| Summary: | Leverhulme Trust : Grant F/00152/Q National Science Foundation NSF grant ARC114010; NSF grant ARC1104372; NSF grant ARC1145300 IPY This thesis tests and applies a new, physically based snow distribution and melt model at spatial scales of tens of metres and temporal scales of days across sub-arctic landscapes, in order to assess the significance of snow variability in sub-arctic human ecodynamics at resolutions relevant to human activities. A wider goal is to contribute to planning in the face of future climate change. Model tests are undertaken based on original field data collected in Sweden and Norway, and secondary data from Idaho, France and Greenland. Model applications focus on the ‘completed experiment’ of the medieval Norse in Greenland, a comparatively isolated population that relied on a combination of pastoralism and hunting for survival. A combination of local calibration based on contemporary meteorological data, customised climate reconstructions based on GCM data, new archaeological survey and new DEM are used in order to apply the model. This thesis shows, for the first time, the likely range of snow depth and duration experienced across the medieval Norse Greenland landscape as a result of climate and vegetation change. Results show that increases in snow cover could have been significant drivers of transformative change in Norse Greenland, and are therefore likely to be key in understanding the potential impact of future climate changes on similar sub-arctic and relatively marginal communities. Selected model analyses simulate the total spring (April-June) snow cover at the homefields to range from 32% cover lasting 6 days in the most favourable climate to 100% cover lasting 45 days in the most unfavourable climate at key elite inner fjord farms. At the more isolated outer fjord farms, total spring snow cover ranges from 33% cover lasting 10 days in the most favourable climate to 100% cover lasting 60 days in the most unfavourable climate. Increased climate variance and recovery times, as ...
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