摘要
Surface snowmelt affects the energy balance through melt-albedo feedback and may endanger the ice shelves in the Antarctic Peninsula (AP) through hydrofracture. Here, we introduce an automatic snowmelt identification algorithm based on Quick Scatterometer and Advanced Scatterometer. The proposed method can provide self-adaptive thresholds for snowmelt detection based on Rosin thresholding, and is able to detect weak melt signals with a wavelet denoising procedure. Results suggest the AP surface snowmelt has slightly declined during 1999–2018 in the context of recent cooling. However, winter melt index (i.e., melt days times the area of melting) has significantly increased (above the 99% confidence level) with a rate of 83% decade−1. Unprecedented winter snowmelt was found in 2015/2016 when about one-third of the AP experienced snowmelt in late May. This may be attributed to the intrusions of marine air and the enhanced föhn-driven warming resulted from the abnormal northwesterly flow driven by a significant deepening of the Amundsen Sea Low.
| 源语言 | 英语 |
|---|---|
| 文章编号 | 111835 |
| 期刊 | Remote Sensing of Environment |
| 卷 | 246 |
| DOI | |
| 出版状态 | 已出版 - 1 9月 2020 |
联合国可持续发展目标
此成果有助于实现下列可持续发展目标:
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可持续发展目标 14 水下生物
指纹
探究 'Enhanced winter snowmelt in the Antarctic Peninsula: Automatic snowmelt identification from radar scatterometer' 的科研主题。它们共同构成独一无二的指纹。引用此
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