Incorporating Snow Albedo Feedback into Downscaled Temperature and Snow Cover Projections for California’s Sierra Nevada
Abstract
California’s Sierra Nevada is a high-elevation mountain range with significant seasonal snow cover. Under anthropogenic climate change, amplification of the warming is expected to occur at elevations near snow margins due to snow albedo feedback. However, climate change projections for the Sierra Nevada made by global climate models (GCMs) and statistical downscaling methods miss this key process. Dynamical downscaling simulates the additional warming due to snow albedo feedback. Ideally, dynamical downscaling would be applied to a large ensemble of 30 or more GCMs to project ensemble-mean outcomes and intermodel spread, but this is far too computationally expensive. To approximate the results that would occur if the entire GCM ensemble were dynamically downscaled, a hybrid dynamical–statistical downscaling approach is used. First, dynamical downscaling is used to reconstruct the historical climate of the 1981–2000 period and then to project the future climate of the 2081–2100 period based on climate changes from five GCMs. Next, a statistical model is built to emulate the dynamically downscaled warming and snow cover changes for any GCM. This statistical model is used to produce warming and snow cover loss projections for all available CMIP5 GCMs. These projections incorporate snow albedo feedback, so they capture the local warmingmore »
- Authors:
-
- Univ. of California, Los Angeles, CA (United States)
- Univ. of California, Los Angeles, CA (United States); Univ. of Missouri, Kansas City, MO (United States)
- Publication Date:
- Research Org.:
- Univ. of California, Los Angeles, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1537008
- Grant/Contract Number:
- SC0014061
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Climate
- Additional Journal Information:
- Journal Volume: 30; Journal Issue: 4; Journal ID: ISSN 0894-8755
- Publisher:
- American Meteorological Society
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; Meteorology & Atmospheric Sciences
Citation Formats
Walton, Daniel B., Hall, Alex, Berg, Neil, Schwartz, Marla, and Sun, Fengpeng. Incorporating Snow Albedo Feedback into Downscaled Temperature and Snow Cover Projections for California’s Sierra Nevada. United States: N. p., 2017.
Web. doi:10.1175/jcli-d-16-0168.1.
Walton, Daniel B., Hall, Alex, Berg, Neil, Schwartz, Marla, & Sun, Fengpeng. Incorporating Snow Albedo Feedback into Downscaled Temperature and Snow Cover Projections for California’s Sierra Nevada. United States. https://doi.org/10.1175/jcli-d-16-0168.1
Walton, Daniel B., Hall, Alex, Berg, Neil, Schwartz, Marla, and Sun, Fengpeng. Tue .
"Incorporating Snow Albedo Feedback into Downscaled Temperature and Snow Cover Projections for California’s Sierra Nevada". United States. https://doi.org/10.1175/jcli-d-16-0168.1. https://www.osti.gov/servlets/purl/1537008.
@article{osti_1537008,
title = {Incorporating Snow Albedo Feedback into Downscaled Temperature and Snow Cover Projections for California’s Sierra Nevada},
author = {Walton, Daniel B. and Hall, Alex and Berg, Neil and Schwartz, Marla and Sun, Fengpeng},
abstractNote = {California’s Sierra Nevada is a high-elevation mountain range with significant seasonal snow cover. Under anthropogenic climate change, amplification of the warming is expected to occur at elevations near snow margins due to snow albedo feedback. However, climate change projections for the Sierra Nevada made by global climate models (GCMs) and statistical downscaling methods miss this key process. Dynamical downscaling simulates the additional warming due to snow albedo feedback. Ideally, dynamical downscaling would be applied to a large ensemble of 30 or more GCMs to project ensemble-mean outcomes and intermodel spread, but this is far too computationally expensive. To approximate the results that would occur if the entire GCM ensemble were dynamically downscaled, a hybrid dynamical–statistical downscaling approach is used. First, dynamical downscaling is used to reconstruct the historical climate of the 1981–2000 period and then to project the future climate of the 2081–2100 period based on climate changes from five GCMs. Next, a statistical model is built to emulate the dynamically downscaled warming and snow cover changes for any GCM. This statistical model is used to produce warming and snow cover loss projections for all available CMIP5 GCMs. These projections incorporate snow albedo feedback, so they capture the local warming enhancement (up to 3°C) from snow cover loss that other statistical methods miss. Capturing these details may be important for accurately projecting impacts on surface hydrology, water resources, and ecosystems},
doi = {10.1175/jcli-d-16-0168.1},
journal = {Journal of Climate},
number = 4,
volume = 30,
place = {United States},
year = {Tue Feb 07 00:00:00 EST 2017},
month = {Tue Feb 07 00:00:00 EST 2017}
}
Web of Science
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