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[1] Boreal regions are an important component of the global carbon cycle because they host large stocks of aboveground and belowground carbon. Since boreal forest evolution is closely related to fire regimes, shifts in climate are likely to induce changes in ecosystems, potentially leading to a large release of carbon and other trace gases to the atmosphere. Prediction of the effect of this potential climate feedback on the Earth system is therefore important and requires the modeling of fire as a climate driven process in dynamic global vegetation models (DGVMs). Here, we develop a new data- based prognostic model, for use in DGVMs, to estimate monthly burned area from four climate (precipitation, temperature, soil water content and relative humidity) and one human-related (road density) predictors for boreal forest. The burned area model is a function of current climatic conditions and is thus responsive to climate change. Model parameters are estimated using a Markov Chain Monte Carlo method applied to on ground observations from the Canadian Large Fire Database. The model is validated against independent observations from three boreal regions: Canada, Alaska and Siberia. Provided realistic climate predictors, the model is able to reproduce the seasonality, intensity and interannual variability of burned area, as well as the location of fire events. In particular, the model simulates well the timing of burning events, with two thirds of the events predicted for the correct month and almost all the rest being predicted 1 month before or after the observed event. The predicted annual burned area is in the range of various current estimates. The estimated annual relative error (standard deviation) is twelve percent in a grid cell, which makes the model suitable to study quantitatively the evolution of burned area with climate.
