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Revealing the footprint of ENSO in the mass balance of mountain glaciers

Subject Area Physical Geography
Term from 2015 to 2018
Project identifier Deutsche Forschungsgemeinschaft (DFG) - Project number 278205752
 
Climate change at high altitudes is poorly understood compared to low levels of the atmosphere, but revealing the causes of glacier mass fluctuations provides a unique chance for improvement. However, a universal problem in the climate system is that climatic trends are masked by strong internal climate variability. For glaciers, previous research could demonstrate the existence of this problem through statistical linkages, but a sound process understanding does not yet exist. This project aims to understand the impact of the strongest mode of climate variability on earth (El Niño Southern Oscillation, ENSO) on glacier mass balance in different climate zones, and throughout the multitude of space-time scales: from the atmospheric surface layer above glaciers, through the mesoscale atmospheric circulation over the mountains, up to the large-scale climate dynamics that set the boundary conditions. Based on this approach, the ultimate goal is to drastically advance our understanding of anomalies in glacier mass and energy balance processes (the "footprint") due to ENSO activity. To achieve the goal a suite of methods is employed: field measurements at high altitude; physically-based glacier mass balance modeling; numerical atmospheric modeling; and large-scale data analyses. This methodical variety, eventually, enables multi-scale modeling of the causality chain between ENSO events and glacier mass balance. In particular, it is possible to simulate the "memory" of mass balance series from ENSO impacts, which is crucial for a better understanding of anthropogenic forcing of glacier mass loss and, hence, for improved detection and attribution of high-altitude climate change. The proposed method to link large-scale climate and a local climate-driven phenomenon by the governing physics, moreover, will open new horizons for all climate impact studies, which mostly rely on statistical models that are known to be unstable in time.
DFG Programme Research Grants
 
 

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