Reference : Sensitivity of the Antarctic surface mass balance to oceanic perturbations
Scientific congresses and symposiums : Poster
Physical, chemical, mathematical & earth Sciences : Earth sciences & physical geography
http://hdl.handle.net/2268/216770
Sensitivity of the Antarctic surface mass balance to oceanic perturbations
English
Kittel, Christoph mailto [Université de Liège - ULiège > Département de géographie > Climatologie et Topoclimatologie >]
Amory, Charles mailto [Université de Liège - ULiège > Département de géographie > Climatologie et Topoclimatologie >]
Agosta, Cécile mailto [Université de Liège - ULiège > Département de géographie > Climatologie et Topoclimatologie >]
Fettweis, Xavier mailto [Université de Liège - ULiège > Département de géographie > Climatologie et Topoclimatologie >]
15-Dec-2017
No
International
AGU Fall meeting 2017
11/12/2017 to 15/12/12017
New-Orleans
The United Stades of America
[en] SMB ; ocean ; Antarctic
[en] Regional climate models (RCMs) are suitable numerical tools to study the surface mass balance (SMB) of the wide polar ice sheets due to their high spatial resolution and polar-adapted physics. Nonetheless, RCMs are driven at their boundaries and over the ocean by reanalysis or global climate model (GCM) products and are thus influenced by potential biases in these large-scale fields. These biases can be significant for both the atmosphere and the sea surface conditions (i.e. sea ice concentration and sea surface temperature). With the RCM MAR, a set of sensitivity experiments has been realized to assess the direct response of the SMB of the Antarctic ice sheet to oceanic perturbations. MAR is forced by ERA-Interim and anomalies based on mean GCM biases are introduced in sea surface conditions. Results show significant increases (decreases) of liquid and solid precipitation due to biases related to warm (cold) oceans. As precipitation is mainly caused by low-pressure systems that intrude into the continent and do not penetrate far inland, coastal areas are more sensitive than inland regions. Furthermore, warm ocean representative biases lead to anomalies as large as anomalies simulated by other RCMs or GCMs for the end of the 21st century.
Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS; CECI
http://hdl.handle.net/2268/216770

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