Spatio-temporal variability of climatic fire risk over the last millennium: A simple

assessment employing earth system model data

Oliver Bothe

CliSAP, University of Hamburg, Hamburg, Germany

c/o

Max-Planck-Institute for Meteorology

Bundesstraße 53 | 20146 Hamburg | Germany

oliver dot bothe at zmaw dot de

A simple fire risk index is modified and utilized to evaluate the temporal evolution of fire risk

on global and regional scales for a member of the Community Simulations of the Last

Millennium performed at the Max Planck Institute for Meteorology. The index is the CPTEC-

INPE-index operationally employed in Brazil. A description of fire risk is obtained based on

daily sums of precipitation, daily minimum relative humidity and daily maximum surface air

temperature as well as monthly estimates of leaf area index (LAI) from the model simulation.

The model simulation is forced by reconstructed estimates of changes in total solar irradiance,

volcanic radiative forcing, land cover change and anthropogenic greenhouse gas emissions.

The number of critical fire risk occurrences is detected per year. Regional fire risk occurrence

numbers mirror the climate classification of the specific region. Zonal averages indicate an

increase in fire risk numbers since the mid 19th

century at near all latitudes. The low frequent

variability is found to align with changes in precipitation and temperature. Nevertheless, large

climate fluctuations in the simulation do not result in large regional changes in fire risk.

Especially, warm and cold episodes in the past display similar changes in fire risk compared

to fire risk in the period 1971 to 2000. Correlations of fire risk with temperature and

precipitation are highest in the southern hemisphere, whereas eastern Asia and the Sahel

correlate with the vegetation cover change. The presently applied fire risk calculation ignores

firstly the fraction of a grid cell covered by vegetation and secondly and most importantly the

differences fuel and moisture limitated fire regimes.

1. Introduction

Wildfires and climate are multiply interactive systems. Fire impacts the climate on

various spatial and temporal scales and climatic parameters are major drivers of fire risk, fire

occurrence, fire evolution and fire loss. The effects of climatic change on wildfire and

ecosystems affected by wildfire have ultimately to be studied in earth-system simulations

including the relevant climate-fire feedbacks. A preliminary assessment is possible utilizing

data from simulations of future and past climates.

Under transient projections of anthropogenic climate change, Liu et al. (2010) find a

likely global increase in wildfire potential. The authors stress the dependence of the results on

the representation of land cover change.

Simulations of the climate of the last millennium are another testbed for studying the

development of global and regional fire risk under changing climatic conditions. They offer

the possibility to assess the natural variations of fire risk under external forcings undisturbed

by anthropogenic greenhouse gas emissions, which further is of relevance for earth system

model development with respect to fire in deep time climates. The effect of anthropogenic

land cover changes on wildfire potential can be evaluated from such long simulations, if these

changes are included in the forcing components of the simulation.

Various measures are meant to approximate the fire risk based on one or several

climatic parameters. One of these is the CPTEC-INPE index, which is operationally applied in

Brazil. The calculation is mainly based on temperature, precipitation and vegetation cover.

Thus its computation includes the major parameters thought to be affected in a changing

climate and the anthropogenic component of land use. However, it ignores the different

limiting factor in different fire regimes (i.e. fuel or moisture limitation).

Applying the CPTEC-INPE, the present study shortly presents how fire risk evolves in

the community simulations of the last millennium. After a short description of the algorithm

and the utilized data, section 3 assesses fire risk and its variability in the virtual reality of the

chosen climate simulation.

2. Data and methods

Model data: Spatio-temporal variability of fire risk is assessed in data taken from the

Community Simulations of the Last Millennium performed at the Max Planck Institute for

Meteorology (Jungclaus et al., 2010). One of three simulations with strong solar forcing

amplitude is selected (out of a total of eight full forcing simulations). 1200 years of data are

utilized for the period from the year 800 to the year 2000. This selection allows the evaluation

of fire risk in one long climate simulation with notable global scale temperature variations

(Figure 1). The Max Planck Institute for Meteorology Earth System Model (MPI-ESM)

consists of the general circulation models for the atmosphere ECHAM5 (Roeckner et al.,

2003) and for the ocean MPI-OM (Marsland et al., 2003). The resolutions are T31 with 19

levels for ECHAM5 (3.75 degree) and conformal mapping with a horizontal resolution from

22 km to 350 km for MPI-OM. Time-varying snow cover influences the surface albedo on

land (Roeckner et al., 2003), however land-ice and glaciers are not time-varying. The carbon

cycle is modeled by the ocean biogeochemistry module HAMOCC5 (Wetzel et al., 2006) and

the land surface scheme JSBACH (Raddatz et al., 2007). Vegetation data stems also from the

JSBACH routines. The forcing includes the land cover change reconstruction of Pongratz et

al. (2008).

For the calculation of fire risk, daily sums of precipitation, daily maximum temperatures

and daily minimum relative humidity values are necessary. In addition, average monthly

mean leaf area index values are utilized for each grid box as obtained from the JSBACH land

subsections. This is the most crude possible approximation of the land cover per grid box.

The algorithm of fire risk calculation nearly completely follows the operational fire risk

calculation of the CPTEC-INPE-index in Brazil as presented by Justino et al. (2010, and

references therein). Some minor modifications are necessary as the application is here

extended to all grid cells of the global model with values of leaf area index. In the following

the sequence of the fire risk index calculation is shortly outlined including comments

concerning the changes, the full description is given in the Appendix based on the description

of Justino et al. (2010). Note again, the calculation ignores possibly fuel-limited fire regimes

and assumes moisture limitation everywhere, which definitely is not reasonable.

For the calculation, first accumulated precipitation in millimeters is determined for a

model grid box and each day for eleven preceding periods of 1, 2, 3, 4, 5, 6–10, 11–15, 16–

30, 31–60, 61–90 and 91–120 days. Then precipitation factors are established (values

bounded by 0 and 1) for the eleven periods following the equations in the Appendix. A

parameter for days of drought is derived.

Afterwards, the basic fire risk potential is computed dependent on the mean monthly

leaf area index of each grid box. The basic fire risk BF is bounded by a maximum value of

0.9. In the operational index, five classes exist for the vegetation with values between 1.715

for dense tropical rain forest and 4 for no forest areas. Here, this parameter, AL, follows the

arbitrary relation AL= 5 – LAI *0.5 with the monthly mean LAI over a range from 0 to

approximately 6.5. This results in some notable deviations in the basic characterization of the

vegetation type in comparison to e.g. the work of Justino et al. (2010), but appears to be in

line with fire risk dependence on the LAI reported by Pechony and Shindell (2009) or Arora

and Boer (2005). Obviously low values of AL imply that vegetation is less sensitive to a

drought period before high fire risk may be assessed.

The operational methodology includes two corrections of the basic risk with respect to

the concurrent weather conditions. In a first step the daily minimum relative humidity is used

to adjust for the humidity impact on fire risk. Risk is reduced for relative humidity larger than

40% and increased below. A second correction considers the influence of daily maximum

temperatures. Here the correction is utilized as presented by Justino et al. (2010). Thus risk

increases for temperatures above 30°C and decreases below. The temperature adjusted fire

risk is then taken as fire risk index (FI) for the specific grid point on that specific day.

Following the equations in the Appendix, the FI then in theory should be bounded by 1

with critical fire risk values being larger than 0.9. However, due to the adjustment to the

vegetation classes and the application to global climate model output, the values of FI can

become larger than 1, as is obvious from the expressions for the corrected fire risk (HF and

TF, see Appendix). Indeed even negative values are possible in certain climates. The further

fire risk classes are minimum (FI smaller than 0.15), low (FI between 0.15 and 0.4), medium

(between 0.4 and 0.7) and high (between 0.7 and 0.9).

3. Analysis of fire risk variability

a. Mean and variability of annual days of fire risk

A global evaluation of the spatial and temporal variability of fire risk over a long model

simulation (1200 years) is alleviated by considering annual values of occurrence of fire risks

above a certain threshold. Here, the number of occurrences are considered which reach at

least a high level of risk (FI>0.7). Annual mean, maximum and minimum frequencies of

occurrence events are depicted in Figure 2.

The pattern of mean conditions for the full 1200 year period agrees broadly with the

mean global Keetch-Byram Drought Index (KBDI) pattern for present times shown by Liu et

al. (2010). The KBDI is seen as another approximation of wildfire potential. Notably different

patterns are found in the northern hemisphere extratropics and especially the high latitudes

with respect to the fire counts of Krawchuk et al. (2009), which base on Satellite Along Track

Scanning Radiometer data. Earlier fire count maps of Mota et al. (2006) better match the

mean risk maps in Figure 2. The largest number of days with high fire risk according to the FI

occurs south of the Sahara, in southern Arabia and in arid southern Asia with regionally above

350 days of high risk. Up to 350 days of high risk are found on average in Australia, western

South Africa, tropical eastern South America and central mid-latitude South America. Central

and southern North America, Central and Southern Asia, the Mediterranean region and wide

areas of South America and southern hemisphere Africa display up to 275 days of at least

high fire risk. Only East Asia, northern Eurasia and northern North America experience less

than 50 high risk days per year.

Maximum numbers of high fire risk days per year display just an amplification of the

mean pattern (Figure 2), and the pattern of minimum numbers is just a weakening of the

mean. Consistently, the difference between maximum and minimum numbers again mainly

reflects the mean, although a more heterogeneous pattern arises in the northern hemisphere

(Figure 3). Largest differences arise in eastern Africa, Australia, southern Asia, southern

North America and eastern South America. Standard deviations underpin the similarity of the

mean and patterns of variability. Coefficients of variation are large in the high latitudes, but

the largest ones occur in regions where the mean is smaller than one day of high risk per year

(parts of Alaska, Chukotka, the Canadian Arctic and central northern Eurasia).

Interestingly but not surprisingly, the geographic pattern can be interpreted in terms of

the Köppen-Geiger climate classification (e.g. Kottek et al., 2006). A simplified classification

is as follows: Polar climates have on average below one day of high fire risk per year, and

snow climates and fully humid warm temperate climates generally show below 50 days of

high fire risk on average. This climatic distinction is more clearly visible in the minimum

numbers. Note, the southern hemisphere fully humid temperate climates give notably higher

numbers. Arid climates are most prone to high fire risk and dry equatorial winter climates as

well display large numbers of high fire risk occurrences per year. The compliance between

Köppen-Geiger classification, the KBDI-maps of Liu et al. (2010) and fire risk numbers for

the last millennium can be seen as basic confirmation, that the applied calculation of fire risk

validly captures the principal climatic controls of fire risk.

b. Assessment of periods of interest

The simulated evolution of global temperature in Figure 1 suggests two notable cold

periods (early 18th

century, late 15th

century) and a period of exceptional warmth (late 12th

century). The average annual days of fire risk for these periods are compared to the 1971 to

2000 period. Note that the latter period represents a transient climate evolution in contrast to

the rather stable climate for the periods 1151 to 1180, 1461 to 1490 and 1811 to 1840.

The two cold periods both follow strong volcanic eruptions or are parallel to clusters of

strong volcanic activity in the utilized forcing. These episodes of volcanic activity coincide

with relative minima of solar activity. The warm period on the other hand is concurrent to the

medieval maximum in the utilized solar forcing. While local warming or cooling influences

the occurrence of high fire risk, changes in vegetation cover have also to be considered.

Differences in annual mean leaf are index and with respect to the four periods (Figure 4)

strongly reflect the changes in agricultural area (Pongratz et al., 2008), which are used as one

forcing component of the simulation (Jungclaus et al., 2010): e.g. reductions in the LAI in

North America and wide regions of Eurasia are due to an increase in crop land, while in

south-eastern South America the increase in pasture is more important in the reduction. More

pasture increases the LAI in Australia and parts of Africa. The above assignment of climatic

zones to fire risk numbers is modified by these land use changes.

Differences in high fire risk occurrences are similar for both cold periods (Figure 5)

with respect to modern times. Recently, fire risk is reduced in high latitudes of central

Eurasia, Pakistan, the eastern Maritime Continent and to some extent eastern North America.

Most other regions, especially eastern South America, southern Africa, Australia and Europe,

display an increase in the modern period. Differences are mostly below 20 days per year. Both

periods differ most notably in North America. There fire risk is reduced in modern times

compared to the 15th

century, while, with respect to the 19th

century, an increase in high fire

risk numbers is found.

Compared to the late 20th

century, high fire risk is up to 1.5 times more frequent in the

late Middle Ages in southern Asia and eastern North America but, otherwise, it is mostly

about 0.75 to 1 times as frequent. These numbers also apply for the 19th

century period. In

northern Europe in all periods changes reach above 5 times the number of modern high risk

occurrences. However, there the modern mean is below 1 day of high fire risk per year. The

change is below half a local standard deviation of interannual variability in most areas and

reaches above one standard deviation only in Central and eastern Asia, southern America and

a sole grid point in southern Africa.

The 12th

century warm period shares most characteristics with the cold periods, but the

pattern is slightly more heterogeneous. Pronounced reductions in high fire risk are visible in

modern times compared to the 12th

century in North America, tropical Africa, central Asia,

western South America and the Iberian Peninsula. Modern time changes are weaker in

southern Africa with respect to the warm period compared to the cold periods. Warm period

fire risk numbers are nearly everywhere between 0.75 and 1.5 times the modern. An increase

over Scandinavia is the most notable change beside large scale signatures in central northern

Eurasia and central northern Canada. Only few grid points show changes exceeding one

standard deviation.

c. Spatio-temporal evolution

The prior description indicates a dependence of the fire risk variability on the changes in

land use. For example, the fire risk in North America appears to continuously reduce from the

12th

century until modern times. In the following, the temporal variability of high fire risk

occurrences and the climatic parameters temperature and precipitation over land are discussed

in terms of global and regional zonal averages. The hemispheric asymmetry of land masses

results in an amplified signal of southern hemispheric anomalies in the global depiction.

Figures in this section generally are 31 year moving averages of anomalies with respect to the

full 1200 year period.

Global zonal averages of moving averaged high fire risk occurrence anomalies are

increased since the mid 19th

century at near all latitudes (Figure 6). High fire risk numbers are

further found near globally in the late 18th

century and less uniformly in the early 17th

century.

These periods are intersected by periods of reduced risk occurrence numbers. Most

pronounced is a reduction in risk from the mid 15th

century until about the year 1600. Earlier,

the fire risk numbers are generally high and reduced numbers occur only in the 11th

and 13th

centuries. Southern hemispheric anomalies are more pronounced. Note the positive anomaly

at the beginning of the studied period.

Zonal averages of moving mean precipitation anomalies display a relative drying since

the early 19th

century in the northern hemisphere. Similar tendencies are found in the southern

hemisphere in the second half of the 20th

century. The northern hemisphere is generally wetter

until 1400 and drier afterwards. Southern hemisphere precipitation is more variable and shows

more strong anomalies with wet episodes in the early 11th

, 14th

and 15th

and late 19th

centuries.

Dryness and wetness agree well with enhanced and reduced numbers of high fire risk

especially in the southern hemisphere.

Temperature anomalies are generally well aligned globally with changes in the high fire

risk occurrence numbers. Phases of globally reduced fire risk are consistent with cooling in

the 11th

century, the late 12th

century, from the mid 15th

until the mid 18th

century and in the

19th

century. Warming at the end of the first millennium AD and in the 12th

century is more

pronounced in the northern hemisphere. Contrary, warming appears to be more intense and

more rapid in the southern hemisphere in modern times.

Considering regional zonal averages (Figure 7), fire risk is generally enhanced in the

larger European sector from the late 17th

century onwards with a prior strong increase in the

12th

century. Occurrence numbers are most notably reduced south of 60 N in Europe in the

15th

century. The evolution of high fire risk is similar for eastern and to some extent also

central Eurasia, but there high risk numbers are reduced in the early 19th

century. Southern

central Eurasia displays a strong increase since the late 19th

century.

Negative anomalies are seen for North America since the early 19th

century and

relatively strong positive anomalies are found there in the 18th

century. Anomalies of reduced

fire risk are prominent south of 55 N in western North America in the 15th

and 16th

centuries.

The tropics and the southern hemisphere show pronounced negative anomalies in the

15th

and 16th

centuries. Otherwise multi-decadal fluctuations dominate of enhanced or reduced

risk numbers with notable negative anomalies in the 19th

, the early 18th

and the late 17th

centuries.

For precipitation, the globally zonal averaged picture remains valid for the northern

hemisphere regions, the first half of the simulated period is wetter and the later period drier

than the average (Figure 8). Notable is the drying since 1800 for Eurasia which is less

pronounced in central longitudes. A wetting tendency can be seen in western North America

from the late 19th

century and for the 20th

century. Anomalies south of 15 S are very similar in

the three southern hemispheric sectors. Precipitation fluctuates on multi-decadal timescales in

the southern hemisphere as also found for the fire risk occurrences. Recent decades are

notably drier in the Australian sector.

Regional temperatures evolve very similar to each other (Figure 9). The 20th

century

warming is more pronounced in the southern hemisphere and in tropical and subtropical

Eurasia. This differs from medieval warmth, when the largest warm anomalies are seen in

high latitudes. A 14th

century cool anomaly is mostly restricted to latitudes south of 50 N.

In sum, temperature changes are globally well aligned with changes in fire risk

occurrence as are changes in annual precipitation amount in the southern hemisphere. The

first half of the last Millennium was rather wet and the second half drier (in the considered

model simulation). Regionally in Europe and Asia risk is notably enhanced since the late 17th

century except for central Eurasia and the increase is especially strong in southern latitudes

since the late 19th

century. Contrastingly, fire risk numbers are reduced in North America

since the early 19th

century. In the southern hemisphere, risk anomalies fluctuate on

multidecadal timescales as do precipitation variations. Northern hemispheric regional

precipitation recovers the global picture with a wetter (drier) early (late) half of the

Millennium. Especially Eurasia dries since the late 18th

century, while western North America

seems to have become wetter in the later Millennium. For the southern hemisphere, we note a

drier Australian sector. Regional temperatures are similar, but the southern hemisphere

warming is more intense than the warming in the northern hemisphere.

The prior description depicts the temporal evolution of the relation between annual high

fire risk occurrence numbers and the three main parameters in its calculation. Mapped

correlation coefficients underpin these results (Figure 10). Strong negative correlations are

found between high fire risk and precipitation in the southern hemisphere and southern Asia

and reasonable high correlations between temperature and fir risk occur in South America and

Africa. Absolute values of correlations between the LAI and high fire risk numbers are largest

in eastern South America, the Sahel, southeastern Asia and the coast of the Arabian Sea.

Relations are overall weaker in the northern hemisphere extratropics.

4. Summary

Fire risk and its variability is assessed based on a simplified risk algorithm in the virtual

reality of an earth system model simulation over the period of the last millennium. Note, the

algorithm neglects possible fuel limitations of regional fire regimes.

Another caveat of the presented assessment has to be mentioned. The applied climate

simulator is known to produce wet high latitude climates, which further biases the fire risk

assessment. Furthermore, presented Figures indicate a temperature dominated fire risk in

southeastern Asia, where one would assume a leaf area index and fuel dominated fire risk.

The following is noted with respect to climate limited fire risk over the considered

period: Fire risk occurrence numbers mainly reflect the climate classification of the

geographic region. Zonal averages display an increase in fire risk numbers since the mid 19th

century at near all latitudes. Low frequent variability of fire risk follows basically the climatic

parameters temperature and precipitation.

However, large simulated climate fluctuations do not necessarily result in large regional

changes in fire risk. That is, both warm and cold episodes in the past display similar changes

in fire risk compared to fire risk in the period 1971 to 2000. Correlations of fire risk with

temperature and precipitation are highest in the southern hemisphere, whereas eastern Asia

and the Sahel correlate with the vegetation cover change.

A. Appendix:

The fire risk calculation: The present algorithm nearly completely follows the operational fire

risk calculation of the CPTEC-INPE-index in Brazil as presented by Justino et al. (2010, and

references therein). Some minor modifications are necessary as the application is here

extended to all grid cells of the global model output with values of leaf area index. In the

following the sequence of the fire risk index calculation is given including comments

concerning the changes.

(i) Determination of accumulated precipitation in millimeters for a model grid box and eleven

preceding periods of 1, 2, 3, 4, 5, 6–10, 11–15, 16–30, 31–60, 61–90 and 91–120 days.

(ii) Establish precipitation factors (values bounded by 0 and 1) for the eleven periods

following these equations

P1 = exp (−0.14Prec) (1)

P2 = exp (−0.07Prec) (2)

P3 = exp (−0.04Prec) (3)

P4 = exp (−0.03Prec) (4)

P5 = exp (−0.02Prec) (5)

P6_10 = exp (−0.01Prec) (6)

P11_15 = exp (−0.008Prec) (7)

P16_30 = exp (−0.004Prec) (8)

P31_60 = exp (−0.002Prec) (9)

P61_90 = exp (−0.001Prec) (10)

P91_120 = exp (−0.0007Prec) (11)

(iii) The relation

DD = 105 × ( P1 × . . . × P91_120 ) (12)

is taken to represent days of drought

(iv) A basic fire risk potential is computed dependent on the mean monthly leaf area index of

each grid box,

BF = 0.9 × ( 1 + sin ( AL × DD - 90)) / 2 (13)

The basic fire risk BF obviously is bounded by a maximum value of 0.9. The equation implies

a reduction in fire risk for high values of AL after a certain threshold. Thus, for length of

drought beyond this threshold, the basic potential is set constant to 0.9 high value.

In the operational index, five classes exist for AL, and values vary between 1.715 for dense

tropical rain forest to 4 for no forest areas. Here, AL follows the arbitrary relation

AL= 5 – LAI *0.5

with the monthly mean LAI over a range from 0 to approximately 6.5. This results in some

notable deviations in the basic characterization of the vegetation type in comparison to e.g.

the work of Justino et al. (2010), but appears to be in line with fire risk dependence on the

LIA reported by Pechony and Shindell (2009) or Arora and Boer (2005). Obviously low

values of AL imply that vegetation is less sensitive to a drought period before high fire risk

may be assessed.

(v) The operational methodology includes two corrections of the basic risk with respect to the

concurrent weather conditions. In a first step the daily minimum relative humidity is used to

adjust for the humidity impact on fire risk. Risk is reduced for relative humidity larger than

40% and increased below. The linear correction follows

HF = BF × (−0.006 × RHmin + 1.3) .

A second correction considers the influence of daily maximum temperatures. Here the

correction is utilized as presented by Justino et al. (2010). Thus risk increases for

temperatures above 30°C and decreases below.

TF = HF × (0.02 × Tmax + 0.4)

The temperature adjusted fire risk is then taken as fire risk for the specific grid point on that

specific day. That means the fire index FI equals TF.

The FI then in theory should be bounded by 1 with critical fire risk values being larger than

0.9. However, due to the adjustment to the vegetation classes and the application to global

climate model output, the values of FI can become larger than 1, as is obvious from the

expressions for HF and TF. Indeed even negative values are possible in certain climates. The

further fire risk classes are minimum (FI smaller than 0.15), low (FI between 0.15 and 0.4),

medium (between 0.4 and 0.7) and high (between 0.7 and 0.9).

Figure 1: Global mean 2 meter temperature. Black annual values, red the 31 year running

mean.

Figure 2: Mean, maximum and minimum fire risk occurrence numbers.

Figure 3: Standard deviation, coefficient of variation and maximum minus minimum

difference of fire risk occurrence numbers.

Figure 4: Leaf Area Index difference between modern times and the noted periods.

Figure 5: Fire risk differences, modern times minus periods of interest as noted.

Figure 6: Zonal averages of high fire risk occurrence numbers, temperature and precipitation

mm/day (top to bottom).

Figure 7: Regional zonal averages of fire risk occurrence numbers. Sectors as in the titles of

the panels.

Figure 8: As Figure 7 but for precipitation.

Figure 9: As Figure 7 but for temperature.

Figure 10: Correlations between fire risk and a) LAI, b) temperature and c) precipitation.

Acknowledgements:

Discussion with Sylvia Kloster is acknowledged.

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