Forecasting net ecosystem CO2 exchange in a subalpine forest using model data assimilation combined with simulated climate and weather generation

摘要: [1] Forecasting the carbon uptake potential of terrestrial ecosystems in face future climate change has proven challenging. Process models, which have been increasingly used to study ecosystem-atmosphere and water exchanges when conditioned with tower-based eddy covariance data, inform us about biogeochemical processes regimes, but only if we can reconcile spatial temporal scales for observed fluxes projected climate. Here, weather generator ecosystem process models on dynamics carbon/water fluxes, embedded them within projections from a suite six Earth Systems Models. Using this combination studied cycle subalpine forest context (2080–2099) regimes. The assimilation daily averaged, net CO2 exchange (NEE) evapotranspiration (ET) into model resulted retrieval NEE level accuracy that was similar following half-daily averaged observations; 30 min or monthly caused degradation model's capacity accurately simulate seasonal patterns NEE. flux data period 2080–2099, predicted greater response lengthening growing season. These results contradict our previous observations reduced longer seasons current (1999–2008) regime. difference between these analyses is due increase frequency rain versus snow during warmer winters future. Our demonstrate sensitivity modeled local variation meteorology, often left unresolved traditional approaches earth systems modeling, importance maintaining similarity timescales driven by downscaled projections.