Glacier-wide mass balance has been measured for more than sixty years and is widely used as an indicator of climate change and to assess the glacier contribution to runoff and sea level rise. Until recently, comprehensive uncertainty assessments have rarely been carried out and mass balance data have often been applied using rough error estimation or without consideration of errors. In this study, we propose a framework for reanalysing glacier mass balance series that includes conceptual and statistical toolsets for assessment of random and systematic errors, as well as for validation and calibration (if necessary) of the glaciological with the geodetic balance results. We demonstrate the usefulness and limitations of the proposed scheme, drawing on an analysis that comprises over 50 recording periods for a dozen glaciers, and we make recommendations to investigators and users of glacier mass balance data. Reanalysing glacier mass balance series needs to become a standard procedure for every monitoring programme to improve data quality, including reliable uncertainty estimates

Capturing and quantifying the world in three dimensions (x,y,z) using light detection and ranging (lidar) technology drives fundamental advances in the Earth and Ecological Sciences (EES). However, additional lidar dimensions offer the possibility to transcend basic 3-D mapping capabilities, including i) the physical time (t) dimension from repeat lidar acquisition and ii) laser return intensity (LRI λ ) data

ABSTRACT. Snow accumulation is an important component of the mass balance of alpine glaciers. To improve our understanding of the processes related to accumulation and their representation in state-ofthe-art mass-balance models, extensive field measurements are required. We present measurements of snow accumulation distribution on Findelengletscher, Switzerland, for April 2010 using (1) in situ snow probings, (2) airborne ground-penetrating radar (GPR) and (3) differencing of two airborne light detection and ranging (lidar) digital elevation models (DEMs). Calculating high-resolution snow depth from DEM-differencing requires careful correction for vertical ice-flow velocity and densification in the accumulation area. All three methods reveal a general increase in snow depth with elevation, but also a significant small-scale spatial variability. Lidar-differencing and in situ snow probings show good agreement for the mean specific winter balance (0.72 and 0.78 m w.e., respectively). The lidar-derived distributed snow depth reveals significant zonal correlations with elevation, slope and curvature in a multiple linear regression model. Unlike lidar-differencing, GPR-derived snow depth is not affected by glacier dynamics or firn compaction, but to a smaller degree by snow density and liquid water content. It is thus a valuable independent data source for validation. The simultaneous availability of the three datasets facilitates the comparison of the methods and contributes to a better understanding of processes that govern winter accumulation distribution on alpine glaciers.

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A re-analysis is presented here of a 10 year mass balance series at Findelengletscher, a temperate mountain glacier in Switzerland. Calculating glacier-wide mass balance from the set of glaciological point balance observations using conventional approaches, such as the profile or contour method, resulted in significant deviations from the reference value given by the geodetic mass change over a 5 year period. This is attributed to the sparsity of observations at high elevations and to the inability of the evaluation schemes to adequately estimate accumulation in unmeasured areas. However, measurements of winter mass balance were available for large parts of the study period from snow probings and density pits. Complementary surveys by helicopter-borne ground-penetrating radar (GPR) were conducted in three consecutive years. The complete set of seasonal observations was assimilated using a distributed mass balance model. This model-based extrapolation revealed a substantial mass loss at Findelengletscher of −0.43 m w.e. a −1 between 2004 and 2014, while the loss was less pronounced for its former tributary, Adlergletscher (−0.30 m w.e. a −1 ). For both glaciers, the resulting time series were within the uncertainty bounds of the geodetic mass change. We show that the model benefited strongly from the ability to integrate seasonal observations. If no winter mass balance measurements were available and snow cover was represented by a linear precipitation gradient, the geodetic mass balance was not matched. If winter balance measurements by snow probings and snow density pits were taken into account, the model performance was substantially improved but still showed a significant bias relative to the geodetic mass change. Thus, the excellent agreement of the model-based extrapolation with the geodetic mass change was owed to an adequate representation of winter accumulation distribution by means of extensive GPR measurements.

Glacier response to a changing climate and its impact on runoff is understood in general terms, but model-based projections are affected by considerable uncertainties. They originate from the driving climate model, input data quality, and simplifications in the glacio-hydrological model and hamper the reliability of the simulations. Here, an integrative assessment of the uncertainty in 21st century glacier runoff is provided based on experiments using the Glacier Evolution Runoff Model (GERM) applied to the catchment of Findelengletscher, Switzerland. GERM is calibrated and validated in a multi-objective approach and is run using nine Regional Climate Models (RCMs) until 2100. Among others, the hydrological impacts of the RCM downscaling procedure, the winter snow accumulation, the surface albedo and the calculation of ice melt and glacier retreat are investigated. All experiments indicate rapid glacier wastage and a transient runoff increase followed by reduced melt season discharge. However, major uncertainties in, e.g., glacier area loss (À100% to À63%) and the change in annual runoff (À57% to +25% relative to today) by 2100 are found. The impact of model assumptions on changes in August runoff is even higher (À94% to À5%). The spread in RCM results accounts for 20-50% of the overall uncertainty in modeled discharge. Initial ice thickness, the amount and spatial distribution of winter snow and the glacier retreat model have the largest effect on the projections, whereas the RCM downscaling procedure, calibration data quality and the melt model (energy balance vs. degree-day approach) are of secondary importance.

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Glacier-wide mass balance has been measured for more than sixty years and is widely used as an indicator of climate change and to assess the glacier contribution to runoff and sea level rise. Until present, comprehensive uncertainty assessments have rarely been carried out and mass balance data have often been applied using rough error estimation or without error considerations. In this study, we propose a framework for re-analyzing glacier mass balance series including conceptual and statistical toolsets for assessment of random and systematic errors as well as for validation and calibration (if necessary) of the glaciological with the geodetic balance results. We demonstrate the usefulness and limitations of the proposed scheme drawing on an analysis that comprises over 50 recording periods for a dozen glaciers and we make recommendations to investigators and users of glacier mass balance data. Reanalysis of glacier mass balance series needs to become a standard procedure for every monitoring programme to improve data quality and provide thorough uncertainty estimates