Inter-annual variability of carbon and water fluxes in Amazonian forest, Cerrado and pasture sites, as simulated by terrestrial biosphere models

•We analyze inter-annual variability of modeled surface fluxes in 8 sites in Brazil.•Carbon and water fluxes at Amazon forest, Cerrado and pasture sites are presented.•We find significant differences between simulations and observed annual totals.•The majority of the models do not reproduce well the observed inter-annual variations.•We study possible drivers of variability of observed and simulated annual fluxes. This study analyzes the inter-annual variability (IAV) of simulations of 21 different land surface model formulations, driven by meteorological conditions measured at 8 flux towers, located in rain forest, forest-savanna ecotone and pasture sites in Amazonia, and one in savanna site in Southeastern Brazil. Annual totals of net ecosystem exchange (NEE) of carbon and evapotranspiration (ET), measured and simulated by each model for each site-year, were compared in terms of year-to-year variability and possible relation to climate drivers. Results have shown that most of models simulations for annual totals of NEE and ET, and IAV of these fluxes, are frequently different from measurements. The average of the model simulations of annual fluxes tend to respond to climatic drivers similarly to the observations, but with noticeable discrepancies. Annual measurements of NEE are negatively correlated to annual rainfall in the forest sites group. Although the ensemble of all models yields a similar result, only three model formulations reproduce a significant negative correlation of simulated NEE with rainfall. For the IAV of ET, tower measurements are controlled by annual variations of radiation and this feature is captured by the ensemble of the models, both at individual sites and when all forest sites are grouped. However, simulated ET values are also significantly correlated to the amount of precipitation in many models and in the model ensemble, while there is no significant correlation in the observations. In general, the surface models are able to reproduce the responses of fluxes to climatic drivers, but improvements are still needed to better capture their inter-annual variability.