Mass-balance parameters derived from a synthetic network of mass-balance glaciers

Abstract

Glacier mass-balance parameters such as the equilibrium-line altitude (ELA) play an important role when working with large glacier samples. While the number of observational mass-balance series to derive such parameters is limited, more and more modeled data are becoming available. Here we explore the possibilities of analyzing such 'synthetic' mass-balance data with respect to mass-balance parameters. A simplified energy-balance model is driven by bias-corrected regional climate model output to model mass-balance distributions for 94 glaciers in the Swiss Alps over 15 years. The modeling results in realistic interannual variability and mean cumulative mass balance. Subsequently model output is analyzed with respect to 18 topographic and mass-balance parameters and a correlation analysis is performed. Well-known correlations such as for ELA and median elevation are confirmed from the synthetic data. Furthermore, previously unreported parameter relationships are found such as a correlation of the balance rate at the tongue with the accumulation-area ratio (AAR) and of the glacier elevation range with the AAR. Analyzing modeled data complements in situ observations and highlights their importance: the small number of accurate mass-balance observations available for validation is a major challenge for the presented approach. ABSTRACT. Glacier mass-balance parameters such as the equilibrium-line altitude (ELA) play an important role when working with large glacier samples. While the number of observational massbalance series to derive such parameters is limited, more and more modeled data are becoming available. Here we explore the possibilities of analyzing such 'synthetic' mass-balance data with respect to massbalance parameters. A simplified energy-balance model is driven by bias-corrected regional climate model output to model mass-balance distributions for 94 glaciers in the Swiss Alps over 15 years. The modeling results in realistic interannual variability and mean cumulative mass balance. Subsequently model output is analyzed with respect to 18 topographic and mass-balance parameters and a correlation analysis is performed. Well-known correlations such as for ELA and median elevation are confirmed from the synthetic data. Furthermore, previously unreported parameter relationships are found such as a correlation of the balance rate at the tongue with the accumulation-area ratio (AAR) and of the glacier elevation range with the AAR. Analyzing modeled data complements in situ observations and highlights their importance: the small number of accurate mass-balance observations available for validation is a major challenge for the presented approach. Mass-balance parameters derived from a synthetic network of mass-balance glaciers

Compiling an Inventory of Glacier-Bed Overdeepenings and Potential New Lakes in De-Glaciating Areas of the Peruvian Andes: Approach, First Results, and Perspectives for Adaptation to Climate Change

“…The factor 0.8 produces an altitude which is lower than the mid-range elevation, which in most other cases would be a realistic approximation for the ELA [37][38][39] under equilibrium conditions. This reflects that (a) accumulation area ratio (AAR) values are especially high for tropical glaciers Based on the estimates for constant and accelerated rates of change, a rough distinction was made between three classes ( Figure 8):…”

“…The factor 0.8 produces an altitude which is lower than the mid-range elevation, which in most other cases would be a realistic approximation for the ELA [37][38][39] under equilibrium conditions. This reflects that (a) accumulation area ratio (AAR) values are especially high for tropical glaciers due to extreme ratios of mass balance gradients in accumulation/ablation areas [18] and that (b) the value of H max in the Peruvian glacier inventories relates to the mountain peaks rather than to the uppermost point of pronounced glacier flow.…”

The Swiss Alpine glaciers’ response to the global ‘2 °C air temperature target’

“…The de-biasing of the RCM fields is done for each grid cell and each variable according to the method described in Machguth et al (2012). For T and S in , the de-biasing is based on 14 high-elevation stations and achieved as follows (cf Machguth et al 2012 for details).…”

“…During the actual model run, all temperature values (at 100 m spatial resolution) are corrected by the corresponding daily bias. Observed cloudiness (n obs ) is calculated from observed S in according to Greuell et al (1997) and cloudiness from the RCM is adjusted to meet n obs by means of cumulative density function matching (Machguth et al 2012). Corrections are temporally and spatially uniform, which guarantees that the spatial pattern as given by the RCM (e.g.…”

“…In particular the large uncertainties in observed high mountain precipitation (Sevruk 1997) hamper the de-biasing procedure and makes it impossible to achieve a level of data accuracy that would allow the calculation of accurate mass balances for each individual glacier. We have approached these limitations by applying a calibration procedure, where the MB-model is driven by the downscaled and de-biased RCM time series for the period 1970-2000 and precipitation is calibrated for each glacier individually in order to achieve a prescribed cumulative mass balance (Machguth et al 2012). Prior to the calibration of precipitation, the mass balance model is adjusted to meet observed summer mass balance as described in Machguth et al (2009Machguth et al ( , 2012.…”