Showing papers by "Nick Rutter published in 2016"

Intercomparison of snow density measurements: bias, precision, and vertical resolution

Abstract: . Density is a fundamental property of porous media such as snow. A wide range of snow properties and physical processes are linked to density, but few studies have addressed the uncertainty in snow density measurements. No study has yet quantitatively considered the recent advances in snow measurement methods such as micro-computed tomography (μCT) in alpine snow. During the MicroSnow Davos 2014 workshop, different approaches to measure snow density were applied in a controlled laboratory environment and in the field. Overall, the agreement between μCT and gravimetric methods (density cutters) was 5 to 9 %, with a bias of −5 to 2 %, expressed as percentage of the mean μCT density. In the field, density cutters overestimate (1 to 6 %) densities below and underestimate (1 to 6 %) densities above a threshold between 296 to 350 kg m−3, dependent on cutter type. Using the mean density per layer of all measurement methods applied in the field (μCT, box, wedge, and cylinder cutters) and ignoring ice layers, the variation between the methods was 2 to 5 % with a bias of −1 to 1 %. In general, our result suggests that snow densities measured by different methods agree within 9 %. However, the density profiles resolved by the measurement methods differed considerably. In particular, the millimeter-scale density variations revealed by the high-resolution μCT contrasted the thick layers with sharp boundaries introduced by the observer. In this respect, the unresolved variation, i.e., the density variation within a layer which is lost by lower resolution sampling or layer aggregation, is critical when snow density measurements are used in numerical simulations.

Microstructure representation of snow in coupled snowpack and microwave emission models

Abstract: . This is the first study to encompass a wide range of coupled snow evolution and microwave emission models in a common modelling framework in order to generalise the link between snowpack microstructure predicted by the snow evolution models and microstructure required to reproduce observations of brightness temperature as simulated by snow emission models. Brightness temperatures at 18.7 and 36.5 GHz were simulated by 1323 ensemble members, formed from 63 Jules Investigation Model snowpack simulations, three microstructure evolution functions, and seven microwave emission model configurations. Two years of meteorological data from the Sodankyla Arctic Research Centre, Finland, were used to drive the model over the 2011–2012 and 2012–2013 winter periods. Comparisons between simulated snow grain diameters and field measurements with an IceCube instrument showed that the evolution functions from SNTHERM simulated snow grain diameters that were too large (mean error 0.12 to 0.16 mm), whereas MOSES and SNICAR microstructure evolution functions simulated grain diameters that were too small (mean error −0.16 to −0.24 mm for MOSES and −0.14 to −0.18 mm for SNICAR). No model (HUT, MEMLS, or DMRT-ML) provided a consistently good fit across all frequencies and polarisations. The smallest absolute values of mean bias in brightness temperature over a season for a particular frequency and polarisation ranged from 0.7 to 6.9 K. Optimal scaling factors for the snow microstructure were presented to compare compatibility between snowpack model microstructure and emission model microstructure. Scale factors ranged between 0.3 for the SNTHERM–empirical MEMLS model combination (2011–2012) and 3.3 for DMRT-ML in conjunction with MOSES microstructure (2012–2013). Differences in scale factors between microstructure models were generally greater than the differences between microwave emission models, suggesting that more accurate simulations in coupled snowpack–microwave model systems will be achieved primarily through improvements in the snowpack microstructure representation, followed by improvements in the emission models. Other snowpack parameterisations in the snowpack model, mainly densification, led to a mean brightness temperature difference of 11 K at 36.5 GHz H-pol and 18 K at V-pol when the Jules Investigation Model ensemble was applied to the MOSES microstructure and empirical MEMLS emission model for the 2011–2012 season. The impact of snowpack parameterisation increases as the microwave scattering increases. Consistency between snowpack microstructure and microwave emission models, and the choice of snowpack densification algorithms should be considered in the design of snow mass retrieval systems and microwave data assimilation systems.

Air temperature distribution and energy-balance modelling of a debris-covered glacier

Abstract: Near-surface air temperature is an important determinant of the surface energy balance of glaciers and is often represented by a constant linear temperature gradients (TGs) in models. Spatiotemporal variability in 2 m air temperature was measured across the debris-covered Miage Glacier, Italy, over an 89 d period during the 2014 ablation season using a network of 19 stations. Air temperature was found to be strongly dependent upon elevation for most stations, even under varying meteorological conditions and at different times of day, and its spatial variability was well explained by a locally derived mean linear TG (MG–TG) of −0.0088°C m−1. However, local temperature depressions occurred over areas of very thin or patchy debris cover. The MG–TG, together with other air TGs, extrapolated from both on- and off-glacier sites, were applied in a distributed energy-balance model. Compared with piecewise air temperature extrapolation from all on-glacier stations, modelled ablation, using the MG–TG, increased by 4% using the environmental ‘lapse rate’. Ice melt under thick debris was relatively insensitive to air temperature, while the effects of different temperature extrapolation methods were strongest at high elevation sites of thin and patchy debris cover.

Modeling subcanopy incoming longwave radiation to seasonal snow using air and tree trunk temperatures

Abstract: Data collected at three Swiss alpine forested sites over a combined eleven-year period were used to evaluate the role of air temperature in modeling sub-canopy incoming longwave radiation to the snow surface. Simulated sub-canopy incoming longwave radiation is traditionally partitioned into that from the sky and that from the canopy, i.e. a two-part model. Initial uncertainties in predicting longwave radiation using the two-part model resulted from vertical differences in measured air temperature. Above-canopy (35m) air temperatures were higher than those within (10m) and below (2m) canopy throughout four snow seasons (Dec-Apr), demonstrating how the forest canopy can act as a cold sink for air. Lowest model RMSE was using above-canopy air temperature. Further investigation of modeling sub-canopy longwave radiation using above-canopy air temperature showed underestimations, particularly during periods of high insolation. In order to explicitly account for canopy temperatures in modeling longwave radiation, the two-part model was improved by incorporating a measured trunk-view component and trunk temperature. Trunk temperature measurements were up to 25°C higher than locally measured air temperatures. This three-part model reduced the RMSE by up to 7.7 Wm-2 from the two-part air temperature model at all sensor positions across the 2014 snowmelt season, and performed particularly well during periods of high insolation when errors from the two-part model were up to 40 Wm-2. A parameterization predicting tree trunk temperatures using measured air temperature and incoming shortwave radiation demonstrate a simple method that can be applied to provide input to the three-part model across mid-latitude coniferous forests.

Measurement of incoming radiation below forest canopies: a comparison of different radiometer configurations

Abstract: Ground-based, subcanopy measurements of incoming shortwave and longwave radiation are frequently used to drive and validate energy balance and snowmelt models. These subcanopy measurements are frequently obtained using different configurations (linear or distributed; stationary or moving) of radiometer arrays that are installed to capture the spatial and temporal variability of longwave and shortwave radiation. Three different radiometer configurations (stationary distributed, stationary linear, and moving linear) were deployed in a spruce forest in the eastern Swiss Alps during a 9-month period, capturing the annual range of sun angles and sky conditions. Results showed a strong seasonal variation in differences between measurements of shortwave transmissivity between the three configurations, whereas differences in longwave enhancement appeared to be seasonally independent. Shortwave transmissivity showed a larger spatial variation in the subcanopy than longwave enhancement at this field site. T...

Brief communication: Improved measurement of ice layer density in seasonal snowpacks

Abstract: . The microstructure and density of ice layers in snowpacks is poorly quantified. Here we present a new field method for measuring the density of ice layers caused by melt or rain-on-snow events. The method was used on 87 ice layer samples taken from natural and artificial ice layers in the Canadian Arctic and mid-latitudes. Mean measured ice layer density was 909 ± 28 kg m−3 with a standard deviation of 23 kg m−3, significantly higher than values typically used in the literature.

Impact of spatial averaging on radar reflectivity at internal snowpack layer boundaries

Abstract: Microwave radar amplitude within a snowpack can be strongly influenced by spatial variability of internal layer boundaries. We quantify the impact of spatial averaging of snow stratigraphy and physical snowpack properties on surface scattering from near-nadir frequency-modulated continuous-wave radar at 12–18 GHz. Relative permittivity, density, grain size and stratigraphic boundaries were measured in-situ at high resolution along the length of a 9 m snow trench. An optimal range of horizontal averaging (4–6 m) was identified to attribute variations in surface scattering at layer boundaries to dielectric contrasts estimated from centimetre-scale measurements of snowpack stratigraphy and bulk layer properties. Single vertical profiles of snowpack properties seldom captured the complex local variability influencing near-nadir radar surface scattering. We discuss implications of scaling in-situ measurements for snow radiative transfer modelling and evaluation of airborne microwave remote sensing of snow.