C32A-01

The response of Northern hemisphere mountain snow to a changing climate

* Mote, P W mote@washington.edu, Climate Impacts Group, UW, JISAO, Box 355672 University of Washington, Seattle, WA 98195, United States
Brown, R D brown.ross@ouranos.ca, Environnement Canada @ Ouranos, 550 Sherbrooke Ouest, 19e etage, Montreal, QC H3A 1B9, Canada

Observations of changes in snow cover, snow depth, and snow water content across the globe in numerous country-scale studies paint a somewhat confusing picture. We use a snowpack model to quantify how changes in temperature and precipitation could affect these and other measures of snow quantity. Some metrics are more sensitive to warming than others; snow cover duration had the highest sensitivity, especially in the western cordillera of North America. Largest decreases have occurred where winter mean air temperatures are above -5°C. These findings are echoed by global climate models, which show large decreases in snow cover duration over the maritime margins of North America and western Europe. The sensitivity analysis suggests a potentially complex dependence on elevation of snow cover duration and seasonal maximum snow water content in mountain regions, owing to nonlinear interactions between the duration of the snow season and snow accumulation rates.

C32A-02

Modeling of High Latitude Spring Freshet from AMSR-E Passive Microwave Observations: Potential for Gauged and Ungauged Basins

* Ramage, J ramage@lehigh.edu, Earth and Environmental Sciences, Lehigh University, Bethlehem, PA 18015, United States
McKenney, R A mckennra@plu.edu, Geosciences/Environmental Studies, Pacific Lutheran University, Tacoma, WA 98447, United States
Yan, F fey206@lehigh.edu, Earth and Environmental Sciences, Lehigh University, Bethlehem, PA 18015, United States

Snowmelt runoff in high latitude drainage basins has significant impacts on local to global climatic, ecologic, and hydrologic systems. Predicting snowmelt runoff timing and magnitude is challenging in remote, high latitude, or mountainous regions with sparse meteorological and streamflow observations. The SWEHydro model was developed to use snowmelt timing and snow water equivalent (SWE) from Advanced Microwave Scanning Radiometer for EOS (AMSR-E) to simulate the spring freshet without meteorological data in the upper Yukon River basin, Canada. The model uses four parameters: snowmelt rate during and after the melt transition, and flow timing during and after the melt transition. Monte Carlo simulations with randomly generated values of the parameters were performed to extract hydrographs unconstrained by user assumptions. Best fit curves were selected by comparisons with ground based streamflow data from the Water Survey of Canada. A normalized mismatch function was used to calculate the best fit. Curves were ranked by lowest error in freshet timing, and peak timing and magnitude. Parameters extracted from the best fit curves can be used to predict flow in similar ungauged basins. The SWEHydro model, developed to use solely remote sensing observations and DEM data, is effective in simulating spring stream runoff in basins lacking sufficient available in-situ weather measurements for conventional models. Sensitivity tests demonstrate that the simulated freshet timing is strongly related to the AMSR-E derived snowmelt timing, and that the modeled hydrograph is strongly dependent on the flow timing parameter. This study shows that AMSR-E passive microwave data observations are a powerful tool to investigate snowmelt timing, snow water equivalent (SWE), and their collective effects on streamflow timing and magnitude. Generalizing the model parameters for ungauged basins will make it feasible to apply the SWEHydro model to other arctic and subarctic watersheds to assist in studies of water resource variability and climate change.

C32A-03 INVITED

Multi-Scale Reconstructions of Snowpack Variability for Key Watersheds in Western North America: Tree-Rings Provide Insights on the Past 500 to 1000 Years

* Pederson, G T gtpeders@email.arizona.edu, School of Natural Resources University of Arizona, School of Natural Resources University of Arizona 325 Biosciences East, Tucson, AZ 85721-0043, United States
* Pederson, G T gtpeders@email.arizona.edu, U.S. Geological Survey Northern Rocky Mountain Science Center, 229 AJM Johnson Hall Montana State University, Bozeman, MT 59717, United States
Gray, S T sgray8@uwyo.edu, University of Wyoming, Water Resources Data System University of Wyoming 100 E. University Ave. Dept 3943, Laramie, WY 82071, United States
Fagre, D B dan_fagre@usgs.gov, U.S. Geological Survey Northern Rocky Mountain Science Center, 229 AJM Johnson Hall Montana State University, Bozeman, MT 59717, United States
Graumlich, L J lisag@cals.arizona.edu, School of Natural Resources University of Arizona, School of Natural Resources University of Arizona 325 Biosciences East, Tucson, AZ 85721-0043, United States

One of the most robust lines of evidence for climate change impacts in the Western US is the decline in snowpack during the latter half of the 20th century. It is critical to ascertain whether this trend is anomalous relative to long-term patterns of snowpack dynamics. We are using tree-ring data networks coupled with NRCS snowcourse based reconstructions of Snow Water Equivalent (SWE) to reconstruct snowpack variability at multiple watershed scales. Snowcourse records provide the raw data needed to generate calibration datasets of April 1 SWE. Records span 1930 to present, and were utilized to generate historic SWE anomalies from the scale of individual USGS level 6 watershed to the entire Upper Colorado River Basin. Across multiple watershed scales historic SWE records were used in combination with more then 600 existing and recently collected tree-ring chronologies to produce 500 to 1000 year records of April 1 SWE variability. Initial work targets key high-mountain headwaters for the Upper Colorado, Upper Yellowstone/Missouri, and Columbia/Saskatchewan Rivers. For the Colorado Plateau region, 9 out of 17 level 6 watersheds achieve quality reconstructions (R2> 0.45) of April 1 SWE, and a reconstruction extending back to 1181 AD was produced for the entire Upper Colorado River Basin. Preliminary analyses show marked interannual to multidecadal variability in total April 1 SWE. Comparisons with existing proxy records of Pacific Basin climate show coupled ENSO and PDO influences on the total amount of mountain snowpack in these regions. The strength and sign of these relationships is shown to vary over time and on a watershed-by- watershed basis. These and other results exemplify why long-term records are essential baseline information for evaluating recent and future changes in mountain snowpack. The overarching goal of this project is to lay the foundation for snowpack reconstructions that encompass high mountain areas in all of western North America.

http://www.snr.arizona.edu/project/snowpack

C32A-04 INVITED

Ensemble Snowpack Projections for the Pacific Northwest from 1950-2100 Based on Downscaled IPCC Scenarios

* Hamlet, A F hamleaf@u.washington.edu, CSES Climate Impacts Group, University of Washington Box 355672, Seattle, WA 98195, United States
* Hamlet, A F hamleaf@u.washington.edu, Dept. of Civil and Environmental Engineering, University of Washington Box 352700, Seattle, WA 98195, United States
McGuire Elsner, M mmcguire@u.washington.edu, CSES Climate Impacts Group, University of Washington Box 355672, Seattle, WA 98195, United States
McGuire Elsner, M mmcguire@u.washington.edu, Dept. of Civil and Environmental Engineering, University of Washington Box 352700, Seattle, WA 98195, United States
Salathe, E salathe@atmos.washington.edu, CSES Climate Impacts Group, University of Washington Box 355672, Seattle, WA 98195, United States
Lettenmaier, D P dennisl@u.washington.edu, CSES Climate Impacts Group, University of Washington Box 355672, Seattle, WA 98195, United States
Lettenmaier, D P dennisl@u.washington.edu, Dept. of Civil and Environmental Engineering, University of Washington Box 352700, Seattle, WA 98195, United States

Over the last decade, and particularly over the last several years, global climate change has been widely recognized as an important issue affecting water resources planning and management in the western U.S. where snowpack plays a crucial role in the hydrologic cycle. Hydrologic impacts of warming in snowmelt- dominant and mixed rain and snow watersheds include loss of spring snowpack, systematic increases in winter flow, earlier and reduced peak flows deriving from snowmelt, and decreased water availability in summer. These temperature-related hydrologic changes have been characterized in observed records by strong elevational gradients, with low lying areas showing little change, and areas close to freezing in mid winter at moderate elevations (such as the west slopes of the Cascades) showing the most pronounced changes. Although the sensitivity of mountain snowpack to warming has been well established for some time, spring snowpack is sensitive to both changes in temperature and precipitation and their seasonality, which are uncertain for future projections derived from GCM simulations. In this study we analyze simulations of snowpack and some related hydrologic variables in the Pacific Northwest from 1950-2100 using a new high-resolution version of the VIC hydrologic model implemented at 1/16th degree and driven by a 40- member ensemble of GCM transient scenarios from the most recent IPCC effort. The time frame of the analysis allows for both comparison with observed snowpack trends in the second half of the 20th century and an evaluation of future impacts extending to the end of the 21st century.

C32A-05

Understanding the Temperature Sensitivity of Mountain Snowfall

* Minder, J R juminder@atmos.washington.edu, University of Washington Department of Atmospheric Science, 408 Atmospheric Sciences - Geophysics (ATG) Building Box 351640, Seattle, WA 98195-1640, United States

Understanding the Temperature Sensitivity of Mountain Snowfall The temperature sensitivity of a mountain snowpack to warming may be defined as the percentage change in area integrated snow water equivalent per degree of climate warming (hereafter λ). For analyzing the impacts of climate change and climate variability on mountain snows, the temperature sensitivity concept is a particularly useful tool. Since λ reflects both the sensitivity of snowfall and ablation processes to temperature, it is a potentially complicated function of numerous topographic, land surface, and climatic factors. While recent work has shown that λ may be a robust feature of some regional climates, a general understanding of how myriad physical processes interact to determine its value for a give region is still lacking. To better understand the controls on λ we investigate in detail the factors that determine the sensitivity of climatological mountain snowfall to temperature (λ_s), and their relative importance. This is accomplished using an idealized model of orographic precipitation run over realistic topography forced by several decades of radiosonde observations. The model includes the fundamental temperature dependencies of mountain snowfall associated with atmospheric moisture content and precipitation phase, and compares favorably with observations and more sophisticated atmospheric models. Our experiments with various climatologies and topographies suggest that λ_s is primarily determined by the relationship between the distribution of upstream freezing-levels and the mountain hypsometry. The climatologies of windspeed, wind direction, and microphysics, as well as storm-to-storm variations in precipitation patterns and intensity are of secondary importance for determining λ_s. While increases in orographic precipitation intensity with temperature may act to reduce λ_s, increased snowfall rates are overwhelmed by reduced snowfall frequency as freezing levels rise and snow changes to rain. We further investigate the importance of λ_s by comparing the sensitivity of mountain snowfall to climate warming with natural inter-annual variability in mountain snowfall. Taken together, our results demonstrate that λ is strongly constrained by some very basic factors that make it a robust characteristic of mountain climates.

C32A-06 INVITED

Snowmelt runoff, remote sensing, and the end of stationarity

* Dozier, J dozier@bren.ucsb.edu, University of California, Bren School, Santa Barbara, CA 93106-5131, United States

In the 21st century, a crucial question for the Sierra Nevada snowpack is: How do we reliably predict snowmelt runoff and associated demand as climate changes, populations grow, land use evolves, and individual and societal choices are made? Our traditional forecasting methods are based on statistical relations developed when human impacts were less intense and the pace of climate change was slower. The rich, hard-won, long-term data that we have document trends already, but uncertainty will get worse without new, more mechanistic approaches. Interpolations from ground measurements of snow-water equivalent, constrained by measurements of snow-covered area, provide a method to estimate the spatial distribution of snow. An energy balance snow-depletion calculation can be used for validation. The combination of models should improve the accuracy of snowmelt runoff forecasts.

C32A-07 INVITED

Inter-annual variability in snow cover depletion and snow water equivalent in the Sierra Nevada inferred from MODIS data

* Molotch, N P noah.p.molotch@jpl.nasa.gov, Department of Civil and Environmental Engineering, University of California, Los Angeles, 5732 Boelter Hall, Los Angeles, CA 90095, United States
* Molotch, N P noah.p.molotch@jpl.nasa.gov, Jet Propulsion Laboratory, 4800 Oak Grove Dr., Pasadena, CA 91109, United States
Margulis, S A margulis@seas.ucla.edu, Department of Civil and Environmental Engineering, University of California, Los Angeles, 5732 Boelter Hall, Los Angeles, CA 90095, United States
Dozier, J dozier@bren.ucsb.edu, Donald Bren School of Environmental Science and Management, University of California, Santa Barbara, Bren Hall, Santa Barbara, CA 93106, United States
Painter, T H painter@geog.utah.edu, Department of Geography, University of Utah, 260 S. Central Campus Dr, Salt Lake City, UT 84112, United States
Shen, D dayong@seas.ucla.edu, Department of Civil and Environmental Engineering, University of California, Los Angeles, 5732 Boelter Hall, Los Angeles, CA 90095, United States
Kwok, A angkwok@ucla.edu, Department of Civil and Environmental Engineering, University of California, Los Angeles, 5732 Boelter Hall, Los Angeles, CA 90095, United States

Hydroclimatological studies of snow cover depletion are now possible as we approach the first decade of Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover observations. Previous studies at watershed scales have shown that these observations can be combined with spatially distributed snowmelt models to reconstruct the spatial distribution of snow water equivalent (SWE). We extend this approach to the Sierra Nevada using a Bayesian SWE reconstruction technique which combines time-series of remote sensing estimates of SCA with a land surface model (LSM) to estimate storm-specific snowfall distribution with a retrospective data assimilation scheme. This approach exploits the inherent relationship between the timing of snow disappearance and the magnitude of initial SWE. In this regard, we show that the MODIS snow cover depletion record from 2001 - 2007 exhibited considerable inter-annual variability in both snow cover persistence and reconstructed snow water equivalent. During wet years (2005-2006) snow cover persistence was up to two months longer than the average persistence over the observation period 2001 - 2007. Conversely, in the drought year of 2007 snow disappeared approximately two months prior to the average date of snow disappearance. Reconstructions of snow water equivalent were consistent with the snow cover persistence anomalies. These snow cover persistence patterns and associated SWE reconstructions provide a means to explore spatially explicit trends in snow accumulation and associated local and meso-scale controls. Such analyses will be described in the context of range-wide and basin-scale hydroclimatological implications.

http://cee.ucla.edu/faculty/molotch.htm

C32A-08

Adaptation and Sustainability of Western U.S. Ski Areas in the 21st Century With a Changing Climate

Lazar, B blazar@stratusconsulting.com, Stratus Consulting, Inc., 1881 Ninth Street, Suite 201, Boulder, CO 80302, United States
* Williams, M W markw@culter.colorado.edu, University of Colorado, Boulder, INSTAAR and Dept. of Geography, Campus Box 450, Boulder, CO 80309, United States

We evaluate how climate change resulting from increased greenhouse gas (GHG) emissions may affect snow coverage for two case studies: Aspen Mountain and Park City Mountain in the years 2030, 2075, and 2100. Snow coverage was evaluated using the Snowmelt Runoff Model. We evaluated climate changes using MAGICC/SCENGEN and the output from five General Circulation Models (GCMs). We bracketed potential climate changes by using the relatively low, mid-range, and high GHG emissions scenarios: B1, A1B, and A1FI. To obtain higher resolution climate change estimates, we spatially downscaled projections using a regional climate model (RCM, MM5), and a statistical downscaling model (SDSM). By 2030, temperatures are estimated to increase 1.8 to 2.5 oC at Aspen Mountain and Park City Mountain, for all GCMs and emission scenarios. The length of the ski season is estimated to decrease by approximately 1 to 1.5 weeks at both ski areas, and the snowline is estimated at 2275 m. In 2100, temperatures are projected to increase 2.9 to 9.4 oC at Aspen Mountain and 4.2 to 8.9 oC at Park City Mountain. The snowline is estimated at 2800 to 2900 m at both ski areas for the A1B and B1 scenarios, and 3100 to 3200m for the A1FI scenario. Here we address questions of adaptation and sustainability of ski areas in the face of these challenges. We address snowmaking, water availability, low-flow scenarios for streams draining ski areas, location of base areas, need for expansion to higher elevation areas, and other adaptation measures.

Authors (2008), Title, Eos Trans. AGU, 89(53), Fall Meet. Suppl., Abstract xxxxx-xx