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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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Glen E. Liston
and
Christopher A. Hiemstra

Abstract

A methodology for assimilating ground-based and remotely sensed snow data within a snow-evolution modeling system (SnowModel) is presented. The data assimilation scheme (SnowAssim) is consistent with optimal interpolation approaches in which the differences between the observed and modeled snow values are used to constrain modeled outputs. The calculated corrections are applied retroactively to create improved fields prior to the assimilated observations. Thus, one of the values of this scheme is the improved simulation of snow-related distributions throughout the entire snow season, even when observations are only available late in the accumulation and/or ablation periods. Because of this, the technique is particularly applicable to reanalysis applications. The methodology includes the ability to stratify the assimilation into regions where either the observations and/or model has unique error properties, such as the differences between forested and nonforested snow environments. The methodologies are introduced using synthetic data and a simple simulation domain. In addition, the model is applied over NASA’s Cold Land Processes Experiment (CLPX), Rabbit Ears Pass, Colorado, observation domain. Simulations using the data assimilation scheme were found to improve the modeled snow water equivalent (SWE) distributions, and simulated SWE displayed considerably more realistic spatial heterogeneity than that provided by the observations alone.

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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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Christopher A. Hiemstra
,
Glen E. Liston
,
Roger A. Pielke Sr.
,
Daniel L. Birkenheuer
, and
Steven C. Albers

Abstract

Meteorological forcing data are necessary to drive many of the spatial models used to simulate atmospheric, biological, and hydrological processes. Unfortunately, many domains lack sufficient meteorological data and available point observations are not always suitable or reliable for landscape or regional applications. NOAA’s Local Analysis and Prediction System (LAPS) is a meteorological assimilation tool that employs available observations (meteorological networks, radar, satellite, soundings, and aircraft) to generate a spatially distributed, three-dimensional representation of atmospheric features and processes. As with any diagnostic representation, it is important to ascertain how LAPS outputs deviate from a variety of independent observations. A number of surface observations exist that are not used in the LAPS system, and they were employed to assess LAPS surface state variable and precipitation analysis performance during two consecutive years (1 September 2001–31 August 2003). LAPS assimilations accurately depicted temperature and relative humidity values. The ability of LAPS to represent wind speed was satisfactory overall, but accuracy declined with increasing elevation. Last, precipitation estimates performed by LAPS were irregular and reflected inherent difficulties in measuring and estimating precipitation.

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Steven J. Fletcher
,
Glen E. Liston
,
Christopher A. Hiemstra
, and
Steven D. Miller

Abstract

In this paper four simple computationally inexpensive, direct insertion data assimilation schemes are presented, and evaluated, to assimilate Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover, which is a binary observation, and Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E) snow water equivalent (SWE) observations, which are at a coarser resolution than MODIS, into a numerical snow evolution model. The four schemes are 1) assimilate MODIS snow cover on its own with an arbitrary 0.01 m added to the model cells if there is a difference in snow cover; 2) iteratively change the model SWE values to match the AMSR-E equivalent value; 3) AMSR-E scheme with MODIS observations constraining which cells can be changed, when both sets of observations are available; and 4) MODIS-only scheme when the AMSR-E observations are not available, otherwise scheme 3. These schemes are used in the winter of 2006/07 over the southeast corner of Colorado and the tri-state area: Wyoming, Colorado, and Nebraska. It is shown that the inclusion of MODIS data enables the model in the north domain to have a 15% improvement in number of days with a less than 10% disagreement with the MODIS observation 24 h later and approximately 5% for the south domain. It is shown that the AMSR-E scheme has more of an impact in the south domain than the north domain. The assimilation results are also compared to station snow-depth data in both domains, where there is up-to-a-factor-of-5 underestimation of snow depth by the assimilation schemes compared with the station data but the snow evolution is fairly consistent.

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Sebastian H. Mernild
,
Glen E. Liston
,
Christopher A. Hiemstra
,
Jacob C. Yde
, and
Gino Casassa

Abstract

We analyzed modeled river runoff variations west of the Andes Cordillera’s continental divide for 1979/80–2013/14 (35 years). Our foci were annual runoff conditions, runoff origins (rain, snowmelt, and glacier ice), and runoff spatiotemporal variability. Low and high runoff conditions were defined as occurrences that fall outside the 10th (low values) and 90th (high values) percentile values of the period of record. SnowModel and HydroFlow modeling tools were used at 4-km horizontal grid increments and 3-h time intervals. NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) datasets were used as atmospheric forcing. This modeling system includes evaporation and sublimation from snow-covered surfaces, but it does not take into account evapotranspiration from bare and vegetation-covered soils and from river and lake surfaces. In general for the Andes Cordillera, the simulated runoff decreased before 1997 and increased afterward. This could be due to a model precipitation artifact in the MERRA forcing. If so, this artifact would influence the number of years with low runoff values, which decreased over time, while the number of high runoff values increased over time. For latitudes south of ~40°S, both the greatest decrease in the number of low runoff values and the greatest increase in high runoff values occurred. High runoff values averaged 84% and 58% higher than low values for nonglacierized and glacierized catchments, respectively. Furthermore, for glacierized catchments, 61% and 62% of the runoff originated from rain-derived runoff during low and high runoff extreme years, respectively; 28% and 30% from snowmelt-derived runoff; and 11% and 8% from glacier-ice-melt-derived runoff. As the results could be MERRA dependent, more work with other precipitation forcings and/or in situ measurements is needed to assess whether these are real runoff behaviors or artifacts.

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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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Glen E. Liston
,
Christopher A. Hiemstra
,
Kelly Elder
, and
Donald W. Cline

Abstract

The Cold Land Processes Experiment (CLPX) had a goal of describing snow-related features over a wide range of spatial and temporal scales. This required linking disparate snow tools and datasets into one coherent, integrated package. Simulating realistic high-resolution snow distributions and features requires a snow-evolution modeling system (SnowModel) that can distribute meteorological forcings, simulate snowpack accumulation and ablation processes, and assimilate snow-related observations. A SnowModel was developed and used to simulate winter snow accumulation across three 30 km × 30 km domains, enveloping the CLPX mesocell study areas (MSAs) in Colorado. The three MSAs have distinct topography, vegetation, meteorological, and snow characteristics. Simulations were performed using a 30-m grid increment and spanned the snow accumulation season (1 October 2002–1 April 2003). Meteorological forcing was provided by 27 meteorological stations and 75 atmospheric analyses grid points, distributed using a meteorological model (MicroMet). The simulations included a data assimilation model (SnowAssim) that adjusted simulated snow water equivalent (SWE) toward ground-based and airborne SWE observations. The observations consisted of SWE over three 1 km × 1 km intensive study areas (ISAs) for each MSA and a collection of 117 airborne gamma observations, each integrating area 10 km long by 300 m wide. Simulated SWE distributions displayed considerably more spatial heterogeneity than the observations alone, and the simulated distribution patterns closely fit the current understanding of snow evolution processes and observed snow depths. This is the result of the MicroMet/SnowModel’s relatively finescale representations of orographic precipitation, elevation-dependant snowmelt, wind redistribution, and snow–vegetation interactions.

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Glen E. Liston
,
Daniel L. Birkenheuer
,
Christopher A. Hiemstra
,
Donald W. Cline
, and
Kelly Elder

Abstract

This paper describes the Local Analysis and Prediction System (LAPS) and the 20-km horizontal grid version of the Rapid Update Cycle (RUC20) atmospheric analyses datasets, which are available as part of the Cold Land Processes Field Experiment (CLPX) data archive. The LAPS dataset contains spatially and temporally continuous atmospheric and surface variables over Colorado, Wyoming, and parts of the surrounding states. The analysis used a 10-km horizontal grid with 21 vertical levels and an hourly temporal resolution. The LAPS archive includes forty-six 1D surface fields and nine 3D upper-air fields, spanning the period 1 September 2001 through 31 August 2003. The RUC20 dataset includes hourly 3D atmospheric analyses over the contiguous United States and parts of southern Canada and northern Mexico, with 50 vertical levels. The RUC20 archive contains forty-six 1D surface fields and fourteen 3D upper-air fields, spanning the period 1 October 2002 through 31 September 2003. The datasets are archived at the National Snow and Ice Data Center (NSIDC) in Boulder, Colorado.

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