TODAY’S STUDY: CLIMATE CHANGE AND WILDFIRES

Climate change and disruptions to global fire activity Max A. Moritz, Marc-Andre Parisien, Enric Batllori, Meg A. Krawchuk, Jeff Van Dorn, David J. Ganz, Ans Katharine Hayhoe, June 2012 (EcoSphere)

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 0.58resolution 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.

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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 near term 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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Introduction

Fire’s pervasive influence on human societies and ecosystem functions has motivated great interest in understanding its environmental drivers and effects, especially in the context of anthropogenic climate change (Bowman et al. 2009, Flannigan et al. 2009, Whitlock et al. 2010). Recent increases in fire activity in some parts of the world have been attributed to climate change (e.g., Pin˜ol et al. 1998, Gillett et al. 2004, Kasischke and Turetsky 2006, Westerling et al. 2006), often overshadowing the potential range of future outcomes that may occur across the planet, including relative stability or decreases in fire activity. Even the latest IPCC AR4 chapter on ecosystem impacts focused almost exclusively on projected increases in fire (Fischlin et al. 2007), despite mixed results from Scholze et al. (2006), who predicted both increases and decreases in fire using a model of vegetation dynamics driven by projections from multiple global climate models (GCMs).

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Given the strong linkage between fire and climate (e.g., Swetnam and Betancourt 1990, Marlon et al. 2008, Aldersley et al. 2011), there is little doubt that climate-induced disruptions to fire activity will occur in many areas. However, the projected magnitude of change, and even whether fire probabilities will increase or decrease, is hotly debated for many parts of the world. Ongoing fire activity requires biomass resources to burn, atmospheric conditions conducive to combustion (i.e., dry, hot, and/or windy periods), and ignitions (Bond and van Wilgen 1996, Moritz et al. 2005, Bradstock 2010). Climate can affect all three of these factors in complex ways and over multiple timescales. The relative importance of different controls on fire activity, as well as inherent sources of uncertainty in them, can be separated into short-term environmental fluctuations versus long-term norms (Fig. 1A). This approach has been used in analyses of climate averages versus interannual climate variability to explain habitat suitability patterns (Zimmermann et al. 2009), and a similar logic has been proposed for examining changes in mean climate values versus episodic events for ecology in general (Jentsch et al. 2007). Here, we focus on coarser, long-term climate norms as they affect fire occurrence across the planet.

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Different modeling schools have emerged for capturing climate-vegetation-fire relationships at broad scales. Dynamic global vegetation models (DGVMs) simulate the climate-based processes controlling plant growth and death in different vegetation types, and many of these models have incorporated a fire module (e.g., Lenihan et al. 1998, Fosberg et al. 1999, Thonicke et al. 2001, Arora and Boer 2005). Recent advances in some DGVMs have improved their ability to represent historical patterns of burning (Thonicke et al. 2010, Prentice et al. 2011), and this remains an active area of research. An alternative approach has been to build statistical models of fire activity, based directly on correlating empirical observations of fire and the key environmental variables that control its occurrence (e.g., McKenzie et al. 2004, Archibald et al. 2009, Preisler et al. 2009, Littell et al. 2009, Balshi et al. 2009, Krawchuk et al. 2009, Parisien and Moritz 2009, Westerling et al. 2011). These empirical fire models are similar to species distribution models often used to project future shifts in habitat ranges (e.g., Guisan and Thuiller 2005, Elith et al. 2006, Hijmans and Graham 2006, Morin and Thuiller 2009, Engler et al. 2011) and thus share some of their strengths and weaknesses. Despite the basic differences between process-based and more correlative modeling approaches, it is encouraging that their predictions can often be similar (Morin and Thuiller 2009, Kearney et al. 2010).

Regardless of the modeling framework, future projections of fire at the global scale are relatively rare (Scholze et al. 2006, Krawchuk et al. 2009, Gonzalez et al. 2010, Liu et al. 2010, Pechony and Shindell 2010). Furthermore, because most of these global studies used different GCMs, there remains a lack of understanding about how variations among GCMs affect future fire projections versus differences in the modeling approaches themselves (Littell et al. 2011). This issue is not trivial because discrepancies among GCMs, especially with respect to precipitation, may be important in the context of fire. There is growing evidence that more precipitation in some warm grasslands and shrublands can lead to higher productivity and increased fire activity during the dry season, whereas in more mesic areas the same precipitation increase could diminish fire activity (Meyn et al. 2007, van der Werf et al. 2008, Littell et al. 2009, Krawchuk and Moritz 2011). Warmer and drier weather may therefore increase fire activity in biomass-rich areas, but have the opposite effect in moisturestressed biomes, as increased evaporative demand decreases growth of biomass necessary to carry fire (Fig. 1B). Along with human behaviors, the continuum of varying constraints on fire are central to understanding responses to global change, and they lead to important questions about GCM-based uncertainties. How well can global fire activity be modeled using long-term climate norms available from GCMs? Where do GCMs agree on the future of fire activity? What climate variables drive these changes, and will they be consistent through time?

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Here we integrate empirically based statistical models of fire occurrence with an ensemble of GCM projections to derive a globally consistent analysis of future fire activity. Of the parameters important for characterizing fire regimes—fire sizes, frequencies, intensities, and seasonality—our models are designed to project fire probabilities over a given time period, results that are directly representative of fire frequencies. Our fire probability models are built from over a decade of remotely sensed fire observations and key environmental variables representing vegetation to burn and fire-conducive atmospheric conditions. The fire models are then driven by projected future climates from 16 different GCMs covering the 2010–2039 and 2070–2099 time periods, statistically downscaled to 0.58, corresponding to the mid-high A2 emissions scenario. From these projected fire probability models, we assess the ensemble mean change in fire probability for the 16 GCMs, the degree of model agreement in projected increases and decreases in fire activity, and the long-term environmental controls contributing to these global changes…

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Conclusion

Projections reported here highlight the potential for rapid disruptions in future fire activity, and consensus on such alterations strengthens through time. In addition to impacting terrestrial carbon stocks and human livelihoods, abrupt changes in fire will stress native flora and fauna as they adjust to climate change (Loarie et al. 2009) and threaten biodiversity in many conservation areas (Myers 2006, Nelson and Chomitz 2009). The ecological severity of projected changes will depend on the degree to which organisms are fire-sensitive or fire-adapted. This will be especially important in marginal or ‘‘trailing edge’’ habitats (Davis and Shaw 2001, Hampe and Petit 2005), which may be vulnerable to relatively sudden, fire-punctuated range contractions instead of more gradual, climate-driven transitions.

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Conversely, future fire may also act as a disturbance that frees up space and resources more quickly than would otherwise occur, facilitating establishment of ‘‘leading edge’’ populations (Landha¨usser et al. 2010). Although sharp decreases in fire activity are less likely to capture our attention, reductions in this key ecological disturbance may have important trickle-down effects in many fire-prone regions (Bond and van Wilgen 1996, Krawchuk et al. 2009), and longer fire intervals could conceivably make some areas more vulnerable to catastrophic wildfires over time. Linking fire probabilities to fire intensities and area burned are thus important next steps. A better quantification of interactions between climate change and fire is crucial for a complete global assessment of vulnerabilities for humans and the ecosystems upon which they depend.

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