A comprehensive evaluation comparing hydroclimate projections derived from six statistically downscaled data sets, including data underpinning the National Climate Change Viewer (NCCV) was recently published.
National Climate Change Viewer data used in an evaluation comparing statistical downscaling methods and data sets
USGS scientists with the Land Change Science Program have published a comprehensive evaluation comparing the hydroclimate projections derived from six statistically downscaled data sets, which includes the data set underpinning the USGS National Climate Change Viewer (NCCV). They use the monthly water balance model from the NCCV to quantify and decompose uncertainties associated with 14 general circulation models (GCMs) and statistical techniques in projections for four snow‐dominated regions in the western United States. The end‐of‐century projections from GCMs exhibit substantial variation in change over the regions (temperature range of 2.8–8.0 °C and precipitation range of −22–31%). The six downscaled data sets exhibit disparate high‐resolution representations of the magnitude and spatial patterns of future temperature (up to 2.2 °C) and precipitation (up to 30%) for a common GCM. Two data sets derived by the same downscaling method (Multivariate Adaptive Constructed Analogs) produce median losses of snow water equivalent over the Upper Colorado of 51% and 81%. The principal causes of the differences among the downscaled projections are related to the gridded observations used to bias correct the historical GCM output. Specifically,
- Whether a fixed atmospheric lapse rate (−6.5 °C/km) or a spatially and temporally varying lapse rate is used to extrapolate lower elevation observations to high‐elevations and
- Whether high‐elevation station data (e.g., SNOTEL) are included in the observations.
The GCM projections are the largest source of uncertainty in the monthly water balance model simulations; however, the differences among seasonal projections produced by downscaled data sets in some regions highlight the need for careful evaluation of the statistically downscaled data in climate impact studies.
Their study can be found at: https://doi.org/10.1029/2018WR023458
The supporting data release is at: https://doi.org/10.5066/P9O9EB1C
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) [Data set in the NCCV]
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 data set home pages:
MACA-L, MACA-M: http://maca.northwestknowledge.net
The GCMs evaluated 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
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