NHESSNatural Hazards and Earth System ScienceNHESSNat. Hazards Earth Syst. Sci.1684-9981Copernicus GmbHGöttingen, Germany10.5194/nhess-15-2037-2015 Multi-variable bias correction: application of forest fire risk in present and future climate in SwedenYangW.wei.yang@smhi.seGardelinM.OlssonJ.https://orcid.org/0000-0001-5907-4061BosshardT.Swedish Meteorological and Hydrological Institute, Norrköping, SwedenW. Yang (wei.yang@smhi.se)11September20151592037205714November201430January201529June201510July2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://nhess.copernicus.org/articles/15/2037/2015/nhess-15-2037-2015.htmlThe full text article is available as a PDF file from https://nhess.copernicus.org/articles/15/2037/2015/nhess-15-2037-2015.pdf
As the risk of a forest fire is largely influenced by weather, evaluating
its tendency under a changing climate becomes important for management and
decision making. Currently, biases in climate models make it difficult to
realistically estimate the future climate and consequent impact on fire
risk. A distribution-based scaling (DBS) approach was developed as a
post-processing tool that intends to correct systematic biases in climate
modelling outputs. In this study, we used two projections, one driven by
historical reanalysis (ERA40) and one from a global climate model (ECHAM5)
for future projection, both having been dynamically downscaled by a regional
climate model (RCA3). The effects of the post-processing tool on relative
humidity and wind speed were studied in addition to the primary variables
precipitation and temperature. Finally, the Canadian Fire Weather Index
system was used to evaluate the influence of changing meteorological
conditions on the moisture content in fuel layers and the fire-spread risk.
The forest fire risk results using DBS are proven to better reflect risk
using observations than that using raw climate outputs. For future periods,
southern Sweden is likely to have a higher fire risk than today, whereas
northern Sweden will have a lower risk of forest fire.
Introduction
A forest fire is an uncontrolled fire event. It can exert a destructive
influence on ecosystems, affecting climate and weather (Flannigan, 2009). On
the other hand, it also has beneficial effects on wilderness areas where
some species depend on prescribed fire for growth and reproduction (Brockway
and Lewis, 1997) and on fire hazard reduction (Fernandes and Botelho, 2003).
Forest fire activity is strongly affected by two factors: weather conditions
and availability of fuels. The weather conditions directly and indirectly
affect fire behaviour during both ignition and burning by influencing the
fuel conditions, especially through the moisture content in the uppermost
dead fuel (Fosberg and Deeming, 1971). Over the past century, global warming
caused by an anthropogenic increase in greenhouse gases has shown its impact
on present climate (IPCC, 2007). This is likely to have even more of an
impact if these gases continue to increase with human activities. The
changing climate will thus likely accelerate the water cycle on a global
scale, subsequently intensify the uneven distribution of precipitation, and
cause more extreme weather conditions locally (IPCC, 2013). Studying the
changes in fuel conditions caused by changing climate is hence important for
decision making, both for public authorities and in forest management.
In an international context, the forest fire risk in Sweden is limited. Owing
to efficient fire suppression, during years with average or low fire hazard,
the total annually burnt area of forest has not commonly exceeded 5000 ha
since 1950s. However, during the high-hazard years the burnt area can be
substantial, for instance, the fires in Gotland (1992, 1000 ha), Tyresta
(1999, 450 ha), Bodträskfors (2006, 1900 ha), Hassela (2008, 1300 ha)
and the most recent one in Sala (2014, 13 100 ha) that caused damage valued
at around SEK 1 billion (MSB, 2015; Skydd and Säkerhet, 2014).
Today, most of the ignitions are human-caused, followed by lightning
ignition (Granström 1993). Extreme weather conditions, such as the
conditions prior to and during the Sala fire (i.e. extremely low relative
humidity, strong wind speed and extreme high temperature), are also one of
the causes that make fuels conductive to ignition and spread (Fendell and Wolff,
2001; Ryan, 2002). Dendrochronological fire studies have indicated a large
temporal and spatial variability in fire activity in Sweden during the last
500 years (Niklasson and Granström, 2000; Drobyshev et al., 2014). A
recent study by Drobyshev et al. (2014) reveals that a geographical division
between one northern and one southern region with different characteristic
fire activity could be found around 60∘ N.
In climate change studies, global climate models (GCMs) and regional climate
models (RCMs) are widely used tools to simulate climate at different scales.
RCMs in general outperform GCMs in many aspects due to (1) a better
representation of geographical features such as orography, thanks to finer
spatial resolution (typically at 25–50 km), and (2) a better description of
physical processes by means of, e.g. sub-grid-scale parameterisation and
more detailed land surface schemes (Giorgi and Marinucci, 1996; Samuelsson et
al., 2010). However, the mismatch between RCM-simulated and observed
climatological conditions still cannot be neglected. A study conducted by
the Swedish Commission on Climate and Vulnerability (SOU, 2007) demonstrated
the limitations of using raw data from a climate model for forest fire
danger estimation, as historically simulated fire danger levels were
consistently lower compared to risk levels estimated using meteorological
observations. This discrepancy is very likely caused by biases in driving
variables from climate model outputs.
One conventional approach to tackle climate model bias is the delta change
method by which an observed data time series is perturbed with a projected
climate change (Flannigan et al., 1991; Stocks et al., 1989; Hay et al.,
2000). Typically, the changes in long-term climatology on a monthly or
seasonal basis are superimposed on the observation records over the entire
frequency distribution, i.e. for both extreme and normal events. This
approach is easy to implement and keeps exactly the same change in
climatological mean in meteorological variables as that in climate
projection, but with two limitations. The first limitation is that only
average change in monthly variables is incorporated. The variance in future
climate comes either from observed data or from perturbed data, but it does
not directly come from climate projection. The second limitation is that
changes in regional climate (i.e. one grid cell) are assumed to be the same
for all locations in the same region, which is very unlikely to be true.
Another widely used approach in forest fire risk studies is built on the
statistical relationship of weather conditions on the point scale
(i.e. single station) and at its corresponding climate model grid cell (Mearns et
al., 1995; Logan et al., 2004). The approach has been applied in a number of
case studies (Bergeron and Flannigan, 1995; Wotton et al., 2003). By this
approach, various correction processes were designed for different
variables: (1) precipitation frequency and humidity magnitude are corrected
using the statistical relationship identified under present climate; (2) noon
temperature is simply estimated as modelled maximum daily temperature minus
2.0 ∘C and (3) wind speed comes directly from model output and
remains uncorrected. This approach makes model output more realistic for use
in fire risk studies; however, it merely treats a small part of the bias in
variables in a simple way, that is, the frequency of rainy days is corrected
but not precipitation magnitude; humidity variables are corrected in terms
of long-term mean but without consideration of variance; no treatment is
carried out for bias in modelled maximum daily temperature and wind speed.
Recently, the quantile-mapping approach has been developed to correct bias
in climate model outputs. The approach mainly focuses on correcting the
biases in precipitation (and/or temperature) from RCMs to better reflect
observations via mapping either parametric or non-parametric cumulative
distribution functions (CDFs) to observed and projected climate variables
(Piani et al., 2010; Themeßl et al., 2011; Yang et al., 2010). A few
studies have focused on correcting RCM bias in other hydrologically relevant
meteorological variables, e.g. relative humidity, wind speed and solar
radiation (Wilcke et al., 2013).
This study presents work regarding the forest fire risk in Sweden under
changing climate. The forest fire model, observations and climate data are
introduced in Sect. 2. The systematic bias originated from RCMs is removed
by one of the quantile-mapping approaches, the distribution-based scaling (DBS),
which is extended to support bias correction of wind speed and
relative humidity (see Sect. 3). Following the experimental set-up in
Sect. 4, the newly developed approach was calibrated and validated, and
then further applied to the impact study. Ultimately, an impact study was
carried out via two RCM simulations, one reanalysis-driven historical run
for method development and validation under present climate and one
GCM-driven future projection for estimating the climate change impact. Their
corresponding results are discussed in Sect. 5. At the end of the paper,
some conclusions and remarks on future development are given in Sect. 6. A
summary of acronyms and variables are listed in Table A1a and b.
Fire risk model and dataFire Weather Index system (FWI)
The Fire Weather Index system, FWI, is a major component of the Canadian
Forest Fire Weather danger rating system (Stocks et al., 1989). It was
originally designed for a standardised forest type in Canada and has lately
been used for fire danger estimation by many other countries (Viegas et al.,
1999; Carvalho et al., 2008).
The details of the application of the FWI can be found in Van Wagner (1987)
and Dowdy et al. (2009). Here, only the key features of each component are
summarised. The FWI system tracks daily moisture content variations in three
stratified fuel layers in forests, coded as primary indices: the Fine Fuel
Moisture Code (FFMC), the Duff Moisture Code (DMC) and the Drought Code (DC).
For every index, two phases are considered: the rainfall phase and the drying
phase. They are determined by a threshold value given as an empirical value
in the FWI literature for the purpose of each index. Any rainfall below the
threshold value is to be ignored in individual layers. As the three layers
differ in fuel type and in their connections to the weather conditions in
the proximity, they play different roles in potential fire behaviour. What
they have in common are the influencing factors. They are present as
moisture content in the fuel, drying rate and weather states of being dry or
wet (i.e. rainy or non-rainy days).
Primary indices: FFMC, DMC and DC
The uppermost surface layer, described by the FFMC, responds rapidly to the
short-term changes in weather conditions that are described by
precipitation, P (mm), temperature, T (∘C), relative
humidity, RH (%) and wind speed, W (m s-1). It is the most important
layer in the FWI and other fire risk models when assessing fire risk.
The middle layer is a loosely compacted organic layer on the forest floor.
The DMC was designed to reflect its average moisture content. It gives an
indication of the slow-drying forest fuel consumed in burning. This layer is
influenced by all input variables except wind speed. Again, the moisture
content, mc (%), is an indicator to reflect the moisture condition in the fuel.
In contrast to the computation in the FFMC layer, the drying rate, k
(log10 % day-1), in the DMC layer is calculated as proportional
not only to temperature and the deficit in relative humidity but also to the
day length varying with season, Le (h).
The bottom layer is a very slow-drying compact organic fuel in the deeper
soil layers. Its corresponding code, DC, reflects the influence of long-term
drying on the fuels (Turner, 1972). It is used to detect extremely long dry
conditions in lower layers of deep duff, which may result in persistent smouldering.
This layer does not have direct contact with the atmosphere. It only absorbs
moisture through rainfall and dries out through the evapotranspiration
process. Therefore, its final code computed from moisture equivalent is a
function of the previous code value and potential evapotranspiration, V
(mm day-1).
Range of FWI (Fire Weather Index) for fire danger classes in Sweden.
Integral indices: Build-Up Index, Initial Spread Index and Fire Weather Index
The Build-Up Index (BUI) and the Initial Spread Index (ISI) are two
intermediate sub-indices computed based on the aforementioned primary
moisture indices. They were designed to describe the fire behaviours, the
available fuel and the rate of fire spread for combustion. BUI is built up
by the combination of the DMC and the DC. It indicates all fuel available
for consumption during the burning process. ISI is computed by combining
moisture content in the fine fuel and W using a wind function, f(W), and a fine
fuel moisture function, g(FFMC) (Van Wagner, 1987). It is used as an indicator for
the potential rate of fire spreading.
Ultimately, the Fire Weather Index (FWI) is an integrated function of a
function of ISI, h(ISI), and a function of ISI, l(BUI), to represent fire intensity as
energy output rate per unit length of fire front.
Application of the FWI system in Sweden
At SMHI, the original FWI system has been run operationally since 1998. In
Gardelin (1997), the FWI model was evaluated by comparison with forest fire
statistics in the eastern parts of Kalmar and Jönköping County where
675 fires were reported from 1989 to 1994. Fire danger classes (FWIX) for
different FWI ranges have, however, been corrected to be suitable for
Swedish conditions (Table 1) (Gardelin, 1997). Since 1999 the system has been used
to make nationwide fire risk forecasts at 11 km × 11 km resolution during the
fire season from April to October. The estimated fire risks serve as the
basis for general forest fire warnings to the public, rescue services and
emergency centres in Sweden. Previous studies concluded that the original
FWI system generally works well for Swedish conditions (Gardelin, 1997;
Granström and Schimmel, 1998). Strong relationships between index levels
(FFMC, DMC and DC) and measured moisture content were found. The
relationships vary highly, depending on the fuel types. Additionally,
the final FWI index well represented the forest fire statistics in terms of
number of fires and burnt area for the forest fire-prone regions during past
and present climate in Sweden. The FWI system is therefore chosen for
climate change impact studies.
DataObservations
Data were compiled from meteorological stations with observed 24 h accumulated
precipitation (P-obs) as well as temperature (T-obs), wind speed (W-obs) and
relative humidity (RH-obs) at 12:00 UTC, covering a reference period
from 1966 to 2005. They were extracted from the Swedish network of
observation stations (see Fig. 1) with at least 30-year long measurements
with less than 20 % missing values in the reference period, to ensure
coverage of various climate phenomena. The following requirements were
considered: (1) data must be geographically evenly distributed to represent most of the
Swedish climatic regions and (2) observations must be of a high quality. It should be
emphasised that wind speed is inherently hard to measure in a consistent way
over long time periods because the instruments are repositioned, nearby
buildings are put up or torn down, forests grow up or get cut, etc.
Nevertheless, some findings can be summarised by analysing the observations,
which will be described in Sect. 5.1.1.
RCM simulations
Two climate simulations, denoted as RCA3-ERA40 and RCA3-E5r3-A1B, were used
in this study. They were both dynamically downscaled to 25 km resolution by
the RCM, the RCA3, but driven by different large-scale forcing data as
lateral boundaries. The RCA3 is the third full release of the Rossby
Centre regional climate model, developed at the Swedish Meteorological and
Hydrological Institute (SMHI) (Samuelsson et al., 2010). For many
near-surface variables, the RCA3 represents the European climate well when
compared to other RCMs (Hagemann et al., 2004).
The RCA3-ERA40 simulation uses the ERA40 reanalysis data as its boundary
condition and covers the period from 1961 to 2000. It is assumed to
represent the reality as represented by local observations and was therefore
used to verify the methodology in this paper. The RCA3-E5r3-A1B transient
projection from 1961–2100 is based on the ECHAM5 GCM (Roeckner et al.,
2006), forced with the IPCC emissions scenario A1B, an intermediate scenario
with respect to the magnitude of future global warming (Nakicénović
et al., 2000). In this experiment, the RCA3-E5r3-A1B projection was first
evaluated for past climate and then used for future impact assessment.
Within the ensemble of 16 climate projection studies by Kjellström et
al. (2011), RCA3-E5r3-A1B represents projections in the small-to-medium
range with respect to the expected future increase of both P and T.
The same variables as those collected at observation stations were extracted
for the following experiment. They are grid-averaged daily precipitation
(P-raw), 2 m temperature (T-raw), 2 m relative humidity (RH-raw) and 10 m wind
speed (W-raw). Time series from the RCA3 grid cell covering each of the
stations were used.
Map showing the locations of the observation stations.
RCM bias correction for fire risk modelling
The DBS method is a parametric quantile-mapping approach. It aims to correct
systematic bias in GCM/RCM outputs while preserving the temporal variability
in meteorological variables resulting from climate projections over time. In
DBS, as opposed to common non-parametric quantile-mapping approaches,
meteorological variables are fitted to appropriate parametric distributions
that allow for generation of values outside the range of the reference
period and thus simulation of previously unobserved conditions in future
climate periods.
The general form of the DBS approach is
xSimCorr=FObs-1FSimxSimOrg,γSim,φSim,γObs,φObs,
where γ and ϕ are distribution parameters estimated from the
climate model (subscript Sim) and from the observations (subscript Obs) by
the maximum likelihood estimator (MLE), the method of moments, iterative or
other approximate methods; xSimOrg is the original output of
variable x simulated by a climate model and xSimCorr is the result
after correction. FSim and FObs-1 stand for the cumulative
distribution function (CDF) and its inverse of a suitable parametric
distribution for each variable of interest.
The distribution parameters of precipitation are estimated for every season,
whereas the distribution parameters of other variables are estimated using a
31-day moving window for every Julian day, and Fourier series are used to
describe the distribution parameters over the year in a smooth way:
γt*=a02+∑k=1Kakcos(kwt⋅)+bksin(kwt⋅)ϕt*=c02+∑k=1Kckcos(kwt⋅)+dksin(kwt⋅),
where a0, ak, bk, c0, ck and dk are the Fourier
coefficients, t* is the day of the year; w equals 2π/n, where n is the
time units per cycle (in our case 365 days) and k stands for the nth harmonic.
Theoretically, (t*/2+ 1) harmonics are able to represent a
complete cycle perfectly, with the drawback of a potential overfitting of
the data. Five harmonics have been found to be sufficient in Yang et al. (2010).
DBS for P and T: an overview
A detailed description of the DBS for P and T correction can be found in a
previous study by Yang et al. (2010). In the following, only a summary is given.
To tackle the common RCM bias in terms of the overestimated frequency of
rainy days with small rainfall amount (i.e. wet frequency bias, “drizzle
effect”) a cut-off value is identified as a threshold to correct the
frequency of rainy days (P> 0.1 mm) in climate projections. Any
drizzle, generated by the RCM model, with intensity smaller than the
threshold is removed, and the day with the drizzle is treated as a dry day.
Dry frequency bias, i.e. the tendency of RCMs to underestimate wet-day
frequency, is rather uncommon in Europe but may occur, e.g. during summer
in south-eastern Europe and in the Alps (Hagemann et al., 2004; Jacob et
al., 2007). In the current DBS method, such wet-day deficit is handled by
adding a small rainfall amount at the end of wet spells, starting with the
longest ones, until the correct frequency is obtained. In-depth analysis and
research work are progressing.
After the precipitation frequency bias has been corrected, the remaining
modelled precipitation is then transformed to match the distribution of
observed precipitation. The full time series is divided into two partitions
separated by the 95th percentile identified from sorted observation
records and model simulation. This approach intends to capture the main
properties of normal low- to medium-intensity precipitation as well as the
high-intensity extremes. A double-gamma distribution, instead of a
conventional gamma distribution, is accordingly implemented. Two sets of
parameters – α, β (normal precipitation) and
α95, β95 (extremes) – are estimated by the maximum
likelihood estimator (MLE) from observations and from the RCM output in the
reference period. The fitted scaling parameters are then applied to correct
the RCM outputs for the entire projection period by Eq. (1). For impact
studies in Europe, four seasons are normally used. They are winter
(December–February), spring (March–May), summer (June–August) and autumn (September–November).
Daily temperature values are described using a Gaussian distribution. For
every Julian day, the distribution parameters, μT and σT,
are estimated from observations and RCM data. Considering the
dependency between P and T, the statistics of temperature are calculated
separately for wet days (i.e. rainy days) and dry days (i.e. non-rainy days).
DBS for RH and W: method development
The approach for correcting RH and W is similar to that for daily P and T. The
factors used to scale RH and W were defined conditioned on the location of the
station and the season of interest. For wind speed scaling, the
precipitation state (i.e. wet or dry) is considered as an influencing factor.
Relative humidity is different than other variables in that its value is
restricted to the interval of [0, 1]. To cope with this property, the
commonly used Beta distribution (Yao, 1974) is chosen, the density
distribution of which is
f(x)=Γ(p+q)Γ(p)Γ(q)xp-1(1-x)q-1,
where p and q are the two parameters of the distribution and Γ is the
gamma function. By different combinations of p and q, a wide range of
distribution shapes maybe represented. The distribution parameters can be
fitted by the method of moments using the equations below:
μ=pp+qσ2=pq(p+q)2(p+q+1),
where μ and σ are the statistical mean and standard deviation
of the data to be fitted.
The Beta density function is not analytically integrable; however, its
cumulative probability, F, can be obtained through numerical methods by
using the incomplete Beta function (Abramowitz and Stegun, 1984; Press et al., 1986).
Wind speed is an atmospheric variable characterised by properties that are
similar to precipitation, i.e. positive skewness and non-negative property.
It is commonly described by the Weibull distribution (Pavia and O'Brien,
1986; Seguro and Lambert, 2000). Its density distribution is given as
f(x)=κλxλexp-xλκ-1κ,λ,x≻0,
where the two parameters κ and λ are shape and scale
parameters, respectively. The shape parameter, κ, describes numerous
shapes with different magnitudes of positive skewness, while the scale
parameter, λ, controls the stretch of the distribution.
The Weibull distribution has several special forms when setting the shape
parameter κ to different values. For instance, the Weibull
distribution is identical to the gamma distribution when κ equals 1,
and it is very similar to the Gaussian distribution when κ equals 3.6.
It can also be transformed to be an extreme value distribution (EVD)
with location parameter μ=log(κ) and scale parameter
σ=λ-1. Because of its particular properties, it can also be used to solve
other distributions after transformation. The distribution parameters of the
Weibull distribution are conventionally estimated using MLE. As its density
function is analytically integrable, as expressed in Eq. (8), it is
straight-forward to calculate the probability and solve the inverse function:
F(x)=1-exp-xλκκ,λ,x≻0.
Experimental set-up and evaluation
RCM-simulated P-raw, T-raw, RH-raw and W-raw at 12:00 UTC were bias-corrected using
observations from meteorological stations (see Sect. 3). Along with
original outputs from RCMs and observed variables, the corrected variables
were used to drive the FWI system for assessing forest fire danger. The
internal variables (FFMC, DMC, DC) as well as the integrated indices BUI,
ISI, the final index (FWI), and the fire danger classes (FWIX) were all used
for evaluating the influence of the DBS approach.
To validate the approach, 1966–1985 (20 years) was used as the calibration
period for both simulations; 1986–2000 (15 years) was used as the validation
period for the RCA3-ERA40 simulation (as the reanalysis data i.e. ERA40
ends by 2000), and 1986–2005 (20 years) was used for the RCA3-E5r3-A1B
simulation. Basic statistics such as the climatological mean (Avg) and the
standard deviation (SD1) were calculated in both the calibration and
validation periods. For P, the mean value of accumulated seasonal
precipitation (Acc) is used to present its long-term mean. Because of the
discrete-continuous property of precipitation and wind speed, an additional
statistic, the frequencies of rainy and windy days are computed to study how
the model captures their properties. In the following, they are denoted as
Freq-P (i.e. occurrence of days with rainfall amount larger than 0.1 mm) and
Freq-Ws (i.e. occurrence of days with wind speed above 0 m s-1).
Moreover, a standard distance (SD2) was included to investigate the spatial
variations of every variable. It is computed as the standard deviation of
the mean values of all stations. A larger value indicates a higher
variability in space, and vice versa.
Apart from that, how well climate models can capture the observed
probability distribution of individual variables was also studied using a
PDF skill score (SS) (Perkins et al., 2007). The SS is a quantitative
assessment of goodness-of-fit in terms of probability distribution to
evaluate the consistency between two data sets. The results reflect the
agreement, with a perfect agreement resulting in an SS of 1.0 and a poor
agreement in an SS close to 0. In this work, the SS is calculated from
observation, raw and corrected RCM outputs. Its formula is expressed as in
Eqs. (9a) and (9b), where m is the number of bins used to calculate the PDF
for a given variable per station, Zraw (and Zcorrected) is the
probability in a given bin from model simulation before and after bias
correction, respectively, and ZObs is the probability in a given bin
from the observed data.
SSraw=∑1mminZraw,ZObsSScorrected=∑1mminZcorrected,ZObs
All these statistics were calculated from the climate projections' output
before and after bias correction, and observations. For P, RH and W, their
relative differences in Avg were used for bias evaluation, whilst for T, the
differences in Avg were used. In terms of the two SD (SD1 and SD2), the
ratio of their values calculated from model outputs and from the
observations was used to identify the differences in describing the variances.
For future climate change (CC) assessment, the scaling parameters obtained
from the reference periods (i.e. 1966–1995) were applied to individual
variables for the future periods in climate projections. Subsequently, the
corrected variables were used to run the FWI system. The transient future
projections were divided into three 30-year time periods – 2011–2040,
2041–2070, 2071–2100 – for analysing the climate change signals and
influence of the DBS method on meteorological variables and further on the
forest fire danger in the near, intermediate and distant future. The results
for the period 2071–2100 are to be presented in this paper.
This paper focuses on the results for the period from March to November, a
typical fire period for Europe. Thus, the three seasons MAM (March–May), JJA
(June–August) and SON (September–November) are studied in the following. One
station – Edsbyn – is used to illustrate the results from the DBS correction, and
another station – Växjö – is used to present the climate change impacts.
Results and discussionEvaluation for present climateMeteorological variables
Sweden is characterised as a mixture of temperate and continental climate
with four distinct seasons. The seasonal temperature varies on average from
-4 ∘C in winter (not shown here) to 18.3 ∘C in
summer (see Table 2). Due to its large coverage in latitude, the temperature
in Sweden varies greatly from north to south, with 12 ∘C
difference in winter temperature and 6 ∘C difference in
summer temperature (not shown here).
Precipitation in Sweden occurs throughout the whole year. In general, it
often rains less in spring and winter, whereas it rains heavily in summer
and autumn with stronger variability. The rainfall frequency in spring is in
the same range as that in summer, but approximately 21 % less compared to
that in autumn; however, the accumulative precipitation amount in spring is
much lower compared to the other two seasons (i.e. 42.8 % compared to
summer and 50.6 % compared to autumn), which implies drier conditions in
spring (see Table 2 and Fig. 2).
In terms of relative humidity, the distribution varies from season to
season. On average, the relative humidity in Sweden appears to be relatively
low in spring and summer (i.e. in the range of 55–65 %) and reaches
its minimum value in summer. From autumn onward, its value continuously
increases until its annual maximum in winter (see Table 2, Figs. 2 and 3).
Annual mean wind speed in Sweden varies between 2 and 5 m s-1, with an
average of 4 m s-1. In southern Sweden it is generally high because
this region is more exposed to westerly and south-westerly wind. Wind speed
closer to the coast features stronger variability than that in the inner
region. Wind speed in the inner regions of central Sweden such as Edsbyn is
characterised as a general weak annual cycle with the weakest wind in winter
(see Fig. 2).
With respect to its spatial distribution (see SD2 in Table 2) precipitation
is a localised variable, while the rest of the variables are largely
influenced by large-scale effects.
Seasonal variation of the FWI inputs (precipitation, temperature, relative
humidity and wind speed) presented as 7-day moving average values at the Edsbyn
station (see Fig. 1). Comparison of observational data and raw output of the
climate models from RCA3-ERA40 and RCA3-E5r3-A1B simulations (calibration
period 1966–1985).
Probability density functions of precipitation, temperature, relative
humidity and wind speed at the Edsbyn station (see Fig. 1). Comparison of
observational data and raw output of the climate models RCA3-ERA40 and
RCA3-E5r3-A1B (calibration period 1966–1985).
As reanalysis data (i.e. ERA40) are generally assumed to be the closest
data set to the real climate, the deviations from observations in the
RCA3-ERA40 run are considered to mainly reflect RCA3 model bias. The main
findings from a comparison between observed and RCA3-ERA40 simulated climate
statistics include the following (see Tables 2 and 3):
The seasonal precipitation amount is generally overestimated for all three
seasons, whereas variability is in general slightly lower than that of the
observations (see SD1 in Table 2). The climate model estimates the frequency of
wet days with the lowest accuracy for summer, in which almost 100 % bias was
found in comparison to the observations; the overestimation in autumn was 66.7 %
and in spring it was 80.8 %. The average SS had a value of 0.60. Again, the
summer precipitation is the least accurately simulated, with an SS value of
approximately 0.56 (see Table 3). Concerning spatial variability, modelled
precipitation tends to be more unevenly distributed than observations in spring
and summer, which is in contrast to the situation in autumn.
A cold bias appears during all fire seasons. The largest bias (-2.3 ∘C)
was found in summer, whereas the lowest bias (-0.9 ∘C) appeared in autumn.
This is also reflected by the SS being 0.80 for spring, 0.85 for autumn and
0.71 for summer (see Table 3). Similar to precipitation, the spatial variability at
point stations is underestimated by the climate model in autumn (-7.7 %),
whereas it is overestimated by ∼ 30 % in spring and summer.
The variability of relative humidity is in general well reproduced, being
within -2.5 ∼+6.1 % of the observed variance. However, the
magnitude in summer is overestimated by 18.8 %. The largest deviation of
relative humidity is found in summer, followed by autumn and spring. The climate
model generates more days with higher Rh-raw than in the observations. The high
SS for spring (i.e. 0.81) indicates a good match between simulation and observation,
but the skill scores for summer (0.72) and autumn (0.74) are relatively lower
(see Table 3). Again, an overestimated spatial variability (i.e. 148.4 % in
spring and 36.4 % in summer) is found in the modelled data for the fire
season except for autumn (-14.3 %).
Wind speed and its variance are evidently underestimated during all seasons
of interest. Its distribution is positively skewed but with a larger proportion
of low wind speeds and a smaller proportion of high wind speeds in the simulated
data (Fig. 3). In the RCM run, Ws-raw of more than 6 m s-1 seldom occurs,
which differs from that in the observations, in which speeds up to 15 m s-1
occur. The SS is on average 0.70 (see Table 3). In contrast to the other variables,
for modelled wind speed the Std2 is significantly lower (∼-75 %)
than that in the observations. Such a damped spatial variability is noted in
all fire seasons, as shown in Table 2.
Summer is always the season with the largest bias.
One source of bias is the mismatch of spatial scale between station data
(point scale) and RCA3 grid cells (25 km × 25 km). Compared to a
GCM (∼ 200 km), the spatial resolution of the RCMs is clearly more
suited for approximating local conditions, but still the difference in
statistical characteristics between point scale and averages over thousands
of km2 is huge for highly spatially varying variables,
notably precipitation and wind. It should be emphasised that bias is also
caused by measurement errors and uncertainties, e.g. precipitation
undercatch, incorrect temperature observations in cold conditions and
changing surroundings affecting wind gauges.
Statistical characteristics of P (precipitation), T (temperature),
RH (relative humidity) and W (wind speed) during the calibration period (1966–1985) over
14 Swedish stations. Comparison between observation, raw RCA3-ERA40 and raw
RCA3-E5r3-A1B. The bold number in brackets presents the biases between the
modelled value and the observed value in % except Avg (average) of T (temperature)
where it is given as the difference between the modelled and observed value.
PDF skill scores (SS) of raw data from RCA3-ERA40 and RCA3-E5r3-A1B
(1966–1985), averaged over 14 Swedish stations.
Precipitation Temperature Relative humidity Wind speed MeanMinMaxMeanMinMaxMeanMinMaxMeanMinMaxMAMRCA3-ERA400.640.590.690.800.740.860.830.760.870.750.650.84RCA3-E5r3-A1B0.650.600.700.800.750.850.810.760.860.690.570.76JJARCA3-ERA400.560.480.600.710.670.760.720.630.780.700.550.83RCA3-E5r3-A1B0.540.450.600.590.540.630.670.600.720.660.520.76SONRCA3-ERA400.620.580.580.850.890.810.780.740.890.760.620.86RCA3-E5r3-A1B0.610.680.650.820.790.790.740.690.840.680.580.83
Apart from that, the biases are also likely caused by limitations in the
climate models' process descriptions. Biases in precipitation may be linked
to an overestimation of cloud fraction in mountainous areas (Willén,
2008), incorrectly solved convective triggering and lack of details in
geographical information, which lead to unrealistic precipitation
simulation. The cold bias (∼-2 ∘C) in summer and in
autumn over northern Europe may be partly because of an overestimation of
cloud water by the RCA3, which leads to too much short-wave radiation being
reflected and subsequently an underestimation of the incoming short-wave
radiation at the surface (Willén, 2008). Additionally, the bias in
relative humidity in summer may be due to overestimated cloud water that
subsequently leads to an underestimation of maximum summer temperatures over
northern Europe (Samuelsson et al., 2010). In terms of wind speed, a general
bias is noted when comparing model output to long-term climatological means.
This can be attributed to the parameterisation utilised in unresolved
orography, and uncaptured small-scale features, for instance, the influence
of hills, lakes, valleys, etc. Furthermore, the incorrect seasonal wind
speed variation generated by the climate model implies that the RCA3 model
captures large-scale forcing well, but no other influencing processes such as
seasonal variations and atmospheric stability over land and water that
largely influence the wind speed (Achberger et al., 2006). For inland
stations, such as Edsbyn, the seasonal variation in stability over the land
is smaller than that over the sea, which reduces the seasonal wind speed
variation compared to stations close to the sea (Achberger et al., 2006).
However, it seems that Edsbyn was modelled as a coastal location where
winter wind speed is enhanced because of less stably stratified atmosphere
over water and the stronger pressure gradient in winter.
Seasonal variation of the FWI inputs (precipitation, temperature, relative
humidity and wind speed) presented as 7-day moving average values at the Edsbyn
station (see Fig. 1). Comparison of observational data, raw output of the
climate models from RCA3-E5r3-A1B simulation and its corresponding
corrected output (validation period 1986–2005).
Probability density functions of precipitation, temperature, relative
humidity and wind speed at the Edsbyn station (see Fig. 1). Comparison of
observational data, the raw output of the climate model, RCA3-E5r3-A1B, and
its corresponding adjusted output (validation period 1986–2005).
Bias in GCM-forced RCM runs reflects the integral influence of GCM and RCM.
In comparison of the two RCA3 simulations, the reanalysis-forced run
(i.e. RCA3-ERA40) is found to outperform the GCM-forced run (i.e. RCA3-E5r3-A1B),
however, the difference is overall small and their annual cycles are very
similar (see Fig. 2). As shown by the statistics in Table 2 and
frequency distribution in Fig. 3, the RCA3-E5r3-A1B generally performs
similarly or worse in terms of the statistical mean and variability. The
largest differences appear for precipitation simulation for which
RCA3-E5r3-A1B generated up to 105 % higher wet-day percentage and 118 %
more accumulated precipitation than present in the observations in summer.
In terms of precipitation frequency distribution, RCA3-ERA40 tends to
generate a slightly higher number of days with small rainfall amount and
fewer days with extreme amounts. Temperature is another variable with
visible differences between the two simulations. Again, the largest
differences appear in summer in which RCA3-E5r3-A1B is inclined to be
slightly colder and with less variability than RCA3-ERA40. The distributions
of relative humidity and wind speed generated from two simulations are in
general almost identical.
Though the two climate projections are driven by different forcing, many of
their characteristics are highly consistent, implying that the majority of
the biases are likely to originate from the RCM. The alternative conclusion
would be that the ERA40 is as bad as the GCM in simulating the statistics of
these four variables.
As the climate projection forced by GCM is the basis for assessing future
impact, we will mainly focus on evaluating the results from RCA3-E5r3-A1B in
the following.
Effect of the DBS approach
Figures 4 and 5 illustrate how the DBS method improves the FWI input variables.
In the calibration period (not shown here) the bias correction effectively
removed the majority of biases in all of the variables, which is expected as
the bias-correction parameters have been calibrated on the same set of data.
In the following we will focus the analysis on the validation period to
illustrate the effect of DBS.
PDF skill scores (SS) of data from raw and corrected RCA3-E5r3-A1B (1986–2005),
averaged over 14 Swedish stations.
Precipitation Temperature Relative humidity Wind speed MeanMin.MaxMeanMin.MaxMeanMinMaxMeanMinMaxMAMRaw0.620.580.650.780.740.850.750.660.810.730.600.88Corrected0.930.860.960.890.830.910.840.750.880.820.730.93JJARaw0.570.530.600.580.530.610.660.570.730.640.510.77Corrected0.930.910.950.910.890.930.810.750.860.830.730.94SONRaw0.600.570.620.830.800.860.720.660.780.770.630.92Corrected0.930.910.950.900.860.920.830.770.890.840.770.92
The correction was first applied to the two primary variables, P and T. The
cut-off values obtained from the parameter estimation process for
precipitation scaling (see Sect. 3.1) range from 0.6 to 3.2 mm over all
stations during the fire seasons. The largest cut-off value always appears
in summer, followed by autumn and then spring. At Edsbyn station, the
cut-off value varies from 0.85 mm day-1 (spring) to 1.56 mm day-1 (summer).
After removing the bias, the corrected P shows a better match with observed
data over all three seasons, though partial biases in volume still remain,
as shown by Fig. 4. The improvement in temperature is noticeable in terms of
both the full distribution and the annual cycle. The major improvement
occurs for summer and spring where the cold bias appears in modelled data.
The corrected T is statistically equivalent to that from the observations in
terms of climatological mean and standard deviation of temperature
conditioned on dry and wet days. As with temperature, the corrected relative
humidity shows a better annual distribution. The overestimation of relative
humidity is largely reduced, but some bias still remains at the tail of the
distribution. Wind speed gets substantially improved in terms of both
magnitude and annual distribution. The overestimated number of days with
small wind speeds is reduced, and the probability of higher wind speed is
largely improved, but the DBS-corrected data tend to overestimate the wind
speeds over 6 m s-1. Taking a closer look at the PDF of meteorological
variables from different data sources by comparison of Figs. 3 and 5, we
find that the effect of the DBS largely depends on the performance of raw
climate projections. Whether the climate model is capable of reflecting the
changes between the calibration and validation period is very significant.
In observation time series, the local climate at the Edsbyn station is found
to be warmer (except for summer) and wetter (except for autumn) in the
validation period than that in the calibration period. The largest rise in
temperature appears in winter (i.e. 2.2 ∘C), followed by a
large rise in spring (i.e. 0.9 ∘C) and a moderate rise in
autumn (i.e. 0.4 ∘C). In summer, the temperature is found
to drop by 0.7 ∘C. For precipitation the seasonal
precipitation is found to be wetter in spring (i.e. 4.3 %) and summer
(i.e. 13.3 %), but drier in winter by 14.0 % and in autumn by
11.7 % (not shown here). In the climate model's output (i.e. the
RCA3-E5r3-A1B) for the same period a similar trend for temperature is found
but with smaller magnitude; however a different trend for precipitation is
found. The climate model simulates generally wetter conditions in the
validation period over the whole year with a rise of more than 10 % per
season except for autumn (i.e. 6.6 %). The increment in spring and
summer may even reach 15.0 and 13.6 %, that is, the climate model
does not correctly capture the trend in variables and also largely
overestimates their changing rate. As a result, unstable statistics in raw
climate projections make it difficult to obtain a correction as good as in
the calibration period, which subsequently leads to an imperfect match in
fire risk index, e.g. the DC in Fig. 6.
Seasonal variation of FFMC, DMC and DC index at the Edsbyn station (see Fig. 1).
Comparison of values based on observations (black line), raw output from
climate model (blue line), RCA3-E5r3-A1B, corrected P (precipitation), T
(temperature) and uncorrected RH (relative humidity) and W (wind speed)
(green line) and corrected P (precipitation), T (temperature), RH (relative
humidity) and W (wind speed) (red line) for period (a) 1966–1985
(calibration) and (b) 1986–2005 (validation). Note that the DC is influenced
by P (precipitation) and T (temperature) (see blue, green and black lines).
Apart from computed statistics, the distribution corrections are also
reflected by the SS. The SS in Table 4 show general improvement in all
variables, i.e. the SS are on average ∼ 0.93 for
precipitation, ∼ 0.90 for temperature, ∼ 0.83
for relative humidity and 0.83 for wind speed, though the seasons differ.
The largest improvements appear in the summer season in which the major
biases tend to occur in the raw climate projections. Similar improvement was
found when the correction was applied to the RCA3-ERA40 run (not shown here).
Forest fire risk indices
The major forest fire risk indices – FFMC, DMC, DC, BUI, ISI and FWI – are
plotted as long-term average annual cycles over the calibration (1966–1985)
and the validation (1986–2005) periods in Figs. 6 and 7.
The calculated fire risk indices using raw RCA3 outputs are at first studied
in comparison to those obtained using station data. The deviation (see blue
and black lines in Figs. 6 and 7) is intuitively understandable. Too many
drizzle days in the raw RCA3 data are very likely to cause oversaturation in
the soil that may not dry out between rainfall events as in the reference
simulation driven by station data. Furthermore, along with lower
temperature, the water content in the deepest fuel layer might be increased,
affecting long-term drying conditions of the soil. Higher relative humidity
as well as lower wind speed leads to a decrease of the drying rate. As a
whole, moisture content in the uppermost layer is overestimated and the
corresponding fire risk described by the FFMC index is underestimated
(Fig. 6). Similar effects also work on the slow-drying fuel layer (DMC) and the
deepest fuel layer (DC). Because of the unrealistic values of the DMC and DC
indices, the modelled BUI and ISI are also, as expected, far from the
observed (Fig. 7). Ultimately, the final FWI is substantially
underestimated. Correction of input variables is thus of uttermost
importance when climate projections are utilised for forest fire risk assessment.
Seasonal variation of BUI, ISI and FWI index at the Edsbyn station (see
Fig. 1). Comparison of values based on observations (black line), raw output from
climate model (blue line), RCA3-E5r3-A1B, corrected P (precipitation) and
T (temperature) uncorrected (raw) RH (relative humidity) and W (wind speed)
(green line) and corrected P (precipitation), T (temperature), RH (relative
humidity) and W (wind speed) (red line) for period (a) 1966–1985
(calibration) and (b) 1986–2005 (validation).
The DC is an integrating index reflecting the combined effect of
precipitation and temperature; it was therefore used to study the correcting
impact induced by the DBS on these two variables. As the rainfall cut-off
values for all stations are seldom above 2.8 mm (i.e. the threshold values
given in the FWI literature, described in Sect. 2.1), the major impact on
the DC values is considered to be from the correction of P and T. During the
drying phase, the moisture depletion is governed by evapotranspiration,
which is proportional to noon temperature and also influenced by the
seasonal day-length. During the rainfall phase, any rainfall more than the
threshold value is first reduced to an effective rainfall by a linear
function and then simply added to the existing moisture equivalent. After
bias was removed, the corrected noon temperature was in general increased,
which led to stronger evapotranspiration. Additionally, the reduction of
precipitation amounts (see Figs. 4 and 5) resulted in less moisture
equivalent. Ultimately, the fire risk in the slow-acting fuel, described
by the DC value, was found to be considerably enlarged in comparison to that
which was computed using raw climate output (see Fig. 6) as well as closer
to that computed using observations.
The DMC represents the moisture content of real slow-drying forest fuel. It
is a function of precipitation, temperature and relative humidity. For the
RCA3-E5r3-A1B the cut-off values for the summer season (i.e. JJA) are often
more than 1.5 mm (i.e. the threshold values given in the FWI literature,
described in Sect. 2.1), but seldom in other seasons. Therefore, for
summer, not only precipitation amount but also precipitation frequency will
affect the DMC value. After applying the DBS, RH became less overestimated and
the cold bias in noon temperature was removed (see Figs. 4 and 5), which led
to the larger drying rate. For the rainfall phase, the DBS not only removed
the small rainfall events but also reduced the portion of medium-size
rainfall events via correcting the precipitation distribution (see Figs. 4 and 5).
As a result, the overestimated moisture level and consequently also the
integral value of the DMC were corrected (see red line in Fig. 6). In
comparison to the DMC value computed by corrected P and T (i.e. denoted as
corrected PT and marked by a green line in Fig. 6), correcting RH and W (red
line) leads to additional improvement. Especially in summer and autumn
seasons, the maximum improvement reaches as much as 50 %. It is likely
because of the removal of drizzle in the precipitation frequency correction
that the moisture content in the fuel reduced.
The FFMC reflects the integrated effect of all meteorological input
variables. In the drying phase, its drying rate varies with temperature,
relative humidity and wind speed. After applying the DBS, the drying rate was
increased upon correcting the cold bias in T, the overestimated RH and the
underestimated W, as shown in Figs. 4 and 5. Moreover, the computed
equilibrium moisture content by drying and by wetting, Ed and Ew,
became smaller (not shown here). In the rainfall phase, only the current
moisture content and rainfall amount matter. As the cut-off values estimated
at all stations were all above 0.5 mm (i.e. the threshold values given in
the FWI literature, described in Sect. 2.1), any correction of
precipitation frequency influenced the final FFMC value. By applying the
DBS, many periods of drizzle were removed and the overestimated
precipitation amount was corrected. As a result, (1) the wet spells were
shortened and the moisture content in the fuels had time to dry out; (2) the
fire risks described by the FFMC value largely increased (see red line in Fig. 6).
In Fig. 7, the fire behaviour indices, the ISI and the BUI, as well as the
final fire risk index, the FWI, were studied. As ISI is a product of wind
speed and fine fuel moisture, it is directly influenced when these two are
changed. As the W was not perfectly corrected, over- or underestimated W after
bias correction caused larger variation in the ISI index in comparison to
that computed using observations. BUI depends on the variation in the DMC
and the DC values, with more weight given to DMC. Hence, the BUI shows a
similar pattern to the DMC index. Ultimately, the final index (the FWI) (Fig. 7)
and the fire danger classes (the FWIX) (Fig. 8) used for issuing fire
risk warnings (i.e. danger class >= 5 in Table 1) were significantly improved.
The fire-risk-related indices generally showed improvement when all
variables were corrected, compared to only a partial bias correction of
precipitation and temperature. This suggests that the bias correction does
not destroy the physical consistency between the variables in such a way
that it would degrade the validation results when multiple variables are
bias-corrected. Apart from that, the improvements imply that the relative
humidity and wind speed do play important roles in final fire danger level,
and appropriate correction of these two variables adds value to fire risk
assessment. Particularly, the wind speed works as a dominant factor for
cases of extremely large forest fire risk (see danger class >= 5 in Fig. 8).
This finding matches the conclusion drawn from a recent study
in Greece (Karali et al., 2014), in which a sensitivity test of the FWI
indices to the meteorological variables was carried out. It was found that
precipitation and wind speed play the most important roles in final indices.
Specifically, for wind speed, even a moderate wind speed leads to index
values over the critical risk thresholds, and a high wind speed results in
an extremely high value of the FWI.
The occurrence frequency of fire danger classes (i.e. FWIX) at the Edsbyn
station (see Fig. 1) calculated from observation, raw climate model output,
RCA3-E5r3-A1B, and after correcting meteorological variables.
Climate change signals in P (precipitation), T (temperature), RH (relative
humidity) and W (wind speed) at Edsbyn and Växjö station (see Fig. 1),
reflected in RCA3-E5r3-A1B before and after correction during 2071–2100.
Figure 10 gives an overview of how often the high risk indices of forest
fire (i.e. FWI >= 5) are likely to occur in past climate
(1966–1995) at the 14 stations used in this study. Colour markers indicate
the average number of days with the FWI index of 5 and 6 per fire season
(the months of April to October). At most stations, the occurrence of high
risk indices derived from simulations forced by observed data are fewer than
20 days per fire season. In the southern parts of Sweden the high risk
indices of forest fire appear more frequently, whereas in northern and
central Sweden the occurrence of high risk indices are lower except at the
Edsbyn station (i.e. 20 days per fire season). In comparison with the risk
level calculated using the observations, the risk level calculated using raw
meteorological variables from the climate projection, RCA3-E5r3-A1B, shows
obvious underestimation. No high risk level is reflected at any of the
14 stations (shown in Fig. 11b). After correcting the biases in meteorological
variables, the fire risk in the reference period is significantly increased
and it shows a similar spatial distribution pattern to that calculated from
observations (see Fig. 11a and c). However, underestimation in the
calculated occurrence of high risk indices (i.e. an average of -6 days per
fire season) still exists. None of the stations reach the number of days
identified from those calculated using the observations. The maximum number
of days calculated using corrected meteorological variables is 20 days.
Future projection (RCA3-E5r3-A1B)
The climate projection was run until the end of 2100 with a transient-mode
simulation, which makes it possible to investigate the evolution of climate
change in a continuous manner (Kjellström et al., 2006). The historical
observations used to obtain the scaling factors cover the period from 1966
to 1995, the longest observation period available for the study area. Topics
that will be discussed in this section include whether the DBS alters the
climate change signals in input variables as well as the FWI index and how
fire risk will evolve in Sweden in the future.
Climate change signals in the FWI reflected in RCA3-E5r3-A1B for the
period of 2071–2100 with respect to the period 1966–1995 at Edsbyn and
Växjö station (see Fig. 1).
Figure 9 presents the climate change signals in all input variables at two
stations, Edsbyn in northern Sweden and Växjö in southern Sweden. As
projected by RCA3-E5r3-A1B, the local climate in Edsbyn will become wetter,
warmer, more humid and slightly windier in the future. During fire seasons,
a general increase in the precipitation amount is found during the complete
future period, particularly during spring in the intermediate and distant
future (∼ 40 % increase). Temperature and relative humidity
are also characterised by a general rise during the whole period. The air
gets warmer and moister at the beginning of spring in the near future and
this tendency is enhanced until 2100. The largest rise appears in spring and
the smallest in summer. Compared to the present climate, it is likely to be
warmer by 5 ∘C in 2071–2100 and moister by 15 % in
2071–2100. The change in wind speed is smaller when compared to other
variables. It varies mainly within the range of -6 to 6 % in the
study periods, with the largest increase in the near future. The maximum
increase appears in autumn in every future period. The local changes in
Växjö are projected to be similar to those in Edsbyn, but with
stronger seasonal variations during the fire season. As in Edsbyn,
temperature and relative humidity exhibit a consistent future increase.
Their rate of increase increases with time until 2100 (i.e. 4 ∘C
warmer and 15 % moister until 2071–2100). The
changes of the other two variables fluctuate around zero with a different
sign at different periods of the year. Precipitation decreases during the
fire season except in spring, whereas wind speed increases in late summer
with a maximum of 10 %.
In general, the corrected data reproduce the climate change signal in the
raw climate model output reasonably well. However, in some cases, DBS was
found to alter the changes projected by the climate model. It might be
caused by non-linearity in RCM biases, that is, the biases caused by an
imperfect model representation of atmospheric physics for the present
climate are likely to be altered by the changes in relevant climatic
variables in the future. For instance, the described changes in temperature
bias can be related to changes in cloud cover and the corresponding response
in radiative surface heating, soil moisture feedback and sea level pressure
(Maraun, 2012), which are not accounted for in the bias-correction approach.
As all bias-correction methods, applying DBS is built upon an assumption of
stationary bias.
By running the FWI system, the integral impact on the long-term mean future
fire risk danger was evaluated (Fig. 10). Because the figures aim to present
the average situations for a 30-year period, extreme values cannot be seen.
However, their relative changes in FWI compared to those for the present
climate are quite consistent though different in magnitude. The differences
in CC signal between the raw and DBS-corrected data are
partly because of biases in driving variables as described in Sect. 5.1.3.
Moreover, as the three primary indices of the FWI (i.e. FFMC, DMC and DC)
are computed for drying and wetting phases that are determined by a
threshold value for each fuel, any correction of precipitation amount may
have an impact on the indices that subsequently influence the final index,
FWI, and its CC signals.
Using the corrected data, autumn at the Edsbyn station is found to become
more prone to forest fire, followed by spring, and then summer (Fig. 10). It
is mainly due to the increase of temperature and wind speed. For today's
main fire risk season, summer, the relative change in the FWI value tends to
be negative (i.e. approximately -20 %). The moister air, the increased
precipitation and relatively stabilised wind speed balance out the effect
of the warmer climate. The fire risk in autumn gradually increases with regard
to the last 30 years, particularly the beginning of autumn, which is most
likely because of relatively drier and warmer air combined with stronger
wind speeds. At the Växjö station, the most fire-prone season in
future is likely to be summer, where less precipitation, warmer temperatures
and higher wind speeds are projected. In the last 30 years, the local
climate has become even wetter, moister and less windy in spring, which reduces
the fire risk level by 15 % compared to the present day. However, the
fire risk in summer increases by 20 % as the climate in the distant
future becomes drier, warmer and windier.
Number of days with high fire risk (FWIX >= 5) during the calibration
period (1966–1995) presented by (a) observation, (b) raw RCA3-E5r3-A1B
and (c) corrected raw RCA3-E5r3-A1B; and changes of number of days with
high fire risk (FWIX >= 5) in percentage during the period of (d) 2071–2100.
The relative changes in the number of days with high fire risk (i.e. the
FWI >= 5) during the fire season are presented in Fig. 11d.
Northern Sweden is likely to be a fire-resistant region in the future
climate where the number of days with high fire risk is found to be lower
than today. In contrast, southern Sweden is projected to become a more fire-prone
region where an increased number of days with a high fire risk are found
in almost all stations in all three periods. The stations located in central
Sweden are projected to face an increased risk of forest fire in the near
future, after which the risk decreases until the end of the century. The changes
at those stations vary from time to time, which is probably because of local
climate factors at different time periods.
Conclusions
In this study, two climate projections driven by different forcing were
investigated for direct use of a climate model (i.e. GCM or GCM/RCM) in
forest fire risk studies. The raw climate model outputs show a clear
mismatch with the observations in all influencing variables used in fire
risk modelling: precipitation, temperature, wind speed and relative
humidity. This is likely caused by uncertainties in observations as well as
improper descriptions of physical processes and coarse resolutions in the
present generation of RCMs.
Two parametric distributions, the Beta distribution and the Weibull
distribution, were tested for correcting the biases in relative humidity and
wind speed, respectively. In a cross-validation, the DBS method is
demonstrated to substantially reduce the bias in driving meteorological
variables and thus facilitates the utilisation of climate projections in
forest fire risk studies. Regarding the simultaneous bias correction of
multiple variables, the result showed an improved description of fire-risk-related
indices when all variables were corrected, compared to only a partial
bias correction of precipitation and temperature. This suggests that the
bias correction does not destroy the physical consistency between the
variables to such an extent that it degrades the validation results when
multiple variables are bias-corrected.
For the present climate, by using bias-corrected meteorological variables,
the FWI model generates realistic results that are well in line with those
derived from observations. The frequency of extremely high fire risk is
significantly better reproduced when compared to directly using raw climate
projection data, though some underestimation remains. Further development of
the DBS method is therefore required to, e.g. better represent the
influencing variables by removing remaining biases, keep consistency amongst
meteorological variables in terms of their temporal and spatial covariance
and capture the non-stationary climate model biases.
Concerning the future climate, the climate projection used here projects a
climate in Sweden that is warmer, wetter and windier than today. Southern
Sweden, where it is normally warmer and windier than in other parts of
Sweden, is likely to become a more fire-prone region in the future, whereas
northern and central Sweden will face a similar or lower fire risk than
today. Forest fire activity and its spread is a result of combinations of
weather, fuels and topography as well as incident management decisions.
Thus, fuel bed structure and fire potential are influencing factors in
addition to the changing climate. This kind of studies for Sweden has
partly been done previously (Granström et al., 2000; Granström and
Schimmel, 1998). With changing climate, there may be a northward
displacement of the broad vegetation belts with an increasing component of
broad-leaved tree species at the expense of spruce (Koca et al., 2006). Fuel
beds in the north may then shift from moss to leaf litter, with unknown
effects on ignition potential and fire behaviour. Apart from reducing
human-caused ignition, experience concerning rescue tactics suppression
methods needs to be collated. An ongoing project will develop a national
preparedness strategy for forest fires with consideration of changing climate.
Our results do not completely agree with the work of Flannigan et al. (2013),
who found significant increases of fire risk in the Northern
Hemisphere by applying a combination of three GCMs and three emission
scenarios. For Sweden, an overall and large increase was projected. One
reason for the differences may be the way the climate change signal is
treated. The DBS approach focuses on preserving the variability produced by
individual climate projection, which is different from the traditional delta
change (DC) approach used by Flannigan et al. (2013) in which the average
changes are transferred onto the observations. Another difference concerns
the spatial and temporal resolutions of the observed reference data.
Compared to the large-scale data used in Flannigan et al. (2013), using
regional/local data is beneficial in studies, including localised variables
such as precipitation and wind speed.
Forest fire regimes with different climatic sensitivity in northern and
southern Sweden have also been revealed in earlier studies. The results in
Drobyshev et al. (2014) pointed towards the presence of two well-defined
zones with characteristic fire activity, geographically divided at
approximately 60∘ N. Such division was also reflected in Dai
et al. (2012) who applied the self-calibrated Palmer drought severity index
to study the global aridity in present and future climate. The calculated
indices indicated drier conditions in southern Sweden than in the northern
part under the present climate. In the future, more precipitation was projected
in northern Sweden in comparison with relative dryness in the southern Sweden.
For improved interpretation of the assessment results, all uncertainties in
the full production chain must be considered. Reliance on a single climate
projection (combination of GCM and RCM) to represent the current and future
climate is not sufficient given the amount of uncertainty involved in the
climate models themselves. As forest fire is largely affected by weather
conditions in close proximity and influencing forcing is very local,
including different projections is required for forest fire impact
assessment. A full-scale evaluation of the future forest fire risk should
include an ensemble of projections covering different aspects such as
parameterisation of sub-grid-scale processes in GCMs and RCMs,
initialisation of GCMs, spatial resolutions and emission scenarios. Also,
other uncertainty sources should be assessed. One concerns the quality of
observation data, which limits the application of the bias-correction method
to the climate projections. Another source is the choice of bias-correction
method, which is likely to influence the results. Finally, the choice of
forest fire model adds uncertainty. For example, the connection between fuel
layers is switched off in the drying process within the FWI, whereas in
other models (e.g. Fosberg, 1975) a more complete drying model that couples
heat and vapour transport is included. The way a model describes the
processes may potentially give a different response to the projected driving
meteorological variables.
(a) List of acronyms and (b) list of variables.
AcronymsDescriptions(a) CCClimate changeCDFCumulative distribution functionsDBSDistribution-based scaling approachERA40ECMWF reanalysis dataECHAM5The EC Hamburg global climate model, version 5EVDExtreme value distributionGCMGlobal climate modelIPCCIntergovernmental Panel on Climate ChangeJJAJune–July–AugustMAMMarch–April–MayMLEMaximum likelihood estimatorMSBMyndigheten för samhällsskydd och Beredskap (Swedish Civil Contingencies Agency)RCA3Rossby Centre atmospheric model, version 3RCA3-ERA40Ensemble 3 of RCA3 projection with ECHAM5 global boundary conditions using ERA40RCA3-E5r3-A1BEnsemble 3 of RCA3 projection with ECHAM5 global boundary conditions using SRES-A1BRCMRegional climate modelSMHISwedish Meteorological and Hydrological InstituteSONSeptember–October–NovemberSOUStatens Offentliga Utredningar (Government offices of Sweden)(b) AccMean value of accumulated precipitation (expressed here as mm)AvgClimatological mean (expressed here as the same unit as the described variables)BUIBuildup index [–]DCDrought Code [–]DMCDuff Moisture Code [–]FFMCFine Fuel Moisture Code [–]Freq-POccurrence of days with rainfall amount larger than 0.1 mm (expressed here as %)Freq-WsOccurrence of days with wind speed above 0 m s-1 (expressed here as %)FWIFire Weather Index [–]FWIXFire danger classes [–]RHRelative humidity at 12:00 UTC (expressed here as %)ISIInitial Spread Index [–]PDaily precipitation (expressed here as mm)SD1Standard deviation (expressed here as the same unit as the described variables)SD2Standard distanceSSPDF skill score [–]TTemperature at 12:00 UTC (expressed here as ∘C)WWind speed at 12:00 UTC (expressed here as m s-1)Acknowledgements
This work was mainly funded by the Swedish Civil Contingencies Agency (MSB),
through project Klimatscenarier Brandrisk FWI (contract no. 2009/729/180)
and Klimatpåverkan på skogsbrandrisk i Sverige. Nulägesanalys,
modellutveckling och framtida scenarier (contract no. 2011-3777). Additional
funding was provided by the Swedish EPA (Naturvårdsverket), through
project CLEO (contract no. 802-0115-09), the Swedish Research Council
Formas, through project HYDROIMPACTS2.0 (contract no. 2009-525) and EU FP7,
through project IMPACT2C (contract no. 282746). The authors would like to
thank Johan Andreásson, Jörgen Sahlberg and Björn Stensen
for their support in this study.
Edited by: B. D. Malamud
Reviewed by: four anonymous referees
References
Abramowitz, M. and Stegun, I. A.: Pocketbook of mathematical functions,
Verlag Harri Deutsch, Frankfurt, 468 pp., 1984.
Achberger, C., Chen, D. L., and Alexandersson, H.: The surface winds of
Sweden during 1999–2000, Int. J. Climatol., 26, 159–178, 2006.
Bergeron, Y. and Flannigan, M. D.: Predicting the effect of climate change on
fire frequency in the southeastern Canadian boreal forest, Water Air
Soil Poll., 82, 437–444, 1995.
Brockway, D. G. and Lewis, C. E.: Long-term effects of dormant-season
prescribed fire on plant community diversity, structure and productivity in
a longleaf pine wiregrass ecosystem, Forest Ecol. Manage.,96, 167–183, 1997.
Carvalho, A., Flannigan, M. D., Logan, K., Miranda, A. I., and Borrego, C.:
Fire activity in Portugal and its relationship to weather and the Canadian
Fire Weather Index System, Int. J. Wildland Fire, 17, 328–338, 2008.Dai, A.: Increasing drought under global warming in observations and models,
Nat. Clim. Change, 3, 52–58, 10.1038/nclimate1633, 2012.
Dowdy, A. J., Mills, G. A., Finkele, K., and de Groot, W. J.: 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, Australia, 2009.Drobyshev, I., Granström, A., Linderholm, H. W., Hellberg, E., Bergeron,
Y., and Niklasson, M.: Multi-century reconstruction of fire activity in
Northern European boreal forest suggests differences in regional fire
regimes and their sensitivity to climate, J. Ecol., 102, 738–748,
10.1111/1365-2745.12235, 2014.
Fendell, F. E. and Wolff, M. F.: Wind-Aided Fire Spread, Forest Fires,
Behavior and Ecological Effects, in: Chapter 6, edited by: Johnson, E. A. and
Miyanishi, K., Academic Press, San Diego, California, 171–223, 2001.
Fernandes, P. M. and Botelho, H. S.: A review of prescribed burning
effectiveness in fire hazard reduction, Int. J. Wildland Fire, 12, 117–128, 2003.Flannigan, M., Cantin, A. S., de Groot, W. J., Wotton, M., Newbery, A., and
Gowman, L. M.: Global wildland fire season severity in the 21st century,
Forest Ecol. Manage., 294, 54–61, 10.1016/j.foreco.2012.10.022, 2013.
Flannigan, M. D. and Van Wagner, C. E.: Climate change and wildfire in Canada,
Can. J. Forest Res., 21, 66–72, 1991.
Flannigan, M. D., Amiro, B. D., Logan, K. A., Stocks, B. J., and Wotton, B. M.:
Forest Fires and Climate Change in the 21st century, Mitig. Adapt. Strat.
Global Change, 11, 847–859, 2009.
Fosberg, M. A.: Heat and water vapour flux in conifer forest litter and
duff: A theoretical model, Rep. RM-152, For. Serv., US Dep. of Agric.,
Washington, D.C., 23 pp., 1975.
Fosberg, M. A. and Deeming, J. E.: Derivation of the 1 and 10-hour timelag fuel
moisture calculations for fire danger rating, US Department of
Agriculture, Forest Service, Research Note RM-207, Rocky Mountain Forest
Experimental Station, Fort Collins, Colorado, 1971.
Gardelin, M.: Brandriskprognoser med hjälp av en kanadensisk
skogsbrandsmodell, Räddningsverket Report, Myndigheten för
samhällsskydd och Beredskap (MSB), Sweden, 1997.
Giorgi, F. and Marinucci, M. R.: Improvements in the simulation of surface
climatology over the European region with a nested modelling system,
Geophys. Res. Lett., 23, 273–276, 1996.
Granström, A.: Spatial and Temporal variation in lightning ignitions in
Sweden, J. Veget. Sci., 4, 737–744, 1993.
Granström, A. and Schimmel, J.: Utvärdering av det kanadensiska
brandrisksystemet – Testbränningar och uttorkningsanalyser,
Räddningsverket, Myndigheten för samhällsskydd och Beredskap
(MSB), Sweden, 1998.
Granström, A., Berglund, L., and Hellberg, E.: Gräsbrand, Uttorkning
och brandspridning i relation till brandriskindex, Grass fuels in Northern
Sweden, Moisture relations and fire spread in relation to fire-weather
indicies, Räddningsverket Report, Myndigheten för samhällsskydd
och Beredskap (MSB), Sweden, 2000.
Hagemann, S., Machenhauer, B., Jones, R., Christensen, O. B., Déqué,
M., Jacob, D., and Vidale, P. L.: Evaluation of water and energy budgets in
regional climate models applied over Europe, Clim. Dynam., 23, 547–567, 2004.
Hay, L. E., Wilby, R. L., and Leavesley, G. H.: A comparison of delta change
and downscaled GCM scenarios for three mountainous basins in the United
States, J. Am. Water Resour. As., 36, 387–398, 2000.
IPCC: Climate Change: Synthesis Report, in: Contribution of Working
Groups I, II and III to the Fourth Assessment Report of the
Intergovernmental Panel on Climate Change, IPCC, Geneva, Switzerland, 104 pp., 2007.IPCC: Climate Change 2013: The Physical Science Basis, in: Contribution of
Working Group I to the Fifth Assessment Report of the Intergovernmental
Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor,
M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P.
M., Cambridge University Press, Cambridge, UK and New York, NY, USA,
1535 pp., 10.1017/CBO9781107415324, 2013.
Jacob, D., Bärring, L., Christensen, O. B., Christensen, J. H., de Castro,
M., Déqué, M., Giorgi, F., Hagemann, S., Hirschi, M., Jones, R.,
Kjellström, E., Lenderink, G., Rockel, B., Sánchez, E., Schär,
C., Seneviratne, S. I., Somot, S., van Ulden, A., and van den Hurk, B.: An
inter-comparison of regional climate models for Europe: model performance in
present-day climate, Climatic Change, 81, 31–52, 2007.Karali, A., Hatzaki, M., Giannakopoulos, C., Roussos, A., Xanthopoulos, G.,
and Tenentes, V.: Sensitivity and evaluation of current fire risk and future
projections due to climate change: the case study of Greece, Nat. Hazards Earth
Syst. Sci., 14, 143–153, 10.5194/nhess-14-143-2014, 2014.
Kjellström, E., Bärring, L., Gollvik, S., Hansson, U., Jones, C.,
Samuelsson, P., Rummukainen, M., Ullerstig, A., Willén, U., and Wyser,
K.: A 140-year simulation of European climate with the new version of the
Rossby Centre regional atmospheric climate model (RCA3), SMHI Rep. Meteorol. Climatol. 108,
Swedish meteorological and hydrological institute (SMHI), Sweden, 2006.Kjellström, E., Nikulin, G., Hansson, U., Strandberg, G., and Ullerstig,
A.: 21st century changes in the European climate: uncertainties derived from
an ensemble of regional climate model simulations, Tellus A, 63, 24–40,
10.1111/j.1600-0870.2010.00475.x, 2011.
Koca, D., Smith, B., and Sykes, M. T.: Modelling regional climate change
effects on potential natural ecosystems in Sweden, Climatic Change, 78, 381–406, 2006.
Logan, K. A., Flannigan, M. D., Wotton, B. M. and Stocks, B. J.: Development
of daily weather and fire danger scenarios using two general circulation
models, in: Proceedings 22nd Tall Timbers Fire Ecology Conference: Fire in
temperate, boreal, and montane ecosystems, edited by: Engstrom, R. T.,
Galley, K. E. M., and de Groot, W. J., Kananaskis Village, Alberta, Canada,
Tall Timbers Research, Inc., Edmonton, Alberta, Canada, 185–190, 2004.Maraun, D.: Nonstationarities of regional climate model biases in European
seasonal mean temperature and precipitation sums, Geophys. Res. Lett., 39,
L06706, 10.1029/2012GL051210, 2012.Mearns, L. O., Giorgi, F., McDaniel, L., and Shields, C.: Analysis of daily
variability of precipitation in a nested regional climate model: comparison
with observations and doubled CO2 results, Global Planet. Change, 10, 55–78, 1995.
MSB: Skogsbranden i Västmanland 2014, Observatörsrapport,
Myndigheten för samhällsskydd och Beredskap (MSB), Sweden, 2015.
Nakicénović, N., Alcamo, J., Davis, G., de Vries, B., Fenhann, J.,
Gaffin, S., Gregory, K., Gruebler, A., Jung, T. Y., Kram, T., La Rovere, E.
L., Michaelis, L., Mori, S., Morita, T., Pepper, W., Pitcher, H., Price, L.,
Riahi, K., Roehrl, A., and Rogner, H.-H.: IPCC Special Report on Emissions
Scenarios, Cambridge University Press, Cambridge, UK, New York, NY, USA, 2000.
Niklasson, M. and Granström, A.: Numbers and sizes of fires: Long term
trends in a Swedish boreal landscape, Ecology, 81, 1496–1499, 2000.
Pavia, E. G. and O'Brien, J. J.: Weibull statistics of wind speed over the
ocean, J. Clim. Appl. Meteorol., 25, 1324–1332, 1986.
Perkins, S. E., Pitman, A. J., Holbrook, N. J., and McAneney, J.: Evaluation of
the AR4 Climate Models' Simulated Daily Maximum Temperature, Minimum
Temperature, and Precipitation over Australia Using Probability Density
Functions, J. Climate, 20, 4356–4376, 2007.
Piani, C., Haerter, J. O., and Coppola, E.: Statistical bias correction for
daily precipitation in regional climate models over Europe, Theor. Appl.
Climatol., 99, 187–192, 2010.
Press, W. H., Flannery, B. P., Teukolsky, S. A., and Vetterling, W. T.:
Numerical Recipes: the Art of Scientific Computing, Cambridge University
Press, Cambridge, UK, 818 pp., 1986.
Roeckner, E., Brokopf, R., Esch, M., Giorgetta, M., Hagemann, S., Kornblueh,
L., Manzini, E., Schlese, U., and Schulzweida, U.: Sensitivity of simulated
climate to horizontal and vertical resolution in the ECHAM5 atmosphere
model, J. Climate, 19, 3771–3791, 2006.
Ryan, K. C.: Dynamic interactions between forest structure and fire behaviour
in boreal ecosystems, Silva Fennica, 36, 13–39, 2002.Samuelsson, P., Jones, C. G., Willén, U., Ullerstig, A., Gollvik, S.,
Hansson, U., Jansson, C., Kjellström, E., Nikulin, G., and Wyser, K.: The
Rossby Centre Regional Climate model RCA3: model description and
performance, Tellus, 63, 4–23, 10.1111/j.1600-0870.2010.00478.x, 2010.
Seguro, J. V. and Lambert, T. W.: Modern estimation of the parameters of the
Weibull wind speed distribution for wind energy analysis, J. Wind Eng. Ind.
Aerod., 85, 75–84, 2000.Skydd and Säkerhet: The cost of the forest fire is approaching one
billion, available at:http://skyddosakerhet.se/nyheter/kostnader-skogsbranden-narmar-sig-en-miljard/,
last access: 3 September 2014.
SOU: Sweden facing climate change – threats and opportunities, Final
report from the Swedish Commission on Climate and Vulnerability, Stockholm, 2007.
Stocks, B. J., Lawson, B. D., Alexander, M. E., Van Wagner, C. E., McAlpine,
R. S., Lynham, T. J., and Dubé, D. E.: The Canadian Forest Fire Danger Rating
System: An Overview, Forest. Chron., 65, 450–457, 1989.
Themeßl, M. J., Gobiet, A., and Leuprecht, A.: Empirical-statistical
downscaling and error correction of daily precipitation from regional
climate models, Int. J. Climatol., 31, 1530–1544, 2011.
Turner, J. A.: The drought code component of the Canadian forest fire
behavior system, Environment Canada, Publication No. 1316, Canadian Forestry
Service, Headquarters, Ottawa, 14 pp., 1972.
Van Wagner, C. E.: Development and structure of the Canadian Forest Fire
Weather Index system, Forestry Technical Report 35, Canadian Forest Service,
Ottawa, Canada, 1987.
Viegas, D. X., Bovio, G., Ferreira, A., Nosenzo, A., and Sol, B.: Comparative
study of various methods of fire danger evaluation in southern Europe, Int.
J. Wildland Fire, 10, 235–246, 1999.
Wilcke, R. A. I., Mendlik, T., and Gobiet, A.: Multi-Variable Error-Correction of
Regional Climate Models, Climatic Change, 120, 871–887, 2013.
Willén, U.: Preliminary use of CM-SAF cloud and radiation products for
evaluation of regional climate simulations, SMHI Rep. Meteorol. Climatol. 131, ]
Swedish meteorological and hydrological institute (SMHI), Sweden, 2008.
Wotton, B. M., Martell, D. L., and Logan, K. A.: Climate change and
people-caused forest fire occurrence in Ontario, Climatic Change, 60, 275–295, 2003.
Yang, W., Andréasson, J., Graham, L. P., Olsson, J., Rosberg, J., and
Wetterhall, F.: Distribution based scaling to improve usability of regional
climate model projections for hydrological climate change impacts studies,
Hydrol. Res., 41, 3–4, 2010.
Yao, A. Y. M.: A statistical model for the surface relative humidity, J. Appl.
Meteorol., 13, 17–21, 1974.