Simulated Greenland Surface Mass Balance in the GISS ModelE2 GCM: Role of the Ice Sheet Surface
Simulated Greenland Surface Mass Balance in the GISS ModelE2 GCM: Role of the Ice Sheet Surface
复制标题
GISS ModelE2 GCM 中模拟的格陵兰表面质量平衡:冰盖表面的作用
DOI:
10.1029/2018jf004772
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发表时间:
2019
期刊:
影响因子:
--
通讯作者:
Schmidt, G. A.
中科院分区:
文献类型:
--
作者:
Alexander, P. M.;LeGrande, A. N.;Fischer, E.;Tedesco, M.;Fettweis, X.;Kelley, M.;Nowicki, S. M. J.;Schmidt, G. A.
The rate of growth or retreat of the Greenland and Antarctic ice sheets remains a highly uncertain component of future sea level change. Here we examine the simulation of Greenland ice sheet surface mass balance (GrIS SMB) in a development branch of the ModelE2 version of the NASA Goddard Institute for Space Studies (GISS) general circulation model (GCM). GCMs are often limited in their ability to represent SMB compared with polar region regional climate models. We compare ModelE2‐simulated GrIS SMB for present‐day (1996–2005) simulations with fixed ocean conditions, at a spatial resolution of 2° latitude by 2.5° longitude (~200 km), with SMB simulated by the Modèle Atmosphérique Régionale (MAR) regional climate model (1996–2005 at a 25‐km resolution). ModelE2 SMB agrees well with MAR SMB on the whole, but there are distinct spatial patterns of differences and large differences in some SMB components. The impacts of changes to the ModelE2 surface are tested, including a subgrid‐scale representation of SMB with surface elevation classes. This has a minimal effect on ice sheet‐wide SMB but corrects local biases. Replacing fixed surface albedo with satellite‐derived values and an age‐dependent scheme has a larger impact, increasing simulated melt by 60%–100%. We also find that lower surface albedo can enhance the effects of elevation classes. Reducing ModelE2 surface roughness length to values closer to MAR reduces sublimation by ~50%. Further work is required to account for meltwater refreezing in ModelE2 and to understand how differences in atmospheric processes and model resolution influence simulated SMB.
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DOI:
--
发表时间:
2017
期刊:
影响因子:
--
作者:
M. Helsen;R. Wal;T. Reerink;R. Bintanja;M. S. Madsen;Shuting Yang;Qiang Li;Qiong Zhang
通讯作者:
Qiong Zhang
影响因子:
2.9
作者:
W. Colgan;J. Box;M. Andersen;X. Fettweis;B. Csathó;R. Fausto;D. van As;J. Wahr
通讯作者:
J. Wahr
DOI:
--
发表时间:
2015
期刊:
影响因子:
--
作者:
N. Schlegel;E. Larour;H. Seroussi;Mathieu Morlighem;J. Box
通讯作者:
J. Box
DOI:
10.1016/j.jqsrt.2013.07.002
发表时间:
2013-11-01
影响因子:
2.3
作者:
Rothman, L. S.;Gordon, I. E.;Wagner, G.
通讯作者:
Wagner, G.
影响因子:
5.2
作者:
Alexander, P. M.;Tedesco, M.;van den Broeke, M. R.
通讯作者:
van den Broeke, M. R.