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.

This paper compares and evaluates four principal methods of dust source and plume identification using MODIS data. The four MODIS methods used here are: (i) Roskovensky and Liou's dust identification algorithm, (ii) Ackerman's model, (iii) Normalized Difference Dust Index (NDDI), and (iv) Deep Blue algorithm. These techniques were applied to three recent significant events in the Middle East region. In addition, true color images and the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model were used to evaluate the result of each technique. To optimize the result of dust detection for each technique, the published dust–nondust thresholds had to be considerably adjusted on an event-by-event basis. Results show that techniques that use brightness temperature (BT) difference are the most reliable techniques for dust source detection in several situations such as multiplume and multimineralogical conditions (unlike the NDDI index and other optical based algorithms). However, these technique... Ce document compare et évalue les quatre principales méthodes de source de poussière et de l'identification à l'aide du panache de données MODIS. Les quatre méthodes utilisées ici sont MODIS: (i) l'algorithme Roskovensky et Liou de la poussière d'identification, (ii) le modèle d'Ackerman, (iii) Indice NDDI, et (iv) algorithme de Deep Blue. Ces techniques sont appliquées à trois récents événements importants dans la région du Moyen-Orient. En outre, l'image vraie couleur et le modèle HYSPLIT ont été utilisés pour évaluer le résultat de chaque technique. Pour optimiser le résultat de détection de poussière pour chaque technique, les seuils publiés poussière–non-poussière dû être considérablement ajustés au niveau événement par événement. Résultat montre que les techniques qui utilisent la différence de température de brillance sont les techniques les plus fiables pour la détection source de poussière dans plusieurs situations telles que les conditions multi-plume et multi-minéralogique (contrairement à l'in...