Does Downscaling in Space and Time Degrade the Dependability of Seasonal Climate Forecasts?

The National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center issues total precipitation and average temperature forecasts for 3-month periods for relatively large areas (forecast divisions are about 9 x 10⁴ km²). Incorporation of the climate forecasts into farm level decision support systems necessitates downscaling the forecasts to local and daily time scales. Since any measure of the skill of seasonal climate forecasts is expected to depend on scale (specifically, more skillful for larger areas and longer time periods), there is concern that downscaling will dilute current forecast skill, possibly to the point of non-utility. As an initial exploration, this study will determine any change in die dependability of precipitation and average temperature forecasts downscaled to local and monthly time scales for several locations in Oklahoma. Dependability is a skill measure that assesses only those forecasts that are discernibly different from climatology. Downscaling to daily time scales is done in two steps, first to monthly forecasts, and then to generated ensembles of daily forecasts. This initial study will examine only the first step, the monthly downscaling. In order to assess the relative impact of downscaling in space versus time, forecasts will be downscaled separately in each, and then downscaled in both. Current forecast dependability for Oklahoma at the larger scales is limited compared to regions of the US that experience stronger ENSO impacts, so any degradation from downscaling to monthly forecasts and specific locations should be readily apparent.