Abstract

To improve the depiction of autumn through spring land–atmosphere interactions and feedbacks within regional and global weather, climate, and hydrologic models, a Subgrid SNOW Distribution (SSNOWD) submodel that explicitly includes subgrid snow-depth and snow-cover variability has been developed. From both atmospheric and hydrologic perspectives, the subgrid snow-depth distribution is an important quantity to account for within large-scale models. In the natural system, these subgrid snow-depth distributions are largely responsible for the mosaic of snow-covered and snow-free areas that develop as the snow melts, and the impacts of these fractional areas must be quantified in order to realistically simulate grid-averaged surface fluxes. SSNOWD's formulation incorporates observational studies showing that snow distributions can be described by a lognormal distribution and the snow-depth coefficient of variation. Using an understanding of the physical processes that lead to these observed snow-depth variations, a global distribution of nine subgrid snow-depth-variability categories was developed, and coefficient-of-variation values were assigned to each category based on published measurements. In addition, SSNOWD adopts the physically realistic approach of performing separate surface-energy-balance calculations over the snow-covered and snow-free portions of each model grid cell and weighing the resulting fluxes according to these fractional areas. Using a climate version of the Regional Atmospheric Modeling System (ClimRAMS) over a North American domain, SSNOWD was compared with a snow-cover formulation similar to those currently used in most general circulation models. The simulations indicated that accounting for snow-distribution variability has a significant impact on snow-cover evolution and associated energy and moisture fluxes.

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.

Abstract

A gridded linear-reservoir runoff routing model (HydroFlow) was developed to simulate the linkages between runoff production from land-based snowmelt and icemelt processes and the associated freshwater fluxes to downstream areas and surrounding oceans. HydroFlow was specifically designed to account for glacier, ice sheet, and snow-free and snow-covered land applications. Its performance was verified for a test area in southeast Greenland that contains the Mittivakkat Glacier, the local glacier in Greenland with the longest observed time series of mass-balance and ice-front fluctuations. The time evolution of spatially distributed gridcell runoffs required by HydroFlow were provided by the SnowModel snow-evolution modeling system, driven with observed atmospheric data, for the years 2003 through 2010. The spatial and seasonal variations in HydroFlow hydrographs show substantial correlations when compared with observed discharge coming from the Mittivakkat Glacier area and draining into the adjacent ocean. As part of its discharge simulations, HydroFlow creates a flow network that links the individual grid cells that make up the simulation domain. The collection of networks that drain to the ocean produced a range of runoff values that varied most strongly according to catchment size and percentage and elevational distribution of glacier cover within each individual catchment. For 2003–10, the average annual Mittivakkat Glacier region runoff period was 200 ± 20 days, with a significant increase in annual runoff over the 8-yr study period, both in terms of the number of days (30 days) and in volume (54.9 × 10⁶ m³).

Abstract

Runoff magnitudes, the spatial patterns from individual Greenland catchments, and their changes through time (1960–2010) were simulated in an effort to understand runoff variations to adjacent seas and to illustrate the capability of SnowModel (a snow and ice evolution model) and HydroFlow (a runoff routing model) to link variations in terrestrial runoff with ocean processes and other components of Earth’s climate system. Significant increases in air temperature, net precipitation, and local surface runoff lead to enhanced and statistically significant Greenland ice sheet (GrIS) surface mass balance (SMB) loss. Total Greenland runoff to the surrounding oceans increased 30%, averaging 481 ± 85 km³ yr⁻¹. Averaged over the period, 69% of the runoff to the surrounding seas originated from the GrIS and 31% came from outside the GrIS from rain and melting glaciers and ice caps. The runoff increase from the GrIS was due to an 87% increase in melt extent, 18% from increases in melt duration, and a 5% decrease in melt rates (87% + 18% − 5% = 100%). In contrast, the runoff increase from the land area surrounding the GrIS was due to a 0% change in melt extent, a 108% increase in melt duration, and an 8% decrease in melt rate. In general, years with positive Atlantic multidecadal oscillation (AMO) index equaled years with relatively high Greenland runoff volume and vice versa. Regionally, runoff was greater from western than eastern Greenland. Since 1960, the data showed pronounced runoff increases in west Greenland, with the greatest increase occurring in the southwest and the lowest increase in the northwest.

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.

Abstract

This paper presents modeled surface and subsurface melt fluxes across near-coastal Antarctica. Simulations were performed using a physical-based energy balance model developed in conjunction with detailed field measurements in a mixed snow and blue-ice area of Dronning Maud Land, Antarctica. The model was combined with a satellite-derived map of Antarctic snow and blue-ice areas, 10 yr (1991–2000) of Antarctic meteorological station data, and a high-resolution meteorological distribution model, to provide daily simulated melt values on a 1-km grid covering Antarctica. Model simulations showed that 11.8% and 21.6% of the Antarctic continent experienced surface and subsurface melt, respectively. In addition, the simulations produced 10-yr averaged subsurface meltwater production fluxes of 316.5 and 57.4 km³ yr⁻¹ for snow-covered and blue-ice areas, respectively. The corresponding figures for surface melt were 46.0 and 2.0 km³ yr⁻¹, respectively, thus demonstrating the dominant role of subsurface over surface meltwater production. In total, computed surface and subsurface meltwater production values equal 31 mm yr⁻¹ if evenly distributed over all of Antarctica. While, at any given location, meltwater production rates were highest in blue-ice areas, total annual Antarctic meltwater production was highest for snow-covered areas due to its larger spatial extent. The simulations also showed higher interannual meltwater variations for surface melt than subsurface melt. Since most of the produced meltwater refreezes near where it was produced, the simulated melt has little effect on the Antarctic mass balance. However, the melt contribution is important for the surface energy balance and in modifying surface and near-surface snow and ice properties such as density and grain size.

Abstract

A new classification system for seasonal snow covers is proposed. It has six classes (tundra, taiga, alpine, maritime, prairie, and ephemeral, each class defined by a unique ensemble of textural and stratigraphic characteristics including the sequence of snow layers, their thickness, density, and the crystal morphology and grain characteristics within each layer. The classes can also be derived using a binary system of three climate variables: wind, precipitation, and air temperature. Using this classification system, the Northern Hemisphere distribution of the snow cover classes is mapped on a 0.5° lat × 0.5° long grid. These maps are compared to maps prepared from snow cover data collected in the former Soviet Union and Alaska. For these areas where both climatologically based and texturally based snow cover maps are available, there is 62% and 90% agreement, respectively. Five of the six snow classes are found in Alaska. From 1989 through 1992, hourly measurements, consisting of 40 thermal and physical parameters, including snow depth, the temperature distribution in the snow, and basal heat flow, were made on four of these classes. In addition, snow stratigraphy and texture were measured every six weeks. Factor analysis indicates that the snow classes can be readily discriminated using four or more winter average thermal or physical parameters. Further, analysis of hourly time series indicates that 84% of the time, spot measurements of the parameters are sufficient to correctly differentiate the snow cover class. Using the new snow classification system, 1) classes can readily be distinguished using observations of simple thermal parameters, 2) physical and thermal attributes of the snow can be inferred, and 3) classes can be mapped from climate data for use in regional and global climate modeling.

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.

Abstract

A coupled Regional Atmospheric Modeling System (RAMS) and ecosystem (CENTURY) modeling system has been developed to study regional-scale two-way interactions between the atmosphere and biosphere. Both atmospheric forcings and ecological parameters are prognostic variables in the linked system. The atmospheric and ecosystem models exchange information on a weekly time step. CENTURY receives as input air temperature, precipitation, radiation, wind speed, and relative humidity simulated by RAMS. From CENTURY-produced outputs, leaf area index, and vegetation transimissivity are computed and returned to RAMS. In this way, vegetation responses to weekly and seasonal atmospheric changes are simulated and fed back to the atmospheric–land surface hydrology model.

The coupled model was used to simulate the two-way biosphere and atmosphere feedbacks from 1 January to 31 December 1989, focusing on the central United States. Validation was performed for the atmospheric portion of the model by comparing with U.S. summary-of-the-day meteorological station observational datasets, and for the ecological component by comparing with advanced very high-resolution radiometer remote-sensing Normalized Difference Vegetation Index datasets. The results show that seasonal vegetation phenological variation strongly influences regional climate patterns through its control over land surface water and energy exchange. The coupled model captures the key aspects of weekly, seasonal, and annual feedbacks between the atmospheric and ecological systems. In addition, it has demonstrated its usefulness as a research tool for studying complex interactions between the atmosphere, biosphere, and hydrosphere.

Abstract

In the Arctic, where wind transport of snow is common, the depth and insulative properties of the snow cover can be determined as much by the wind as by spatial variations in precipitation. Where shrubs are more abundant and larger, greater amounts of drifting snow are trapped and suffer less loss due to sublimation. The snow in shrub patches is both thicker and a better thermal insulator per unit thickness than the snow outside of shrub patches. As a consequence, winter soil surface temperatures are substantially higher, a condition that can promote greater winter decomposition and nutrient release, thereby providing a positive feedback that could enhance shrub growth. If the abundance, size, and coverage of arctic shrubs increases in response to climate warming, as is expected, snow–shrub interactions could cause a widespread increase (estimated 10%–25%) in the winter snow depth. This would increase spring runoff, winter soil temperatures, and probably winter CO₂ emissions. The balance between these winter effects and changes in the summer energy balance associated with the increase in shrubs probably depends on shrub density, with the threshold for winter snow trapping occurring at lower densities than the threshold for summer effects such as shading. It is suggested that snow–shrub interactions warrant further investigation as a possible factor contributing to the transition of the arctic land surface from moist graminoid tundra to shrub tundra in response to climatic warming.