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.
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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 Niño, 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
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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 (1.875°x1.25°) 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).
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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 Quéré 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
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to determine emissions and then radiative forcings, whereas the RCPs use defined radiative forcing levels as a starting point (Moss et al., 2010). The E1 mitigation scenario results provide a useful comparison with the results produced using the RCP 2.6 scenario (sometimes also referred to as RCP 3-PD) since both scenarios follow a similar trajectory in total radiative forcing (van Vuuren et al., 2007) as shown in Figure 1b. RCP 2.6 is the low-end forcing scenario of 2.6
Wm-2
, the others being RCP 4.5, RCP 6.0, and RCP 8.5. Here, the results for the RCPs are obtained from the HadGEM2-ES, and make use of an experimental set up following the protocols for Phase 5 of the Coupled Model Intercomparison Project (CMIP5), described in more detail by Jones et al. (2011).
Figure 1: (a) Global mean CO2-equivalent (all well-mixed greenhouse gases, CFCs including tropospheric and stratospheric O3) concentration used to drive the A1B and E1 simulations (Top), and (b) corresponding radiative forcing (bottom). Profiles for the RCPs are also shown. Adapted from Johns et al. (2011).
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2.3 Fire Weather Indices
The key meteorological factors which affect wildfires are temperature, precipitation, relative humidity and wind speed. A number of fire weather indices are in use, but the most commonly used tend to be based upon the McArthur Forest Fire Danger Index (FFDI), developed in Australia, and the Canadian Fire Weather Index (FWI). Dowdy et al. (2009) compared the McArthur FFDI and Canadian FWI over Australia using eight years of gridded data, and it was found that they were similar on the broad scale and most sensitive to wind speed, followed by relative humidity, then temperature. Although the indices are formulated slightly differently, they can be deemed to be complementary.
The McArthur FFDI (Luke and McArthur, 1978) is a weather-based index derived empirically in south-eastern Australia. It indicates the probability of a fire starting, its rate of spread, intensity, and difficulty of suppression. It was originally defined in the late-1960s to assist foresters to relate the weather to the associated fire danger. Originally the “calculation” took the form of a set of cardboard wheels, into which the user dialled the observations. Later, Noble et al. (1980) converted the FFDI into a form suitable for use by computers.
FFDI = 2exp(0.987logD – 0.45 + 0.0338T + 0.0234V – 0.0345H)
H = relative humidity from 0-100 (%)
T = daily maximum air temperature (°C)
V = daily mean wind-speed 10-metres above the ground (km/hr)
D = drought factor in the range 0-10
The drought factor (D) is calculated as:
D=0.191(I+104)(N+1)1.5 / [3.52(N+1)1.5+R-1)
N = No. of days since the last rain (days)
R = Total rainfall in the most recent 24h with rain (mm)
I = Amount of rain needed to restore the soil’s moisture content to 200mm (mm). A constant of 120mm has been substituted here, as suggested by Sirakoff (1985).
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The FFDI has been used extensively in its native Australia (e.g. Hennessy et al., 2005) but also in other regions such as Amazonia (Golding and Betts, 2008), where it was used to assess the future risk of fire during the 21st Century. An associated Grassland Fire Danger Index (GFDI) is also in use.
Fire Danger Rating
FFDI Range
Difficulty of suppression
Low
0-5
Fires easily suppressed with hand tools.
Moderate
5-12
Fire usually suppressed with hand tools and easily suppressed with bulldozers. Generally the upper limit for prescribed burning.
High
12-25
Fire generally controlled with bulldozers working along the flanks to pinch the head out under favourable conditions. Back burning1 may fail due to spotting.
Very High
25-50
Initial attack generally fails but may succeed in some circumstances. Back burning1 will fail due to spotting. Burning-out2 should be avoided.
Extreme
50-100+
Fire suppression virtually impossible on any part of the fire line due to the potential for extreme and sudden changes in fire behaviour. Any suppression actions such as burning out2 will only increase fire behaviour and the area burnt.
Very Extreme
75+
Unofficial category after Lucas et al. (2007).
Catastrophic
100+
Unofficial category after Lucas et al. (2007).
Table 1: Categories of Fire Danger Rating (FDR). Taken from Vercoe (2003) with modification after Lucas et al. (2007). 1Back burning is setting fire downwind of the head fire in order to create a break wide enough to stop the head fire. 2Burning out is setting fire to consume unburned fuel inside the control line.
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It is very important to note that this model was developed in Australia and that climate and vegetation characteristics could be quite different in other parts of the world. A simple verification exercise comparing FFDI values for the baseline period with a reconstructed dataset (Mouillot and Field, 2005) and a satellite dataset (Global Fire Emissions Database Version 3) found substantial variation in the ability of the FFDI to represent fire occurrence in different regions. However, it was found to produce a reasonable (>0.5 and similar to Australian values) correlation in many regions including Russia, Europe, Africa, North America and the Amazon region, comparable to another fire model tested. Further investigation into the use of the FFDI on a regional scale would be advised if location-specific studies were required.
Where we refer to the danger categories throughout this report, they should be interpreted in a relative sense on the global scale. A high fire danger rating in Australia may not represent a similarly high risk in a different region for example.
A Fire Danger Rating (FDR) is often used by fire agencies to summarise the FFDI calculation, and to reflect aspects of fire behaviour and control. Table 1 shows the categories of FFDI for a standardised fuel (in this case dry sclerophyll (hard leaf) forest with an available fuel load of 12t/ha) on flat ground. The fuel load is an important factor since uncontrollable fire behaviour can occur at progressively lower FFDI values, even for modest increases in fuel load.
Moving beyond the indices, examining changes in the individual meteorological variables will give a clearer picture of the main climatic changes which may influence changes in fire danger in the future. Since we have more confidence in changes in some variables, such as temperature, compared to others, such as regional precipitation or wind speed, an assessment of which variables are driving future changes will provide an initial qualitative indication of our level of confidence in future changes in forest fire danger.
2.4 Global forest coverage
As an estimate of global forest coverage we use the International Geosphere-Biosphere Programme (IGBP) dataset (Loveland et al., 2000). This consists of 17 land cover classes, which were translated into proportional cover and characteristics of the plant-functional types of the UK community land-surface model, JULES (Pacifico et al., 2011). JULES has five plant- functional types (namely broadleaf trees, needle leaf trees, C3 grass, C4 grass, and shrubs) and uses a further four surface types (urban, inland water, bare soil and ice). Here, we show forest coverage according to grid boxes containing a combined broadleaf and needle leaf tree fraction of greater than zero, as shown in Figure 2. Since we are assessing changes in the
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meteorological characteristics affecting fire, we do not consider the potential for changing forest area since this is subject to additional uncertainties.
Figure 2: Fractions of broadleaf and needle leaf forest cover represented by the IGBP dataset. Regions selected for further study are shown by boxes.
3. Results
3.1 Global changes in Forest Fire Danger Index
Figure 3 shows the annual mean FFDI for global land grid boxes with present-day forest cover (see Figure 2) from 1971 to 2100. For 1971-2000, mean FFDI is around 14, which falls within the high fire danger category. FFDI is projected to increase under all future scenarios by 2100, with values of up to around 20 under the high-emissions scenarios of A1B-IMAGE and RCP8.5, although this remains within the high danger category. FFDI under the lower emissions scenarios are around 16 for E1, and 15 for RCP2.6. Clearly, global forest fire risk increases more under the high emissions scenarios compared to the low emissions scenarios where forest fire risk stabilises around the middle of the 21st Century.
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Figure 3: Forest Fire Danger Index projections for the HadGEM2-AO scenarios, and the HadGEM2-ES RCPs. Global mean represents land area with present-day forest coverage, as shown in Figure 2.
3.2 Global changes in Forest Fire Danger Index components
The projections of the components of the FFDI are shown in Figure 4. These indicate the expected increase in maximum temperatures (Tmax) in the higher emissions A1B scenarios, compared with E1 where global mean temperatures stabilise around the 2050s. Note that these values are averaged over land-only. Precipitation shows an interesting division between higher values in E1 and A1B-IMAGE, compared to lower values in A1B-SRES. This has been investigated elsewhere (e.g. Johns et al., 2011) and has been ascribed to differences in the aerosol loading between the IMAGE and SRES derived scenarios. Relative humidity, being closely related to temperature, shows a similar pattern, with the largest decreases in humidity in A1B-IMAGE, followed by the A1B-SRES simulations.
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Figure 4: Time series of forest fire danger index components covering the simulation of the historic period and projections for the 21st Century. Global mean values represent the land area with present-day forest coverage, as shown in Figure 2.
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Wind speed shows relatively little change, but there is a discernible difference between the IMAGE and SRES runs, which again may be linked to precipitation and the different aerosol loadings.
To help to understand the relative contributions of the components to changing fire danger over the 21st Century, we recalculate the 21st Century FFDI with each component in turn fixed at the year 2000 value (Figure 5). We do this for the A1B-IMAGE scenario, since this has the largest change in FFDI through the 21st Century for HadGEM2-AO, therefore making it potentially easier to discern differences between different components. The results indicate that FFDI increases in all situations, but the lowest increase occurs when we keep Tmax fixed at present- day levels. This results in an increase in FFDI of just one unit by 2100. With relative humidity fixed, the increase is only slightly larger. Keeping the drought factor fixed at present-day levels results in a slight underestimate of FFDI by 2100, but keeping wind speed fixed has no affect on the outcome.
Changes in maximum temperature therefore have the biggest impact on FFDI on the global scale, followed by changes in relative humidity, which is linked to temperature change. Changes in precipitation have a relatively small impact on the global scale, though this is likely related to the fact that precipitation may increase or decrease depending on region. Also, whilst global precipitation is generally projected to increase with increasing global temperatures (e.g. IPCC, 2007), this increase is more clearly seen over ocean regions, with more modest increases in precipitation over land areas. Therefore it is important to assess these influences on a regional scale. Wind speed changes show virtually no impact upon the FFDI values for the late 21st Century, which is consistent with the relatively sparse areas of change indicated later in Figure 11.
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Figure 5: Projections of Forest Fire Danger Index for the A1B-IMAGE scenario. The dotted line represents the actual projection, and the coloured lines show the effect of fixing each component in turn at the year 2000 value. For example, the line labelled FFDI-Tmax represents a projection of FFDI with Tmax fixed at year 2000 values, but with all other components varying as projected through the 21st Century. The FFDI represents the global mean for land areas with present-day forest cover as shown in Figure 2.
3.3 Regional changes in Forest Fire Danger Index
Maps of FFDI for the 2090s are shown in Figure 6 along with the ‘avoided’ changes for the 2090s i.e. the difference in FFDI between the A1B and the E1 scenarios. It is important to note that, by the end of the 21st Century, FFDI displays large absolute values over large areas such as North Africa, western Australia and the Middle East where the forest density is currently very low, but here we have masked values according to present-day forest coverage (as shown in Figure 2). However, this study makes no assumptions about changes in land use and forest cover through the 21st Century.
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Figure 6: Forest Fire Danger Index for the period 2090-2099. (a) E1 represents a mean of the two E1 simulations, and (b) A1B represents a mean of the two A1B-SRES and the single A1BIMAGE simulations. (c) Avoided changes for the 2090s. Land area is masked by present-day observed forest cover, as shown in Figure 2.
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Figure 7: Percentage change in Forest Fire Danger Index for the period 2090-2099 relative to
1971-2000.
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The percentage changes in FFDI over different parts of the globe are shown in Figure 7. There are larger changes under the higher emissions scenarios of A1B-SRES and A1B-IMAGE compared to E1. Under all scenarios, the largest percentage increases in FFDI occur over Europe, China, and Amazonia. Under higher emissions, the increases over these regions increase in magnitude, and there are also large changes over North America and Central Africa. The results from the RCP simulations (not shown) indicate similar patterns of changes, with RCP2.6 being similar to E1 in the general spatial distribution and magnitudes of change, and RCP8.5 being similar to the A1B simulations, particularly A1B-IMAGE.
3.4 Regional changes in Forest Fire Danger Index components
Figure 8 shows projected changes in maximum temperature for the 2090s. The largest percentage increases occur over the northern latitudes, but increases are generally larger in the A1B scenarios compared to E1.
Projected relative humidity changes are shown in Figure 9. There are relatively small changes in the E1 scenario, with the largest decreases over the Amazon. There are much larger decreases over the Amazon under the A1B scenarios, but also large decreases over the southwest USA, southern Africa, and the Mediterranean.
Projected percentage changes in precipitation by the 2090s for the three main scenarios (E1, A1B-SRES and A1B-IMAGE) are shown in Figure 10. Under E1, few regions experience a decrease in precipitation, with the main exceptions being NE Brazil, central Australia, and parts of southern Africa. Under the A1B projections, there is a much larger precipitation decrease over the Amazon region, and also over the Mediterranean regions of Africa and Europe. The Southwest USA also shows a decrease, particularly under A1B-IMAGE.
Figure 11 shows projected changes in wind speed, and under the E1 scenario the main regions of change occur over the Amazon and central Africa, where wind speed is projected to increase slightly. Larger increases in wind speed are projected over these regions under the A1B scenario, but other regions indicate relatively minor changes.
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Figure 8: Projected percentage change in maximum temperatures for the 2090s relative to
1971-2000 under the three scenarios. A1B-SRES and E1 represent the ensemble mean of two
simulations, whereas A1B-IMAGE is a single simulation.
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Figure 9: Projected percentage change in minimum relative humidity for the 2090s relative to
1971-2000 under the three scenarios. A1B-SRES and E1 represent the ensemble mean of two
simulations, whereas A1B-IMAGE is a single simulation.
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Figure 10: Projected percentage change in mean precipitation for the 2090s relative to 19712000 under the three scenarios. A1B-SRES and E1 represent the ensemble mean of two
simulations, whereas A1B-IMAGE is a single simulation.
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Figure 11: Projected percentage change in mean 10-m wind speed for the 2090s relative to
1971-2000 under the three scenarios. A1B-SRES and E1 represent the ensemble mean of two
simulations, whereas A1B-IMAGE is a single simulation.
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Figure 12: Projections of Forest Fire Danger Index for the A1B-IMAGE scenario, showing differences between the actual A1B-IMAGE projection and the projection using (a) maximum temperature, (b) relative humidity, (c) wind speed, and (d) drought factor, fixed at year 2000 values. The period shown is 2070-2099.
Figure 12 shows the regional contributions to the FFDI projections from the individual components for the A1B-IMAGE scenario for the period 2070-2099. The maps show the difference between the standard A1B-IMAGE FFDI projection and the projections where each component in turn is fixed at year 2000 values (this figure can be compared with the time series in Figure 5). Maximum temperature contributes positively to FFDI changes over all regions, particularly over the regions with larger projected increases in FFDI. Changes in relative humidity also contribute over most regions, particularly over Brazil. Changes in wind speed (Figure 12c) show only small contributions to FFDI changes, with the exception of NE Brazil. Drought contributions are also relatively small, but greater over NE Brazil, eastern Australia, and parts of the USA (Figure 12d).
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3.5 Regional FFDI case studies
In this section, we investigate in more detail the changes in FFDI and its components over the regions identified in Figure 2. Changes in FFDI in each individual region are shown in Figure 13, and the results are discussed for each country in the following sections. Plots showing the time series of the FFDI components are not shown, but relative changes in the components are commented on.
3.5.1 Amazonia
In the Amazon region, FFDI is projected to increase from a present-day baseline of around 13 to around 22 under the A1B scenarios (Figure 13a). Both of these values fall within the high danger category. Under mitigation, this increase is much lower, to around a FFDI of 15, though still within the high danger category. Temperatures are projected to increase more under the A1B scenarios by about 2-3°C compared with the E1 scenario. Precipitation decreases more rapidly under A1B, as does relative humidity. Wind speeds are projected to be higher under A1B than E1. All of these changes to individual components act to increase the fire danger risk.
3.5.2 West Africa
In the West Africa region, the rate of increase of FFDI under A1B-SRES and E1 is similar throughout the 21st Century, but after 2050 is discernibly higher under A1B-IMAGE (Figure 13b). Projected maximum temperature under A1B-SRES is around 33°C compared with 32°C under E1 by 2100. A1B-IMAGE shows a stronger increase of up to 35°C by 2100. Precipitation is lower by approximately 0.5mm/day under A1B-SRES, though this does not apply to A1BIMAGE, where precipitation remains at a level similar to E1. There is little separation between the A1B-SRES and E1 scenarios in minimum daily relative humidity. But relative humidity under A1B-IMAGE is noticeably lower. Winds speeds show only small increases throughout the
21st
Century, however projections under A1B are marginally higher than for E1 by 2100. So for this region, the higher FFDI projections under A1B-IMAGE appear to be a result of a combination of higher temperatures and higher wind speeds.
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Figure 13: Projected changes in Forest Fire Danger Index for the five selected regions shown in Figure 2.
3.5.3 Pacific Northwest
Under the A1B scenarios, FFDI over the west of Canada and the north-western United States shows an increase from a present-day value of around 7 to around 10 by 2100 (Figure 13c), though these values both fall within the moderate danger category. Under mitigation, this
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increase is stabilised at around 8 by the mid-21st Century. In this region, mitigation could avoid an increase in mean maximum temperatures of around 3°C. There is little difference in projected precipitation between the scenarios, though precipitation under A1B-SRES is slightly lower than E1 throughout much of the 21st Century. Minimum relative humidity is lower under A1B compared to E1 by 2100, and wind speed is also lower under A1B. Higher temperature and lower relative humidity under A1B would both contribute to a higher fire risk, but lower wind speed would mitigate against an increase.
3.5.4 Eastern Australia
There is little clear difference between the scenarios regarding the projected FFDI values for the 21st
Century (Figure 13d). The region experiences quite high inter-annual variability, which masks any differences between the scenarios. Temperature projections under A1B are higher by around 2-3°C over Eastern Australia compared to the E1 scenario, which would enhance fire risk. There is little discernible difference in precipitation or relative humidity between the scenarios throughout the 21st Century. There is also relatively little difference in wind speed between the scenarios, though A1B is marginally lower throughout the 21st Century, which would contribute to a decreased risk of fire. Therefore it appears that temperature change is the primary driver of the small increased fire risk over Eastern Australia.
3.5.5 England & Wales
Here we include the England and Wales region despite it having a relatively low density of forestry (c.f. Figure 2), as fires, often associated with moorland are known to be an issue (e.g. Albertson et al., 2010). Under A1B, the region shows an increase in FFDI from around 6 to around 9 by 2100 (Figure 13e). However, this change falls within the moderate danger category and is small compared with the inter-annual variability. With mitigation, the increase in FFDI is reduced with values of around 7 or below by 2100. The results show that mitigation could avoid a change in maximum temperatures of around 2-3°C by 2100. There is high variability in precipitation, and little discernible difference between the scenarios by 2100, though precipitation under A1B appears marginally lower than under E1. For relative humidity, by 2100 the A1B scenarios seem to be a few percent lower than under E1. Wind speed is also highly variable and shows no clear differences between the scenarios through the 21st Century. Again, temperature would appear to be the primary driver of the modest projected increases in FFDI for this region.
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4. Discussion and Conclusions
Changes in fire risk (as defined by FFDI) over a region are partly dependent upon the relative changes in the meteorological factors which contribute to fires. It should be noted that changes in land cover and vegetation will strongly influence how the FFDI applies in practice. Also other non-meteorological changes, such as population, will also influence the risk of forest fires.
We have identified that the primary meteorological driver of projected changes in forest fire danger on a global scale is generally 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. We have least confidence in projection of wind speeds, but these appear to change relatively little over most parts of the globe, and have the smallest contribution to the changes in the FFDI.
Fire danger is projected to increase over most parts of the world compared to present-day values. Most of this increase is driven by increasing temperatures which will increase daily maximum temperatures, and also act to reduce relative humidity. The largest proportional increases are seen under the A1B scenarios for Europe, Amazonia and parts of North America and East Asia. Increases in fire danger are lower under the mitigation scenario (E1), but generally affecting the same regions as under A1B. Amazonia sees the largest projected increases in forest fire danger, along with high absolute values, as a result of all of the contributing meteorological components changing so as to increase the risk of fire danger. Over this region, a combination of increasing temperatures and wind speed, and decreasing precipitation and wind speed will act together to increase the fire danger. Although most regions of the globe are projected to warm in the future, in some regions the fire danger increase is diminished as a result of projected increases in precipitation and/or relatively humidity, and projected decreases in wind speed, though these tend to be small.
The use of policies to limit carbon emissions will help to mitigate future increases in fire danger. Although all of the scenarios considered in this report suggest some increases in forest fire danger, the highest emissions scenarios suggest potential increases three times greater than the increases under the lowest emissions scenarios. This is largely a result of the lower global mean temperature projections achieved through mitigation.
This study has used annual means of fire danger and its constituent components. For regions of high fire danger within the tropics, fire danger is likely to remain relatively high throughout the year. As of the present-day climate, fire danger generally becomes more seasonal at higher
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latitudes. As a result, the use of annual mean fire danger may underplay the risks associated with the summer season and does not fully account for intra-annual variability. Future research should therefore focus upon the seasonality of fire danger, and the assessment of fire danger during the seasons of highest risk. Also, using a higher resolution regional climate model may better capture the daily variability and extremes of the meteorological components, in particular precipitation. In areas where the mean fire risk does not increase, it is possible that there may be an increase in variability of fire risk, and therefore an increase in the number of days with an enhanced fire danger rating. The daily FFDI data that has underpinned this global assessment could be usefully used to assess the distributions of high fire danger days (e.g. the frequency of events above the 90th percentile), and also the clustering of high fire danger days. Further assessment of the underlying uncertainties could also be obtained through use of alternative GCMs.
Finally, some mention should be made of the uncertainties inherent in any modelling study and those particular to this study. The work presented here has aimed to capture a measure of the uncertainty resulting from different emissions scenarios and has shown results from both several SRES scenarios and for the more recently developed RCP scenarios based on a range of atmospheric concentrations of greenhouse gases. However, by using only two models and these from the same family of models this uncertainty range is still limited and may therefore be considered a subset of the possible uncertainty.
It may be beneficial to consider in a similar way the full complement of CMIP5 GCMs and predictions from these models of the factors identified here as key drivers of fire risk in the future. This will help to gauge whether the results presented in this study are representative of the range of possible outcomes and therefore of the risks that may be avoided by mitigation.
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