Refined climate downscaling for the Intermountain West
Large biases associated with climate projections are problematic when it comes to their regional application in the assessment of water resources and ecosystems. We produced a set of regional climate projections that have the systematic biases reduced. The dataset first utilized a statistical regression technique and a global reanalysis dataset to correct biases in the globally-simulated variables that are subsequently used to drive the regional model. The bias-corrected global simulation data led to a more realistic regional climate simulation of precipitation and associated atmospheric dynamics, as well as snow water equivalent (SWE) in comparison to the original globally-driven simulation. This effective and economical method provides a useful tool to reduce biases in regional climate downscaling simulations of water resource variables.
Gillies, Robert; Li, Rong; Wang, Shih-Yu; and Jin, Jiming, "Refined climate downscaling for the Intermountain West" (2014). Browse all Datasets. Paper 7.
CCSM_2001_2010_precip_adjusted.nc (134446 kB)
CCSM_2056_2065_precip_adjusted.nc (134446 kB)
Metadata.pdf (32 kB)
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