Estimation of Total Surface and Subsurface Meltwater Amounts across Greenland Ice Sheet

Greenland ice sheet (GrIS) melting has been a significant concern in the warming climate. Accurate quantification of total surface and subsurface meltwater amount across the pan-Greenland scale is crucial to understanding GrIS mass balance dynamics, thus better projecting global sea level rise. We used multi-year L-band observations from the NASA Soil Moisture Active Passive (SMAP) mission to quantify the GrIS surface and sub-surface meltwater amounts and examine their spatiotemporal variability. We employed an empirical algorithm to detect surface and subsurface melt events. Then, we applied a physics-based retrieval algorithm to estimate the intensity and physical properties of the melt events. Finally, we validated the retrieval by meltwater derived from an energy balance model calibrated locally with in situ observations from the PROMICE automatic weather station (AWS) network. The retrievals generally demonstrated stronger agreement within the percolation zone than the ablation and upper accumulation zones. The retrieval and validation results are presented and analyzed.

在气候变暖的背景下，格陵兰冰盖（Greenland Ice Sheet, GrIS）消融始终是备受关注的重要科学议题。精准量化全格陵兰范围内地表与次表层融水的总储量，对于理解格陵兰冰盖的质量平衡动力学机制，进而更可靠地预测全球海平面上升至关重要。本研究采用美国国家航空航天局（National Aeronautics and Space Administration, NASA）土壤湿度主动-被动卫星（Soil Moisture Active Passive, SMAP）任务获取的多年L波段观测数据，对格陵兰冰盖的地表与次表层融水总量进行量化，并分析其时空变异性。研究首先通过经验算法识别地表与次表层融水事件，随后运用基于物理原理的反演算法估算融水事件的强度与物理属性。最终，本研究借助由PROMICE自动气象站（Automatic Weather Station, AWS）网络的原位观测数据本地化校准的能量平衡模型所得到的融水数据，对上述反演结果开展验证。相较于消融区与上部积累区，反演结果在渗流区内的吻合度整体更高。本研究对反演与验证结果进行了呈现与分析。