The Sensitivity of Mountain Snowpack Accumulation to Climate Warming

Author:

Minder Justin R.1

Affiliation:

1. Department of Atmospheric Sciences, University of Washington, Seattle, Washington

Abstract

Abstract Controls on the sensitivity of mountain snowpack accumulation to climate warming (λS) are investigated. This is accomplished using two idealized, physically based models of mountain snowfall to simulate snowpack accumulation for the Cascade Mountains under current and warmed climates. Both models are forced from sounding observations. The first model uses the linear theory (LT) model of orographic precipitation to predict precipitation as a function of the incoming flow characteristics and uses the sounding temperatures to estimate the elevation of the rain–snow boundary, called the melting level (ML). The second “ML model” uses only the ML from the sounding and assumptions of uniform and constant precipitation. Both models simulate increases in precipitation intensity and elevated storm MLs under climate warming. The LT model predicts a 14.8%–18.1% loss of Cascade snowfall per degree of warming, depending on the vertical structure of the warming. The loss of snowfall is significantly greater, 19.4%–22.6%, if precipitation increases are neglected. Comparing the two models shows that the predominant control on λS is the relationship between the distribution of storm MLs and the distribution of topographic area with elevation. Although increases in precipitation due to warming may act to moderate λS, the loss of snow accumulation area profoundly limits the ability of precipitation increases to maintain the snowpack under substantial climate warming (beyond 1°–2°C). Circulation changes may act to moderate or exacerbate the loss of mountain snowpack under climate change via impacts on orographic precipitation enhancement.

Publisher

American Meteorological Society

Subject

Atmospheric Science

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3