Exploring the impact of explicit assimilation of dust-on snow albedo information on snow estimates from a snow reanalysis framework
Abstract
Snowpack impurities caused by dust and black carbon deposition affects the timing and amount of peak snow water equivalent (SWE) and snowmelt. Yet these factors are rarely included in snow modeling and estimation frameworks, introducing the possibility of biases in SWE and snowmelt estimates. This study explores the explicit assimilation of information about dust-on snow impacts on albedo into a snow reanalysis framework, with the goal of better capturing the variability of snowmelt and thereby enhancing seasonal snow estimates. The MODIS Dust Radiative Forcing in Snow (MODDRFS) product is used to provide dust-on snow albedo information that can inform how the uncertain modeled snow albedo should be adjusted to reflect dust impacts in space and time. The assimilation of MODDRFS data is combined with a reanalysis framework that currently assimilates fractional snow covered area (fSCA) to test how snow estimates change based on the explicit dust-on-snow information. Validation and sensitivity test are conducted across a wide range of sample regimes, from relatively dust-free (i.e., in the Sierra Nevada (CA)) to areas where dust is known to play a larger role (i.e., the Rocky Mountains of Colorado, and the Hindu Kush Himalaya in High Mountain Asia). The method is tested against validation data (where available) and versus the baseline case with fSCA assimilation only to assess the value added by the MODDRFS data.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFM.C33E1621F
- Keywords:
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- 0736 Snow;
- CRYOSPHERE;
- 0740 Snowmelt;
- CRYOSPHERE;
- 0758 Remote sensing;
- CRYOSPHERE;
- 1863 Snow and ice;
- HYDROLOGY