气候变化领域集成服务门户(CCPortal): Estimating early-winter Antarctic sea ice thickness from deformed ice morphology

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DOI	10.5194/tc-13-2915-2019
	Estimating early-winter Antarctic sea ice thickness from deformed ice morphology
	Jeffrey Mei M.; Maksym T.; Weissling B.; Singh H.
发表日期	2019
ISSN	19940416
EISSN	13
起始页码	2915
结束页码	2934
卷号	13 期号:11
英文摘要	Satellites have documented variability in sea ice areal extent for decades, but there are significant challenges in obtaining analogous measurements for sea ice thickness data in the Antarctic, primarily due to difficulties in estimating snow cover on sea ice. Sea ice thickness (SIT) can be estimated from snow freeboard measurements, such as those from airborne/satellite lidar, by assuming some snow depth distribution or empirically fitting with limited data from drilled transects from various field studies. Current estimates for large-scale Antarctic SIT have errors as high as ĝ1/450%, and simple statistical models of small-scale mean thickness have similarly high errors. Averaging measurements over hundreds of meters can improve the model fits to existing data, though these results do not necessarily generalize to other floes. At present, we do not have algorithms that accurately estimate SIT at high resolutions. We use a convolutional neural network with laser altimetry profiles of sea ice surfaces at 0.2m resolution to show that it is possible to estimate SIT at 20m resolution with better accuracy and generalization than current methods (mean relative errors ĝ1/415%). Moreover, the neural network does not require specification of snow depth or density, which increases its potential applications to other lidar datasets. The learned features appear to correspond to basic morphological features, and these features appear to be common to other floes with the same climatology. This suggests that there is a relationship between the surface morphology and the ice thickness. The model has a mean relative error of 20% when applied to a new floe from the region and season. This method may be extended to lower-resolution, larger-footprint data such as such as Operation IceBridge, and it suggests a possible avenue to reduce errors in satellite estimates of Antarctic SIT from ICESat-2 over current methods, especially at smaller scales. © 2019 Copernicus GmbH. All rights reserved.
学科领域	estimation method; ice thickness; ICESat; lidar; measurement method; satellite data; sea ice; snow cover; vertical distribution; winter; Antarctica
语种	英语
scopus关键词	estimation method; ice thickness; ICESat; lidar; measurement method; satellite data; sea ice; snow cover; vertical distribution; winter; Antarctica
来源期刊	The Cryosphere
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/118957
作者单位	Department of Applied Ocean Science and Engineering, Woods Hole Oceanographic Institution, Woods Hole, MA 02540, United States; Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States; Department of Geological Sciences, University of Texas at El Paso, El Paso, TX 79968, United States; Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, United States
推荐引用方式 GB/T 7714	Jeffrey Mei M.,Maksym T.,Weissling B.,et al. Estimating early-winter Antarctic sea ice thickness from deformed ice morphology[J],2019,13(11).
APA	Jeffrey Mei M.,Maksym T.,Weissling B.,&Singh H..(2019).Estimating early-winter Antarctic sea ice thickness from deformed ice morphology.The Cryosphere,13(11).
MLA	Jeffrey Mei M.,et al."Estimating early-winter Antarctic sea ice thickness from deformed ice morphology".The Cryosphere 13.11(2019).