Greenland's surface is inhospitable, sparsely sampled, and increasingly prone to melt. Multiple studies note the key roles that clouds play in modulating Greenland's surface melt. However, active satellite sensors (CALIPSO) and climate models observe and simulate much different spatial patterns of cloud effects than observed by passive sensors and analyses.
To better quantify this, we exploit 29 automatic weather stations (AWS) to provide, for the first time, a multi-year analysis of cloud effects on seasonal and hourly timescales across both accumulation and ablation zones. Longwave sensors show that a bimodal distribution of clouds occurs on the diurnal timescale. The close relationship between net cloud effects and albedo over dark surfaces suggests a stabilizing feedback: the net cloud effect is cooling at low albedo caused by snow melt and snow metamorphism, and thus tends to increase albedo and decelerate surface melt. We also intercompare the spatial distributions of cloud effects estimated from AWS, satellite, reanalyses, and models (MERRA2, ERA, CERES, ASR, CESM, E3SM). With AWS as "ground truth", we quantify the fidelity of the gridded datasets, and identify the physical causes of discrepencies. Overall, AWS observations reveal that clouds consistently enhance surface melt in the higher accumulation zone and reduce surface melt in the lower ablation zone.