Refined climate downscaling for the Intermountain West

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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.

CCSM_1969_1999_precip_adjusted.nc (416765 kB)
MD5: f6e45d0a3647b413e6209c8551cadbb7

CCSM_2001_2010_precip_adjusted.nc (134446 kB)
MD5: 8494004139689aa279abd35213130982

CCSM_2056_2065_precip_adjusted.nc (134446 kB)
MD5: 46ea37288d6d7a73497fd0383f5f1e33

Metadata.pdf (32 kB)
MD5: 4a6eb22a9e7ec50245b237d26424614d

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