Otto I. Lang, Patrick Naple, Derek Mallia, Ty Hosler, Bradley Adams, S. McKenzie Skiles
{"title":"Two Decades of Dust Radiative Forcing on Snow Cover Across the Great Salt Lake Basin","authors":"Otto I. Lang, Patrick Naple, Derek Mallia, Ty Hosler, Bradley Adams, S. McKenzie Skiles","doi":"10.1029/2024JF007957","DOIUrl":null,"url":null,"abstract":"<p>Seasonal snowpacks in mountain drainages of the Great Salt Lake Basin (GSLB), western United States, are the primary surface water supply to regional agriculture, the metropolitan Wasatch Front, and the terminal Great Salt Lake. Spring dust emissions from the eastern Great Basin result in a dust-darkened GSLB snowpack, locally accelerating snowmelt relative to dust-free conditions. Such acceleration has been linked to streamflow forecasting errors in the adjacent Colorado River Basin, but snow darkening impacts within the GSLB are largely uninvestigated. To quantify the dust impact, we analyzed patterns in dust radiative forcing (RF<sub>dust</sub>) over the MODIS record (2001–2023) using spatially and temporally complete RF<sub>dust</sub> and fractional snow-covered area products. For validation, retrievals were cross-referenced with in situ RF<sub>dust</sub> observations. Results showed that RF<sub>dust</sub> was present every year and had no significant trend over the record. Spatially, RF<sub>dust</sub> was similar across all three subbasins. Temporally, RF<sub>dust</sub> exhibited high interannual variability (−30 to +40 Wm<sup>−2</sup> from record means) and has declined slightly in regions of the eastern GSLB. Controls of RF<sub>dust</sub> may be linked to seasonal meteorology and drought conditions, but drivers remain uncertain. Further understanding of the distribution and controls of RF<sub>dust</sub> in the GSLB during changing climate and weather patterns may allow us to predict snowmelt more accurately.</p>","PeriodicalId":15887,"journal":{"name":"Journal of Geophysical Research: Earth Surface","volume":"130 2","pages":""},"PeriodicalIF":3.5000,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024JF007957","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Geophysical Research: Earth Surface","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1029/2024JF007957","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Seasonal snowpacks in mountain drainages of the Great Salt Lake Basin (GSLB), western United States, are the primary surface water supply to regional agriculture, the metropolitan Wasatch Front, and the terminal Great Salt Lake. Spring dust emissions from the eastern Great Basin result in a dust-darkened GSLB snowpack, locally accelerating snowmelt relative to dust-free conditions. Such acceleration has been linked to streamflow forecasting errors in the adjacent Colorado River Basin, but snow darkening impacts within the GSLB are largely uninvestigated. To quantify the dust impact, we analyzed patterns in dust radiative forcing (RFdust) over the MODIS record (2001–2023) using spatially and temporally complete RFdust and fractional snow-covered area products. For validation, retrievals were cross-referenced with in situ RFdust observations. Results showed that RFdust was present every year and had no significant trend over the record. Spatially, RFdust was similar across all three subbasins. Temporally, RFdust exhibited high interannual variability (−30 to +40 Wm−2 from record means) and has declined slightly in regions of the eastern GSLB. Controls of RFdust may be linked to seasonal meteorology and drought conditions, but drivers remain uncertain. Further understanding of the distribution and controls of RFdust in the GSLB during changing climate and weather patterns may allow us to predict snowmelt more accurately.