Abstract. Snowpack microstructure controls the transfer of heat to, as well as the temperature of, the underlying soils. In situ measurements of snow and soil properties from four field campaigns during two winters (March and November 2018, January and March 2019) were compared to an ensemble of CLM5.0 (Community Land Model) simulations, at Trail Valley Creek, Northwest Territories, Canada. Snow micropenetrometer profiles allowed for snowpack density and thermal conductivity to be derived at higher vertical resolution (1.25 mm) and a larger sample size (n=1050) compared to traditional snowpit observations (3 cm vertical resolution; n=115). Comparing measurements with simulations shows CLM overestimated snow thermal conductivity by a factor of 3, leading to a cold bias in wintertime soil temperatures (RMSE=5.8 ∘C). Two different approaches were taken to reduce this bias: alternative parameterisations of snow thermal conductivity and the application of a correction factor. All the evaluated parameterisations of snow thermal conductivity improved simulations of wintertime soil temperatures, with that of Sturm et al. (1997) having the greatest impact (RMSE=2.5 ∘C). The required correction factor is strongly related to snow depth (R2=0.77,RMSE=0.066) and thus differs between the two snow seasons, limiting the applicability of such an approach. Improving simulated snow properties and the corresponding heat flux is important, as wintertime soil temperatures are an important control on subnivean soil respiration and hence impact Arctic winter carbon fluxes and budgets.

Abstract. Snowpack microstructure controls the transfer of heat to, and the temperature of, the underlying soils. In situ measurements of snow and soil properties from four field campaigns during two different winters (March and November 2018, January and March 2019) were compared to an ensemble of CLM5.0 (Community Land Model) simulations, at Trail Valley Creek, Northwest Territories, Canada. Snow MicroPenetrometer profiles allowed snowpack density and thermal conductivity to be derived at higher vertical resolution (1.25 mm) and a larger sample size (n = 1050) compared to traditional snowpit observations (3 cm vertical resolution; n = 115). Comparing measurements with simulations shows CLM overestimated snow thermal conductivity by a factor of 3, leading to a cold bias in wintertime soil temperatures (RMSE = 5.8 °C). Bias-correction of the simulated thermal conductivity (relative to field measurements) improved simulated soil temperatures (RMSE = 2.1 °C). Multiple linear regression shows the required correction factor is strongly related to snow depth (R2 = 0.77, RMSE = 0.066) particularly early in the winter. Furthermore, CLM simulations did not adequately represent the observed high proportions of depth hoar. Addressing uncertainty in simulated snow properties and the corresponding heat flux is important, as wintertime soil temperatures act as a control on subnivean soil respiration, and hence impact Arctic winter carbon fluxes and budgets.

Abstract. Snowpack microstructure controls the transfer of heat to, and the temperature of, the underlying soils. In situ measurements of snow and soil properties from four field campaigns during two different winters (March and November 2018, January and March 2019) were compared to an ensemble of CLM5.0 (Community Land Model) simulations, at Trail Valley Creek, Northwest Territories, Canada. Snow MicroPenetrometer profiles allowed snowpack density and thermal conductivity to be derived at higher vertical resolution (1.25 mm) and a larger sample size (n = 1050) compared to traditional snowpit observations (3 cm vertical resolution; n = 115). Comparing measurements with simulations shows CLM overestimated snow thermal conductivity by a factor of 3, leading to a cold bias in wintertime soil temperatures (RMSE = 5.8 °C). Bias-correction of the simulated thermal conductivity (relative to field measurements) improved simulated soil temperatures (RMSE = 2.1 °C). Multiple linear regression shows the required correction factor is strongly related to snow depth (R2 = 0.77, RMSE = 0.066) particularly early in the winter. Furthermore, CLM simulations did not adequately represent the observed high proportions of depth hoar. Addressing uncertainty in simulated snow properties and the corresponding heat flux is important, as wintertime soil temperatures act as a control on subnivean soil respiration, and hence impact Arctic winter carbon fluxes and budgets.

Abstract. Seasonal snow cover is the largest single component of the cryosphere in areal extent, covering an average of 46 million square km of Earth's surface (31 % of the land area) each year, and is thus an important expression of and driver of the Earth’s climate. In recent years, Northern Hemisphere spring snow cover has been declining at about the same rate (~ −13 %/decade) as Arctic summer sea ice. More than one-sixth of the world’s population relies on seasonal snowpack and glaciers for a water supply that is likely to decrease this century. Snow is also a critical component of Earth’s cold regions' ecosystems, in which wildlife, vegetation, and snow are strongly interconnected. Snow water equivalent (SWE) describes the quantity of snow stored on the land surface and is of fundamental importance to water, energy, and geochemical cycles. Quality global SWE estimates are lacking. Given the vast seasonal extent combined with the spatially variable nature of snow distribution at regional and local scales, surface observations will not be able to provide sufficient SWE information. Satellite observations presently cannot provide SWE information at the spatial and temporal resolutions required to address science and high socio-economic value applications such as water resource management and streamflow forecasting. In this paper, we review the potential contribution of X- and Ku-Band Synthetic Aperture Radar (SAR) for global monitoring of SWE. We describe radar interactions with snow-covered landscapes, characterization of snowpack properties using radar measurements, and refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. SAR can image the surface during both day and night regardless of cloud cover, allowing high-frequency revisit at high spatial resolution as demonstrated by missions such as Sentinel-1. The physical basis for estimating SWE from X- and Ku-band radar measurements at local scales is volume scattering by millimetre-scale snow grains. Inference of global snow properties from SAR requires an interdisciplinary approach based on field observations of snow microstructure, physical snow modelling, electromagnetic theory, and retrieval strategies over a range of scales. New field measurement capabilities have enabled significant advances in understanding snow microstructure such as grain size, densities, and layering. We describe radar interactions with snow-covered landscapes, the characterization of snowpack properties using radar measurements, and the refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. This review serves to inform the broader snow research, monitoring, and applications communities on progress made in recent decades, and sets the stage for a new era in SWE remote-sensing from SAR measurements.

Abstract. Seasonal snow cover is the largest single component of the cryosphere in areal extent, covering an average of 46 million square km of Earth's surface (31 % of the land area) each year, and is thus an important expression of and driver of the Earth’s climate. In recent years, Northern Hemisphere spring snow cover has been declining at about the same rate (~ −13 %/decade) as Arctic summer sea ice. More than one-sixth of the world’s population relies on seasonal snowpack and glaciers for a water supply that is likely to decrease this century. Snow is also a critical component of Earth’s cold regions' ecosystems, in which wildlife, vegetation, and snow are strongly interconnected. Snow water equivalent (SWE) describes the quantity of snow stored on the land surface and is of fundamental importance to water, energy, and geochemical cycles. Quality global SWE estimates are lacking. Given the vast seasonal extent combined with the spatially variable nature of snow distribution at regional and local scales, surface observations will not be able to provide sufficient SWE information. Satellite observations presently cannot provide SWE information at the spatial and temporal resolutions required to address science and high socio-economic value applications such as water resource management and streamflow forecasting. In this paper, we review the potential contribution of X- and Ku-Band Synthetic Aperture Radar (SAR) for global monitoring of SWE. We describe radar interactions with snow-covered landscapes, characterization of snowpack properties using radar measurements, and refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. SAR can image the surface during both day and night regardless of cloud cover, allowing high-frequency revisit at high spatial resolution as demonstrated by missions such as Sentinel-1. The physical basis for estimating SWE from X- and Ku-band radar measurements at local scales is volume scattering by millimetre-scale snow grains. Inference of global snow properties from SAR requires an interdisciplinary approach based on field observations of snow microstructure, physical snow modelling, electromagnetic theory, and retrieval strategies over a range of scales. New field measurement capabilities have enabled significant advances in understanding snow microstructure such as grain size, densities, and layering. We describe radar interactions with snow-covered landscapes, the characterization of snowpack properties using radar measurements, and the refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. This review serves to inform the broader snow research, monitoring, and applications communities on progress made in recent decades, and sets the stage for a new era in SWE remote-sensing from SAR measurements.

The ability to correct for the influence of forest cover is crucial for retrieval of surface geophysical parameters such as snow cover and soil properties from microwave remote sensing. Existing co...

The ability to correct for the influence of forest cover is crucial for retrieval of surface geophysical parameters such as snow cover and soil properties from microwave remote sensing. Existing co...

Estimating snow water equivalent (SWE) in the northern high latitudesis important from climate, ecological and human perspectives since it enables us to track changes in spatiotemporal distribution...

Estimating snow water equivalent (SWE) in the northern high latitudesis important from climate, ecological and human perspectives since it enables us to track changes in spatiotemporal distribution...

Mid-latitude snow is understudied compared to snow in the northern high latitudes despite its importance as a source of freshwater to this economically significant region. The mid-latitudes provide...

Mid-latitude snow is understudied compared to snow in the northern high latitudes despite its importance as a source of freshwater to this economically significant region. The mid-latitudes provide...