Observations show that glaciers around the world are in retreat and losing mass. Internationally coordinated for over a century, glacier monitoring activities provide an unprecedented dataset of glacier observations from ground, air and space. Glacier studies generally select specific parts of these datasets to obtain optimal assessments of the mass-balance data relating to the impact that glaciers exercise on global sea-level fluctuations or on regional runoff. In this study we provide an overview and analysis of the main observational datasets compiled by the World Glacier Monitoring Service (WGMS). The dataset on glacier front variations (∼42 000 since 1600) delivers clear evidence that centennial glacier retreat is a global phenomenon. Intermittent readvance periods at regional and decadal scale are normally restricted to a subsample of glaciers and have not come close to achieving the maximum positions of the Little Ice Age (or Holocene). Glaciological and geodetic observations (∼5200 since 1850) show that the rates of early 21st-century mass loss are without precedent on a global scale, at least for the time period observed and probably also for recorded history, as indicated also in reconstructions from written and illustrated documents. This strong imbalance implies that glaciers in many regions will very likely suffer further ice loss, even if climate remains stable.

Dynamic ice-flow models for 12 glaciers and ice caps have been forced with various climate change scenarios. The volume of this sample spans three orders of magnitude. Six climate scenarios were considered: from 1990 onwards linear warming rates of 0.01, 0.02 and 0.04 K a\, with and without concurrent changes in precipitation. The models, calibrated against the historic record of glacier length where possible, were integrated until 2100. The differences in individual glacier responses are very large. No straightforward relationship between glacier size and fractional change of ice volume emerges for any given climate scenario. The hypsometry of individual glaciers and ice caps plays an important role in their response, thus making it difficult to generalize results. For a warming rate of 0.04 K a\, without increase in precipitation, results indicate that few glaciers would survive until 2100. On the other hand, if the warming rate were to be limited to 0.01 K a\ with an increase in precipitation of 10% per degree warming, we predict that overall loss would be restricted to 10 to 20% of the 1990 volume.

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The sensitivity of glaciers to climatic change is key information in assessing the response and sea-level implications of projected future warming. New Zealand glaciers are important globally as an example of how maritime glaciers will contribute to sea-level rise. A spatially distributed energy-balance model is applied to Brewster Glacier, New Zealand, in order to calculate glacier mass balance, run-off and sensitivity to climate change. The model successfully simulates four annual mass-balance cycles. Close to half (52%) of the energy available for melt on the glacier is supplied by turbulent heat fluxes, with radiation less important, except during the winter. Model sensitivity to temperature change is one of the largest reported on Earth, at −2.0 m w.e. a−1 °C−1. In contrast, a 50% change in precipitation is required to offset the mass-balance change resulting from a 1 °C temperature change. Meltwater runoff sensitivity is also very high, increasing 60% with a 1°C warming. The extreme sensitivity of mass balance to temperature change suggests that significant ice loss will occur with even moderate climate warming.

Abstract. Knowledge of the ice thickness distribution of glaciers and ice caps is an important prerequisite for many glaciological and hydrological investigations. A wealth of approaches has recently been presented for inferring ice thickness from characteristics of the surface. With the Ice Thickness Models Intercomparison eXperiment (ITMIX) we performed the first coordinated assessment quantifying individual model performance. A set of 17 different models showed that individual ice thickness estimates can differ considerably – locally by a spread comparable to the observed thickness. Averaging the results of multiple models, however, significantly improved the results: on average over the 21 considered test cases, comparison against direct ice thickness measurements revealed deviations on the order of 10 ± 24 % of the mean ice thickness (1σ estimate). Models relying on multiple data sets – such as surface ice velocity fields, surface mass balance, or rates of ice thickness change – showed high sensitivity to input data quality. Together with the requirement of being able to handle large regions in an automated fashion, the capacity of better accounting for uncertainties in the input data will be a key for an improved next generation of ice thickness estimation approaches.