Preprints
https://doi.org/10.5194/tc-2021-211
https://doi.org/10.5194/tc-2021-211

  27 Sep 2021

27 Sep 2021

Review status: this preprint is currently under review for the journal TC.

Evaluating sources of an apparent cold bias in MODIS land surface temperatures in the St. Elias Mountains, Yukon, Canada

Ingalise Kindstedt1, Kristin Schild1,2, Dominic Winski1,2, Karl Kreutz1,2, Luke Copland3, Seth Campbell1,2, and Erin McConnell1 Ingalise Kindstedt et al.
  • 1Climate Change Institute, University of Maine, Orono, Maine, USA
  • 2School of Earth and Climate Sciences, University of Maine, Orono, Maine, USA
  • 3Department of Geography, Environment and Geomatics, University of Ottawa, Ottawa, Ontario, Canada

Abstract. Remote sensing data are a crucial tool for monitoring climatological changes and glacier response in areas inaccessible for in situ measurements. The Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) product provides temperature data for remote glaciated areas where weather stations are sparse or absent, such as the St. Elias Mountains (Yukon, Canada). However, MODIS LSTs in the St. Elias Mountains have shown a cold bias relative to available weather station measurements, the source of which is unknown. Here, we show that the MODIS cold bias likely results from the occurrence of near-surface temperature inversions rather than from the MODIS sensor’s large footprint size or from poorly constrained snow emissivity values used in LST calculations. We find that a cold bias in remote sensing temperatures is present not only in MODIS LST products, but also in Advanced Spaceborne Thermal Emissions Radiometer (ASTER) and Landsat surface temperature products, both of which have a much smaller footprint (90–120 m) than MODIS (1 km). In all three datasets, the cold bias was most pronounced in the winter (mean cold bias > 8 °C), and least pronounced in the spring and summer (mean cold bias < 2 °C). We also find this enhanced seasonal bias in MODIS brightness temperatures, before the incorporation of snow surface emissivity into the LST calculation. Finally, we find the MODIS cold bias to be consistent in magnitude and seasonal distribution with modeled temperature inversions, and to be most pronounced under conditions that facilitate near-surface inversions, namely low incoming solar radiation and wind speeds, at study sites Icefield Divide (60.68° N, 139.78° W, 2,603 m a.s.l) and Eclipse Icefield (60.84° N, 139.84° W, 3,017 m a.s.l.). These results demonstrate that efforts to improve the accuracy of MODIS LSTs should focus on understanding near-surface physical processes rather than refining the MODIS sensor or LST algorithm. In the absence of a physical correction for the cold bias, we apply a statistical correction, enabling the use of mean annual MODIS LSTs to qualitatively and quantitatively examine temperatures in the St. Elias Mountains and their relationship to melt and mass balance.

Ingalise Kindstedt et al.

Status: open (until 22 Nov 2021)

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Ingalise Kindstedt et al.

Ingalise Kindstedt et al.

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Short summary
We show that neither the large spatial footprint of the MODIS sensor nor poorly constrained snow emissivity values explain the observed cold bias in MODIS land surface temperatures (LSTs) in the St. Elias. Instead, the bias is most prominent under conditions associated with near-surface temperature inversions. This work represents an advance in the application of MODIS LSTs to glaciated alpine regions, where we often depend solely on remote sensing products for temperature information.