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Summary Aim Possible effects of current and future climates on boreal vegetation dynamics and carbon (C) cycling were investigated using the CENTURY 4.0 soil process model and a modified version of the FORSKA2 forest patch model. Location Eleven climate station locations distributed along a transect across the boreal zone of central Canada. Methods Both models were driven by detrended long‐term monthly climate data. Using a climate change signal derived from the GISS general circulation model (GCM) 2×CO 2 equilibrium climate scenario, the output from the two models was then used to compare simulated current and possible future total ecosystem C storage at the climate station locations. Results After allowing for their different underlying structures, comparison of output from both models showed good agreement with local field data under current climate conditions. CENTURY 4.0 was able to reproduce spatial variation in soil and litter C densities satisfactorily but tended to overestimate biomass productivity. FORSKA2 reproduced aboveground biomass productivity and spatially averaged biomass densities relatively well. Under the GISS 2×CO 2 scenario, both models generally predicted small increases in aboveground biomass C density for forest and tundra locations, but CENTURY 4.0 predicted greater decreases in soil and litter pools, for overall decreases in ecosystem C storage in the range 16–19%. Main conclusions With some caveats, results imply that effects of increased precipitation (as simulated by the GISS GCM) would more than compensate for any negative effects of increased temperature on forest growth. Increased temperature would also increase decomposition rates of soil and litter organic matter, however, for a net overall decrease in total ecosystem C storage.
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Summary Aim To investigate effects of within‐season and interannual climate variability on the behaviour of boreal forest ecosystems as simulated by the FORSKA2 patch model. Location Eleven climate station locations distributed along a transect across the boreal zone of central Canada. Methods FORSKA2′s water balance submodel was modified to enable it to behave more realistically under a varying climate. Long‐term actual monthly time‐series of temperature and precipitation data were detrended and used to drive the modified model. Long‐term monthly averages of the same detrended data were used to drive the unmodified model. Results Modifications created significant improvements when simulating species composition at sites in boreal Canada. Simulated forest biomass values were slightly higher than those obtained from the unmodified model using averaged climate records, but resembled the observed distribution of vegetation more closely. Main conclusions Modified FORSKA2 suggests that boreal forest composition and distribution may be more sensitive to changes in monthly rainfall data than to changes in temperature. Climate variability affects seasonal water balances and should be considered when using patch models to forecast vegetation dynamics during and following a period of climate transition. The modified model provided improved representation of the latitudinal trend in spatially averaged biomass density in this region.
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Abstract. Earth system processes exhibit complex patterns across time, as do the models that seek to replicate these processes. Model output may or may not be significantly related to observations at different times and on different frequencies. Conventional model diagnostics provide an aggregate view of model–data agreement, but usually do not identify the time and frequency patterns of model–data disagreement, leaving unclear the steps required to improve model response to environmental drivers that vary on characteristic frequencies. Wavelet coherence can quantify the times and timescales at which two time series, for example time series of models and measurements, are significantly different. We applied wavelet coherence to interpret the predictions of 20 ecosystem models from the North American Carbon Program (NACP) Site-Level Interim Synthesis when confronted with eddy-covariance-measured net ecosystem exchange (NEE) from 10 ecosystems with multiple years of available data. Models were grouped into classes with similar approaches for incorporating phenology, the calculation of NEE, the inclusion of foliar nitrogen (N), and the use of model–data fusion. Models with prescribed, rather than prognostic, phenology often fit NEE observations better on annual to interannual timescales in grassland, wetland and agricultural ecosystems. Models that calculated NEE as net primary productivity (NPP) minus heterotrophic respiration (HR) rather than gross ecosystem productivity (GPP) minus ecosystem respiration (ER) fit better on annual timescales in grassland and wetland ecosystems, but models that calculated NEE as GPP minus ER were superior on monthly to seasonal timescales in two coniferous forests. Models that incorporated foliar nitrogen (N) data were successful at capturing NEE variability on interannual (multiple year) timescales at Howland Forest, Maine. The model that employed a model–data fusion approach often, but not always, resulted in improved fit to data, suggesting that improving model parameterization is important but not the only step for improving model performance. Combined with previous findings, our results suggest that the mechanisms driving daily and annual NEE variability tend to be correctly simulated, but the magnitude of these fluxes is often erroneous, suggesting that model parameterization must be improved. Few NACP models correctly predicted fluxes on seasonal and interannual timescales where spectral energy in NEE observations tends to be low, but where phenological events, multi-year oscillations in climatological drivers, and ecosystem succession are known to be important for determining ecosystem function. Mechanistic improvements to models must be made to replicate observed NEE variability on seasonal and interannual timescales.