Abstract

AbstractSpatial patterns of interannual variability in US precipitation and their forcing mechanisms are very different between the cool and warm seasons, as determined by the recent observational record. In this work, the dominant continental scale patterns of warm season precipitation variability, in the form of the standardized precipitation index (SPI), are related to their large‐scale atmospheric teleconnection forcing patterns. To account for intraseasonal differences in atmospheric teleconnection patterns, the 2‐month SPI is considered for the separate periods of early, June–July (JJ), and late, August–September (AS), periods, as well as the 6‐month SPI for the cool season (November–April). Rotated empirical orthogonal function analysis and canonical correlation analysis are applied to determine the dominant spatial modes of SPI, their relationship to large‐scale teleconnection patterns, and their possible forcing mechanisms, as seen in anomalies of 500‐mb geopotential height, sea surface temperature (SST), and outgoing longwave radiation (OLR). Two dominant quasi‐stationary Rossby wavetrain teleconnections appear to govern US warm season precipitation variability: (1) a mode that reflects the well‐known out‐of‐phase relationship in summer precipitation between the central United States and southwest, which is related to Pacific SST forcing in early summer and Indian monsoon convection in later summer, and (2) Two phases of the Circumglobal Teleconnection pattern that are more related to precipitation variability in the south central and eastern United States The southwest United States region relating to the variability of the North American Monsoon is considered within the continental scale variability patterns associated with the warm season. This work is a subset of a larger project to determine if tree‐ring records from the southwest United States are reliable proxies for extending the warm season climate record. It also provides a benchmark for assessing how US warm season climate patterns may be assessed in regional climate models used for seasonal forecast or climate change projection purposes.

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