Irreversible mass loss of Canadian Arctic Archipelago glaciers

The Canadian Arctic Archipelago (CAA) contains the largest volume of glacier ice on Earth outside of Antarctica and Greenland. In the absence of significant calving, CAA glacier mass balance is governed by the difference between surface snow accumulation and meltwater runoff—surface mass balance. He...

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Bibliographic Details
Published inGeophysical research letters Vol. 40; no. 5; pp. 870 - 874
Main Authors Lenaerts, Jan T. M., van Angelen, Jan H., van den Broeke, Michiel R., Gardner, Alex S., Wouters, Bert, van Meijgaard, Erik
Format Journal Article
LanguageEnglish
Published Washington Blackwell Publishing Ltd 16.03.2013
John Wiley & Sons, Inc
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Summary:The Canadian Arctic Archipelago (CAA) contains the largest volume of glacier ice on Earth outside of Antarctica and Greenland. In the absence of significant calving, CAA glacier mass balance is governed by the difference between surface snow accumulation and meltwater runoff—surface mass balance. Here we use a coupled atmosphere/snow model to simulate present‐day and 21st century CAA glacier surface mass balance. Through comparison with Gravity Recovery and Climate Experiment mass anomalies and in situ observations, we show that the model is capable of representing present‐day CAA glacier mass loss, as well as the dynamics of the seasonal snow cover on the CAA tundra. Next, we force this model until 2100 with a moderate climate warming scenario (AR5 RCP4.5). We show that enhanced meltwater runoff from CAA glaciers is not sufficiently compensated by increased snowfall. Extrapolation of these results toward an AR5 multimodel ensemble results in sustained 21st century CAA glacier mass loss in the vast majority (>99%) of the ~7000 temperature realizations. Key Points RACMO2 is capable of simulating present‐day CAA mass loss Increase of meltwater runoff is not compensated by increased snowfall Persistent 21 century glacier mass loss in nearly all AR5 projections
Bibliography:ArticleID:GRL50214
istex:978F6C416581237DA97D4D7F8E99C9F8C1F1C50C
ark:/67375/WNG-QVZCP1X7-4
ISSN:0094-8276
1944-8007
DOI:10.1002/grl.50214