ALBERT

Description: [1]  Forecasting the carbon uptake potential of terrestrial ecosystems in the face of future climate change has proven challenging. Process models, which have been increasingly used to study ecosystem-atmosphere carbon and water exchanges when conditioned with tower-based eddy covariance data, have the potential to inform us about biogeochemical processes in future climate regimes; but, only if we can reconcile the spatial and temporal scales used for observed fluxes and projected climate. Here, we used weather generator and ecosystem process models conditioned on observed weather dynamics and carbon/water fluxes, and embedded them within climate projections from a suite of six Earth Systems Models (ESMs). Using this combination of models we studied carbon cycle processes in a subalpine forest within the context of future (2080-2099) climate regimes. The assimilation of daily-averaged, observed net ecosystem CO 2 exchange (NEE) and evapotranspiration (ET) into the ecosystem process model resulted in retrieval of projected NEE with a level of accuracy that was similar to that following the assimilation of half-daily averaged observations; the assimilation of 30-minute averaged fluxes or monthly-averaged fluxes caused degradation in the model's capacity to accurately simulate seasonal patterns in observed NEE. Using daily-averaged flux data with daily-averaged weather data projected for the period 2080-2099, we predicted greater forest net CO 2 uptake in response to a lengthening of the growing season. These results contradict our previous observations of reduced CO 2 uptake in response to longer growing seasons in the current (1999-2008) climate regime. The difference between these analyses is due to a projected increase in the frequency of rain versus snow during warmer winters of the future. Our results demonstrate the sensitivity of modeled processes to local variation in meteorology, which is often left unresolved in traditional approaches to earth systems modeling, and the importance of maintaining similarity in the time scales used in ecosystem process models driven by downscaled climate projections.