ALBERT

Description: Energy and mass-balance modelling of glaciers is a key tool for climate impact studies of future glacier behaviour. By incorporating many of the physical processes responsible for surface accumulation and ablation, they offer more insight than simpler statistical models and are believed to suffer less from problems of stationarity when applied under changing climate conditions. However, this view is challenged by the widespread use of parameterizations for some physical processes which introduces a statistical calibration step. We argue that the reported uncertainty in modelled mass balance (and associated energy flux components) are likely to be understated in modelling studies that do not use spatio-temporal cross-validation and use a single performance measure for model optimization. To demonstrate the importance of these principles, we present a rigorous sensitivity and uncertainty assessment workflow applied to a modelling study of two glaciers in the European Alps, extending classical best guess approaches. The procedure begins with a reduction of the model parameter space using a global sensitivity assessment that identifies the parameters to which the model responds most sensitively. We find that the model sensitivity to individual parameters varies considerably in space and time, indicating that a single stated model sensitivity value is unlikely to be realistic. The model is most sensitive to parameters related to snow albedo and vertical gradients of the meteorological forcing data. We then apply a Monte Carlo multi-objective optimization based on three performance measures: model bias and mean absolute deviation in the upper and lower glacier parts, with glaciological mass balance data measured at individual stake locations used as reference. This procedure generates an ensemble of optimal parameter solutions which are equally valid. The range of parameters associated with these ensemble members are used to estimate the cross-validated uncertainty of the model output and computed energy components. The parameter values for the optimal solutions vary widely, and considering longer calibration periods does not systematically result in better constrained parameter choices. The resulting mass balance uncertainties reach up to 1300 kg m−2, with the spatial and temporal transfer errors having the same order of magnitude. The uncertainty of surface energy flux components over the ensemble at the point scale reached up to 50 % of the computed flux. The largest absolute uncertainties originate from the short-wave radiation and the albedo parameterizations, followed by the turbulent fluxes. Our study highlights the need for due caution and realistic error quantification when applying such models to regional glacier modelling efforts, or for projections of glacier mass balance in climate settings that are substantially different from the conditions in which the model was optimized.

Description: Energy and mass balance modeling of glaciers is a key tool for climate impact studies of future glacier behaviour. By incorporating many of the physical processes responsible for surface accumulation and ablation, they offer more insight than simpler statistical models and are believed to suffer less from problems of stationarity when applied under changing climate conditions. However, this view is challenged by the widespread use of parameterizations for some physical processes introduces a statistical calibration step. We argue that the reported uncertainty in modelled mass balance (and associated energy flux components) are likely to be understated in modelling studies that do not use spatio-temporal cross-validation and use a single performance measure for model optimization. To demonstrate the importance of these principles, we present a rigorous sensitivity and uncertainty assessment workflow applied to a modelling study of two glaciers in the European Alps. The procedure begins with a reduction of the model parameter space using a global sensitivity assessment that identifies the parameters to which the model responds most sensitively. We find that the model sensitivity to individual parameters varies considerably in space and time, indicating that a single stated model sensitivity value is unlikely to be realistic. The model is most sensitive to parameters related to snow albedo and vertical gradients of the meteorological forcing data. We then apply a Monte Carlo multi-objective optimization based on three performance measures: Model bias and mean absolute deviation in the upper and lower glacier parts, with glaciological mass balance data measured at individual stake locations used as reference. This procedure generates an ensemble of optimal parameter solutions which are equally valid. The range of parameters associated with these ensemble members are used to estimate the cross-validated uncertainty of the model output and computed energy components. The parameter values for the optimal solutions vary widely, and considering longer calibration periods does not systematically result in more constrained parameter choices. The resulting mass balance uncertainties reach up to 1300kgm−2, with the spatial and temporal transfer errors having the same order of magnitude. The uncertainty of surface energy flux components over the ensemble at the point scale reached up to 50% of the computed flux. The largest absolute uncertainties originate from the short-wave radiation and the albedo parametrizations, followed by the turbulent fluxes. Our study highlights the need for due caution, and realistic error quantification when applying such models to regional glacier modelling efforts, or for projections of glacier mass balance in climate settings that are substantially different from the conditions in which the model was optimized.

Description: Observations and models agree that the Greenland Ice Sheet (GrIS) surface mass balance (SMB) has decreased since the end of the 1990s due to an increase in meltwater runoff and that this trend will accelerate in the future. However, large uncertainties remain, partly due to different approaches for modelling the GrIS SMB, which have to weigh physical complexity or low computing time, different spatial and temporal resolutions, different forcing fields, and different ice sheet topographies and extents, which collectively make an inter-comparison difficult. Our GrIS SMB model intercomparison project (GrSMBMIP) aims to refine these uncertainties by intercomparing 13 models of four types which were forced with the same ERA-Interim reanalysis forcing fields, except for two global models. We interpolate all modelled SMB fields onto a common ice sheet mask at 1 km horizontal resolution for the period 1980–2012 and score the outputs against (1) SMB estimates from a combination of gravimetric remote sensing data from GRACE and measured ice discharge; (2) ice cores, snow pits and in situ SMB observations; and (3) remotely sensed bare ice extent from MODerate-resolution Imaging Spectroradiometer (MODIS). Spatially, the largest spread among models can be found around the margins of the ice sheet, highlighting model deficiencies in an accurate representation of the GrIS ablation zone extent and processes related to surface melt and runoff. Overall, polar regional climate models (RCMs) perform the best compared to observations, in particular for simulating precipitation patterns. However, other simpler and faster models have biases of the same order as RCMs compared with observations and therefore remain useful tools for long-term simulations or coupling with ice sheet models. Finally, it is interesting to note that the ensemble mean of the 13 models produces the best estimate of the present-day SMB relative to observations, suggesting that biases are not systematic among models and that this ensemble estimate can be used as a reference for current climate when carrying out future model developments. However, a higher density of in situ SMB observations is required, especially in the south-east accumulation zone, where the model spread can reach 2 m w.e. yr−1 due to large discrepancies in modelled snowfall accumulation.

Description: The Greenland Ice Sheet (GrIS) mass loss has been accelerating at a rate of about 20 ± 10 Gt/yr2 since the end of the 1990's, with around 60 % of this mass loss directly attributed to enhanced surface meltwater runoff. However, in the climate and glaciology communities, different approaches exist on how to model the different surface mass balance (SMB) components using: (1) complex physically-based climate models which are computationally expensive; (2) intermediate complexity energy balance models; (3) simple and fast positive degree day models which base their inferences on statistical principles and are computationally highly efficient. Additionally, many of these models compute the SMB components based on different spatial and temporal resolutions, with different forcing fields as well as different ice sheet topographies and extents, making inter-comparison difficult. In the GrIS SMB model intercomparison project (GrSMBMIP) we address these issues by forcing each model with the same data (i.e., the ERA-Interim reanalysis) except for two global models for which this forcing is limited to the oceanic conditions, and at the same time by interpolating all modelled results onto a common ice sheet mask at 1 km horizontal resolution for the common period 1980–2012. The SMB outputs from 13 models are then compared over the GrIS to (1) SMB estimates using a combination of gravimetric remote sensing data from GRACE and measured ice discharge, (2) ice cores, snow pits, in-situ SMB observations, and (3) remotely sensed bare ice extent from MODerate-resolution Imaging Spectroradiometer (MODIS). Our results reveal that the mean GrIS SMB of all 13 models has been positive between 1980 and 2012 with an average of 340 ± Gt/yr, but has decreased at an average rate of −7.3 Gt/yr2 (with a significance of 96 %), mainly driven by an increase of 8.0 Gt/yr2 (with a significance of 98 %) in meltwater runoff. Spatially, the largest spread among models can be found around the margins of the ice sheet, highlighting the need for accurate representation of the GrIS ablation zone extent and processes driving the surface melt. In addition, a higher density of in-situ SMB observations is required, especially in the south-east accumulation zone, where the model spread can reach 2 mWE/yr due to large discrepancies in modelled snowfall accumulation. Overall, polar regional climate models (RCMs) perform the best compared to observations, in particular for simulating precipitation patterns. However, other simpler and faster models have biases of same order than RCMs with observations and remain then useful tools for long-term simulations. Finally, it is interesting to note that the ensemble mean of the 13 models produces the best estimate of the present day SMB relative to observations, suggesting that biases are not systematic among models.

Description: Snow samples were taken on a daily basis along a 100 m wind-parallel transect at the EastGRIP ice core deep drilling site. The snow was collected in the morning at 11 positions with 10 m spacing into three cumulative samples – each for one depth interval. The depth intervals are top 0.5 cm, top 1 cm, and top 2 cm. Each day, undisturbed snow was sampled and the exact sample location was marked to avoid sampling disturbed snow during the next sampling event. The samples were shipped frozen to the Alfred-Wegener-Institut and stored at -25 °C. Prior to measurements, the samples were melted in the sample bags at room temperature. For the measurement of the isotopic composition, a Picarro L2130-i with a high-throughput vaporizer (model A0212) was used. The measurement set-up followed the Van-Geldern protocol. Each sample was injected four times and the standard deviation is computed. We calculate the average over all the standard deviations as a measure of uncertainty. We find this average to be 0.04 permil for δ18O (with stdev 0.02) and 0.13 permil for δD (stdev 0.07). The maximum standard deviation within the data set was found to be 0.14 for δ18O and 0.48 for δD. As a measure of accuracy, the offset between the defined and the measured value of the quality check standard for each measurement run is provided. We calculate the average of this offset for the whole data set and obtain a value of 0.07 permil for δ18O (stdev 0.03) and -0.11 permil for δD (stdev 0.08). The maximum offset found within the data set was 0.12 permil for δ18O and -0.25 permil for δD.

Description: Snow samples were taken on a daily basis along a 100 m wind-parallel transect at the EastGRIP ice core deep drilling site. The snow was collected in the morning at 11 positions with 10 m spacing into three cumulative samples – each for one depth interval. The depth intervals are top 0.5 cm, top 1 cm, and top 2 cm. Each day, undisturbed snow was sampled and the exact sample location was marked to avoid sampling disturbed snow during the next sampling event. The samples were shipped frozen to the Alfred-Wegener-Institut and stored at -25 °C. Prior to measurements, the samples were melted in the sample bags at room temperature. For the measurement of the isotopic composition, a Picarro L2130-i with a high-throughput vaporizer (model A0212) was used. The measurement set-up followed the Van-Geldern protocol. Each sample was injected four times and the standard deviation is computed. We calculate the average over all the standard deviations as a measure of uncertainty. We find this average to be 0.04 permil for δ18O (with stdev 0.02) and 0.13 permil for δD (stdev 0.07). The maximum standard deviation within the data set was found to be 0.14 for δ18O and 0.48 for δD. As a measure of accuracy, the offset between the defined and the measured value of the quality check standard for each measurement run is provided. We calculate the average of this offset for the whole data set and obtain a value of 0.07 permil for δ18O (stdev 0.03) and -0.11 permil for δD (stdev 0.08). The maximum offset found within the data set was 0.12 permil for δ18O and -0.25 permil for δD.

Description: Snow samples were taken on a daily basis along a 100 m wind-parallel transect at the EastGRIP ice core deep drilling site. The snow was collected in the morning at 11 positions with 10 m spacing into three cumulative samples – each for one depth interval. The depth intervals are top 0.5 cm, top 1 cm, and top 2 cm. Each day, undisturbed snow was sampled and the exact sample location was marked to avoid sampling disturbed snow during the next sampling event. The samples were shipped frozen to the Alfred-Wegener-Institut and stored at -25 °C. Prior to measurements, the samples were melted in the sample bags at room temperature. For the measurement of the isotopic composition, a Picarro L2130-i with a high-throughput vaporizer (model A0212) was used. The measurement set-up followed the Van-Geldern protocol. Each sample was injected four times and the standard deviation is computed. We calculate the average over all the standard deviations as a measure of uncertainty. We find this average to be 0.04 permil for δ18O (with stdev 0.02) and 0.13 permil for δD (stdev 0.07). The maximum standard deviation within the data set was found to be 0.14 for δ18O and 0.48 for δD. As a measure of accuracy, the offset between the defined and the measured value of the quality check standard for each measurement run is provided. We calculate the average of this offset for the whole data set and obtain a value of 0.07 permil for δ18O (stdev 0.03) and -0.11 permil for δD (stdev 0.08). The maximum offset found within the data set was 0.12 permil for δ18O and -0.25 permil for δD.