Terrestrial ecosystems are currently large carbon sinks, sequestering approximately 30% of anthropogenic emissions globally over 1850(Friedlingstein et al., 2019. Their past and present ability to sequester carbon, as well as the many other ecosystem services they provide, make "natural climate solutions" an appealing class of climate mitigation strategies (Anderegg et al., 2020;Griscom et al., 2017). In fact, enhanced

Fires in boreal forests of Alaska are changing, threatening human health and ecosystems. Given expected increases in fire activity with climate warming, insight into the controls on fire size from the time of ignition is necessary. Such insight may be increasingly useful for fire management, especially in cases where many ignitions occur in a short time period. Here we investigated the controls and predictability of final fire size at the time of ignition. Using decision trees, we show that ignitions can be classified as leading to small, medium or large fires with 50.4 AE 5.2% accuracy. This was accomplished using two variables: vapour pressure deficit and the fraction of spruce cover near the ignition point. The model predicted that 40% of ignitions would lead to large fires, and those ultimately accounted for 75% of the total burned area. Other machine learning classification algorithms, including random forests and multi-layer perceptrons, were tested but did not outperform the simpler decision tree model. Applying the model to areas with intensive human management resulted in overprediction of large fires, as expected. This type of simple classification system could offer insight into optimal resource allocation, helping to maintain a historical fire regime and protect Alaskan ecosystems.

Wildfires and their emissions reduce air quality in many regions of the world, contributing to thousands of premature deaths each year. Smoke forecasting systems have the potential to improve health outcomes by providing future estimates of surface aerosol concentrations (and health hazards) over a period of several days. In most operational smoke forecasting systems, fire emissions are assumed to remain constant during the duration of the weather forecast and are initialized using satellite observations. Recent work suggests that it may be possible to improve these models by predicting the temporal evolution of emissions. Here, we develop statistical models to predict fire activity one to five days into the future using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite fire counts and weather data from ERA-interim reanalysis. Our predictive framework consists of two-Poisson regression models that separately represent new ignitions and the dynamics of existing fires on a coarse resolution spatial grid. We use ten years of active fire detections in Alaska to develop the model and use a cross-validation approach to evaluate model performance. Our results show that regression methods are significantly more accurate in predicting daily fire activity than persistence-based models (which suffer from an overestimation of fire counts by not accounting for fire extinction), with vapor pressure deficit being particularly effective as a single weather-based predictor in the regression approach.

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