De-Tuning Albedo Parameters in a Coupled Climate Ice Sheet Model to Simulate the North American Ice Sheet at the Last Glacial Maximum
De-Tuning Albedo Parameters in a Coupled Climate Ice Sheet Model to Simulate the North American Ice Sheet at the Last Glacial Maximum
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解调耦合气候冰盖模型中的反照率参数以模拟末次盛冰期的北美冰盖
DOI：
10.1029/2023jf007250
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Gandy N
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文献类型：
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作者：
Gandy N
北美冰盖的末次冰期最大范围在经验上受到很好的限制，但事实证明，用耦合的气候-冰盖模型进行模拟具有挑战性。气候-冰盖耦合模型通常计算成本太高，无法充分探索输入参数的不确定性，并且校准以重现现代冰盖的值不太可能重现末次冰盛期的已知冰层范围。为了解决这个问题，我们运行了一个耦合气候-冰盖模型（FAMOUS‐ice）的集合，模拟了最后一次北美冰盖最大范围增长的最后阶段。使用这种大的合奏方法，我们探讨了许多不确定的冰盖的影响，冰盖动力学，大气和海洋参数的冰盖范围。我们发现，冰盖的不确定性参数确定模拟末次冰期最大的北美冰盖时，大多数。重要的是，需要不同的Applido参数，以产生一个很好的匹配，以末次冰期最大的北美冰盖比以前被用来模拟当代格陵兰冰盖由于消融区云量的差异。因此，在一个冰盖上校准耦合气候-冰盖模型可能会在模型应用于新领域时产生强烈的偏差。
The Last Glacial Maximum extent of the North American Ice Sheets is well constrained empirically but has proven to be challenging to simulate with coupled Climate‐Ice Sheet models. Coupled Climate‐Ice Sheet models are often too computationally expensive to sufficiently explore uncertainty in input parameters, and it is unlikely that values calibrated to reproduce modern ice sheets will reproduce the known extent of the ice at the Last Glacial Maximum. To address this, we run an ensemble with a coupled Climate‐Ice Sheet model (FAMOUS‐ice), simulating the final stages of growth of the last North American Ice Sheets' maximum extent. Using this large ensemble approach, we explore the influence of numerous uncertain ice sheet albedo, ice sheet dynamics, atmospheric, and oceanic parameters on the ice sheet extent. We find that ice sheet albedo parameters determine the majority of uncertainty when simulating the Last Glacial Maximum North American Ice Sheets. Importantly, different albedo parameters are needed to produce a good match to the Last Glacial Maximum North American Ice Sheets than have previously been used to model the contemporary Greenland Ice Sheet due to differences in cloud cover over ablation zones. Thus, calibrating coupled climate‐ice sheet models on one ice sheet may produce strong biases when the model is applied to a new domain.