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  • Author or Editor: Christopher A. Hiemstra x
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Glen E. Liston
and
Christopher A. Hiemstra

Abstract

Arctic snow presence, absence, properties, and water amount are key components of Earth’s changing climate system that incur far-reaching physical and biological ramifications. Recent dataset and modeling developments permit relatively high-resolution (10-km horizontal grid; 3-h time step) pan-Arctic snow estimates for 1979–2009. Using MicroMet and SnowModel in conjunction with land cover, topography, and 30 years of the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) atmospheric reanalysis data, a distributed snow-related dataset was created including air temperature, snow precipitation, snow-season timing and length, maximum snow water equivalent (SWE) depth, average snow density, snow sublimation, and rain-on-snow events. Regional variability is a dominant feature of the modeled snow-property trends. Both positive and negative regional trends are distributed throughout the pan-Arctic domain, featuring, for example, spatially distinct areas of increasing and decreasing SWE or snow season length. In spite of strong regional variability, the data clearly show a general snow decrease throughout the Arctic: maximum winter SWE has decreased, snow-cover onset is later, the snow-free date in spring is earlier, and snow-cover duration has decreased. The domain-averaged air temperature trend when snow was on the ground was 0.17°C decade−1 with minimum and maximum regional trends of −0.55° and 0.78°C decade−1, respectively. The trends for total number of snow days in a year averaged −2.49 days decade−1 with minimum and maximum regional trends of −17.21 and 7.19 days decade−1, respectively. The average trend for peak SWE in a snow season was −0.17 cm decade−1 with minimum and maximum regional trends of −2.50 and 5.70 cm decade−1, respectively.

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Glen E. Liston
and
Christopher A. Hiemstra

Abstract

A vegetation-protruding-above-snow parameterization for earth system models was developed to improve energy budget calculations of interactions among vegetation, snow, and the atmosphere in nonforested areas. These areas include shrublands, grasslands, and croplands, which represent 68% of the seasonally snow-covered Northern Hemisphere land surface (excluding Greenland). Snow depth observations throughout nonforested areas suggest that mid- to late-winter snowpack depths are often comparable or lower than the vegetation heights. As a consequence, vegetation protruding above the snow cover has an important impact on snow-season surface energy budgets. The protruding vegetation parameterization uses disparate energy balances for snow-covered and protruding vegetation fractions of each model grid cell, and fractionally weights these fluxes to define grid-average quantities. SnowModel, a spatially distributed snow-evolution modeling system, was used to test and assess the parameterization. Simulations were conducted during the winters of 2005/06 and 2006/07 for conditions of 1) no protruding vegetation (the control) and 2) with protruding vegetation. The spatial domain covered Colorado, Wyoming, and portions of the surrounding states; 81% of this area is nonforested. The surface net radiation, energy, and moisture fluxes displayed considerable differences when protruding vegetation was included. For shrubs, the net radiation, sensible, and latent fluxes changed by an average of 12.7, 6.9, and −22.7 W m−2, respectively. For grass and crops, these fluxes changed by an average of 6.9, −0.8, and −7.9 W m−2, respectively. Daily averaged flux changes were as much as 5 times these seasonal averages. As such, the new parameterization represents a major change in surface flux calculations over more simplistic and less physically realistic approaches.

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Sebastian H. Mernild
,
Glen E. Liston
, and
Christopher A. Hiemstra

Abstract

Mass changes and mass contribution to sea level rise from glaciers and ice caps (GIC) are key components of the earth’s changing sea level. GIC surface mass balance (SMB) magnitudes and individual and regional mean conditions and trends (1979–2009) were simulated for all GIC having areas greater or equal to 0.5 km2 in the Northern Hemisphere north of 25°N latitude (excluding the Greenland Ice Sheet). Recent datasets, including the Randolph Glacier Inventory (RGI; v. 2.0), the NOAA Global Land One-km Base Elevation Project (GLOBE), and the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) products, together with recent SnowModel developments, allowed relatively high-resolution (1-km horizontal grid; 3-h time step) simulations of GIC surface air temperature, precipitation, sublimation, evaporation, surface runoff, and SMB. Simulated SMB outputs were calibrated against 1422 direct glaciological annual SMB observations of 78 GIC. The overall GIC mean annual and mean summer air temperature, runoff, and SMB loss increased during the simulation period. The cumulative GIC SMB was negative for all regions. The SMB contribution to sea level rise was largest from Alaska and smallest from the Caucasus. On average, the contribution to sea level rise was 0.51 ± 0.16 mm sea level equivalent (SLE) yr−1 for 1979–2009 and ~40% higher 0.71 ± 0.15 mm SLE yr−1 for the last decade, 1999–2009.

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Sebastian H. Mernild
,
Glen E. Liston
,
Christopher A. Hiemstra
,
Jens H. Christensen
,
Martin Stendel
, and
Bent Hasholt

Abstract

A regional atmospheric model, the HIRHAM4 regional climate model (RCM) using boundary conditions from the ECHAM5 atmosphere–ocean general circulation model (AOGCM), was downscaled to a 500-m gridcell increment using SnowModel to simulate 131 yr (1950–2080) of hydrologic cycle evolution in west Greenland’s Kangerlussuaq drainage. Projected changes in the Greenland Ice Sheet (GrIS) surface mass balance (SMB) and runoff are relevant for potential hydropower production and prediction of ecosystem changes in sensitive Kangerlussuaq Fjord systems. Mean annual surface air temperatures and precipitation in the Kangerlussuaq area were simulated to increase by 3.4°C and 95 mm water equivalent (w.eq.), respectively, between 1950 and 2080. The local Kangerlussuaq warming was less than the average warming of 4.8°C simulated for the entire GrIS. The Kangerlussuaq SMB loss increased by an average of 0.3 km3 because of a 0.4 km3 rise in precipitation, 0.1 km3 rise in evaporation and sublimation, and 0.6 km3 gain in runoff (1950–2080). By 2080, the spring runoff season begins approximately three weeks earlier. The average modeled SMB and runoff is approximately −0.1 and 1.2 km3 yr−1, respectively, indicating that ∼10% of the Kangerlussuaq runoff is explained by the GrIS SMB net loss. The cumulative net volume loss (1950–2080) from SMB was 15.9 km3, and runoff was 151.2 km3 w.eq. This runoff volume is expected to have important hydrodynamic and ecological impacts on the stratified salinity in the Kangerlussuaq Fjord and on the transport of freshwater to the ocean.

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