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Daniel Hogg | Applied Mathematics, University of Waterloo
High-Resolution Analysis of Snow Albedo Interactions in the Arctic
The Snow Albedo Feedback (SAF) is an important contributor to Arctic warming, however models disagree significantly on the strength of this effect. Previous work has investigated the influence of vegetation on surface albedo, however the accuracy has been limited by the resolution of model output. In this work, we perform a pan-Arctic survey using Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data and Coupled Model Intercomparison Project (CMIP6) model output, to perform a comprehensive analysis of the effects of vegetation on SAF. We computed pan-Arctic composites of MODIS observational data at the 500m scale and compared the results with the CMIP6 ensemble.
Using MODIS data, we found a mean SAF of -2.17 %/K from April-July over the climatological period 2001-2019, which is stronger than previous results in the literature. Additionally, we identified the source of this discrepancy - models currently do not adequately capture the dynamics of late-season melt-off in the high Arctic grassland and barren regions, which results in an underestimate of SAF. In this work, we demonstrate that land cover changes have a small but nonzero (≤10%) contribution to overall changes in SAF on the timescale of decades, indicating the importance of dynamic vegetation models. Furthermore, we identify upscaling resolution as a major source of local error in SAF, however due to cancellation of errors this has minimal impact on estimates of pan-Arctic mean SAF. Finally, we identify a logarithmic relationship between LAI (Leaf Area Index) and SAF. This work can benefit modelling groups seeking to better capture SAF dynamics, by explaining SAF errors in terms of vegetation dynamics, and by demonstrating the existence of spatial structure in SAF fields at sub-model grid scales.