Abstract. Future disruptions to fire activity will threaten ecosystems and human well-being throughout the world, yet there are few fire projections at global scales and almost none from a broad range of global climate models (GCMs). Here we integrate global fire datasets and environmental covariates to build spatial statistical models of fire probability at a 0.58 resolution and examine environmental controls on fire activity. Fire models are driven by climate norms from 16 GCMs (A2 emissions scenario) to assess the magnitude and direction of change over two time periods, 2010-2039 and 2070-2099. From the ensemble results, we identify areas of consensus for increases or decreases in fire activity, as well as areas where GCMs disagree. Although certain biomes are sensitive to constraints on biomass productivity and others to atmospheric conditions promoting combustion, substantial and rapid shifts are projected for future fire activity across vast portions of the globe. In the near term, the most consistent increases in fire activity occur in biomes with already somewhat warm climates; decreases are less pronounced and concentrated primarily in a few tropical and subtropical biomes. However, models do not agree on the direction of nearterm changes across more than 50% of terrestrial lands, highlighting major uncertainties in the next few decades. By the end of the century, the magnitude and the agreement in direction of change are projected to increase substantially. Most far-term model agreement on increasing fire probabilities (;62%) occurs at mid-to high-latitudes, while agreement on decreasing probabilities (;20%) is mainly in the tropics.Although our global models demonstrate that long-term environmental norms are very successful at capturing chronic fire probability patterns, future work is necessary to assess how much more explanatory power would be added through interannual variation in climate variables. This study provides a first examination of global disruptions to fire activity using an empirically based statistical framework and a multi-model ensemble of GCM projections, an important step toward assessing fire-related vulnerabilities to humans and the ecosystems upon which they depend.

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Climate change, Downscaling, General circulation model, Greenhouse gas emission scenarios, Northeast United States,

Abstract:Long-term data from the Hubbard Brook Experimental Forest in New Hampshire show that air temperature has increased by about 1°C over the last half century. The warmer climate has caused significant declines in snow depth, snow water equivalent and snow cover duration. Paradoxically, it has been suggested that warmer air temperatures may result in colder soils and more soil frost, as warming leads to a reduction in snow cover insulating soils during winter. Hubbard Brook has one of the longest records of direct field measurements of soil frost in the United States. Historical records show no long-term trends in maximum annual frost depth, which is possibly confounded by high interannual variability and infrequency of major soil frost events. As a complement to field measurements, soil frost can be modelled reliably using knowledge of the physics of energy and water transfer. We simulated soil freezing and thawing to the year 2100 using a soil energy and water balance model driven by statistically downscaled climate change projections from three atmosphere-ocean general circulation models under two emission scenarios. Results indicated no major changes in maximum annual frost depth and only a slight increase in number of freeze-thaw events. The most important change suggested by the model is a decline in the number of days with soil frost, stemming from a concurrent decline in the number of snow-covered days. This shortening of the frost-covered period has important implications for forest ecosystem processes such as tree phenology and growth, hydrological flowpaths during winter, and biogeochemical processes in soil. Published in

ABSTRACT:The asynchronous regional regression model (ARRM) is a flexible and computationally efficient statistical model that can downscale station-based or gridded daily values of any variable that can be transformed into an approximately symmetric distribution and for which a large-scale predictor exists. This technique was developed to bridge the gap between large-scale outputs from atmosphere-ocean general circulation models (AOGCMs) and the fine-scale output required for local and regional climate impact assessments. ARRM uses piecewise regression to quantify the relationship between observed and modelled quantiles and then downscale future projections. Here, we evaluate the performance of three successive versions of the model in downscaling daily minimum and maximum temperature and precipitation for 20 stations in North America from diverse climate zones. Using cross-validation to maximize the independent comparison period, historical downscaled simulations are evaluated relative to observations in terms of three different quantities: the probability distributions, giving a visual image of the skill of each model; root-mean-square errors; and bias in nine quantiles that represent both means and extremes. Successive versions of the model show improved accuracy in simulating extremes, where AOGCMs are often most biased and which are frequently the focus of impact studies. Overall, the quantile regression-based technique is shown to be efficient, robust, and highly generalizable across multiple variables, regions, and climate model inputs.

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