1. AVOID WS2 D1 31 Fire 1 Crown copyright 2008 _ _ _ _ - Author(s): J. Caesar and N. Golding Institute: Met Office Hadley Centre Reviewer: Richard Betts Institutes: Met Office Hadley Centre Date: 21/12/2011 AVOID: Avoiding dangerous climate change AVOID is a DECC/Defra funded research programme led by the Met Office in a consortium with the Walker Institute, Tyndall Centre and Grantham Institute Meteorological factors influencing forest fire risk under climate change mitigation AVOID is an LWEC accredited activity
2. Key outcomes / non-technical summary Forest fires present a serious hazard to humans and ecosystems in many parts of the world, and fires over large forest ecosystems can be a major agent of conversion of biomass and soil organic matter to CO2. Here we make use of the McArthur Forest Fire Danger Index, which is calculated from daily maximum temperature, daily minimum relative humidity, daily mean wind speed, and a drought factor which is based upon daily precipitation. We do not take account of other factors such as changing extent or characteristics of vegetation cover or population changes. We identify that the primary meteorological driver of projected changes in forest fire danger on the global scale is temperature, followed by relative humidity which itself is strongly influenced by temperature. In terms of global and regional climate projections, we have more confidence in the direction and magnitude of these projected changes compared to changes in precipitation and wind speed, which make less of a contribution to the results. Fire danger is projected to increase over most parts of the world compared to present-day values. The largest proportional increases are seen under the A1B SRES and IMAGE (Integrated Model for Assessment of Greenhouse Effect) scenarios for Europe, Amazonia and parts of North America and East Asia. These scenarios were described by the IPCC (Intergovernmental Panel on Climate Change) to help make projections of future climate change. Increases in fire danger are lower under the mitigation scenario (E1), but generally affecting the same regions as under both of the A1B scenarios considered here.
3. AV/WS2/D1/31 Meteorological factors influencing forest fire risk under climate change mitigation John Caesar & Nicola Golding 1. Introduction A combination of high temperatures and drought conditions raises the risk of wildfires, and therefore climate change could have an impact on the frequency and severity of wildfires in the future. Prominent areas where fires have a significant impact on developed world populations are south-eastern Australia, southern Europe, and the western United States and Canada, in particular California and British Columbia. Forest fires are also a particular problem over large forest ecosystems such as Amazonia, whether ignited naturally or by human activities, where it can be a major agent of conversion of biomass and soil organic matter to CO2. Wildfires oxidise 1.7 to 4.1 GtC per year which represents about 3-8% of total terrestrial Net Primary Productivity (IPCC, 2007). Severe drought conditions in Amazonia in 1998 resulted in 40,000km2 of fire in standing forests (Nepstad et al., 2004) and the resulting carbon release contributed approximately 5% of annual anthropogenic emissions (0.4Pg, de Mendoca et el., 2004). Fires in Southeast Asia, linked to the 1997-98 El Nio, are estimated to have released 0.8-2.6 GtC. It has been estimated that the CO2 source from fire could increase in the future (Flannigan et al., 2005). Working Group II of the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4; IPCC, 2007) cautioned that trends in disturbance resulting from forest fires remains a subject of controversy. The IPCC (2007) noted that there has been a decrease in fire frequency over some regions, including the USA and Europe, and an increase in others, including Amazonia, Southeast Asia, and Canada. The reasons for these regional differences are complex; in some cases climate change is a contributing factor, but other factors such as changes to forest management can also be important. Gillet et al. (2004) has provided evidence that climate change has contributed to an increase in fire frequency in Canada whereby about half of the increase in burnt area is in agreement with simulated warming from a GCM. However, another study found that fire frequency in Canada has decreased in response 3
4. AV/WS2/D1/31 to better fire protection, and notes that the effects of climate change on fire are complex (Bergeron et al., 2004). A more recent study (Westerling et al., 2006) found a sudden increase in large wildfire activity in the western USA during the mid-1980s, associated with increased temperatures and earlier spring snow melt. An increase in fires in England and Wales between 1965 and 1998 may be attributable to a trend towards warmer and drier summer conditions (Cannell et al., 1999). Golding and Betts (2008) investigated changing fire risk in Amazonia in the HadCM3 model and found significant future increases in fire risk, with over 50% of the Amazon forest projected to experience high fire danger by 2080. Other studies also suggest that increased temperatures, increased aridity, and a longer growing season will elevate fire risk (Williams et al., 2001; Flannigan et al., 2005; Schlyter et al., 2006). Crozier and Dwyer (2006) found a 10% increase in the seasonal severity of fire hazard over much of the United States under changed climate. Flannigan et al. (2005) projected a 74-118% increase of the area burned in Canada by the end of the 21st Century under a 3XCO2 scenario. There are a variety of ways to approach the modelling of fire, some of which take a comprehensive assessment of factors, including changes in the occurrence of trigger mechanisms (which may take account of population). In Amazonia, fires lit intentionally for the purpose of forest clearance can spread and become uncontrollable. Lightning is another common trigger. In more populous regions, arson can be a factor, as can changes in land use and management. An alternative approach, which we use here, is to assess the underlying conditions which may increase the risk of fire starting and spreading. 2. Methods 2.1 Climate models This work is based upon climate simulations from the Hadley Centre Global Environment Model version 2 (HadGEM2; Collins et al., 2008). We primarily use the HadGEM2-AO configuration with the atmosphere coupled to a fully dynamical ocean. The higher resolution of HadGEM2 (1.875x1.25) over previous Hadley Centre models is a particular benefit for studying changes in climate extremes, including the factors related to forest fires. We also make use of new simulations from the Earth System version of HadGEM2, known as HadGEM2-ES (Collins et al., 2011). 4
5. AV/WS2/D1/31 2.2 Future climate scenarios To compare the effects of reducing greenhouse gas emissions in the future we focus upon five model experiments which use three different emissions pathways; two based upon a non- mitigation business-as-usual scenario (i.e. with no explicit climate policy intervention), and the third using an aggressive mitigation scenario. The main business-as-usual scenario is the A1B-SRES scenario (Nakicenovic and Swart, 2000), a medium-high emissions scenario which assumes a future of strong economic growth leading to an increase in the rate of greenhouse gas emissions. The atmospheric carbon dioxide equivalent (CO2eq) concentration rises throughout the 21st Century to around 900ppm by 2100 (Figure 1a). We use the A1B-SRES scenario as it provides overlap and consistency with much existing climate modelling work, and it is fairly consistent with observed carbon emissions over the past two decades (van Vuuren and Riahi, 2008; Le Qur et al., 2009). Two simulations using the A1B-SRES simulation were available for this study. The European Union ENSEMBLES project has developed an aggressive mitigation scenario known as E1 (Lowe et al., 2009), and was the first international multi-model inter-comparison project to make use of such a scenario (Johns et al., 2011). The E1 scenario has a peak in the CO2eq concentration at around 535 parts per million (ppm) in 2045, before stabilising at around 450ppm during the 22nd Century (Figure 1a). CO2eq emissions start to reduce early in the 21st Century, and decline to almost zero by 2100. The IMAGE 2.4 model was used to provide CO2 concentrations and land use changes (MNP, 2006). In addition to the A1B-SRES scenario, we have available a single simulation of the A1B-IMAGE scenario (van Vuuren et al., 2007). An important difference between the A1B-SRES and A1BIMAGE scenarios is that the sulphate aerosol burden is markedly different during the early 21st Century with the A1B-IMAGE scenario containing lower sulphur emissions. The E1 scenario also has a lower sulphur burden as it is derived from the A1B-IMAGE scenario, and because of the mitigation policies used to construct the scenario (Johns et al., 2011). A new range of scenarios have been defined for use in the IPCC Fifth Assessment Report (AR5) and are being implemented in GCM experiments at climate modelling groups around the world (Moss et al., 2010; Arora et al., 2011). These are referred to as Representative Concentration Pathways (RCPs) and use a different approach from the SRES scenarios. The SRES scenarios were developed by working forwards from their socio-economic assumptions 5