avoid ws2 d1_31_fire

AVOID WS2 D1 31 Fire 1

copy Crown copyright 2008

_ _ _ _ - ndash

Author(s) J Caesar and N Golding

Institute Met Office Hadley Centre

Reviewer Richard Betts

Institutes Met Office Hadley Centre

Date 21122011

AVOID

Avoiding dangerous climate change

AVOID is a DECCDefra 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

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

AVWS2D131

Meteorological factors influencing forest

fire risk under climate change mitigation

John Caesar amp 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

17 to 41 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 40000km2 of fire in

standing forests (Nepstad et al 2004) and the resulting carbon release contributed

approximately 5 of annual anthropogenic emissions (04Pg de Mendoca et el 2004) Fires

in Southeast Asia linked to the 1997-98 El Nintildeo are estimated to have released 08-26 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

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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

21 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

(1875degx125deg) 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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22 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 (ie 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 Queacutereacute 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 24 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 A1Bshy

IMAGE 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 ldquoforwardsrdquo 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 26 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 26 is the low-end forcing scenario of 26

Wm-2 the others being RCP 45 RCP 60 and RCP 85 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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23 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 ldquocalculationrdquo 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(0987logD ndash 045 + 00338T + 00234V ndash 00345H)

H = relative humidity from 0-100 ()

T = daily maximum air temperature (degC)

V = daily mean wind-speed 10-metres above the ground (kmhr)

D = drought factor in the range 0-10

The drought factor (D) is calculated as

D=0191(I+104)(N+1)15 [352(N+1)15+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 soilrsquos 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 (eg 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 (gt05 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

12tha) 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

24 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

31 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 RCP85

although this remains within the high danger category FFDI under the lower emissions

scenarios are around 16 for E1 and 15 for RCP26 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

10

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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

32 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 (eg 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 (eg

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

13

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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

33 Regional changes in Forest Fire Danger Index

Maps of FFDI for the 2090s are shown in Figure 6 along with the lsquoavoidedrsquo changes for the

2090s ie 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 A1Bshy

IMAGE 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

RCP26 being similar to E1 in the general spatial distribution and magnitudes of change and

RCP85 being similar to the A1B simulations particularly A1B-IMAGE

34 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

17

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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

18

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Figure 9 Projected percentage change in minimum relative humidity for the 2090s relative to



19

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Figure 10 Projected percentage change in mean precipitation for the 2090s relative to 1971shy

2000 under the three scenarios A1B-SRES and E1 represent the ensemble mean of two


20

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Figure 11 Projected percentage change in mean 10-m wind speed for the 2090s relative to



21

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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)

22

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35 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

351 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 2-3degC 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

352 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 33degC compared with 32degC under

E1 by 2100 A1B-IMAGE shows a stronger increase of up to 35degC by 2100 Precipitation is

lower by approximately 05mmday under A1B-SRES though this does not apply to A1Bshy

IMAGE 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

23

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Figure 13 Projected changes in Forest Fire Danger Index for the five selected regions shown

in Figure 2

353 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

24

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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 3degC 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

354 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 2-3degC 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

355 England amp Wales

Here we include the England and Wales region despite it having a relatively low density of

forestry (cf Figure 2) as fires often associated with moorland are known to be an issue (eg

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 2-3degC 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

25

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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


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 andor 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

26

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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 (eg 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

27

AVWS2D131

References

Albertson K Aylen J Cavan G McMorrow J (2010) Climate change and the future

occurrence of moorland wildfires in the Peak District of the UK Climate Research 45105-118

Arora V K Scinocca JF Boer GJ Christian JR Denman KL Flato GM Kharin VV

Lee WG and Merryfield WJ (2011) Carbon emission limits required to satisfy future

representative concentration pathways of greenhouse gases Geophys Res Lett 38 L05805

doi1010292010GL046270

Bergeron Y Flaanigan M Gauthier S Leduc A and Lefort P (2004) Past current and

future fire frequency in the Canadian boreal forest implications for sustainable forest

management Ambio 33 356-360

Cannell MGR Palutikof JP and Sparks TH (1999) Indicators of Climate Change in the

UK DETR London 87 pp

Collins WJ and co-authors (2008) Evaluation of the HadGEM2 model Hadley Centre Tech

Note 74 Met Office Exeter UK (Available at

httpwwwmetofficegovukresearchhadleycentrepubsHCTNindexhtml

Collins W J and co-authors (2011) Development and evaluation of an Earth-system model

HadGEM2 Geosci Model Dev Discuss 4 997ndash1062 doi105194gmdd-4-997-2011

de Mendonccedila MJC Vera Diaz MDC Nepstad D da Motta RSAlencar A Gomes JC and Ortiz RA (2004) The economic cost of the use of fire in the Amazon Ecological Economics 49 (1) 89ndash105

Dowdy AJ Mills GA Finkele K and de Groot W (2009) Australian fire weather as

represented by the McArthur Forest Fire Danger Index and the Canadian Forest Fire Weather

Index CAWCR Technical Report No 10 Centre for Australian Weather and Climate Research

Flannigan MD and co-authors (2005) Future area burned in Canada Climatic Change 721shy

16

Gillett NP Weaver AJ Zwiers FW and Flannigan MD (2004) Detecting the effect of

climate change on Canadian forest fires Geophys Res Lett 31 L18211

doi1010292004GL020876

Golding N and Betts R (2008) Fire risk in Amazonia due to climate change in the HadCM3

climate model Potential interactions with deforestation Global Biogeochemical Cycles 22Doi

1010292007gb003166

Hennessey K Lucas C Nicholls N Bathols J Suppiah R and Ricketts J (2005) Climate

change impacts on fire-weather in southeast Australia CSIRO Atmospheric Research

Consultancy Report 91 pp

IPCC (2007) Climate Change 2007 The Physical Science Basis Contribution of Working

Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change

[Solomon S D Qin M Manning Z Chen M Marquis KB Averyt M Tignor and HL Miller

(eds)] Cambridge University Press Cambridge United Kingdom and New York NY USA

996pp

28

AVWS2D131

Johns TC and co-authors (2006) The new Hadley Centre Climate Model (HadGEM1)

Evaluation of coupled simulations J Climate 19 1327-1353

Johns TC and co-authors (2011) Climate change under aggressive mitigation The

ENSEMBLES multi-model experiment Climate Dynamics DOI 101007s00382-011-1005-5

Jones C D and co-authors (2011) The HadGEM2-ES implementation of CMIP5 centennial

simulations Geosci Model Dev 4 543-570 doi105194gmd-4-543-2011

Le Queacutereacute C and co-authors (2009) Trends in the sources and sinks of carbon dioxide Nature

Geosciences 2 831 - 836

Loveland T R Reed B C Brown J F Ohlen D O Zhu Z Yang L and Merchant J W

(2000) Development of a global land cover characteristics database and IGBP DISCover from

1km AVHRR data Int J Remote Sens 21(6ndash7) 1303ndash1330

Lowe J A Hewitt CD van Vuuren DP Johns TC Stehfest E Royer J-F and van der

Linden PJ (2009) New Study For Climate Modeling Analyses and Scenarios Eos Trans

AGU 90(21) doi1010292009EO210001

Lucas C and co-authors (2007) Bushfire Weather in Southeast Australia Recent Trends and

Projected Climate Change Impacts

httpwwwclimateinstituteorgauimagesstoriesbushfirefullreportpdf

Luke R H and McArthur AG (1978) Bushfires in Australia Aust Gov Publ Serv

Canberra A C T

Martin G M and co-authors (2011) The HadGEM2 family of Met Office Unified Model Climate

configurations Geosci Model Dev Discuss 4 765ndash841 doi105194gmdd-4-765-2011

MNP (2006) (Edited by AF Bouwman T Kram and K Klein Goldewijk) Integrated modelling of

global environmental change An overview of IMAGE 24 Netherlands Environmental

Assessment Agency (MNP) Bilthoven The Netherlands

Moss R H and co-authors (2010) The next generation of scenarios for climate change

research and assessment Nature 463(7282) 747-756

Mouillot F and Field C B (2005) Fire history and the global carbon budget a 1degtimes 1degfire

history reconstruction for the 20th century Global Change Biology 11 398ndash420 doi

101111j1365-2486200500920x

Nakicenovic N and Swart R (eds) (2000) Special Report on Emissions Scenarios A Special

Report of Working Group III of the Intergovernmental Panel on Climate Change 600 pp

Cambridge Univ Press Cambridge UK

Nepstad D Lefebvre P Silva U L Tomasella J Schlesinger P Solorzano L Moutinho

P Ray D amp Benito J G (2004) Amazon drought and its implications for forest flammability

and tree growth a basin-wide analysis Glob Change Biol 10 704ndash717 (doi101111j1529shy

8817200300772x)

Noble IR Barry GAV and Gill AM (1980) McArthurrsquos fire-danger meters expressed as

equations Australian Journal of Ecology 5 201-203

29

AVWS2D131

Pacifico F and co-authors (2011) Evaluation of a photosynthesis-based biogenic isoprene

emission scheme in JULES and simulation of isoprene emissions under present-day climate

conditions Atmos Chem Phys 11 4371ndash4389

Sirakoff C (1985) A correction to the equations describing the McArthur forest fire danger

meter Austral Ecol 10(4) 481

van Vuuren D and Riahi K (2008) Do recent emission trends imply higher emissions forever

Climatic Change 91 237-248

van Vuuren D M den Elzen P Lucas B Eickhout B Strengers B van Ruijven S Wonink

R van Houdt (2007) Stabilizing greenhouse gas concentrations at low levels an assessment of

reduction strategies and costs Climatic Change doi101007s10584-006-9172-9

Vercoe T (2003) lsquoWhoever owns the fuel owns the firersquo - Fire management for forest growers

AFG Special Liftout no 65 26(3) 8pp

httpwwwcoagbushfireenquirygovausubs_pdf57_2_ragg_afgpdf

Westerling AL Hidalgo HG Cayan DR and Swetnam TW (2006) Warming and earlier

spring increases Western US forest fire activity Science 313 940-943

30

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


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

AVWS2D131




1 Introduction



future












2005)











3

AVWS2D131





















2 Methods

21 Climate models







2011)

4

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5

AVWS2D131














6

AVWS2D131






















D=0191(I+104)(N+1)15 [352(N+1)15+R-1)





7

AVWS2D131




also in use









burning1




will fail


should be

avoided





burning out2


and the area burnt







8

AVWS2D131

































9

AVWS2D131





3 Results










10

AVWS2D131



shown in Figure 2











11

AVWS2D131




12

AVWS2D131



loadings










the outcome










Figure 11

13

AVWS2D131















14

AVWS2D131





15

AVWS2D131


1971-2000

16

AVWS2D131



























17

AVWS2D131




18

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19

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20

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21

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22

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commented on

351 Amazonia








352 West Africa












23

AVWS2D131


in Figure 2





24

AVWS2D131
































25

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26

AVWS2D131












GCMs












27

AVWS2D131

References






doi1010292010GL046270
















16



doi1010292004GL020876



1010292007gb003166








996pp

28

AVWS2D131














AGU 90(21) doi1010292009EO210001





Canberra A C T










101111j1365-2486200500920x







8817200300772x)



29

AVWS2D131
















30

AVWS2D131




1 Introduction



future












2005)











3

AVWS2D131





















2 Methods

21 Climate models







2011)

4

AVWS2D131
































5

AVWS2D131














6

AVWS2D131






















D=0191(I+104)(N+1)15 [352(N+1)15+R-1)





7

AVWS2D131




also in use









burning1




will fail


should be

avoided





burning out2


and the area burnt







8

AVWS2D131

































9

AVWS2D131





3 Results










10

AVWS2D131



shown in Figure 2











11

AVWS2D131




12

AVWS2D131



loadings










the outcome










Figure 11

13

AVWS2D131















14

AVWS2D131





15

AVWS2D131


1971-2000

16

AVWS2D131



























17

AVWS2D131




18

AVWS2D131




19

AVWS2D131




20

AVWS2D131




21

AVWS2D131















22

AVWS2D131






commented on

351 Amazonia








352 West Africa












23

AVWS2D131


in Figure 2





24

AVWS2D131
































25

AVWS2D131


































26

AVWS2D131












GCMs












27

AVWS2D131

References






doi1010292010GL046270
















16



doi1010292004GL020876



1010292007gb003166








996pp

28

AVWS2D131














AGU 90(21) doi1010292009EO210001





Canberra A C T










101111j1365-2486200500920x







8817200300772x)



29

AVWS2D131
















30

AVWS2D131





















2 Methods

21 Climate models







2011)

4

AVWS2D131
































5

AVWS2D131














6

AVWS2D131






















D=0191(I+104)(N+1)15 [352(N+1)15+R-1)





7

AVWS2D131




also in use









burning1




will fail


should be

avoided





burning out2


and the area burnt







8

AVWS2D131

































9

AVWS2D131





3 Results










10

AVWS2D131



shown in Figure 2











11

AVWS2D131




12

AVWS2D131



loadings










the outcome










Figure 11

13

AVWS2D131















14

AVWS2D131





15

AVWS2D131


1971-2000

16

AVWS2D131



























17

AVWS2D131




18

AVWS2D131




19

AVWS2D131




20

AVWS2D131




21

AVWS2D131















22

AVWS2D131






commented on

351 Amazonia








352 West Africa












23

AVWS2D131


in Figure 2





24

AVWS2D131
































25

AVWS2D131


































26

AVWS2D131












GCMs












27

AVWS2D131

References






doi1010292010GL046270
















16



doi1010292004GL020876



1010292007gb003166








996pp

28

AVWS2D131














AGU 90(21) doi1010292009EO210001





Canberra A C T










101111j1365-2486200500920x







8817200300772x)



29

AVWS2D131
















30

AVWS2D131
































5

AVWS2D131














6

AVWS2D131






















D=0191(I+104)(N+1)15 [352(N+1)15+R-1)





7

AVWS2D131




also in use









burning1




will fail


should be

avoided





burning out2


and the area burnt







8

AVWS2D131

































9

AVWS2D131





3 Results










10

AVWS2D131



shown in Figure 2











11

AVWS2D131




12

AVWS2D131



loadings










the outcome










Figure 11

13

AVWS2D131















14

AVWS2D131





15

AVWS2D131


1971-2000

16

AVWS2D131



























17

AVWS2D131




18

AVWS2D131




19

AVWS2D131




20

AVWS2D131




21

AVWS2D131















22

AVWS2D131






commented on

351 Amazonia








352 West Africa












23

AVWS2D131


in Figure 2





24

AVWS2D131
































25

AVWS2D131


































26

AVWS2D131












GCMs












27

AVWS2D131

References






doi1010292010GL046270
















16



doi1010292004GL020876



1010292007gb003166








996pp

28

AVWS2D131














AGU 90(21) doi1010292009EO210001





Canberra A C T










101111j1365-2486200500920x







8817200300772x)



29

AVWS2D131
















30

AVWS2D131














6

AVWS2D131






















D=0191(I+104)(N+1)15 [352(N+1)15+R-1)





7

AVWS2D131




also in use









burning1




will fail


should be

avoided





burning out2


and the area burnt







8

AVWS2D131

































9

AVWS2D131





3 Results










10

AVWS2D131



shown in Figure 2











11

AVWS2D131




12

AVWS2D131



loadings










the outcome










Figure 11

13

AVWS2D131















14

AVWS2D131





15

AVWS2D131


1971-2000

16

AVWS2D131



























17

AVWS2D131




18

AVWS2D131




19

AVWS2D131




20

AVWS2D131




21

AVWS2D131















22

AVWS2D131






commented on

351 Amazonia








352 West Africa












23

AVWS2D131


in Figure 2





24

AVWS2D131
































25

AVWS2D131


































26

AVWS2D131












GCMs












27

AVWS2D131

References






doi1010292010GL046270
















16



doi1010292004GL020876



1010292007gb003166








996pp

28

AVWS2D131














AGU 90(21) doi1010292009EO210001





Canberra A C T










101111j1365-2486200500920x







8817200300772x)



29

AVWS2D131
















30

AVWS2D131






















D=0191(I+104)(N+1)15 [352(N+1)15+R-1)





7

AVWS2D131




also in use









burning1




will fail


should be

avoided





burning out2


and the area burnt







8

AVWS2D131

































9

AVWS2D131





3 Results










10

AVWS2D131



shown in Figure 2











11

AVWS2D131




12

AVWS2D131



loadings










the outcome










Figure 11

13

AVWS2D131















14

AVWS2D131





15

AVWS2D131


1971-2000

16

AVWS2D131



























17

AVWS2D131




18

AVWS2D131




19

AVWS2D131




20

AVWS2D131




21

AVWS2D131















22

AVWS2D131






commented on

351 Amazonia








352 West Africa












23

AVWS2D131


in Figure 2





24

AVWS2D131
































25

AVWS2D131


































26

AVWS2D131












GCMs












27

AVWS2D131

References






doi1010292010GL046270
















16



doi1010292004GL020876



1010292007gb003166








996pp

28

AVWS2D131














AGU 90(21) doi1010292009EO210001





Canberra A C T










101111j1365-2486200500920x







8817200300772x)



29

AVWS2D131
















30

AVWS2D131




also in use









burning1




will fail


should be

avoided





burning out2


and the area burnt







8

AVWS2D131

































9

AVWS2D131





3 Results










10

AVWS2D131



shown in Figure 2











11

AVWS2D131




12

AVWS2D131



loadings










the outcome










Figure 11

13

AVWS2D131















14

AVWS2D131





15

AVWS2D131


1971-2000

16

AVWS2D131



























17

AVWS2D131




18

AVWS2D131




19

AVWS2D131




20

AVWS2D131




21

AVWS2D131















22

AVWS2D131






commented on

351 Amazonia








352 West Africa












23

AVWS2D131


in Figure 2





24

AVWS2D131
































25

AVWS2D131


































26

AVWS2D131












GCMs












27

AVWS2D131

References






doi1010292010GL046270
















16



doi1010292004GL020876



1010292007gb003166








996pp

28

AVWS2D131














AGU 90(21) doi1010292009EO210001





Canberra A C T










101111j1365-2486200500920x







8817200300772x)



29

AVWS2D131
















30

AVWS2D131

































9

AVWS2D131





3 Results










10

AVWS2D131



shown in Figure 2











11

AVWS2D131




12

AVWS2D131



loadings










the outcome










Figure 11

13

AVWS2D131















14

AVWS2D131





15

AVWS2D131


1971-2000

16

AVWS2D131



























17

AVWS2D131




18

AVWS2D131




19

AVWS2D131




20

AVWS2D131




21

AVWS2D131















22

AVWS2D131






commented on

351 Amazonia








352 West Africa












23

AVWS2D131


in Figure 2





24

AVWS2D131
































25

AVWS2D131


































26

AVWS2D131












GCMs












27

AVWS2D131

References






doi1010292010GL046270
















16



doi1010292004GL020876



1010292007gb003166








996pp

28

AVWS2D131














AGU 90(21) doi1010292009EO210001





Canberra A C T










101111j1365-2486200500920x







8817200300772x)



29

AVWS2D131
















30

AVWS2D131





3 Results










10

AVWS2D131



shown in Figure 2











11

AVWS2D131




12

AVWS2D131



loadings










the outcome










Figure 11

13

AVWS2D131















14

AVWS2D131





15

AVWS2D131


1971-2000

16

AVWS2D131



























17

AVWS2D131




18

AVWS2D131




19

AVWS2D131




20

AVWS2D131




21

AVWS2D131















22

AVWS2D131






commented on

351 Amazonia








352 West Africa












23

AVWS2D131


in Figure 2





24

AVWS2D131
































25

AVWS2D131


































26

AVWS2D131












GCMs












27

AVWS2D131

References






doi1010292010GL046270
















16



doi1010292004GL020876



1010292007gb003166








996pp

28

AVWS2D131














AGU 90(21) doi1010292009EO210001





Canberra A C T










101111j1365-2486200500920x







8817200300772x)



29

AVWS2D131
















30

AVWS2D131



shown in Figure 2











11

AVWS2D131




12

AVWS2D131



loadings










the outcome










Figure 11

13

AVWS2D131















14

AVWS2D131





15

AVWS2D131


1971-2000

16

AVWS2D131



























17

AVWS2D131




18

AVWS2D131




19

AVWS2D131




20

AVWS2D131




21

AVWS2D131















22

AVWS2D131






commented on

351 Amazonia








352 West Africa












23

AVWS2D131


in Figure 2





24

AVWS2D131
































25

AVWS2D131


































26

AVWS2D131












GCMs












27

AVWS2D131

References






doi1010292010GL046270
















16



doi1010292004GL020876



1010292007gb003166








996pp

28

AVWS2D131














AGU 90(21) doi1010292009EO210001





Canberra A C T










101111j1365-2486200500920x







8817200300772x)



29

AVWS2D131
















30

AVWS2D131




12

AVWS2D131



loadings










the outcome










Figure 11

13

AVWS2D131















14

AVWS2D131





15

AVWS2D131


1971-2000

16

AVWS2D131



























17

AVWS2D131




18

AVWS2D131




19

AVWS2D131




20

AVWS2D131




21

AVWS2D131















22

AVWS2D131






commented on

351 Amazonia








352 West Africa












23

AVWS2D131


in Figure 2





24

AVWS2D131
































25

AVWS2D131


































26

AVWS2D131












GCMs












27

AVWS2D131

References






doi1010292010GL046270
















16



doi1010292004GL020876



1010292007gb003166








996pp

28

AVWS2D131














AGU 90(21) doi1010292009EO210001





Canberra A C T










101111j1365-2486200500920x







8817200300772x)



29

AVWS2D131
















30

AVWS2D131



loadings










the outcome










Figure 11

13

AVWS2D131















14

AVWS2D131





15

AVWS2D131


1971-2000

16

AVWS2D131



























17

AVWS2D131




18

AVWS2D131




19

AVWS2D131




20

AVWS2D131




21

AVWS2D131















22

AVWS2D131






commented on

351 Amazonia








352 West Africa












23

AVWS2D131


in Figure 2





24

AVWS2D131
































25

AVWS2D131


































26

AVWS2D131












GCMs












27

AVWS2D131

References






doi1010292010GL046270
















16



doi1010292004GL020876



1010292007gb003166








996pp

28

AVWS2D131














AGU 90(21) doi1010292009EO210001





Canberra A C T










101111j1365-2486200500920x







8817200300772x)



29

AVWS2D131
















30

AVWS2D131















14

AVWS2D131





15

AVWS2D131


1971-2000

16

AVWS2D131



























17

AVWS2D131




18

AVWS2D131




19

AVWS2D131




20

AVWS2D131




21

AVWS2D131















22

AVWS2D131






commented on

351 Amazonia








352 West Africa












23

AVWS2D131


in Figure 2





24

AVWS2D131
































25

AVWS2D131


































26

AVWS2D131












GCMs












27

AVWS2D131

References






doi1010292010GL046270
















16



doi1010292004GL020876



1010292007gb003166








996pp

28

AVWS2D131














AGU 90(21) doi1010292009EO210001





Canberra A C T










101111j1365-2486200500920x







8817200300772x)



29

AVWS2D131
















30

AVWS2D131





15

AVWS2D131


1971-2000

16

AVWS2D131



























17

AVWS2D131




18

AVWS2D131




19

AVWS2D131




20

AVWS2D131




21

AVWS2D131















22

AVWS2D131






commented on

351 Amazonia








352 West Africa












23

AVWS2D131


in Figure 2





24

AVWS2D131
































25

AVWS2D131


































26

AVWS2D131












GCMs












27

AVWS2D131

References






doi1010292010GL046270
















16



doi1010292004GL020876



1010292007gb003166








996pp

28

AVWS2D131














AGU 90(21) doi1010292009EO210001





Canberra A C T










101111j1365-2486200500920x







8817200300772x)



29

AVWS2D131
















30

AVWS2D131


1971-2000

16

AVWS2D131



























17

AVWS2D131




18

AVWS2D131




19

AVWS2D131




20

AVWS2D131




21

AVWS2D131















22

AVWS2D131






commented on

351 Amazonia








352 West Africa












23

AVWS2D131


in Figure 2





24

AVWS2D131
































25

AVWS2D131


































26

AVWS2D131












GCMs












27

AVWS2D131

References






doi1010292010GL046270
















16



doi1010292004GL020876



1010292007gb003166








996pp

28

AVWS2D131














AGU 90(21) doi1010292009EO210001





Canberra A C T










101111j1365-2486200500920x







8817200300772x)



29

AVWS2D131
















30

AVWS2D131



























17

AVWS2D131




18

AVWS2D131




19

AVWS2D131




20

AVWS2D131




21

AVWS2D131















22

AVWS2D131






commented on

351 Amazonia








352 West Africa












23

AVWS2D131


in Figure 2





24

AVWS2D131
































25

AVWS2D131


































26

AVWS2D131












GCMs












27

AVWS2D131

References






doi1010292010GL046270
















16



doi1010292004GL020876



1010292007gb003166








996pp

28

AVWS2D131














AGU 90(21) doi1010292009EO210001





Canberra A C T










101111j1365-2486200500920x







8817200300772x)



29

AVWS2D131
















30

AVWS2D131




18

AVWS2D131




19

AVWS2D131




20

AVWS2D131




21

AVWS2D131















22

AVWS2D131






commented on

351 Amazonia








352 West Africa












23

AVWS2D131


in Figure 2





24

AVWS2D131
































25

AVWS2D131


































26

AVWS2D131












GCMs












27

AVWS2D131

References






doi1010292010GL046270
















16



doi1010292004GL020876



1010292007gb003166








996pp

28

AVWS2D131














AGU 90(21) doi1010292009EO210001





Canberra A C T










101111j1365-2486200500920x







8817200300772x)



29

AVWS2D131
















30

AVWS2D131




19

AVWS2D131




20

AVWS2D131




21

AVWS2D131















22

AVWS2D131






commented on

351 Amazonia








352 West Africa












23

AVWS2D131


in Figure 2





24

AVWS2D131
































25

AVWS2D131


































26

AVWS2D131












GCMs












27

AVWS2D131

References






doi1010292010GL046270
















16



doi1010292004GL020876



1010292007gb003166








996pp

28

AVWS2D131














AGU 90(21) doi1010292009EO210001





Canberra A C T










101111j1365-2486200500920x







8817200300772x)



29

AVWS2D131
















30

AVWS2D131




20

AVWS2D131




21

AVWS2D131















22

AVWS2D131






commented on

351 Amazonia








352 West Africa












23

AVWS2D131


in Figure 2





24

AVWS2D131
































25

AVWS2D131


































26

AVWS2D131












GCMs












27

AVWS2D131

References






doi1010292010GL046270
















16



doi1010292004GL020876



1010292007gb003166








996pp

28

AVWS2D131














AGU 90(21) doi1010292009EO210001





Canberra A C T










101111j1365-2486200500920x







8817200300772x)



29

AVWS2D131
















30

AVWS2D131




21

AVWS2D131















22

AVWS2D131






commented on

351 Amazonia








352 West Africa












23

AVWS2D131


in Figure 2





24

AVWS2D131
































25

AVWS2D131


































26

AVWS2D131












GCMs












27

AVWS2D131

References






doi1010292010GL046270
















16



doi1010292004GL020876



1010292007gb003166








996pp

28

AVWS2D131














AGU 90(21) doi1010292009EO210001





Canberra A C T










101111j1365-2486200500920x







8817200300772x)



29

AVWS2D131
















30

AVWS2D131















22

AVWS2D131






commented on

351 Amazonia








352 West Africa












23

AVWS2D131


in Figure 2





24

AVWS2D131
































25

AVWS2D131


































26

AVWS2D131












GCMs












27

AVWS2D131

References






doi1010292010GL046270
















16



doi1010292004GL020876



1010292007gb003166








996pp

28

AVWS2D131














AGU 90(21) doi1010292009EO210001





Canberra A C T










101111j1365-2486200500920x







8817200300772x)



29

AVWS2D131
















30

AVWS2D131






commented on

351 Amazonia








352 West Africa












23

AVWS2D131


in Figure 2





24

AVWS2D131
































25

AVWS2D131


































26

AVWS2D131












GCMs












27

AVWS2D131

References






doi1010292010GL046270
















16



doi1010292004GL020876



1010292007gb003166








996pp

28

AVWS2D131














AGU 90(21) doi1010292009EO210001





Canberra A C T










101111j1365-2486200500920x







8817200300772x)



29

AVWS2D131
















30

AVWS2D131


in Figure 2





24

AVWS2D131
































25

AVWS2D131


































26

AVWS2D131












GCMs












27

AVWS2D131

References






doi1010292010GL046270
















16



doi1010292004GL020876



1010292007gb003166








996pp

28

AVWS2D131














AGU 90(21) doi1010292009EO210001





Canberra A C T










101111j1365-2486200500920x







8817200300772x)



29

AVWS2D131
















30

AVWS2D131
































25

AVWS2D131


































26

AVWS2D131












GCMs












27

AVWS2D131

References






doi1010292010GL046270
















16



doi1010292004GL020876



1010292007gb003166








996pp

28

AVWS2D131














AGU 90(21) doi1010292009EO210001





Canberra A C T










101111j1365-2486200500920x







8817200300772x)



29

AVWS2D131
















30

AVWS2D131


































26

AVWS2D131












GCMs












27

AVWS2D131

References






doi1010292010GL046270
















16



doi1010292004GL020876



1010292007gb003166








996pp

28

AVWS2D131














AGU 90(21) doi1010292009EO210001





Canberra A C T










101111j1365-2486200500920x







8817200300772x)



29

AVWS2D131
















30

AVWS2D131












GCMs












27

AVWS2D131

References






doi1010292010GL046270
















16



doi1010292004GL020876



1010292007gb003166








996pp

28

AVWS2D131














AGU 90(21) doi1010292009EO210001





Canberra A C T










101111j1365-2486200500920x







8817200300772x)



29

AVWS2D131
















30

AVWS2D131

References






doi1010292010GL046270
















16



doi1010292004GL020876



1010292007gb003166








996pp

28

AVWS2D131














AGU 90(21) doi1010292009EO210001





Canberra A C T










101111j1365-2486200500920x







8817200300772x)



29

AVWS2D131
















30

AVWS2D131














AGU 90(21) doi1010292009EO210001





Canberra A C T










101111j1365-2486200500920x







8817200300772x)



29

AVWS2D131
















30

AVWS2D131
















30

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