Inherent Predictability of Observed Seasonal Mean Precipitation Variations Over the Continental United States

Trustees Of Boston University, Boston

Investigators

Abstract

This project seeks to quantify the inherent predictability of observed precipitation variations over the U.S. on seasonal to multi-decadal timescales. The questions to be addressed include: 1) What amount of observed seasonal-mean precipitation variability is uniquely related to variations in the background state of the observed system during a given time-period? 2) When and where do these inherently predictable variations in seasonal-mean precipitation occur within the U.S.? 3) What is the inherent predictability of short-term (<12-months) and long-term (>12-month) drought occurrences? 4) Do numerical climate models adequately reproduce the inherent predictability of precipitation variations over the U.S.? To answer these questions, the investigators will (i) construct stochastic weather-generation models based upon observed station-based daily precipitation, which can be used to establish the envelope of variability that arises solely from the random behavior of precipitation events, (ii) identify observed interannual to multi-decadal scale variations in seasonal-mean precipitation across the U.S. that lie outside this envelope produced by chance, (iii) use the observed and stochastically-generated daily-precipitation time-series to detect inherently-predictable variations in the severity of drought, as represented by the Standard Precipitation Index, and (vi) evaluate numerical model capability in reproducing the observed inherent predictability of seasonal-mean precipitation and drought over the U.S., using daily precipitation estimates from numerical model representations of the climate of the 20th century. The broader impacts of this project will include improving our confidence in climate forecasts for many regions of the U.S by identifying "hot-spot" regions of historical predictability that are strongly influenced by climate-change processes. Further it will establish the groundwork for a better understanding of the role that regional- and large-scale atmospheric circulations and ocean- and land-surface forcings play in modifying regional precipitation on interannual to multi-decadal time-scales. It will also guide future investigations into numerical model systems' portrayal of physical mechanisms that give rise to observed predictability. Finally, it will help determine the applicability of using stochastic weather-generation models for identifying and analyzing inherently predictable precipitation variations in other regions of the world.