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Accumulation rates (2009–2017) in Southeast Greenland derived from airborne snow radar and comparison with regional climate models
Annals of Glaciology ( IF 2.9 ) Pub Date : 2020-02-21 , DOI: 10.1017/aog.2020.8
Lynn Montgomery , Lora Koenig , Jan T. M. Lenaerts , Peter Kuipers Munneke

Since the year 2000, Greenland ice sheet mass loss has been dominated by a decrease in surface mass balance rather than an increase in solid ice discharge. Southeast Greenland is an important region to understand how high accumulation rates can offset increasing Greenland ice sheet meltwater runoff. To that end, we derive a new 9-year long dataset (2009–17) of accumulation rates in Southeast Greenland using NASA Operation IceBridge snow radar. Our accumulation dataset derived from internal layers focuses on high elevations (1500–3000 m) because at lower elevations meltwater percolation obscured internal layer structure. The uncertainty of the radar-derived accumulation rates is 11% [using Firn Densification Model (FDM) density profiles] and the average accumulation rate ranges from 0.5 to 1.2 m w.e. With our observations spanning almost a decade, we find large inter-annual variability, but no significant trend. Accumulation rates are compared with output from two regional climate models (RCMs), MAR and RACMO2. This comparison shows that the models are underestimating accumulation in Southeast Greenland and the models misrepresent spatial heterogeneity due to an orographically forced bias in snowfall near the coast. Our dataset is useful to fill in temporal and spatial data gaps, and to evaluate RCMs where few in situ measurements are available.

中文翻译:

格陵兰东南部的累积率(2009-2017)来自机载雪地雷达并与区域气候模型比较

自 2000 年以来,格陵兰冰盖的质量损失主要是表面质量平衡的减少,而不是固体冰排放量的增加。格陵兰东南部是了解高积累率如何抵消格陵兰冰盖融水径流增加的重要地区。为此,我们使用 NASA 冰桥行动雪地雷达推导出了一个新的 9 年长的数据集(2009-17 年),该数据集是格陵兰东南部的累积率。我们从内层派生的累积数据集侧重于高海拔(1500-3000 m),因为在低海拔地区,融水渗透掩盖了内层结构。雷达衍生积累率的不确定性为 11% [使用 Firn 致密模型 (FDM) 密度剖面],平均积累率范围为 0.5 至 1.2 m,我们的观察跨越了近十年,我们发现年际变化很大,但没有显着趋势。累积率与两个区域气候模型 (RCM) MAR 和 RACMO2 的输出进行比较。这种比较表明,这些模型低估了格陵兰东南部的累积,并且由于海岸附近降雪的地形强迫偏差,这些模型歪曲了空间异质性。我们的数据集可用于填补时间和空间数据空白,并用于评估很少有现场测量可用的 RCM。这种比较表明,这些模型低估了格陵兰东南部的累积,并且由于海岸附近降雪的地形强迫偏差,这些模型歪曲了空间异质性。我们的数据集可用于填补时间和空间数据空白,并用于评估很少有现场测量可用的 RCM。这种比较表明,这些模型低估了格陵兰东南部的累积,并且由于海岸附近降雪的地形强迫偏差,这些模型歪曲了空间异质性。我们的数据集可用于填补时间和空间数据空白,并用于评估很少有现场测量可用的 RCM。
更新日期:2020-02-21
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