Extended seasonal prediction of spring precipitation over the Upper Colorado River Basin

Siyu Zhao; Rong Fu; Michael L Anderson; Sudip Chakraborty; Jonathan H Jiang; Hui Su; Yu Gu

doi:10.1007/s00382-022-06422-x

Extended seasonal prediction of spring precipitation over the Upper Colorado River Basin

Clim Dyn. 2023;60(5-6):1815-1829. doi: 10.1007/s00382-022-06422-x. Epub 2022 Jul 21.

Authors

Siyu Zhao¹, Rong Fu¹, Michael L Anderson², Sudip Chakraborty³, Jonathan H Jiang³, Hui Su³, Yu Gu¹

Affiliations

¹ Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, CA USA.
² California Department of Water Resources, Sacramento, CA USA.
³ Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA USA.

Abstract

This study provides extended seasonal predictions for the Upper Colorado River Basin (UCRB) precipitation in boreal spring using an artificial neural network (ANN) model and a stepwise linear regression model, respectively. Sea surface temperature (SST) predictors are developed taking advantage of the correlation between the precipitation and SST over three ocean basins. The extratropical North Pacific has a higher correlation with the UCRB spring precipitation than the tropical Pacific and North Atlantic. For the ANN model, the Pearson correlation coefficient between the observed and predicted precipitation exceeds 0.45 (p-value < 0.01) for a lead time of 12 months. The mean absolute percentage error (MAPE) is below 20% and the Heidke skill score (HSS) is above 50%. Such long-lead prediction skill is probably due to the UCRB soil moisture bridging the SST and precipitation. The stepwise linear regression model shows similar prediction skills to those of ANN. Both models show prediction skills superior to those of an autoregression model (correlation < 0.10) that represents the baseline prediction skill and those of three of the North American Multi-Model Ensemble (NMME) forecast models. The three NMME models exhibit different skills in predicting the precipitation, with the best skills of the correlation ~ 0.40, MAPE < 25%, and HSS > 40% for lead times less than 8 months. This study highlights the advantage of oceanic climate signals in extended seasonal predictions for the UCRB spring precipitation and supports the improvement of the UCRB streamflow prediction and related water resource decisions.

Supplementary information: The online version contains supplementary material available at 10.1007/s00382-022-06422-x.

Keywords: Artificial neural network; Extended seasonal prediction; North American Multi-Model Ensemble; Sea surface temperature; Statistical forecast; Upper Colorado River Basin precipitation.