Climate change information simulated by global climate models is downscaled using statistical methods to translate spatially course regional projections to finer resolutions needed by researchers and managers to assess local climate impacts. Several statistical downscaling methods have been developed over the past fifteen years, resulting in multiple datasets derived by different methods. We apply a simple monthly water-balance model (MWBM) to demonstrate how the differences among these datasets result in disparate projections of snow loss and future changes in runoff. We apply the MWBM to six statistically downscaled datasets for 14 general circulation models (GCMs) from the Climate Model Intercomparison Program Phase 5 (CMIP5) for the RCP 8.5 emission scenario (1950 – 2099).
The statistically downscaled datasets are as follows:
BCCA: Bias Corrected Constructed Analogs (Reclamation, 2013)
BCSD-C: Bias Corrected Spatial Disaggregation (Reclamation, 2013)
BCSD-F: Bias Corrected Spatial Disaggregation (Thrasher et al., 2013)
LOCA: Localized Constructed Analogs (Pierce et al., 2014)
MACA-L: Multivariate Adaptive Constructed Analogs (Abatzoglou & Brown, 2012, bias corrected by Livneh et al., 2013)
MACA-M: Multivariate Adaptive Constructed Analogs (Abatzoglou & Brown, 2012, bias corrected by METDATA, Abatzoglou, 2013)
Users interested in the downscaled temperature and precipitation files are referred to the dataset home pages:
BCCA, BCSD-C: http://gdo-dcp.ucllnl.org/downscaled_cmip_projections/dcpInterface.html
MACA-L, MACA-M: http://maca.northwestknowledge.net
The GCMs are the following:
bcc-csm1-1, CanESM2, CNRM-CM5, CSIRO-Mk3-6-0, GFDL-ESM2G, GFDL-ESM2M, inmcm4, IPSL-CM5A-LR, IPSL-CM5A-MR, MIROC-ESM, MIROC-ESM-CHEM, MIROC5, MRI-CGCM3, NorESM1-M
The dataset contains snow and runoff projections simulated by the monthly water-balance model (MWBM) when driven by temperature and precipitation time series from six commonly used statistically downscaled datasets. Differences in hydroclimate projections highlight uncertainty stemming from both the GCMs and statistically downscaling methods.
We are providing FTP and Thredds access to the dataset as a mirror to USGS ScienceBase.
USGS ScienceBase: https://doi.org/10.5066/P9O9EB1C
Alder, J.R., and Hostetler, S.W., 2019, Data Release for The dependence of hydroclimate projections in snow-dominated regions of the western U.S. on the choice of statistically downscaled climate data: U.S. Geological Survey data release, https://doi.org/10.5066/P9O9EB1C.
Abatzoglou, J. T. (2013). Development of gridded surface meteorological data for ecological applications and modelling. International Journal of Climatology, 33(1), 121–131. https://doi.org/10.1002/joc.3413
Abatzoglou, J. T., & Brown, T. J. (2012). A comparison of statistical downscaling methods suited for wildfire applications. International Journal of Climatology, 32(5), 772–780. https://doi.org/10.1002/joc.2312
Livneh, B., Rosenberg, E. A., Lin, C., Nijssen, B., Mishra, V., Andreadis, K. M., et al. (2013). A Long-Term Hydrologically Based Dataset of Land Surface Fluxes and States for the Conterminous United States: Update and Extensions. J. Climate, 26(23), 9384–9392. https://doi.org/10.1175/JCLI-D-12-00508.1
Pierce, D. W., Thrasher, B. L., & Cayan, D. R. (2014). Statistical Downscaling Using Localized Constructed Analogs (LOCA). Journal of Hydrometeorology, 15(6), 1–28. https://doi.org/10.1175/JHM-D-14-0082.1
Reclamation. (2013). Downscaled CMIP3 and CMIP5 Climate Projections: Release of Downscaled CMIP5 Climate Projections, Comparison with Preceding Information, and Summary of User Needs. Denver, Colorado: U.S. Department of the Interior, Bureau of Reclamation, Technical Service Center. Retrieved from http://gdo-dcp.ucllnl.org/downscaled_cmip_projections/techmemo/downscaled_climate.pdf
Thrasher, B., Xiong, J., Wang, W., Melton, F., Michaelis, A., & Nemani, R. (2013). Downscaled Climate Projections Suitable for Resource Management. Eos, Transactions American Geophysical Union, 94(37), 321–323. https://doi.org/10.1002/2013EO370002