Multi‐RCM ensemble downscaling of NCEP CFS winter season forecasts: Implications for seasonal hydrologic forecast skill. Issue 19 (3rd October 2013)

• Record Type: Journal Article
• Authors: Shukla, Shraddhanand; Lettenmaier, Dennis P.
• Is Part Of: Journal of geophysical research. Volume 118:Issue 19(2013)
• Page Start: 10, 770
• Page End: 10, 790
• Publication Date: 2013-10-03
• Subjects: Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/jgrd.50628
• Languages: English
• ISSNs: 2169-897X

Abstract

<abstract abstract-type="main"> <title>Abstract</title> <p>[1] We assess the value of dynamical versus statistical downscaling of National Centers for Environmental Prediction's (NCEP) Climate Forecast System (CFS) winter season forecasts for seasonal hydrologic forecasting. Dynamically downscaled CFS forecasts for 1 December to 30 April of 1982–2003 were obtained from the Multi‐RCM Ensemble Downscaling (MRED) project that used multiple Regional Climate Models (RCMs) to downscale CFS forecasts. Statistical downscaling of CFS forecasts was achieved by a much simpler bias correction and spatial downscaling method. We evaluate forecast accuracy of runoff (RO), soil moisture (SM), and snow water equivalent produced by a hydrology model forced with dynamically (the MRED forecasts) and statistically downscaled CFS forecasts in comparison with predictions of those variables produced by forcing the same hydrology model with gridded observations (reference data set). Our results show that the MRED forecasts produce modest skill beyond what results from statistical downscaling of CFS. Although the improvement in hydrologic forecast skill associated with the ensemble average of the MRED forecasts (Multimodel) relative to statistical downscaled CFS forecasts is field significant for RO and SM forecasts with up to 3 months lead, the region of improvement is mainly limited to parts of the northwest and north central U.S. In general, one or more RCMs outperform the other RCMs as well as the Multimodel. Hence, we argue that careful selection of RCMs (based on their hindcast skill over any given region) is critical to improving hydrologic forecast skill using dynamical downscaling.</p> </abstract>