSO and PDO on winter precipitation were assessed in 165 climate divisions throughout the southern United States. A continuous region of significantly (P < 0.05) increased (decreased) winter precipitation in response to El Niño (La Niña) conditions in the preceding summer (June–September Southern Oscillation Index (SOI)) extends across the entire southern United States and as far north as South Dakota. Within this region stronger correlations (r ≤ −0.45) are found along the Gulf of Mexico, southern Arizona, and central Nebraska. Winter precipitation differs significantly (P < 0.1) between warm and cold phase PDO periods only in the south central region, with greatest significance centered in Oklahoma. Enhanced negative La Niña anomalies during PDO cold phases are dominant in the central region (Texas to South Dakota) whereas enhanced positive El Niño anomalies during PDO warm phases are dominant in the southwest (Arizona, Nevada, and California) and southeast (Louisiana to Florida). Validation tests of winter precipitation predictions based on summer SOI and/or PDO‐phase show a decrease of 9% to 16% in the relative Mean Absolute Error (MAE) from the MAE obtained by using the mean as a predictor in areas with strong correlation (r < −0.45) between SOI and precipitation. Logistic regression probability models of having above or below average winter precipitation had up to 77% successful predictions. The advantage of having probabilities of exceeding certain precipitation thresholds at the beginning of a hydrologic year makes logistic regression models attractive for decision makers.