Remote Sensing, Vol. 12, Pages 756: New Insights in Regional Climate Change: Coupled Land Albedo Change Estimation in Greenland from 1981 to 2017 (Remote Sensing)

Land albedo is an essential variable in land surface energy balance and climate change. Within regional land, albedo has been altered in Greenland as ice melts and runoff increases in response to global warming against the period of the pre-industrial revolution. The assessment of spatiotemporal variation in albedo is a prerequisite for accurate prediction of ice sheet loss and future climate change, as well as crucial prior knowledge for improving current climate models. In our study, we employed the satellite data product from the global land surface satellite (GLASS) project to obtain the spatiotemporal variation of albedo from 1981 to 2017 using the non-parameter-based M-K (Mann-Kendall) method. It was found that the albedo generally showed a decreasing trend in the past 37 years (-0.013±0.001 decade-1, p<0.01); in particular, the albedo showed a significant increasing trend in the middle part of the study area but a decreasing trend in the coastal area. The interannual and seasonal variations of albedo showed strong spatial-temporal heterogeneity. Additionally, based on natural and anthropogenic factors, in order to further reveal the potential effects of spatiotemporal variation of albedo on the regional climate, we coupled climate model data with observed data documented by satellite and adopted a conceptual experiment for detections and attributions analysis. Our results showed that both the greenhouse gas forcing and aerosol forcing induced by anthropogenic activities in the past 37 decades were likely to be the main contributors (46.1%) to the decrease of albedo in Greenland. Here, we indicated that overall, Greenland might exhibit a local warming effect based on our study. Albedo–ice melting feedback is strongly associated with local temperature changes in Greenland. Therefore, this study provides a potential pathway to understanding climate change on a regional scale based on the coupled dataset.