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dc.contributor.authorBroxton, Patrick D.
dc.contributor.authorZeng, Xubin
dc.contributor.authorDawson, Nicholas
dc.date.accessioned2017-12-04T23:58:17Z
dc.date.available2017-12-04T23:58:17Z
dc.date.issued2017-11
dc.identifier.citationThe Impact of a Low Bias in Snow Water Equivalent Initialization on CFS Seasonal Forecasts 2017, 30 (21):8657 Journal of Climateen
dc.identifier.issn0894-8755
dc.identifier.issn1520-0442
dc.identifier.doi10.1175/JCLI-D-17-0072.1
dc.identifier.urihttp://hdl.handle.net/10150/626187
dc.description.abstractAcross much of the Northern Hemisphere, Climate Forecast System forecasts made earlier in the winter (e.g., on 1 January) are found to have more snow water equivalent (SWE) in April-June than forecasts made later (e.g., on 1 April); furthermore, later forecasts tend to predict earlier snowmelt than earlier forecasts. As a result, other forecasted model quantities (e.g., soil moisture in April-June) show systematic differences dependent on the forecast lead time. Notably, earlier forecasts predict much colder near-surface air temperatures in April-June than later forecasts. Although the later forecasts of temperature are more accurate, earlier forecasts of SWE are more realistic, suggesting that the improvement in temperature forecasts occurs for the wrong reasons. Thus, this study highlights the need to improve atmospheric processes in the model (e.g., radiative transfer, turbulence) that would cause cold biaseswhen a more realistic amount of snowis on the ground. Furthermore, SWE differences in earlier versus later forecasts are found to much more strongly affect April-June temperature forecasts than the sea surface temperature differences over different regions, suggesting the major role of snowpack in seasonal prediction during the spring-summer transition over snowy regions.
dc.description.sponsorshipNASA [NNX14AM02G]en
dc.language.isoenen
dc.publisherAMER METEOROLOGICAL SOCen
dc.relation.urlhttp://journals.ametsoc.org/doi/10.1175/JCLI-D-17-0072.1en
dc.rights© 2017 American Meteorological Society.en
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.titleThe Impact of a Low Bias in Snow Water Equivalent Initialization on CFS Seasonal Forecastsen
dc.typeArticleen
dc.contributor.departmentUniv Arizona, Dept Hydrol & Atmospher Scien
dc.identifier.journalJournal of Climateen
dc.description.note6 month embargo; Published online: 2 October 2017en
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en
dc.eprint.versionFinal published versionen
dc.contributor.institutionDepartment of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Arizona
dc.contributor.institutionDepartment of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Arizona
dc.contributor.institutionDepartment of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Arizona
refterms.dateFOA2018-04-02T00:00:00Z
html.description.abstractAcross much of the Northern Hemisphere, Climate Forecast System forecasts made earlier in the winter (e.g., on 1 January) are found to have more snow water equivalent (SWE) in April-June than forecasts made later (e.g., on 1 April); furthermore, later forecasts tend to predict earlier snowmelt than earlier forecasts. As a result, other forecasted model quantities (e.g., soil moisture in April-June) show systematic differences dependent on the forecast lead time. Notably, earlier forecasts predict much colder near-surface air temperatures in April-June than later forecasts. Although the later forecasts of temperature are more accurate, earlier forecasts of SWE are more realistic, suggesting that the improvement in temperature forecasts occurs for the wrong reasons. Thus, this study highlights the need to improve atmospheric processes in the model (e.g., radiative transfer, turbulence) that would cause cold biaseswhen a more realistic amount of snowis on the ground. Furthermore, SWE differences in earlier versus later forecasts are found to much more strongly affect April-June temperature forecasts than the sea surface temperature differences over different regions, suggesting the major role of snowpack in seasonal prediction during the spring-summer transition over snowy regions.


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