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

Handle URI:
http://hdl.handle.net/10754/552177
Title:
Demonstration of a geostatistical approach to physically consistent downscaling of climate modeling simulations
Authors:
Jha, Sanjeev Kumar; Mariethoz, Gregoire; Evans, Jason P.; McCabe, Matthew ( 0000-0002-1279-5272 )
Abstract:
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 of the Murray-Darling basin in southeast Australia, using model outputs at two spatial resolutions of 50 and 10 km. The data used in this study cover 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 10 km resolution using the 50 km resolution data as input. The MPS geostatistical downscaling approach reproduces the spatial distribution of TSK, SMOIS, and LH at 10 km 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.
KAUST Department:
Water Desalination and Reuse Research Center (WDRC)
Citation:
Demonstration of a geostatistical approach to physically consistent downscaling of climate modeling simulations 2013, 49 (1):245 Water Resources Research
Journal:
Water Resources Research
Issue Date:
Jan-2013
DOI:
10.1029/2012WR012602
Type:
Article
ISSN:
00431397
Additional Links:
http://doi.wiley.com/10.1029/2012WR012602
Appears in Collections:
Articles; Water Desalination and Reuse Research Center (WDRC)

Full metadata record

DC FieldValue Language
dc.contributor.authorJha, Sanjeev Kumaren
dc.contributor.authorMariethoz, Gregoireen
dc.contributor.authorEvans, Jason P.en
dc.contributor.authorMcCabe, Matthewen
dc.date.accessioned2015-05-04T16:34:59Zen
dc.date.available2015-05-04T16:34:59Zen
dc.date.issued2013-01en
dc.identifier.citationDemonstration of a geostatistical approach to physically consistent downscaling of climate modeling simulations 2013, 49 (1):245 Water Resources Researchen
dc.identifier.issn00431397en
dc.identifier.doi10.1029/2012WR012602en
dc.identifier.urihttp://hdl.handle.net/10754/552177en
dc.description.abstractA 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 of the Murray-Darling basin in southeast Australia, using model outputs at two spatial resolutions of 50 and 10 km. The data used in this study cover 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 10 km resolution using the 50 km resolution data as input. The MPS geostatistical downscaling approach reproduces the spatial distribution of TSK, SMOIS, and LH at 10 km 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.en
dc.relation.urlhttp://doi.wiley.com/10.1029/2012WR012602en
dc.rightsArchived with thanks to Water Resources Researchen
dc.titleDemonstration of a geostatistical approach to physically consistent downscaling of climate modeling simulationsen
dc.typeArticleen
dc.contributor.departmentWater Desalination and Reuse Research Center (WDRC)en
dc.identifier.journalWater Resources Researchen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionConnected Waters Initiative; University of New South Wales; Sydney; Australiaen
dc.contributor.institutionConnected Waters Initiative; University of New South Wales; Sydney; Australiaen
dc.contributor.institutionClimate Change Research Center; University of New South Wales; Sydney; Australiaen
kaust.authorMcCabe, Matthewen
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