An enhanced temperature-index glacier melt model, incorporating incoming shortwave radiation and albedo, is presented. The model is an attempt to combine the high temporal resolution and accuracy of physically based melt models with the lower data requirements and computational simplicity of empirical melt models, represented by the ‘degree-day’ method and its variants. The model is run with both measured and modelled radiation data, to test its applicability to glaciers with differing data availability. Five automatic weather stations were established on Haut Glacier d’Arolla, Switzerland, between May and September 2001. Reference surface melt rates were calculated using a physically based energy-balance melt model. The performance of the enhanced temperature-index model was tested at each of the four validation stations by comparing predicted hourly melt rates with reference melt rates. Predictions made with three other temperature-index models were evaluated in the same way for comparison. The enhanced temperature-index model offers significant improvements over the other temperature-index models, and accounts for 90–95% of the variation in the reference melt rate. The improvement is lower, but still significant, when the model is forced by modelled shortwave radiation data, thus offering a better alternative to existing models that require only temperature data input.

Urban heat islands (UHIs) exacerbate the risk of heat-related mortality associated with global climate change. The intensity of UHIs is known to vary with population size and mean annual precipitation but a unifying argument is missing, and geographically targeted guidelines for heat mitigation remain elusive. Here we analyze urban-rural surface temperature differences (∆T s) worldwide and find a nonlinear increase of ∆T s with precipitation that is controlled by water/energy limitations on evapotranspiration and that modulates the scaling of ∆T s with city size. We introduce a coarse-grained model linking population, background climate, and UHI intensity and we show that urban-rural changes in evapotranspiration and convection efficiency are the main determinants for warming. The direct implication of these nonlinearities is that mitigation strategies aimed at increasing green cover and albedo are more efficient in dry regions, while cooling tropical cities is a challenge that will require innovative solutions.

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[1] Snow entrainment alters the speed and hence the run-out distance of avalanches, yet little is known about this significant process. We studied entrainment in snow avalanches using observations from (1) the Swiss Vallée de la Sionne test site, (2) the Italian Pizzac site, (3) catastrophic avalanches that occurred during the winter 1998-1999 in Switzerland, and (4) a medium-sized spontaneous avalanche that occurred in 2000 in Davos, Switzerland. We determined mass and energy balances for 18 avalanche events. On average, the mass increased by a factor of 4. The primary mode of entrainment appeared to be frontal ploughing, although entrainment behind the avalanche front was also observed.Step entrainment, where a snow cover layer fractures and is entirely consumed by the avalanche, also occurred. Basal erosion was negligible. Mass availability and snow cover structure were the limiting factors governing entrainment. Other factors such as track topography and avalanche dimension played a secondary role. Using the experimental results, we introduced an entrainment model into a Saint-Venant type flow model where the internal shear deformation of the avalanche is governed by a Bagnold law and the shear stress at the basal layer is treated as a Voellmy fluid. The model with entrainment not only improves the prediction of the velocities and flow heights in comparison to measurements, but also reproduces the variations in run-out distances, which characterize avalanches with similar terminal velocities but different masses.Citation: Sovilla, B., P. Burlando, and P. Bartelt (2006), Field experiments and numerical modeling of mass entrainment in snow avalanches,

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[1] In mountain regions wind is known to cause snow redistribution. While physically based models of snow redistribution have been developed for flat to gently rolling terrain, extension of these findings to steep terrain has been limited by the complexity of wind fields in such areas. In this study, we applied a nonhydrostatic and compressible atmospheric prediction model to steep alpine topography and compared the results to a fully distributed data set of snow depth estimations. The results show reduced horizontal wind velocity as well as an increasing downward vertical wind velocity over areas with the largest winter accumulation, which are mostly glacierized. We show that the wind velocity normal to the local surface, which should be zero in a nondivergent flow field and is a direct measure of increased or decreased local deposition, is a function of small-scale features of local topography. The correlation between wind fields, snow accumulation, and glacierization suggests that accurate modeling of wind fields over glacierized areas in complex terrain is a key factor for understanding the mass balance distribution of glaciers.Citation: Dadic, R., R. Mott, M. Lehning, and P. Burlando (2010), Wind influence on snow depth distribution and accumulation over glaciers,

[1] Physically based hydrological models describe natural processes more accurately than conceptual models but require extensive data sets to produce accurate results. To identify the value of different data sets for improving the performance of the distributed hydrological model TOPKAPI we combine a multivariable validation technique with Monte Carlo simulations. The study is carried out in the snow and ice-dominated Rhonegletscher basin, as these types of mountainous basins are generally the most critical with respect to data availability and sensitivity to climate fluctuations. Each observational data set is used individually and in combination with the other data sets to determine a subset of best parameter combinations out of 10,000 Monte Carlos runs performed with randomly generated parameter sets. We validate model results against discharge, glacier mass balance, and satellite snow cover images for a 14 year time period (1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007). While the use of all data sets combined provides the best overall model performance (defined by the concurrent best agreement of simulated discharge, snow cover and mass balance with their respective measurements), the use of one or two variables for constraining the model results in poorer performance. Using only one data set for constraining the model glacier mass balance proved to be the most efficient observation leading to the best overall model performance. Our main result is that a combination of discharge and satellite snow cover images is best for improving model performance, since the volumetric information of discharge data and the spatial information of snow cover images are complementary.Citation: Finger, D., F. Pellicciotti, M. Konz, S. Rimkus, and P. Burlando (2011), The value of glacier mass balance, satellite snow cover images, and hourly discharge for improving the performance of a physically based distributed hydrological model, Water Resour.

[1] Dynamic vegetation models have been widely used for analyzing ecosystem dynamics and their interactions with climate. Their performance has been tested extensively against observations and by model intercomparison studies. In the present analysis, Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS), a state-of-the-art ecosystem model, was evaluated by performing a global sensitivity analysis. The study aims at examining potential model limitations, particularly with regard to long-term applications. A detailed sensitivity analysis based on variance decomposition is presented to investigate structural model assumptions and to highlight processes and parameters that cause the highest variability in the output. First-and total-order sensitivity indices were calculated for selected parameters using Sobol's methodology. In order to elucidate the role of climate on model sensitivity, different climate forcings were used based on observations from Switzerland. The results clearly indicate a very high sensitivity of LPJ-GUESS to photosynthetic parameters. Intrinsic quantum efficiency alone is able to explain about 60% of the variability in vegetation carbon fluxes and pools for a wide range of climate forcings. Processes related to light harvesting were also found to be important together with parameters affecting forest structure (growth, establishment, and mortality). The model shows minor sensitivity to hydrological and soil texture parameters, questioning its skills in representing spatial vegetation heterogeneity at regional or watershed scales. In the light of these results, we discuss the deficiencies of LPJ-GUESS and possibly that of other, structurally similar, dynamic vegetation models and we highlight potential directions for further model improvements.Citation: Pappas, C., S. Fatichi, S. Leuzinger, A. Wolf, and P. Burlando (2013), Sensitivity analysis of a process-based ecosystem model: Pinpointing parameterization and structural issues,