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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 experienced by the Norse, are potential early warning signals of threshold-crossing change. Model results show that these signals could have been masked for the Norse decision making elite because they were located in the most favourable and least snow covered locations. Masking could have been further increased through the intensified seal hunting implemented by the Norse as an adaption strategy, and these actions could have developed into a rigidity trap. When the conjunctures of the 15th century developed in terms of increased sea ice, snow cover, storminess, culture contact, changing trade and sea level rise, it was too late to develop different responses. Whilst current populations have improved technology and knowledge relative to the Norse Greenlanders, there is a risk that adaptations will lack long-term utility, spatially restricted indications of change may be ignored, and rigidity traps develop. This thesis provides an additional tool for understanding a key element of both the past and possible futures of subarctic human ecodynamics.

The representation of high-latitude surface processes and quantifying surface-climate feedbacks are some of the most serious shortcomings of present day Arctic land surface modelling. The energy balance of seasonally snow-covered sparse deciduous forests at high latitudes is poorly understood and inaccurately represented within hydrological and climate models. Snow cover plays an important role in wintertime fluxes of energy, water and carbon, controlling the length of the active growing season and hence the overall carbon balance of Arctic ecosystems. Snow cover is non-uniform and spatially variable, as wind redistributes snow from areas of exposed open tundra to sheltered areas within the forest, where a deeper snowpack develops. Low solar zenith angles, coupled with sparse deciduous leafless trees, cast shadows across the snow surface. The spatial distribution of canopy gaps determines the timing of direct radiation which penetrates down through the canopy to the snow surface. The forest canopy also excludes incoming longwave radiation and yet also emits longwave radiation to the snow surface. Consequently the forest canopy plays a key role in the radiation balance of sparse forests. To improve our knowledge of these complex processes, meteorological and field observations were taken in an area of highly heterogeneous birch Betula pubescens ssp. czerepanovii forest in Abisko, Sweden during the spring of 2008 and 2009. Detailed measurements of short and longwave radiation above and below the canopy, hemispherical photographs, tree temperatures and snow surveys were conducted to quantify the radiation balance of the sparse deciduous forest. An array of below canopy pyranometers found the mean canopy transmissivity to be 74 % in 2008 and 76 % in 2009. Hemispherical photographs taken at the pyranometer locations analysed with Gap Light Analyzer (GLA) showed reasonable agreement with a mean canopy transmissivity of 75 % in 2008 and 74 % in 2009. The canopy transmissivity was found to be independent of the diffuse fraction of radiation as the canopy is very sparse. A series of survey grids and transects were established to scale up from the below canopy pyranometers to the landscape scale. Hemispherical photographs analysed with GLA showed the sparse forest canopy had a mean transmissivity of 78 % and a mean LAI of 0.25, whereas the open tundra had a mean transmissivity of 97 % and a mean LAI of < 0.01. Snow surveys showed the sparse forest snow depth to vary between 0.34 and 0.55 m, whereas the snow depth in the open tundra varied between 0.12 and 0.18 m. Observations of canopy temperatures showed a strong influence of incident shortwave radiation warming the tree branches to temperatures up to 15 °C warmer than ambient air temperature on the south facing sides of the trees, and up to 6 °C on the north facing sides of the trees. To reproduce the observed radiation balance, two canopy models (Homogenous and Clumped) were developed. The Homogeneous canopy model assumes a single tree tile with a uniform sparse canopy. The Clumped canopy model assumes a tree and a grass tile, where the tree tile is permanently in shade from the canopy and the grass tile receives all the incoming radiation. These canopy models identified the need for a parameter that accounts for the spatial and temporal variation of the shaded gaps within the sparse forest. JULES (Joint UK Land Environment Simulator) is the community land surface model used in the UK Hadley Centre GCM suite. Modifications of the land-surface interactions were included in JULES to represent the shaded gaps within the sparse deciduous forest. New parameterisations were developed for the time-varying sunlit fractions of the gap (flit), the sky-view fraction (fv), and the longwave radiation emitted from the canopy (LWtree). These model developments were informed by field observations of the forest canopy and evaluated against the below canopy short and longwave radiation observed data sets. The JULES Shaded gap model output showed a strong positive relationship with the observations of below canopy shortwave and longwave radiation. The JULES Shaded gap model improves the ratio of observed to modelled short and longwave radiation on sunny days compared to the JULES model. The JULES Shaded gap model reduces the time to snow melt by 2 to 4 days compared to the JULES model, making the model output more aligned with in-situ observational data. This shortening of the modelled snow-season directly impacts on the simulated carbon and water balance regionally and has wider relevance at the pan-Arctic scale. When JULES Shaded Gap was evaluated on the global scale, it improved the modelled snowmass across large areas of sparse forest in northern Canada, Scandinavia and Northern Russia with respect to GlobSnow. The performance of the land surface-snow-vegetation interactions of JULES was improved by using the Shaded gap to model the radiation balance of sparse forests in climate-sensitive Arctic regions. Furthermore these observational data can be used to develop and evaluate high latitude land-surface processes and biogeochemical feedbacks in other earth system models.