Pixel‐scale evaluation of SSM/I sea‐ice algorithms in the marginal ice zone during early fall freeze‐up

Observed reduction in recent sea ice areal extent and thickness has focused attention on the fact that the Arctic marine system appears to be responding to global‐scale climate variability and change. Passive microwave remote‐sensing data are the primary source underpinning these reports, yet problems remain in geophysical inversion of information on ice type and concentration. Uncertainty in sea‐ice concentration (SIC) retrievals is highest in the summer and fall, when water occurs in liquid phase within the snow–sea‐ice system. Of particular scientific interest is the timing and rate of new ice formation due to the control that this form of sea ice has on mass, energy and gas fluxes across the ocean–sea‐ice–atmosphere interface. In this paper we examine the critical fall freeze‐up period using in situ data from a ship‐based and aerial survey programme known as the Canadian Arctic Shelf Exchange study combined with microwave and optical Earth observations data. Results show that: (1) the overall physical conditions observed from aerial survey photography were well matched with coincident moderate‐resolution imaging spectroradiometer data and Radarsat ScanSAR imagery; (2) the shortwave albedo was linearly related to old ice concentration derived from survey photography; (3) the three SSM/I SIC algorithms (NASA Team (NT), NASA Team 2 (NT2), and Bootstrap (BT)) showed considerable discrepancies in pixel‐scale comparison with the Radarsat ScanSAR SICs well calibrated by the aerial survey data. The major causes of the discrepancies are attributed to (1) the inherent inability to detect the new thin ice in the NT and BT algorithms, (2) mismatches of the thin‐ice tie point of the NT2 algorithm, and (3) sub‐pixel ambiguity between the thin ice and the mixture of open water and sea ice. These results suggest the need for finer resolution of passive microwave sensors, such as AMSR‐E, to improve the precision of the SSM/I SIC algorithms in the marginal ice zone during early fall freeze‐up. Copyright © 2006 John Wiley & Sons, Ltd.     

Uncertainty of snow water equivalent retrieved from AMSR‐E brightness temperature in northeast Asia

Accurate estimation of snow water equivalent (SWE) has been significantly recognized to improve management and analyses of water resource in specific regions. Although several studies have focused on developing SWE values based on remotely sensed brightness temperatures obtained by microwave sensor systems, it is known that there are still a number of uncertainties in SWE values retrieved from microwave radiometers. Therefore, further research for improving remotely sensed SWE values including global validation should be conducted in unexplored regions such as Northeast Asia. In this regard, we evaluated SWE through comparison of values produced by the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR‐E) from December 2002 to February 2011 with in situ SWE values converted from snow‐depth observation data from four regions in the South Korea. The results from three areas showed similarities which indicated that the AMSR‐E SWE values were overestimated when compared with in situ SWE values, and their Mean Absolute Errors (MAE) by month were relatively small (1.1 to 6.5 mm). Contrariwise, the AMSR‐E SWE values of one area were significantly underestimated when compared with in situ SWE values and the MAE were much greater (4.9 to 35.2 mm). These results were closely related to AMSR‐E algorithm‐related error sources, which we analyzed with respect to topographic characteristics and snow properties. In particular, we found that snow density data used in the AMSR‐E SWE algorithm should be based on reliable in situ data as the current AMSR‐E SWE algorithm cannot reflect the spatio‐temporal variability of snow density values. Additionally, we derived better results considering saturation effect of AMSR‐E SWE. Despite the demise of AMSR‐E, this study's analysis is significant for providing a baseline for the new sensor and suggests parameters important for obtaining more reliable SWE. Copyright © 2013 John Wiley & Sons, Ltd.     

On the winter evolution of snow thermophysical properties over land‐fast first‐year sea ice

The geophysical, thermodynamic and dielectric properties of snow are important state variables that are known to be sensitive to Arctic climate variability and change. Given recent observations of changes in the Arctic physical system (Arctic Climate Impact Assessment, 2004), it is important to focus on the processes that give rise to variability in the horizontal, vertical and temporal dimensions of the life‐history of snow on sea ice. The objectives in this study are to present these ‘state’ variables and to investigate the processes that govern variability in the vertical, horizontal and temporal dimension by using a case study over land‐fast first‐year sea ice for the period December 2003 to June 2004. Results from two sampling areas (thin and thick snowpacks) show that differences in snowpack thickness can substantially change the vertical and temporal evolution of snow properties. During the late fall and early winter (cooling period) we measured no significant changes in the physical properties, except for thin snow‐cover salinity, which decreased throughout the period. Fall‐snow desalination was only observed under thin snowpacks with a rate of ?0·12 ppt day^?1. Significant changes occurred in the late winter and early spring (warming period), especially for snow grain size. Snow grain kinetic growth of 0·25–0·48 mm·day^?1 was measured coincidently with increasing salinity and wetness for both thin and thick snowpacks. Copyright © 2006 John Wiley & Sons, Ltd.     

Comparison of the multi‐layer HUT snow emission model with observations of wet snowpacks

Information on snow properties plays an important role in hydrological, meteorological and climatological applications. Passive microwave remote sensing is an effective method to retrieve snowpack parameters; however, the observations can be obscured if there is wet snow in the satellite footprint. To study the emission properties of wet snow and check its response to snow wetness, this paper applies the multi‐layer Helsinki University of Technology (HUT) snow emission model coupled with the Advanced Integral Equation Model to simulate the low‐wetness snowpack observed at Luancheng in November 2009, and the high wetness snowpack observed at Weissfluhjoch in June 1995. Input parameters are acquired by the in‐situ snow pit measurements, while the snow grain size is fitted by comparing model predictions with the observed passive microwave signals at a range of observing angles. Results show that the application of a multi‐layer model is capable to consider the distribution pattern of the snow wetness along the snow profile and the refrozen ice crust of the snow surface. The multi‐layer HUT model is able to reproduce the wet snow emission properties, with an rms error of 4.4 K (at Luancheng) and 5.7 K (at Weissfluhjoch) at vertical polarization, and an rms error of 7.9 K (at Luancheng) and 11.4 K (at Weissfluhjoch) at horizontal polarization. Copyright © 2012 John Wiley & Sons, Ltd.     

Global Snow Mass Measurements and the Effect of Stratigraphic Detail on Inversion of Microwave Brightness Temperatures

Snow provides large seasonal storage of freshwater, and information about the distribution of snow mass as snow water equivalent (SWE) is important for hydrological planning and detecting climate change impacts. Large regional disagreements remain between estimates from reanalyses, remote sensing and modelling. Assimilating passive microwave information improves SWE estimates in many regions, but the assimilation must account for how microwave scattering depends on snow stratigraphy. Physical snow models can estimate snow stratigraphy, but users must consider the computational expense of model complexity versus acceptable errors. Using data from the National Aeronautics and Space Administration Cold Land Processes Experiment and the Helsinki University of Technology microwave emission model of layered snowpacks, it is shown that simulations of the brightness temperature difference between 19 and 37 GHz vertically polarised microwaves are consistent with advanced microwave scanning radiometer-earth observing system and special sensor microwave imager retrievals once known stratigraphic information is used. Simulated brightness temperature differences for an individual snow profile depend on the provided stratigraphic detail. Relative to a profile defined at the 10-cm resolution of density and temperature measurements, the error introduced by simplification to a single layer of average properties increases approximately linearly with snow mass. If this brightness temperature error is converted into SWE using a traditional retrieval method, then it is equivalent to ±13 mm SWE (7 % of total) at a depth of 100 cm. This error is reduced to ±5.6 mm SWE (3 % of total) for a two-layer model.     

Combining MODIS and AMSR‐E observations to improve MCD43A3 short‐time snow‐covered Albedo estimation

Land surface albedo plays an important role in the radiation budget and global climate models. NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) provide 16‐day albedo product with 500‐m resolution every 8 days (MCD43A3). Some in‐situ albedo measurements were used as the true surface albedo values to validate the MCD43A3 product. As the 16‐day MODIS albedo retrievals do not include snow observations when there is ephemeral snow on the ground surface in a 16‐day period, comparisons between MCD43A3 and 16 day averages of field data do not agree well. Another reason is that the MODIS cannot detect the snow when the area is covered by clouds. The Advanced Microwave Scanning Radiometer for EOS (AMSR‐E) data are not affected by weather conditions and are a good supplement for optical remote sensing in cloudy weather. When the surface is covered by ephemeral snow, the AMSR‐E data can be used as the additional information to retrieve the snow albedo. In this study, we developed an improved method by using the MODIS products and the AMSR‐E snow water equivalent (SWE) product to improve the MCD43A3 short‐time snow‐covered albedo estimation. The MODIS daily snow products MOD10A1 and MYD10A1 both provide snow and cloud information from observations. In our study region, we updated the MODIS daily snow product by combining MOD10A1 and MYD10A1. Then, the product was combined with the AMSR‐E SWE product to generate new daily snow‐cover and SWE products at a spatial resolution of 500 m. New SWE datasets were integrated into the Noah Land Surface Model snow model to calculate the albedo above a snow surface, and these values were then utilized to improve the MODIS 16‐day albedo product. After comparison of the results with in‐situ albedo measurements, we found that the new corrected 16‐day albedo can show the albedo changes during the short snowfall season. For example, from January 25 to March 14, 2007 at the BJ site, the albedo retrieved from snow‐free observations does not indicate the albedo changes affected by snow; the improved albedo conforms well to the in‐situ measurements. The correlation coefficient of the original MODIS albedo and the in‐situ albedo is 0.42 during the ephemeral snow season, but the correlation coefficient of the improved MODIS albedo and the in‐situ albedo is 0.64. It is concluded that the new method is capable of capturing the snow information from AMSR‐E SWE to improve the short‐time snow‐covered albedo estimation. Copyright © 2012 John Wiley & Sons, Ltd.     

Evaluating the potential of a blended passive microwave‐interactive multi‐sensor product for improved mapping of snow cover and estimations of snow water equivalent

The retrieval of Snow Water Equivalent (SWE) from remote sensing satellites continues to be a very challenging problem. In this paper, we evaluate the accuracy of a new SWE product derived from the blending of a passive microwave SWE product based on the Advanced Microwave Sounding Unit (AMSU) with a multi‐sensor snow cover extent product based on the Interactive Multi‐sensor Snow and Ice Mapping System (IMS). The microwave measurements have the ability to penetrate the snow pack, and thus, the retrieval of SWE is best accomplished using the AMSU. On the other hand, the IMS maps snow cover more reliably due to the use of multiple satellite and ground observations. The evolution of global snow cover from the blended, the AMSU and the IMS products was examined during the 2006 snow season. Despite the overall good inter‐product agreement, it was shown that the retrievals of snow cover extent in the blended product are improved when using IMS, with implications for improved microwave retrievals of SWE. In a separate investigation, the skill of the microwave SWE product was also examined for its ability to correctly estimate SWE globally and regionally. Qualitative evaluation of global SWE retrievals suggested dependence on land surface temperature: the lower the temperature, the higher the SWE retrieved. This temperature bias was attributed in part to temperature effects on those snow properties that impact microwave response. Therefore, algorithm modifications are needed with more dynamical adjustments to account for changing snow cover. Quantitative evaluation over Slovakia in central Europe, for a limited period in 2006, showed reasonably good performance for SWE less than 100 mm. Sensitivity to deeper snow decreased significantly. Copyright © 2007 John Wiley & Sons, Ltd.     

Detection and classification of surface roughness in an Arctic marginal sea ice zone

Sea ice dynamic and thermodynamic processes are important and highly variable elements of the marginal ice zone (MIZ). This study examines the detection and classification of statistically separable sea ice classes in the MIZ through a range of temporal and spatial scales. A helicopter‐based laser system was used to obtain large‐scale and a ship‐based laser profiler to identify small‐scale roughness types, respectively. The analysis of variance of surface height data from helicopter‐ and ship‐based laser systems, active microwave (AMW) C‐band backscattering data and passive microwave (PMW) (37 and 89 GHz) brightness temperature data reveal different classes that statistically differ from one another. We found significant statistical difference in variances in AMW data with six classes that differ in VV polarization, three classes in VH polarization, and five classes in HH polarization in the MIZ (e.g. snow‐covered first‐year ice, ice rubble, pancake ice, frost flowers, melt pond, flooded ice, and ice edge) of southeastern Beaufort Sea. The PMW emission was not as effective at discrimination, yielding only one statistically separable class. The results can potentially be extended to satellite‐based investigations of the MIZ at regional scales. Copyright © 2012 John Wiley & Sons, Ltd.     

On the evaluation of snow water equivalent estimates over the terrestrial Arctic drainage basin

Comparisons between snow water equivalent (SWE) and river discharge estimates are important in evaluating the SWE fields and to our understanding of linkages in the freshwater cycle. In this study, we compared SWE drawn from land surface models and remote sensing observations with measured river discharge (Q) across 179 Arctic river basins. Over the period 1988‐2000, basin‐averaged SWE prior to snowmelt explains a relatively small (yet statistically significant) fraction of interannual variability in spring (April–June) Q, as assessed using the coefficient of determination (R²). Averaged across all basins, mean R²s vary from 0·20 to 0·28, with the best agreement noted for SWE drawn from a simulation with the Pan‐Arctic Water Balance Model (PWBM) forced with data from the European Centre for Medium‐Range Weather‐Forecasts (ECMWF) Re‐analysis (ERA‐40). Variability and magnitude in SWE derived from Special Sensor Microwave Imager (SSM/I) data are considerably lower than the variability and magnitude in SWE drawn from the land surface models, and generally poor agreement is noted between SSM/I SWE and spring Q. We find that the SWE versus Q comparisons are no better when alternate temporal integrations–using an estimate of the timing in basin thaw–are used to define pre‐melt SWE and spring Q. Thus, a majority of the variability in spring discharge must arise from factors other than basin snowpack water storage. This study demonstrates how SWE estimated from remote sensing observations, or general circulation models (GCMs), can be evaluated effectively using monthly discharge data or SWE from a hydrological model. Copyright © 2007 John Wiley & Sons, Ltd.     

First‐year sea ice spring melt transitions in the Canadian Arctic Archipelago from time‐series synthetic aperture radar data, 1992–2002

This paper synthesizes 10‐years' worth of interannual time‐series space‐borne ERS‐1 and RADARSAT‐1 synthetic aperture radar (SAR) data collected coincident with daily measurement of snow‐covered, land‐fast first‐year sea ice (FYI) geophysical and surface radiation data collected from the Seasonal Sea Ice Monitoring and Modeling Site, Collaborative‐Interdisciplinary Cryospheric Experiment and 1998 North Water Polynya study over the period 1992 to 2002. The objectives are to investigate the seasonal co‐relationship of the SAR time‐series dataset with selected surface mass (bulk snow thickness) and climate state variables (surface temperature and albedo) measured in situ for the purpose of measuring the interannual variability of sea ice spring melt transitions and validating a time‐series SAR methodology for sea ice surface mass and climate state parameter estimation. We begin with a review of the salient processes required for our interpretation of time‐series microwave backscatter from land‐fast FYI. Our results suggest that time‐series SAR data can reliably measure the timing and duration of surface albedo transitions at daily to weekly time‐scales and at a spatial scales that are on the order of hundreds of metres. Snow thickness on FYI immediately prior to melt onset explains a statistically significant portion of the variability in timing of SAR‐detected melt onset to pond onset for SAR time‐series that are made up of more than 25 images. Our results also show that the funicular regime of snowmelt, resolved in time‐series SAR data at a temporal resolution of approximately 2·5 images per week, is not detectable for snow covers less than 25 cm in thickness. Copyright © 2007 John Wiley & Sons, Ltd.     

Simulation and parameterization of superimposed ice formation

In cold Arctic snowpacks, meltwater retention is a significant factor controlling the timing and magnitude of runoff. Meltwater percolates vertically through the snowpack until it reaches an impermeable horizon, whereupon a saturated zone is established. If the underlying media is below the freezing point, accretive ice formation takes place. This process has previously been crudely parameterized or modelled numerically. Such ice is called either superimposed ice on glaciers or basal ice on bare land. Using theory derived from sea‐ice formation, an analytical solution to basal ice growth is proposed. Results are compared against growth rates derived from numerical modelling. In addition, model results are compared to field observations of ice temperatures. The analytical solution is further extended to account for the temperature gradient inside the underlying media and the variable thermal properties of the underlying media. In the analysis, observations and references have predominantly relied on knowledge from glaciers. However, the process of accretive ice growth is equally important in seasonal snow packs with a cold snow‐ground interface and on Arctic sea ice where the ice‐snow interface is well below freezing point. The simplification of this accretive ice growth problem makes the solution attractive for incorporation in large‐scale cryospheric models. Copyright © 2007 John Wiley & Sons, Ltd.     

Observations of late winter Canadian tundra snow cover properties

Tundra snow cover is important to monitor as it influences local, regional, and global‐scale surface water balance, energy fluxes, as well as ecosystem and permafrost dynamics. Observations are already showing a decrease in spring snow cover duration at high latitudes, but the impact of changing winter season temperature and precipitation on variables such as snow water equivalent (SWE) is less clear. A multi‐year project was initiated in 2004 with the objective to quantify tundra snow cover properties over multiple years at a scale appropriate for comparison with satellite passive microwave remote sensing data and regional climate and hydrological models. Data collected over seven late winter field campaigns (2004 to 2010) show the patterns of snow depth and SWE are strongly influenced by terrain characteristics. Despite the spatial heterogeneity of snow cover, several inter‐annual consistencies were identified. A regional average density of 0.293 g/cm³ was derived and shown to have little difference with individual site densities when deriving SWE from snow depth measurements. The inter‐annual patterns of SWE show that despite variability in meteorological forcing, there were many consistent ratios between the SWE on flat tundra and the SWE on lakes, plateaus, and slopes. A summary of representative inter‐annual snow stratigraphy from different terrain categories is also presented. © 2013 Her Majesty the Queen in Right of Canada. Hydrological Processes. © 2013 John Wiley & Sons, Ltd.     

Assimilating remotely sensed snow observations into a macroscale hydrology model

Accurate forecasting of snow properties is important for effective water resources management, especially in mountainous areas like the western United States. Current model-based forecasting approaches are limited by model biases and input data uncertainties. Remote sensing offers an opportunity for observation of snow properties, like areal extent and water equivalent, over larger areas. Data assimilation provides a framework for optimally merging information from remotely sensed observations and hydrologic model predictions. An ensemble Kalman filter (EnKF) was used to assimilate remotely sensed snow observations into the variable infiltration capacity (VIC) macroscale hydrologic model over the Snake River basin. The snow cover extent (SCE) product from the moderate resolution imaging spectroradiometer (MODIS) flown on the NASA Terra satellite was used to update VIC snow water equivalent (SWE), for a period of four consecutive winters (1999–2003). A simple snow depletion curve model was used for the necessary SWE–SCE inversion. The results showed that the EnKF is an effective and operationally feasible solution; the filter successfully updated model SCE predictions to better agree with the MODIS observations and ground surface measurements. Comparisons of the VIC SWE estimates following updating with surface SWE observations (from the NRCS SNOTEL network) indicated that the filter performance was a modest improvement over the open-loop (un-updated) simulations. This improvement was more evident for lower to middle elevations, and during snowmelt, while during accumulation the filter and open-loop estimates were very close on average. Subsequently, a preliminary assessment of the potential for assimilating the SWE product from the advanced microwave scanning radiometer (AMSR-E, flown on board the NASA Aqua satellite) was conducted. The results were not encouraging, and appeared to reflect large errors in the AMSR-E SWE product, which were also apparent in comparisons with SNOTEL data.     

Performance characteristics of a new electronic snow water equivalent sensor in different climates

The US Army ERDC CRREL and the US Department of Agriculture Natural Resources Conservation Service developed a square electronic snow water equivalent (e‐SWE) sensor as an alternative to using fluid‐filled snow pillows to measure SWE. The sensors consist of a centre panel to measure SWE and eight outer panels to buffer edge stress concentrations. Seven 3 m square e‐SWE sensors were installed in five different climate zones. During the 2011–2012 winter, 1.8 and 1.2 m square e‐SWE sensors were installed and operated in Oregon. With the exception of New York State and Newfoundland, the e‐SWE sensors accurately measured SWE, with R² values between the sensor and manual SWE measurements of between 0.86 and 0.98. The e‐SWE sensor at Hogg Pass, Oregon, accurately measured SWE during the past 8 years of operations. In the thin, icy snow of New York during midwinter 2008–2009, the e‐SWE sensors overmeasured SWE because of edge stress concentrations associated with strong icy layers and a shallow snow cover. The New York e‐SWE sensors' measurement accuracy improved in spring 2009 and further improved during the 2011–2012 winter with operating experience. At Santiam Junction, measured SWE from the 1.8 and 1.2 m square e‐SWE sensors agreed well with the snow pillow, 3 m square e‐SWE sensor, and manual SWE measurements until February 2013, when dust and gravel blew onto the testing area resulting in anomalous measurements. © 2014 The Authors. Hydrological Processes published by John Wiley & Sons Ltd.     

Intercomparison of snow water equivalent observations in the Northern Great Plains

In the Northern Great Plains, melting snow is a primary driver of spring flooding, but limited knowledge of the magnitude and spatial distribution of snow water equivalent (SWE) hampers flood forecasting. Passive microwave remote sensing has the potential to enhance operational river flow forecasting but is not routinely incorporated in operational flood forecasting. We compare satellite passive microwave estimates from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E) to the National Oceanic and Atmospheric Administration Office of Water Prediction (OWP) airborne gamma radiation snow survey and U.S. Army Corps of Engineers (USACE) ground snow survey SWE estimates in the Northern Great Plains from 2002 to 2011. AMSR‐E SWE estimates compare favourably with USACE SWE measurements in the low relief, low vegetation study area (mean difference = ?3.8 mm, root mean squared difference [RMSD] = 34.7 mm), but less so with OWP airborne gamma SWE estimates (mean difference = ?9.5 mm, RMSD = 42.7 mm). An error simulation suggests that up to half of the error in the former comparison is potentially due to subpixel scale SWE variability, limiting the maximum achievable RMSD between ground and satellite SWE to approximately 26–33 mm in the Northern Great Plains. The OWP gamma versus AMSR‐E SWE comparison yields larger error than the point‐scale USACE versus AMSR‐E comparison, despite a larger measurement footprint (5–7 km² vs. a few square centimetres, respectively), suggesting that there are unshared errors between the USACE and OWP gamma SWE data.     

Comparison of five snow water equivalent estimation methods across categories

Snow water equivalent (SWE) is an important indicator used in hydrology, water resources, and climate change impact. There are various methods of estimating SWE (falling in 3 categories: indirect sensors, empirical models, and process‐based models), but few studies that provide comparison across these different categories to help users make decisions on monitoring site design or method selection. Five SWE estimation methods were compared against manual snow course data collected over 2 years (2015–2016) from the Dorset Environmental Science Centre, including the gamma‐radiation‐based CS725 sensor, 3 empirical estimation models (Sexstone snow density model, McCreight & Small snow density model, and a meteorology‐based model), and the University of British Columbia Watershed Model snow energy‐balance model. Snow depth, density, and SWE were measured at the Dorset Environmental Science Centre weather station in south‐central Ontario, on a daily basis over 6 winters from 2011 to 2016. The 2 snow density‐based models, requiring daily snow depth as input, gave the best performance (R² of .92 and .92 for McCreight & Small and Sexstone models, respectively). The CS725 sensor that receives radiation coming from soil penetrating the snowpack provided the same performance (R² = .92), proving that the sensor is an applicable method, although it is expensive. The meteorology‐based empirical model, requiring daily climate data including temperature, precipitation and solar radiation, gave the poorest performance (R² = .77). The energy‐balance‐based University of British Columbia Watershed Model snow module, only requiring climate data, worked better than the empirical meteorology‐based model (R² = .9) but performed worse than the density models or CS725 sensor. Given differences in application objectives, site conditions, and budget, this comparison across SWE estimation methods may help users choose a suitable method. For ongoing and new monitoring sites, installation of a CS725 sensor coupled with intermittent manual snow course measurements (e.g., weekly) is recommended for further SWE method estimation testing and development of a snow density model.     

An examination of snow redistribution over smooth land‐fast sea ice

An understanding of temporal evolution of snow on sea ice at different spatial scales is essential for improvement of snow parameterization in sea ice models. One of the problems we face, however, is that long‐term climate data are routinely available for land and not for sea ice. In this paper, we examine the temporal evolution of snow over smooth land‐fast first‐year sea ice using observational and modelled data. Changes in probability density functions indicate that depositional and drifting events control the evolution of snow distribution. Geostatistical analysis suggests that snowdrifts increased over the study period, and the orientation was related to the meteorological conditions. At the microscale, the temporal evolution of the snowdrifts was a product of infilling in the valleys between drifts. Results using two shore‐based climate reporting stations (Paulatuk and Tuktoyuktuk, NWT) suggest that on‐ice air temperature and relative humidity can be estimated using air temperature recorded at either station. Wind speed, direction and precipitation on ice cannot be accurately estimated using meteorological data from either station. The temporal evolution of snow distribution over smooth land‐fast sea ice was modelled using SnowModel and four different forcing regimes. The results from these model runs indicate a lack of agreement between observed distribution and model outputs. The reasons for these results are lack of meteorological measurements prior to the end of January, lack of spatially adequate surface topography and discrepancies between meteorological variables on land and ice. Copyright © 2009 John Wiley & Sons, Ltd.     

Probabilistic SWE reanalysis as a generalization of deterministic SWE reconstruction techniques

Snow accumulation and melt is highly variable in space and time in complex mountainous environments. Therefore, it is necessary to provide high‐resolution spatially and temporally distributed estimates of sub‐basin snow water equivalent (SWE) to accurately predict the timing and magnitude of snowmelt runoff. In this study, we compare two reconstruction techniques (a commonly used deterministic reconstruction vs a probabilistic data assimilation framework). The methods retrospectively estimate SWE from a time series of remotely sensed maps of fractional snow‐covered area (FSCA). In testing both methods over the Tokopah watershed in the Sierra Nevada (California), the probabilistic reconstruction approach is shown to be a more robust generalization of the deterministic reconstruction. Under idealized conditions, both probabilistic and deterministic approaches perform reasonably well and yield similar results when compared with in situ verification data, whereas the probabilistic reconstruction was found to be in slightly better agreement with snow‐pit observations. More importantly, the probabilistic approach was found to be more robust: unaccounted for biases in solar radiation impacted the probabilistic SWE estimates less than the deterministic case (4% vs 7% errors for water year (WY)1997 and 0% vs 3% errors for WY1999); the probabilistic reconstruction was found to be less sensitive to the number of available observations (6% vs 10% errors in WY1997 and 13% vs 44% errors in WY1999 from the nominal cases when four fewer FSCA images were available). Finally, results from the probabilistic reconstruction approach, which requires precipitation inputs (unlike the deterministic approach), were found to be relatively robust to bias in prior precipitation estimates, where the nominal case mean estimates were recovered even when an underestimated prior precipitation was used. The additional robustness of the probabilistic SWE reconstruction technique should prove useful in future applications over larger basins and longer periods in mountainous terrain. Copyright © 2013 John Wiley & Sons, Ltd.     

New satellite derived sea ice motion tracks Arctic contamination

Sea ice has been reported to contain contaminants from atmospheric and nearshore sediment resuspension processes. In this study successive passive microwave images from the 85.5 GHz channels on the Special Sensor Microwave Imager (SSM/I) were merged with drifting buoy trajectories from the International Arctic Buoy Program to compute Arctic sea ice motion in the Russian Arctic between 1988 and 1994. Smooth daily motion fields were averaged to prepare monthly maps making it possible to compute the 7-year mean and mean seasonal ice motions as well as principal components of directional variability of sea ice motion for the entire Arctic and surrounding basins. These mean motion vectors are used to simulate the advection of contaminants deposited on or contained within the sea ice and subsequently transported into the Arctic Ocean in order to predict both their mean trajectories and dispersal over time. The 3-year displacement of contaminants from a number of Russian sites and one American site display various behaviours from substantial displacement and dispersal to almost no movement. This computational procedure could be applied to realtime SSM/I and ice buoy data to provide detailed, all-weather, vector motion maps of ice circulation to predict the path and dispersal of any new substance introduced to the sea ice and transported into the Arctic or Antarctic ocean surface.     

Sensitivity of model parameterizations for simulated latent heat flux at the snow surface for complex mountain sites

The snowcover energy balance is typically dominated by net radiation and sensible and latent heat fluxes. Validation of the two latter components is rare and often difficult to undertake at complex mountain sites. Latent heat flux, the focus of this paper, is the primary coupling mechanism between the snow surface and the atmosphere. It accounts for the critical exchange of mass (sublimation or condensation), along with the associated snowcover energy loss or gain. Measured and modelled latent heat fluxes at a wind‐exposed and wind‐sheltered site were compared to evaluate variability in model parameters. A well‐tested and well‐validated snowcover energy balance model, Snobal, was selected for this comparison because of previously successful applications of the model at these sites and because of the adjustability of the parameters specific to latent heat transfer within the model. Simulated latent heat flux and snow water equivalent (SWE) were not sensitive to different formulations of the stability profile functions associated with heat transfer calculations. The model parameters of snow surface roughness length and active snow layer thickness were used to improve latent heat flux simulations while retaining accuracy in the simulation of the SWE at an exposed and sheltered study site. Optimal parameters for simulated latent heat flux and SWE were found at the exposed site with a shorter roughness length and thicker active layer, and at the sheltered site with a longer roughness length and thinner active layer. These findings were linked to physical characteristics of the study sites and will allow for adoption into other snow models that use similar parameters. Physical characteristics of wind exposure and cover could also be used to distribute critical parameters in a spatially distributed modelling domain and aid in parameter selection for application to other watersheds where detailed information is not available. Copyright © 2012 John Wiley & Sons, Ltd.