| Summary: | For models to predict arctic ecosystems’ responses to climate change, it is important to first predict soil thermal responses. However, soil heat-budget models are generally too finely scaled and detailed to couple with large-scale biogeochemical models. Appropriate data sets to derive simple models capable of predicting active soil layer behavior on a time scale appropriate to biogeochemical models do not exist. One solution is to scale a highly detailed, physical heat model. The fine-scale predictions of this model can be aggregated to generate coarse-scale data, which can be used to derive an appropriately scaled model. We develop an hourly, spatially detailed model of soil temperature based on well-understood physical and biological processes and driven by detailed data. Under a range of environmental conditions, the hourly model predicts active layer behavior. From these predictions we construct a daily model that requires far fewer data about the climatic, environmental, and physical conditions to predict the volume of thawed soil over longer time scales. Arctic; Permafrost; Model aggregation; Soil temperature; Climate change; Soil carbon;
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