Demonstration of a geostatistical approach to physically-consistent downscaling of climate modeling simulations.

Jha, S.K., G. Mariethoz, J.P. Evans and M.F. McCabe
Water Resources Research, 49(1), 245-259, doi:10.1029/2012WR012602, 2013.


A downscaling approach based on multiple-point geostatistics (MPS) is presented. The key concept underlying MPS is to sample spatial patterns from within training images, which can then be used in characterizing the relationship between different variables across multiple scales. The approach is used here to downscale climate variables including skin surface temperature (TSK), soil moisture (SMOIS) and latent heat flux (LH). The performance of the approach is assessed by applying it to data derived from a regional climate model (RCM) of the Murray-Darling basin in south-east Australia, using model outputs at two spatial resolutions of 50km and 10km. The data used in this study covers the period from 1985 to 2006, with 1985 to 2005 used for generating the training images that define the relationships of the variables across the different spatial scales. Subsequently, the spatial distributions for the variables in the year 2006 are determined at 10km resolution using the 50km resolution data as input. The MPS geostatistical downscaling approach reproduces the spatial distribution of TSK, SMOIS and LH at 10km resolution with the correct spatial patterns over different seasons, while providing uncertainty estimates through the use of multiple realizations. The technique has the potential to not only bridge issues of spatial resolution in regional and global climate model simulations, but also in feature sharpening in remote sensing applications through image fusion, filling gaps in spatial data, evaluating downscaled variables with available remote sensing images, and aggregating/disaggregating hydrological and groundwater variables for catchment studies.

Key Figure

downscaling for LH

Figure 6: Summary of seasonal downscaling for the LH. WRF output at 50km resolution used as conditioning data in the DS approach (first column); spatial distribution of LH (W/m ) obtained from a single realization of DS (second column); reference values of the variables obtained from the WRF model (third column); and a plot showing difference in values between mean of 50 realizations and the reference (fourth column). The rows from the top down indicate results corresponding to results in summer, autumn, winter and spring respectively, for the year 2006. The colorbar represents the actual limits on LH.

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