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    Source: Journal of Hydrometeorology:;2017:;Volume( 018 ):;issue: 009::page 2425
    Author:
    McCrary, Rachel R.;McGinnis, Seth;Mearns, Linda O.
    DOI: 10.1175/JHM-D-16-0264.1
    Publisher: American Meteorological Society
    Abstract: AbstractThis study evaluates snow water equivalent (SWE) over North America in the reanalysis-driven NARCCAP regional climate model (RCM) experiments. Examination of SWE in these runs allows for the identification of bias due to RCM configuration, separate from inherited GCM bias. SWE from the models is compared to SWE from a new ensemble observational product to evaluate the RCMs? ability to capture the magnitude, spatial distribution, duration, and timing of the snow season. This new dataset includes data from 14 different sources in five different types. Consideration of the associated uncertainty in observed SWE strongly influences the appearance of bias in RCM-generated SWE. Of the six NARCCAP RCMs, the version of MM5 run by Iowa State University (MM5I) is found to best represent SWE despite its use of the Noah land surface model. CRCM overestimates SWE because of cold temperature biases and surface temperature parameterization options, while RegCM3 (RCM3) does so because of excessive precipitation. HadRM3 (HRM3) underestimates SWE because of warm temperature biases, while in the version of WRF using the Grell scheme (WRFG) and ECPC-RSM (ECP2), the misrepresentation of snow in the Noah land surface model plays the dominant role in SWE bias, particularly in ECP2 where sublimation is too high.
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    contributor authorMcCrary, Rachel R.;McGinnis, Seth;Mearns, Linda O.
    date accessioned2018-01-03T11:02:00Z
    date available2018-01-03T11:02:00Z
    date copyright8/8/2017 12:00:00 AM
    date issued2017
    identifier otherjhm-d-16-0264.1.pdf
    identifier urihttp://138.201.223.254:8080/yetl1/handle/yetl/4246321
    description abstractAbstractThis study evaluates snow water equivalent (SWE) over North America in the reanalysis-driven NARCCAP regional climate model (RCM) experiments. Examination of SWE in these runs allows for the identification of bias due to RCM configuration, separate from inherited GCM bias. SWE from the models is compared to SWE from a new ensemble observational product to evaluate the RCMs? ability to capture the magnitude, spatial distribution, duration, and timing of the snow season. This new dataset includes data from 14 different sources in five different types. Consideration of the associated uncertainty in observed SWE strongly influences the appearance of bias in RCM-generated SWE. Of the six NARCCAP RCMs, the version of MM5 run by Iowa State University (MM5I) is found to best represent SWE despite its use of the Noah land surface model. CRCM overestimates SWE because of cold temperature biases and surface temperature parameterization options, while RegCM3 (RCM3) does so because of excessive precipitation. HadRM3 (HRM3) underestimates SWE because of warm temperature biases, while in the version of WRF using the Grell scheme (WRFG) and ECPC-RSM (ECP2), the misrepresentation of snow in the Noah land surface model plays the dominant role in SWE bias, particularly in ECP2 where sublimation is too high.
    publisherAmerican Meteorological Society
    typeJournal Paper
    journal volume18
    journal issue9
    journal titleJournal of Hydrometeorology
    identifier doi10.1175/JHM-D-16-0264.1
    journal fristpage2425
    journal lastpage2452
    treeJournal of Hydrometeorology:;2017:;Volume( 018 ):;issue: 009
    contenttypeFulltext
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