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Sturm, Matthew

Publications and source records attributed to Sturm, Matthew.

An Examination of Water-Related Melt Processes in Arctic Snow on Tundra and Sea-Ice

From April through June in 2019 and 2022, we monitored snow melt at three sites near Utqiaġvik, Alaska. Along 200-m lines we measured snow depth, density, stratigraphy, snow-covered area, and spectral albedo. Site 1 (ARM) was sloped tundra drained by water tracks. Site 2 (BEO) was flat polygonal tundra. Site 3 (ICE) was on undeformed landfast sea ice. All three sites were within a 6 km radius. Despite similar pre-melt snow distributions and weather, the melt progression differed markedly between sites. In 2019, by mid-melt, there was 40% less snow-covered area at ARM versus ICE, and 34% less snow-covered area at ARM versus BEO. The 2022 melt started 2 weeks later than in 2019 and was rapid, so smaller differences in snow-covered areas developed. In both years meltout dates varied by up to 25 days between sites, and more than 20 days within sites, with melt rates at locations only meters apart differing by up to a factor of seven. This melt diachroneity led to highly heterogeneous meltout patterns at all three sites. Our measurements and observations indicate that, in addition to reductions in snow reflective properties and wind-driven heat advection, the fate of meltwater plays a key role in producing melt diachroneity. We identify seven snow-water mechanisms that can enhance or inhibit melt rates, all largely controlled by the local topography and the nature of the substrate. These mechanisms are important because the most rapid changes in albedo coincide with the peak of water-snow melt interactions.

54 ENVIRONMENTAL SCIENCES↗

Spectral Albedo for the 2019 SALVO Campaign

The springtime, surface-albedo transition in the Alaskan Arctic and the forcings that determine the duration and nature of that transition are the focus of our Snow ALbedo eVOlution (SALVO I & II) campaign. The SALVO team are assessing the “whys” and “how longs” of the stages of the spring melt during which albedo values drop from 0.8 to 0.1, the largest and most significant change of the year. The near-shore location of the ARM NSA observatory makes this an ideal place to investigate these melt stages.

54 ENVIRONMENTAL SCIENCES↗

Orthomosaic Images and Digital Elevation Models for the 2022 SALVO Campaign

The springtime, surface-albedo transition in the Alaskan Arctic and the forcings that determine the duration and nature of that transition are the focus of our Snow ALbedo eVOlution (SALVO I & II) campaign. The SALVO team are assessing the “whys” and “how longs” of the stages of the spring melt during which albedo values drop from 0.8 to 0.1, the largest and most significant change of the year. The near-shore location of the ARM NSA observatory makes this an ideal place to investigate these melt stages.

54 ENVIRONMENTAL SCIENCES↗

Orthomosaic Images and Digital Elevation Models for the 2019 SALVO Campaign

The springtime, surface-albedo transition in the Alaskan Arctic and the forcings that determine the duration and nature of that transition are the focus of our Snow ALbedo eVOlution (SALVO I & II) campaign. The SALVO team are assessing the “whys” and “how longs” of the stages of the spring melt during which albedo values drop from 0.8 to 0.1, the largest and most significant change of the year. The near-shore location of the ARM NSA observatory makes this an ideal place to investigate these melt stages.

54 ENVIRONMENTAL SCIENCES↗

pars2 (b1)

A laser disdrometer (LDIS) measures the drop size spectra and fall velocity of hydrometeors during precipitation events. The passage of hydrometeors through a horizontal area positioned between an eye-safe laser and an array detector results in blockage of the laser beam in proportion to the size of the drop or particle. In addition to drop/particle size and fall speed, precipitation type is classified. ARM also deploys video disdrometers (VDIS).

54 ENVIRONMENTAL SCIENCES↗

Snow in the Changing Sea-Ice Systems

Snow is the most reflective, and also the most insulative, natural material on Earth. Consequently, it is an integral part of the sea-ice and climate systems. However, the spatial and temporal heterogeneities of snow pose challenges for observing, understanding and modelling those systems under anthropogenic warming. Here, we survey the snow-ice system, then provide recommendations for overcoming present challenges. These include: collecting process-oriented observations for model diagnostics and understanding snow-ice feedbacks, and improving our remote sensing capabilities of snow for monitoring large-scale changes in snow on sea ice. These efforts could be achieved through stronger coordination between the observational, remote sensing and modelling communities, and would pay dividends through distinct improvements in predictions of polar environments.

Snow↗

Snow Dunes: A Controlling Factor of Melt Pond Distribution on Arctic Sea Ice

The location of snow dunes over the course of the ice-growth season 2007/08 was mapped on level landfast first-year sea ice near Barrow, Alaska. Landfast ice formed in mid-December and exhibited essentially homogeneous snow depths of 4-6 cm in mid-January; by early February distinct snow dunes were observed. Despite additional snowfall and wind redistribution throughout the season, the location of the dunes was fixed by March, and these locations were highly correlated with the distribution of meltwater ponds at the beginning of June. Our observations, including ground-based light detection and ranging system (lidar) measurements, show that melt ponds initially form in the interstices between snow dunes, and that the outline of the melt ponds is controlled by snow depth contours. The resulting preferential surface ablation of ponded ice creates the surface topography that later determines the melt pond evolution.

arctic↗

Microwave Signatures of Snow on Sea Ice: Observations

Part of the Earth Observing System Aqua Advanced Microwave Scanning Radiometer (AMSR-E) Arctic sea ice validation campaign in March 2003 was dedicated to the validation of snow depth on sea ice and ice temperature products. The difficulty with validating these two variables is that neither can currently be measured other than in situ. For this reason, two aircraft flights on March 13 and 19,2003, were dedicated to these products, and flight lines were coordinated with in situ measurements of snow and sea ice physical properties. One flight was in the vicinity of Barrow, AK, covering Elson Lagoon and the adjacent Chukchi and Beaufort Seas. The other flight was farther north in the Beaufort Sea (about 73 N, 147.5 W) and was coordinated with a Navy ice camp. The results confirm the AMSR-E snow depth algorithm and its coefficients for first-year ice when it is relatively smooth. For rough first-year ice and for multiyear ice, there is still a relationship between the spectral gradient ratio of 19 and 37 GHz, but a different set of algorithm coefficients is necessary. Comparisons using other AMSR-E channels did not provide a clear signature of sea ice characteristics and, hence, could not provide guidance for the choice of algorithm coefficients. The limited comparison of in situ snow-ice interface and surface temperatures with 6-GHz brightness temperatures, which are used for the retrieval of ice temperature, shows that the 6-GHz temperature is correlated with the snow-ice interface temperature to only a limited extent. For strong temperature gradients within the snow layer, it is clear that the 6-GHz temperature is a weighted average of the entire snow layer.

Markus, Thorsten↗

Impact of Surface Roughness on AMSR-E Sea Ice Products

This paper examines the sensitivity of Advanced Microwave Scanning Radiometer (AMSR-E) brightness temperatures (Tbs) to surface roughness by a using radiative transfer model to simulate AMSR-E Tbs as a function of incidence angle at which the surface is viewed. The simulated Tbs are then used to examine the influence that surface roughness has on two operational sea ice algorithms, namely: 1) the National Aeronautics and Space Administration Team (NT) algorithm and 2) the enhanced NT algorithm, as well as the impact of roughness on the AMSR-E snow depth algorithm. Surface snow and ice data collected during the AMSR-Ice03 field campaign held in March 2003 near Barrow, AK, were used to force the radiative transfer model, and resultant modeled Tbs are compared with airborne passive microwave observations from the Polarimetric Scanning Radiometer. Results indicate that passive microwave Tbs are very sensitive even to small variations in incidence angle, which can cause either an over or underestimation of the true amount of sea ice in the pixel area viewed. For example, this paper showed that if the sea ice areas modeled in this paper mere assumed to be completely smooth, sea ice concentrations were underestimated by nearly 14% using the NT sea ice algorithm and by 7% using the enhanced NT algorithm. A comparison of polarization ratios (PRs) at 10.7,18.7, and 37 GHz indicates that each channel responds to different degrees of surface roughness and suggests that the PR at 10.7 GHz can be useful for identifying locations of heavily ridged or rubbled ice. Using the PR at 10.7 GHz to derive an "effective" viewing angle, which is used as a proxy for surface roughness, resulted in more accurate retrievals of sea ice concentration for both algorithms. The AMSR-E snow depth algorithm was found to be extremely sensitive to instrument calibration and sensor viewing angle, and it is concluded that more work is needed to investigate the sensitivity of the gradient ratio at 37 and 18.7 GHz to these factors to improve snow depth retrievals from spaceborne passive microwave sensors.

Stroeve, Julienne C.↗

March 2003 EOS Aqua AMSR-E Arctic Sea Ice Field Campaign

An overview of the March 2003 coordinated sea ice field campaign in the Alaskan Arctic is presented with reference to the papers in this special section. This campaign is part of the program to validate the Aqua Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) sea ice products. Standard AMSR-E sea ice products include sea ice concentration, sea ice temperature, and snow depth on sea ice. The validation program consists of three elements, namely: 1) satellite data comparisons; 2) coordinated satellite/aircraft surface measurements; and 3) modeling and sensitivity analyses. Landsat-7 and RADARSAT observations were used in comparative studies with the retrieved AMSR-E sea ice concentrations. The aircraft sensors provided high-resolution microwave imagery of the surface, atmospheric profiles of temperature and humidity, and digital records of sea ice conditions. When combined with in situ measurements, aircraft data were used to validate the AMSR-E sea ice temperature and snow-depth products. The modeling studies helped interpret the field-data comparisons, provided insight on the limitations of the AMSR-E sea ice algorithms, and suggested potential improvements to the AMSR-E retrieval algorithms.

Cavalieri, Donald J.↗

Remote sensing of sea ice thickness by a combined spatial and frequency domain interferometer : formulations, instrument design & development

The thickness of Arctic sea ice plays a critical role in Earth's climate and ocean circulation. An accurate measurement of this parameter on synoptic scales at regular intervals would enable characterization of this important component for the understanding of ocean circulation and the global heat balance. Presented in this paper is a low frequency VHF interferometer technique and associated radar instrument design to measure sea ice thickness based on the use of backscatter correlation functions. The sea ice medium is represented as a multi-layered medium consisting of snow, seaice and sea water, with the interfaces between layers characterized as rough surfaces. This technique utilizes the correlation of two radar waves of different frequencies and incident and observation angles, scattered from the sea ice medium. The correlation functions relate information about the sea ice thickness. Inversion techniques such as the genetic algorithm, gradient descent, and least square methods, are used to derive sea ice thickness from the phase information related by the correlation functions.

remote sensing↗