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.
.nc is the extension for NetCDF format, a binary data format commonly used for climate model output data. These NetCDF files contain metadata which aid interpretation of the contents. The metadata and data can be explored using the free Panoply software tool (http://www.giss.nasa.gov/tools/panoply/
Utah State University
This work is licensed under a Creative Commons Attribution 4.0 License.
Gillies, R. R., Li, R., Wang, S.-Y., & Jin, J. (2014). Refined climate downscaling for the Intermountain West. Utah State University. https://doi.org/10.15142/T3TS3V
Additional FilesCCSM_1969_1999_precip_adjusted.nc (416765 kB)
CCSM_2001_2010_precip_adjusted.nc (134446 kB)
CCSM_2056_2065_precip_adjusted.nc (134446 kB)
Metadata.pdf (32 kB)