| Summary: | Feedbacks between the climate system and the high-latitude carbon cycle will substantially influence the intensity of future climate change. It is therefore crucial that the net ecosystem exchange of CO2 (NEE) between the high-latitude land surface and the atmosphere is accurately quantified, where NEE refers to the difference between ecosystem respiration (R) and photosynthesis (gross ecosystem exchange, GEE): NEE=-GEE+R in umol/m^2/s. NEE can only be directly measured over areas of 1 km^2 through eddy covariance, and modeling approaches such as the Vegetation Photosynthesis Respiration Model (VPRM) are required to upscale NEE. VPRM is a remote sensing based model that calculates R as a linear function of air temperature (Ta) when air temperature is above a given threshold (Tlow), and sets respiration to a constant value when Ta 50%, and as a linear function of air temperature when SCA<50%, thereby reflecting the influence of snow on decoupling soil/air temperatures. Representing the effect of SCA on NEE therefore reduced uncertainty in VPRM estimates of NEE. In order to represent spatial variability in high-latitude estimates of NEE due to vegetation type, Arctic-specific vegetation classes were created for PolarVPRM by combining and aggregating two existing vegetation classifications: the Synergetic Land Cover Product and the Circumpolar Arctic Vegetation Map. Levene's test indicated that the PolarVPRM vegetation classes divided the pan-Arctic region into heterogeneous distributions in terms of net primary productivity, and passive microwave derived estimates of snow and growing season influences on NEE. A non-parametric statistical approach of Alternating Conditional Expectations found significant, non-linear associations to exist between passive microwave derived estimates of snow and growing season drivers of NEE. Furthermore, the shape of these associations varied according to the vegetation class over which they were examined. Further support was therefore provided to the idea that uncertainty in ...
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