Seasonal forecasts of wildfire frequency and burned area in the western United States using a stochastic machine learning fire model

One of the main challenges for forecasting fire activity is the tradeoff between accuracy at finer spatial scales relevant to local decision making and predictability over seasonal (next 2-4 months) and subseasonal-to-seasonal (next 2 weeks to 2 months) timescales. To achieve predictability at long lead times and high spatial resolution, several analyses in the literature have constructed statistical models of fire activity using only antecedent climate predictors. However, in this talk, I will present preliminary seasonal forecasts of wildfire frequency and burned area for the western United States using SMLFire1.0, a stochastic machine learning (SML) fire model, that relies on both observed antecedent climate and vegetation predictors and seasonal forecasts of fire month climate. In particular, I will discuss results obtained by forcing the SMLFire1.0 model with seasonal forecasts from: a) downscaled and bias-corrected North American Multi-Model Ensemble (NMME) outputs, and b) skill-weighted climate analogs constructed using an autoregressive ML model. I will also comment upon the relative contribution of uncertainties, from climate forecasts and fire model simulations respectively, in projections of wildfire frequency and burned area across several spatial scales and lead times.