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Title: 1
Climate changes and wildfire emissions of atmospheric pollutants in 2
Europe 3
Authors: 4
Wolfgang Knorr*1, Frank Dentener2, Stijn Hantson3, Leiwen Jian4, 5, Zbigniew 5
Klimont6 & Almut Arneth3 6
1Physical Geography and Ecosystem Analysis, Lund University, Sölvegatan 12, 7
22362 Lund, Sweden 8
2 European Commission, Joint Research Centre, Institute for Environment and 9
Sustainability, Ispra, Italy. 10
3 Karlsruhe Institute of Technology, Institute of Meteorology and Climate research, 11
Atmospheric Environmental Research, 82467 Garmisch-Partenkirchen, Germany. 12
4 Asian Demographic Research Institute, Shanghai University 13
5 National Center for Atmospheric Research, Boulder, Colorado, USA 14
6 International Institute for Applied Systems Analysis, Laxenburg, Austria 15
*Corresponding author’s email: [email protected] 16
17
Abstract: 18
Wildfires are not only a threat to human property and a vital element of many 19
ecosystems, but also an important source of air pollution. In this study, we first review 20
the available evidence for a past or possible future climate-driven increase in wildfire 21
emissions in Europe. We then introduce an ensemble of model simulations with a 22
coupled wildfire – dynamic ecosystem model, which we combine with published 23
spatial maps of both wildfire and anthropogenic emissions of several major air 24
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pollutants to arrive at air pollutant emission projections for several time slices during 25
the 21st century. The results indicate moderate wildfire-driven emission increases until 26
2050, but the possibility of large increases until the last decades of this century at high 27
levels of climate change. We identify southern and north-eastern Europe as potential 28
areas where wildfires may surpass anthropogenic pollution sources during the summer 29
months. Under a scenario of high levels of climate change (Representative 30
Concentration Pathway, RCP, 8.5), emissions from wildfires in central and northern 31
Portugal and possibly southern Italy and along the west coast of the Balkan peninsula 32
are projected to reach levels that could affect annual mean particulate matter 33
concentrations enough to be relevant for meeting WHO air quality targets. 34
1 Introduction 35
1.1 Wildfire impact on air quality and the role of climate change 36
Air quality is strongly influenced by local to global emissions of air-borne pollutants, 37
atmospheric chemistry, removal mechanisms, as well as atmospheric transport 38
(Seinfeld and Pandis 2012). While most pollutants of anthropogenic origin are subject 39
to increasingly strict legislation, which has avoided further deterioration of air quality 40
with economic growth and led to an overall significant decrease in emissions in 41
Europe and improvement of European air quality (Cofala et al. 2007; Monks et al. 42
2009; Amann et al. 2011; Klimont et al. 2013; EMEP Assessment Report, in 43
preparation; European Commission National Emissions Ceiling directive: 44
http://ec.europa.eu/environment/air/pollutants/ceilings.htm), wildfires, which emit 45
large amounts of aerosols and chemically reactive gases (Langmann et al. 2009), are 46
predicted to increase with climate change (Scholze et al. 2006, Krawchuk et al.. 2009, 47
Pechony and Shindell 2010, Moritz et al., 2012, Kloster et al. 2012, Knorr et al. 48
2015). 49
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Meteorological fire indices are routinely used to assess the likelihood of fire 50
occurrence, and they generally predict an increased fire risk with warmer and drier 51
weather (van Wagner and Forest 1987). This is consistent with evidence from 52
charcoal records which have revealed a higher fire activity associated with a warmer 53
climate (Marlon et al. 2008). A large increase in the forest area burned annually in the 54
United States in recent decades (Liu et al. 2013) has also been associated with 55
warming and drying trends, at least for the south-western part of the country 56
(Westerling et al., 2006). For Europe, some recent publications based on climate 57
model output combined with fire danger indices have predicted large increases in fire 58
activity in Europe (Amatulli et al. 2013, Bedia et al. 2014). This has important 59
consequences for air quality management, because wildfires are mostly outside the 60
reach of policy measures as they are influenced by humans in complex and often 61
unpredictable ways (Bowman et al. 2011, Guyette et al. 2002, Mollicone et al. 2006, 62
Archibald et al. 2008, Syphard et al. 2009,). Large fires once started often escape 63
human control altogether (Chandler et al. 1983) and, more significantly, human 64
control through fire suppression may increase fire risk in the long term (Fellows and 65
Goulden 2008) resulting in less frequent but more severe wildfires. 66
The most abundant pollutants emitted by fires in extra-tropical forests, which includes 67
typical wildland fires in the Mediterranean, are carbon monoxide (CO), particulate 68
matter (aerosols, including organic carbon and soot), methane (CH4), and various non-69
methane hydrocarbons and volatile organic compounds (Andreae and Merlet, 2001) 70
Not all of these species are explicitly included in large-scale emissions inventories, 71
for example organic carbon, a major part of total primary particulate matter emitted 72
by fires. However, it appears that in general, total wildfire emissions of most 73
components aggregated for Europe are one to two orders of magnitude lower than 74
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those from anthropogenic sources (Granier et al. 2011). During large fire events, 75
however, forest fires in Europe can have a major impact on air quality (Miranda et al. 76
2008; Konovalov et al. 2011). 77
The aim of the present contribution is twofold: First to review published evidence and 78
assess whether past changes in European climate have led to an increase in air 79
pollutant emissions from wildfires, and second, to combine inventories, scenarios and 80
model-based future projections of anthropogenic and wildfire emissions with climate, 81
terrestrial-ecosystem and fire model simulations in order to identify potential 82
geographical hot spots where certain pollutants from wildfires might reach or exceed 83
anthropogenic emission levels as a first indication of where potentially health related 84
risks may be caused by climate change induced forest fires. 85
1.2 Impact of past climate change on European wildfire emissions 86
Before addressing the question of whether past climate change has had an impact on 87
wildfire emissions in Europe, it is useful to consider how these emissions are 88
described in simulation models. Mathematically, emissions from wildfires are 89
routinely calculated as the product of area burned, fuel load, the combustion 90
completeness of the fuel, and the emission factor which translates combusted biomass 91
into emissions of a particular species or group of aerosols. Little is known about 92
whether climate change has affected emission factors or combustion completeness. 93
Fuel load can be expected to change with vegetation productivity, which is influenced 94
by climate and atmospheric CO2, as well as by landscape management. While again 95
little is known about the impact of changing landscape management, dynamic 96
vegetation models can in principle be used to address the impact of climate and CO2. 97
The remaining factor is the change in burned area, and the attribution of changing 98
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burned area to climate change as the main possibility of attributing changes in 99
emissions to climate change. 100
The most prominent example of a regional increase in wildfire activity and severity 101
that has been attributed to recent climate change is found in the Western United States 102
(Westerling et al. 2006) where progressively earlier snowmelt in response to warming 103
has led to forests drying up earlier in the year, and thus making them more flammable. 104
The Western U.S. is a region characterized by exceptionally low atmospheric 105
humidity during the summer, as well as by low human population density. A very 106
close correlation was observed between climate factors and fire frequency, which 107
showed a clear upward trend since the 1970s. 108
The situation for other regions, including Europe, however, is more ambiguous. Fire 109
emissions from boreal forests, where human population density can be as low as in 110
the Western U.S., represent only a small part of European wildfire emissions (van der 111
Werf et al. 2010), and Finland and Sweden in particular have very low wildfire 112
emissions (JRC2013). The Mediterranean and southern European regions, on the 113
other hand, where most wildfires in Europe occur (San Miguel and Camia 2010), are 114
characterized by much more intense human land management going back thousands 115
of years. The period since the 1970s, in particular, was one where large tracts of land, 116
previously managed intensively for grazing and browsing, were abandoned. A study 117
by Koutsias et al. (2013) shows an upward trend in burned area for Greece from about 118
1970 similar to the one found for the Western U.S., and a significant correlation 119
between burned area and climatic factors, even though their study did not analyse the 120
role of any socio-economic drivers as possible causes. However, Pausas and 121
Fernandez-Muñoz (2012) in a study for eastern Spain attributed a very similar 122
temporal trend in fire frequency to an increasing lack of fuel control as a result of 123
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massive land flight. Along the same lines, Moreira et al. (2011) found that during 124
recent decades, changes in land use have generally increased flammability in southern 125
Europe, mainly due to land abandonment and associated fuel build-up, and the spread 126
of more flammable land cover types such as shrublands. In fact, a closer inspection of 127
the data series by Koutsias et al. reveals that most of the increase happened during the 128
1970s, indicating land abondonment as a possible cause. Data by the European Forest 129
Fire Information System (EFFIS) show no apparent trend in burned area for Greece 130
for 1980 to 2012, nor for the five southern European Union member states combined 131
(Portugal, Spain, France, Italy and Greece). Data for Italy even show a downward 132
trend in burned area since 1980, but – as data for Greece by Koutsias et al. – an 133
upward trend during the 1970s. Of the other EU countries, only Croatia has 134
comparable levels of burned area per year as the southern European countries already 135
referred to (i.e. above 20,000 ha/year on average), but shows no trend. Bulgaria shows 136
extremely large year-to-year fluctuations in burned area, but no discernable trend. No 137
large-scale data are available for the European part of Russia (JRC 2013). There is 138
therefore no evidence that burned area from wildfires has increased in Europe over 139
the past decades, and by implication no evidence a climate-driven increase in 140
pollutant emissions from wildfires. 141
1.3 Predicting changes in wildfires emissions 142
As for past changes, any predictions of future changes in pollutant emissions from 143
wildfires suffer from the fact that little is known about the determinants of several of 144
the factors used to compute emission rates: burned area, fuel load, combustion 145
completeness, and emission factors (Knorr et al. 2012). In particular, no study has so 146
far considered changes in emission factors, and even complex global fire models only 147
use a fixed set of values for combustion completeness depending on the type of 148
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biomass combusted (Kloster et al. 2012). At the most, model-based predictions of fire 149
emissions are based on simulated changes in burned area and fuel load alone, 150
assuming no change in either emission factors or combustion completeness as a result 151
of changes in climate, management or ecosystem function. Because there are no large-152
scale direct observations of fuel load, values of fuel simulated by models carry a large 153
margin of uncertainty (Knorr et al. 2012, Lasslop and Kloster 2015). 154
To add to the uncertainty, of the few studies attempting to predict future changes in 155
fire patterns, only two predict burned area. The pioneering global studies by 156
Krawchuk et al. (2009) and Pechony and Shindell (2010) essentially predict number 157
of fires – which the authors call “fire activity”. These studies are therefore not suitable 158
for predicting changes in fire emissions, unless one would assume not only constant 159
emission factors and combustion completeness, but also no change in fuel load and 160
average size of fire. Fuel load, however, has been shown to change substantially with 161
climate and CO2 fertilisation (Kloster et al. 2012, Martin Calvo and Prentice 2015, 162
Lasslop and Kloster 2015) and to have a major impact on predicted changes in total 163
fire-related carbon emissions (Knorr et al. 2015). It has also been observed that 164
average fire size changes substantially with human population density (Archibald et 165
al. 2010, Hantson et al. 2015). 166
While Pechony and Shindell (2010) still concluded that temperature would become 167
the dominant control on fire activity during the 21st century, Moritz et al. (2012) 168
found that precipitation and plant productivity will also play a key role. Using an 169
empirical model based on plant productivity and a range of climate drivers and 170
predicting the number of fires, they found a mixed picture, but no universal increasing 171
trend towards more fires, with large parts of the tropics and subtropics likely seeing a 172
decrease in fire activity, rather than an universal increasing trend towards more fires. 173
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.Contrary to the statistical approaches by Archibald et al. (2010), Knorr et al. (2014) 174
and Bistinas et al. (2014), who also found that increasing human population leads to 175
less burned area, Pechony and Shindell (2010) use an approach first developed by 176
Venevsky et al. (2002), where the number of fires is modelled in proportion to the 177
number of ignitions, most of them human. Human ignitions are assumed to increase 178
proportionally with human population until some threshold, where fire suppression 179
leads to a downward modification. More comprehensive fire models predict not only 180
number of fires, but also fire spread and thus burned area. In fact, most of the existing 181
global fire models to-date that are able to predict burned area use the approach by 182
Venevsky et al. (2002), where burned area is considered at the end of a chain of 183
predictions that starts from the number of ignitions. This applies to the global models 184
of Arora and Boer (2005), Thonicke et al. (2010), Kloster et al. (2010), and Prentice et 185
al. (2011). 186
This inherent view that burned area is driven mainly by the number of ignitions has 187
recently been criticised by Knorr et al. (2014) who, using several independent 188
satellite-observed burned-area data sets, developed a semi-empirical model of fire 189
frequency based on climatic indices and human population density alone. Based on 190
statistical analysis, the study came to the conclusion that human presence 191
overwhelmingly leads to a decrease in burned area, even for areas with very low 192
population density, as for example in large parts of the Australian continent. The same 193
view is supported by a review of the impacts of land management on fire hazard by 194
Moreira et al. (2011), showing that at least in southern Europe, land use changes 195
associated with fewer people almost always lead to increased fire risk, and vice versa. 196
Other statistical studies by Lehsten et al. (2010) for Africa and by Bistinas et al. 197
(2013, 2014) for the globe also found a predominantly negative impact of population 198
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density on burned area, supporting the view that most fire regimes on the globe are 199
not ignition limited but rather ignition saturated (Guyette et al. 2002, Bowman et al. 200
2011). Since the view of ignition saturation is in direct contrast to the implicit 201
assumption of burned area increasing with number of ignitions – all else being equal – 202
that is included in most large-scale fire models, it must be concluded that there is so 203
far no consensus on the mechanisms that drive changes in fire frequency, be they 204
climatic or socio-economic, or both in combination. 205
At the regional scale, a few studies have attempted to predict future changes in fire 206
regime, most of them by predicting changes in fire weather: e.g. Stocks et al. (1998), 207
Flannigan et al. (2005), and for Europe, Moriondo et al. (2006) and Bedia et al. 208
(2014). One study, Amatulli et al. (2013), goes beyond those by developing a 209
statistical model of burned area based on a selection of indicators that form part of the 210
Canadian Fire Weather Index (van Wagner and Forest, 1987). One problem faced by 211
the latter study is that the future climate regime simulated by climate models is often 212
outside the training regime used to develop the statistical model, leading to uncertain 213
results. 214
An overview of relevant model results for Europe is offered in Table 1. The study by 215
Amatulli et al. (2013) previously referred to is also the one that predicts the most 216
extreme changes in burned area in the Mediterranean (Table 1). This might be 217
attributable to a lack of representation of vegetation effects on fire spread or burned 218
area: when precipitation decreases, while meteorological fire risk increases, fire 219
spread is increasingly impeded by lower and lower fuel continuity (Spessa et al. 220
2005). However, as much as this study appears to be an outlier, all predict an increase 221
in either carbon emission or burned area in Europe towards the later part of the 21st 222
century, mostly in southern and eastern Europe. There is, however, no consensus, on 223
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the underlying mechanism of the increase. For instance, while Migliavacca et al. 224
(2013) predict a rate of increase for emissions greater than the rate of increase for 225
burned area – i.e. more fuel combusted per area – Knorr et al. (2015) predict the 226
opposite, but with a climate effect on burned area that still overrides the effect of 227
decreasing fuel load. Or Wu et al. (in press) predict a population driven increase for 228
eastern Europe using SIMFIRE, but mainly a climate driven increase when using 229
SPITFIRE, more similar to the results by Kloster et al. (2012) and Migliavacca et al. 230
(2013). 231
2 Methods 232
None of the published simulation studies of future European fire emissions consider 233
emissions at the level of chemical species or amounts of specific aerosols, and hence 234
do not provide indications on the significance for air quality. Therefore, we have 235
taken existing simulations by Knorr et al. (2015) that predict emissions in combusted 236
carbon amounts, and combined them with biome-dependent emissions factors by 237
Andreae and Merlet (2001; updated 2009). Each grid box is assigned one biome type. 238
To avoid too large areas of tropical rainforests being classified as savannahs, we 239
increased the threshold of total grass leaf area that separates the biome "savannah and 240
grassland" from the two possible forest biomes from 20% to 30% (cf. Knorr et al. 241
2012). 242
Simulations of wildfire carbon emissions are based on an ensemble of eight climate 243
model simulations from the Climate Model Intercomparison Project 5 (Taylor et al. 244
2012). For each climate model, two runs are used, each one driven by greenhouse gas 245
emissions from either RCP 4.5 (medium climate stabilisation case) or 8.5 (baseline 246
case for greenhouse gas emission, van Vuuren et al. 2011). Gridded fields of monthly 247
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simulated precipitation, diurnal mean and range of temperature and solar radiation are 248
bias corrected against mean observations (Harris et al. 2014) for 1961-1990 and 249
together with global mean observed and future-scenario CO2 concentrations used to 250
drive simulations of the LPJ-GUESS global dynamic vegetation model (Smith et al. 251
2001) coupled to the SIMFIRE fire model (Knorr et al. 2012, 2014). Plant mortality 252
during fire and the fraction of living and dead biomass consumed by the fire are all 253
assumed fixed across time (see Knorr et al. 2012). The simulations are carried out on 254
an equal-area grid with a spacing of 1° in latitudinal direction and 1° in longitudinal 255
direction at the equator, increasing in degrees longitude towards the poles (with 256
approximately constant 110 km by 110 km grid spacing). 257
Population density until 2005 is taken from gridded HYDE data (Klein-Goldewijk et 258
al. 2010). Future population scenarios are from the Shared Socio-Economic Pathways 259
(SSPs, Jiang 2014), using SSP5 (a conventional development scenarios assuming high 260
population growth and fast urbanisation for Europe, or slight population decline in 261
some eastern European countries, differing from most of the rest of the world with 262
low population growth and fast urbanisation for developing regions), SSP2 (middle of 263
the road scenario, with medium population growth and urbanisation for Europe and 264
the rest of the world), and SSP3 (a fragmented world, assuming low population 265
growth, or strong population decline, combined with slow urbanisation for Europe, as 266
compared to high population growth and slow urbanisation for developing regions). 267
Gridded population distributions beyond 2005 are produced by separate re-scaling of 268
the urban and rural populations from HYDE of 2005 (see Knorr et al. 2015 for 269
details). 270
In order to simulate realistic scenarios of the spatial patterns of wildfire emissions in 271
Europe, we use emission data from the Global Fire Emissions Database Version 4.1 272
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(GFED4s) based on an updated version of van der Werf et al. (2010) with burned area 273
from Giglio et al. (2013) boosted by small fire burned area (Randerson et al., 2012), 274
available from http://www.falw.vu/~gwerf/GFED/GFED4/. We use the mean annual 275
course of monthly emissions at a resolution of 0.5° by 0.5° from the sum of boreal 276
and temperate forest fires during the years 1997 to 2014 as a climatology of present 277
wildfire emissions for black carbon (BC), CO, NOx, particulate matter up to 2.5 278
microns (PM2.5) and SO2. In order to avoid as much as possible the inclusion of 279
agricultural burning erroneously classified as wildfires, we only use the months May 280
to October from the climatology. We then calculate future emissions by averaging 281
simulated annual emissions for the same chemical species by European country using 282
the Gridded Population of the World Version 3 country grid. We restrict the area of 283
analysis to Europe west of 40°E. Only those countries resolved on the 1° equal area 284
grid are included. Two groups of countries are treated as a single unit, namely 285
Belgium, Netherlands and Luxemburg as "Benelux", and the countries of former 286
Yugoslavia plus Albania as "Yugoslavia & Albania". The observed climatology of 287
emissions is then scaled at each grid cell according to which country it is located in. 288
The scaling factor equals the mean annual simulated emission of each species of this 289
country during the future period divided by the mean annual emissions of this species 290
during 1997 to 2014, inclusive. 291
Two further simulations were performed where the standard parameterisation of 292
SIMFIRE has been changed against one derived from optimisation against MCD45 293
global burned area (Roy et al. 2008). This was done only with one climate model 294
(MPI-ESM-LR, see Knorr et al. 2015), in order to test the sensitivity of the SIMFIRE 295
simulations against changes in its parameterisation, which normally is derived by 296
optimisation against GFED3 burned area (van der Werf et al. 2010). 297
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For anthropogenic emissions of air pollutants, we use the GAINS model (Amann et 298
al., 2011) estimates developed within the ECLIPSE project (Stohl et al., 2015). 299
Specifically, we use the GAINS version 4a global emissions fields (Kimont et al. 300
2013, Klimont et al., in preparation, Granier et al. 2011), which are available for 2010 301
(base year), 2030 and 2050 at 0.5° by 0.5° resolution from the GAINS model website 302
(www.iiasa.ac.at/web/home/research/researchPrograms/Global_emissions.html). The 303
future emissions for 2030 and 2050 are available for two scenarios: current legislation 304
(CLE), which assumes efficient implementation of existing air pollution laws, and the 305
maximum technically feasible reduction (MFR), where all technical air pollution 306
control measures defined in the GAINS model are introduced irrespective of their 307
cost. We do not use PEGASOS PBL emissions (Braspenning-Radu et al., in review) 308
because they do not include particulate matter, but instead compare them to the 309
emission scenarios used here (Table 1). In order to obtain a scenario with some 310
further declining emissions, we extend the ECLIPSE CLE anthropogenic emissions 311
dataset to 2090 by scaling emissions in 2050 by the relative change of the population 312
in each grid cell between 2050 and 2090 according to the SSP3 population scenario 313
(low population growth and slow urbanisation for Europe). For MFR, we assume that 314
emissions for all species in 2090 are half of what they are for 2050. A comparison of 315
the extended ECLIPSE anthropogenic emission trends after 2050 can be made using 316
the independent set of emission scenarios provided by the PEGASOS PBL emissions 317
dataset (Braspenning-Radu et al., 2015, in review). Since this dataset does not provide 318
PM2.5 emissions, the comparison is limited to CO, BC, NOx and SO2. For CO and 319
BC, the PEGASOS PBL CLE data show a stronger decline by than our extended 320
ECLIPSE emissions, but for NOx and SO2, the changes from 2050 to 2090 are very 321
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similar. For MFR, PEGASOS MFR-KZN has about the same total emission as those 322
used here by 2090 (Table 2). 323
In the following, we compare anthropogenic and wildfire emissions of BC (black 324
carbon), CO, NOx, PM2.5 (particulate matter up to 2.5 µm diameter) and SO2 both on 325
an annual average basis, and for the peak month of the fire season, i.e. during the 326
month with highest wildfire emissions on average at the corresponding grid cell. We 327
approximate monthly emissions at the peak of the fire season as total anthropogenic 328
emissions minus emissions from the category "residential and commercial 329
combustion" per month. Subtraction of the latter sector, with a large contribution from 330
domestic heating in winter, focuses on the relative contribution of emissions in the 331
summer 332
3 Results and Discussion 333
3.1 Current observed patterns of air pollution against population density 334
By and large, we expect anthropogenic emissions to be spatially associated with areas 335
of high population density, and it is therefore interesting to consider how the two 336
quantities are related. For emissions from wildfires one would expect a different 337
relationship, as large wildfires are often associated with remote and sparsely 338
populated areas, such as the boreal zone. As Figure 1 shows, current anthropogenic 339
emissions of CO, PM2.5 and BC are generally about two orders of magnitude higher 340
than wildfire emissions on average in a given category, and, contrary to expectations, 341
this applies even to the most sparsely populated areas. Anthropogenic emissions 342
increase monotonically against population density up until 100 or more inhabitants / 343
km2, when emissions either saturate or slightly decrease (for CO, PM2.5). 344
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For wildfires, we see the highest emissions in the range 10 to 100 inhabitants / km2, 345
and the lowest in the most sparsely populated regions. We find that CO and PM2.5 346
are the dominant pollutants emitted both by wildfires or human activities. The decline 347
of total fire emissions towards dense population is consistent with the SIMFIRE 348
model, which predicts generally declining burned area with increasing population 349
density. By contrast, the declining emissions towards low population values at first 350
sight seem contradictory with the current model formulation, which assumes burned 351
area being largest in these low population regions, with only a very small effect at 352
very low population levels (Knorr et al., 2014). However, co-variation of other 353
environmental variables that drive fire occurrence with population density (Bistinas et 354
al. 2014) explain the more complex relationship seen in Figure 1 (Knorr et al., ). 355
Areas with fewer than 3 inhabitants / km2 (see Appendix, Figure A1) are all situated 356
in boreal regions or northern highlands, with low fire occurrence (Giglio et al. 2013). 357
If we compare the two sources of emissions on a monthly instead of an annual basis 358
and choose the month where wildfire emissions are highest, we find CO emissions for 359
large parts of Portugal to be of comparable magnitude to the large Russian wildfires 360
near Moscow in July and August 2010 (Kaiser et al. 2010). Even though these fires 361
were only one event in a 14 year record, they show up clearly in Figure 2b around 362
54°N, 39°E (Moscow can be located by high anthropogenic emissions slightly to the 363
west), as do the fire in the western Peloponnese in 2007 (Boschetti et al. 2008). 364
PM2.5 emissions of comparable magnitude are more widespread and are found again 365
for Portugal and east of Moscow, but also along the western the coastal regions of 366
Yugoslavia and Albania and southern Greece. The large forest fires in southern 367
Europe (Pereira et al., 2005; Boschetti et al. 2008) and the 2010 fires east of Moscow 368
all show peak emissions in August (Figure 2c). If we sum over all wildfire emissions 369
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of the European study region (including western Russia) during June to October, the 370
emissions also show a clear peak in August (Figure 2f). 371
3.2 Predicted changes in wildfire emissions 372
Simulated wildfire emissions of PM2.5 from Europe (Figure 3) show a minor 373
decrease over the 20th century, which is consistent with the lack of evidence for a 374
change in European fire activity discussed in Section 1.2. Between 2000 and 2050, 375
both climate scenarios show a similar slight increase with almost no discernible 376
impact of the specific choice of population scenario. Only after 2050, simulations 377
with a high climate change scenario (RCP8.5) show a marked increase, including a 378
doubling of current emission levels for the highest ensemble members, while for 379
RCP4.5, emissions barely increase any further. Differences between population 380
scenarios have only a small impact on emissions in Europe, with SSP5 leading to the 381
lowest, and SSP3 population and urbanisation to the highest emissions. 382
The SSP5 scenario assumes high levels of fertility, life expectancy and net 383
immigration for western Europe under optimistic economic prospects, but opposite 384
demographic trends, similar to developing countries, in eastern Europe. By contrast, 385
SSP3 assumes slow economic development in a fragmented world with low 386
migration, fertility and life expectancy, and therefore low population growth for the 387
developed world, including Europe. As a result, projected wildfire emission trends 388
differ greatly from those for the global scale, where emissions are dominated by 389
demographic trends in developing countries (Knorr et al. 2015), with SSP5 leading to 390
the highest emissions. The reason for the difference is that in developing countries 391
under SSP5, low population growth and fast urbanisation both lead to lower 392
population in rural areas, thus increasing fire emissions. In developed countries, 393
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higher population growth leads to lower but slower urbanisation to higher emissions. 394
Because Europe is already highly urbanised and the scope for further urbanisation 395
small, the population growth effect dominates over the urbanisation effect, and as a 396
result SSP5 has the lowest emissions. The exact opposite happens for SSP3. 397
Of the regions or countries analysed (Table 3), Portugal clearly stands out, 398
representing not only around 27% of European wildfire emissions (here of PM2.5, but 399
relative results are similar for other pollutants), its emissions are also more than one 400
order of magnitude higher per area than the European average (Pereira et al. 2005, 401
JRC 2013). Other countries or regions with high emissions per area are Russia (20%), 402
Yugoslavia & Albania (9%), Spain (16%) and Greece (4% of European emissions), 403
and these countries together contribute as much as 77% of total European PM2.5 404
wildfire emissions using the GFED4.1s data. Most of the remainder is made up of 405
Italy, France, Ukraine and Belarus (18% of total), while Northern European countries 406
emit marginal quantities of fire emissions especially relative to the anthropogenic 407
emissions. 408
Portugal is estimated to experience a 23 to 42% increase in PM2.5 emissions by 2050, 409
depending on the climate scenario. For 2090 and high levels of climate change 410
(RCP8.5), the ensemble average (over eight GCMs and three SSP scenarios) indicates 411
almost a doubling of emissions (93%), with the highest ensemble estimate reaching 412
+134%. By comparison, western Russia is simulated to experience only small 413
emission increases or even a decrease. Spain, France, Italy, Yugoslavia & Albania and 414
Greece have similar increases in emissions to Portugal, all but Spain and France 415
showing extremely high ensemble maxima for 2090 that amount approximately to a 416
tripling or quadrupling (Italy) of emissions by that point in time. Some countries or 417
regions, like Benelux, Germany, Czech Republic and Switzerland, have even higher 418
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ensemble-mean estimated relative increases and ensemble maximum increases for 419
RCP8.5 that represent an upward shift of almost an order of magnitude. However, 420
these regions have very low wildfire emissions currently, making them unlikely to 421
contribute significantly total pollutant emissions in the future. A more important result 422
is therefore that ensemble maxima for some of the strongly emitting regions are also 423
very high. For example, the simulations indicate that Greece could triple and Italy and 424
Portugal quadruple their wildfire emissions until around 2090 for the RCP8.5 climate 425
change scenario. 426
Results of the sensitivity study using the alternative SIMFIRE parameterisation are 427
shown in the Appendix (Figure A3, Table A1). For all European regions, LPJ-428
GUESS-SIMFIRE simulates ca. 30% lower burned area compared to the standard 429
parameterisation, an offset that is rather stable across the simulation period, leading to 430
a small impact on relative changes in emissions (Table A1, bottom row) . On a 431
region/country basis, however, the differences can be quite large, especially for 432
changes from 2010 to 2090 and the RCP8.5 scenario. For example, using the MPI 433
climate model and the MCD45 parameterisation, Greece is predicted to increase 434
wildfire carbon emissions by 350% compared to +209% for the standard 435
parameterisation and +211% for PM2.5 and the ensemble maximum (Table 3). 436
3.3 Future patterns of exposure and interaction with population density 437
The character of the wildfire emission – population density relationship (Figure 1), 438
which largely follows the relationship for anthropogenic emissions but more with a 439
more than two orders smaller magnitude, makes it improbable that wildfires could 440
ever become a significant source of air pollution in Europe in even the more remote 441
areas of Europe. In fact, even when we compare the highest case for wildfire 442
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emissions, combining high RCP8.5 climate and CO2 change with SSP3 rapid 443
population decline over large parts of Europe (Figure A2), with the scenario of 444
maximum feasible reduction (MFR) in anthropogenic emissions, European wildfire 445
emissions always remain much below those from anthropogenic sources (see 446
Appendix, Figure A4; this case would require that most greenhouse gas emissions 447
leading to RCP8.5 would have to originate outside of Europe). 448
On a seasonal basis, however, wildfire emissions may come close to those of human 449
origin (Figure 4) for regions with population densities between 3 and 100 inhabitants / 450
km2, and CO and PM2.5.. In this case, we combine both RCP4.5 (Figure 4a) and 451
RCP8.5 (Figure 4b) with the SSP5 scenario (fast urbanisation and high population 452
growth, or slow decline in eastern Europe), so that differences in simulated wildfire 453
emissions between the two sub-figures are solely due to differences in the degree of 454
climate and CO2 change. It has to be taken into account that the population scenario 455
used by the GAINS projections of anthropogenic emissions are different from the SSP 456
scenarios used here, which were not available at that time (Klimont et al. in 457
preparation, Jiang 2014).The climate and CO2 effect leads to higher wildfire 458
emissions compared to present day. For RCP4.5, however, the increase is confined to 459
areas with less than 10 inhabitants / km2, caused mainly by widespread abandonment 460
of remote areas due to increasing population concentration in cities under the SSP5 461
fast-urbanisation scenario (Figure A2), leading to increases in the areal extent of the 462
sparsely populated regions (translating into higher emission in that category even if 463
per area emissions stayed the same). For RCP8.5, there is also a marked emission 464
increase by 2090 across the entire range of population densities, consistent with 465
Figure 4. For the CLE scenario, which we compare with RCP4.5/SSP5, wildfire BC 466
and CO emissions always remain more than one order of magnitude below 467
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anthropogenic emissions for all population density categories, even at the peak of the 468
fire season. For PM2.5, wildfire emissions may reach around 10% of the 469
anthropogenic counterpart for less than 10 inhabitants / km2. Even for MFR (Figure 470
4b), CO from wildfires remain a minor source, but for BC and PM2.5 (except for the 471
most densely populated regions), wildfires reach anthropogenic-emission levels. 472
The importance of wildfire emissions will further increase with under stronger climate 473
change, but the main reason is a reduction in anthropogenic emissions. It is therefore 474
mainly a combination of climate warming and strong reduction in anthropogenic 475
emissions that could make wildfire emissions a significant contributor to air pollution 476
during the fire season. This could mean that fire management will have to be 477
improved in the areas concerned if air quality targets are to be met. 478
While on a long-term annual basis, wildfire emissions are unlikely to develop into an 479
important source of air pollution for Europe as a whole, some areas have already now 480
comparatively high emissions (Figure 2). A spatially explicit analysis of future 481
emissions using again RCP8.5, SSP5 population and MFR anthropogenic emissions, 482
reveals that by 2090 wildfires could become the dominant source of BC for much of 483
Portugal (Figure 5a). For PM2.5 in Portugal or BC and PM2.5 in boreal regions, this 484
could already be the case as soon as these maximum feasible emission reductions 485
have been achieved (2030). CO is only likely to play an important role in Portugal, 486
but only by 2090 because of large increases in wildfire emissions due to high levels of 487
climate change. 488
During the peak of the fire season (Figure 5b), in 2030 fire emissions are dominating 489
for most of Portugal, coastal regions of former Yugoslavia and Albania, western 490
Greece plus some scattered parts of Spain, Italy and Bulgaria, and the northern part of 491
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eastern Europe (Russia, Ukraine, Belarus), as soon as maximum feasible reduction of 492
anthropogenic emission reductions are implement – considering that by 2030 the 493
degree of climate driven increases will be minimal. The areas affected more strongly 494
are predicted to increase further by 2050, especially for BC in north-eastern Europe, 495
and 2090, in particular in southern Europe. 496
These results may change when a different anthropogenic emissions data set is 497
chosen. There are, for example, considerable differences between the present scenario 498
assuming half of 2050 ECLIPSE GAINS 4a emissions by 2090, and the PEGASOS 499
BPL v2 emissions for the same year. For example, PEGASOS has much lower CO 500
emissions in north-western Russia and Finland, but our extended ECLIPSE data set 501
lower emissions in the southern Balkans, which would affect results shown in Figure 502
5b. In general, however, there is a reasonable agreement between the two scenarios. 503
Only when MFR is combined with assumed further technical advancement and a 504
stringent climate policy (PEGASOS scenario 450-MFR-KZN, see Table 1) emissions 505
are projected to fall even further by 2090. In this case, however, we also expect 506
smaller increases in wildfire emissions due to limited climate change. Another 507
important point to consider in further studies is that atmospheric aerosols from 508
anthropogenic pollutant emissions itself have either a cooling (Ramanathan et al. 509
2001) or warming (Ramanathan and Carmichael, 2008) effect on climate, and also 510
influence plant productivity (Mercado et al. 2009), creating potentially important 511
cross-links and feedbacks between air pollution and wildfire emissions. 512
3.4 Policy relevance of results 513
In order to be relevant for air pollution policy, wildfires we assumed that wildfires 514
must (1) contribute a considerable fraction of pollutant emissions, and (2) the 515
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emissions need to be large enough so that limit values of air pollutant concentrations 516
are exceeded. Modelling air pollutant emissions from wildfires in Europe remains a 517
challenge for science and policy alike, from an observational and even more so a 518
modelling standpoint. Observing present-day patterns and their changes, and the 519
attribution of observed changes to climate change or socio-economic drivers is 520
difficult, which makes it also hard to provide reasonable future projections. Current 521
wildfire emission estimates are also uncertain owing to differences in burned area, 522
emissions factors or the assumed fraction of combusted plant material, which could 523
easily double or halve the emissions values when assumptions are modified (Knorr et 524
al. 2012). Likewise, the uncertainty in the published range of even the present 525
anthropogenic emissions is of similar relative magnitude (Granier et al. 2011). 526
However, given the large differences by orders of magnitude found at the European 527
level, it is clear that air pollution from wildfire emissions presently and in most cases 528
also in the future only plays a minor role in most of Europe under current conditions 529
of air pollution. 530
Answering the question whether the importance of wildfire emissions has changed 531
over the last century is difficult, but there is no strong evidence that this has been the 532
case. The reason for the lack of evidence for climate-driven increases in European 533
wildfire emissions may simply be that these emissions during the 20th century have 534
tended to slightly decrease, due to socioeconomic changes, rather than increase, as 535
several modelling studies suggest, including the present one. 536
For the future, however, fire emissions may become relatively important (condition 1) 537
if stringent policy measures are taken to further limit anthropogenic emissions. The 538
question therefore remains whether the magnitude can also reach levels sufficiently 539
high to interfere with air quality policy aimed at limiting anthropogenic sources. To 540
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illustrate this, we focus on the most relevant air pollutant component, PM2.5. In the 541
following, we derive an approximate threshold for peak-month wildfire PM2.5 542
emissions (EPM2.5p.m.) above which these might interfere with air quality goals. 543
According to Figure 2e, the highest emissions in central and northern Portugal are 544
around 0.05g/m2 during the peak month. Assuming that the peak month contributes 545
about half the annual wildfire emissions (Figure 2f), a boundary height h=1000 m (as 546
a compromise between night and day time) and a life time of the emissions of 547
τ=1/50 yr (7.3 days), and that the impact on mean annual mean (not peak-month) 548
PM2.5 concentrations corresponds roughly to the steady state concentrations, CPM2.5, 549
with EPM2.5p.m.=0.05 g/(m^2 month), we obtain: 550
CPM2.5 = EPM2.5p.m. * 2 months/year * τ / h 551
= 0.05 * 40 µg / m3 552
= 2 µg / m3. (1) 553
During the peak fire month, this would amount to six times this level, i.e. 12 µg / m3 554
(half of the amount emitted in 1/12 of the time). For 2012, most air quality stations in 555
central to north Portugal report mean annual PM2.5 values of up to 10 µg / m3 (EEA 556
2014, Map 4.2). Fire activity during that year was moderately below average, with 557
around 80% of the long-term average burned area (JRC 2013). Assuming burned area 558
to scale with emissions, we would expect around 1.6 µg / m3 as the wildfire 559
contribution for 2012 in the areas with the highest emissions, which would be 560
consistent with the report air quality data. 561
If the European Union in the future moved from its own air quality directive's target 562
of 25 µg/m3 annual average (EEA 2014) to the more stringent World Health 563
Organization guideline of 10 µg/m3 (WHO 2006), a contribution of 3 µg / m3 would 564
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probably be considered policy relevant. According to Eq. (1), such annual mean levels 565
would require roughly an emissions of 0.07 g/m2 PM2.5 emissions during the peak 566
fire month, which we adopt as a practical lower threshold for when these emissions 567
might become relevant for meeting air quality policy goals. According to Figure 6, 568
such levels are currently not met, and indeed central to northern Portugal has air 569
quality readings that are towards the lower end of European air quality measurements 570
(EEA 2014). However, such conditions could be met later during this century with 571
high levels of climate change. For the remaining European areas with high wildfire 572
emission, the emissions are likely to remain below this threshold according to the 573
present estimate. However, these regions could still emit enough pollutants from 574
wildfires to be policy relevant, either seasonally, or on an annual basis if 575
meteorological conditions are more conducive to high pollutant concentrations as it is 576
implied in the calculation above, or if the emissions or emission change estimates 577
used in the present study turn out to be on the low side. 578
579
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4 Summary and Conclusions 580
• The evidence for changes in fire regimes in Europe for the past several decades is 581
not clear enough to attribute any changes to climatic drivers. A certain role of land 582
abandonment leading to larger fires and higher fire frequency is often reported but 583
has not been universally demonstrated. 584
• Confidence in future predictions of fire emissions for Europe is generally low. 585
Partly this is because important factors, such as changes in emission factors or fuel 586
combustion completeness have never been taken into account. Another reason is 587
that model-based simulations of fire emissions in Europe cannot be properly 588
validated because the multi-decadal data are too ambiguous. Finally, there is no 589
consensus about the main drivers of fire frequency and in particular the way land 590
use impacts average fire size. This caveat is valid also for the following statements. 591
• Future demographic trends are an important factor for fire emissions especially for 592
emerging areas of low population density. 593
• For Europe, only a moderate increase in fire emissions is plausible until 2050. 594
However, a doubling of fire emissions between now and the late 21st century is 595
possible under higher climate change / CO2 emissions trajectories. For some 596
southern European countries, uncertainties are higher, and tripling or even 597
quadrupling of emissions appear plausible, even if unlikely. 598
• The highest ratio of wildfire to anthropogenic emissions for CO, BC, and PM2.5 is 599
found for Portugal. During the fire season, emissions of these pollutants might 600
already exceed those from anthropogenic sources. Emissions are generally 601
projected to increase further with climate change. 602
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• If air pollution standards are further tightened, in large parts of Mediterranean and 603
north-eastern Europe, wildfires could become the main source of air pollution 604
during the fire season, unless improved fire management systems would be 605
considered. 606
Acknowledgements 607
This work was supported by EU contracts 265148 (Pan-European Gas-Aerosol-608
climate interaction Study, PEGASOS), 603542 (Land-use change: assessing the net 609
climate forcing, and options for climate change mitigation and adaptation, LUC4C) 610
and 603445 (Impact of Biogenic versus Anthropogenic emissions on Clouds and 611
Climate, BACCHUS). Anthropogenic emissions data were provided by the ECCAD-612
GEIA database at 0.5 degree resolution on 18 July 2014-07-18 and downloaded from 613
the ECCAD site. We thank Jesus San-Miguel of JRC for sharing information prior to 614
publication. 615
Author contributions: WK conceived of the study, carried out the analysis and wrote 616
the first draft of the manuscript, FD contributed to conception of paper, and scenario 617
analysis. All authors contributed to discussions and writing. 618
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36/5
0
Tab
les
Tabl
e 1:
Ove
rvie
w o
f clim
ate
chan
ge m
odel
ling
resu
lts fo
r wild
fires
. 84
8 R
efer
ence
O
utpu
t D
omai
n M
etho
d In
put
Res
ult f
or E
urop
e Sc
holz
e et
al.
(200
6)
burn
ed a
rea
Glo
be
LPJ-
Glo
bFirM
ve
geta
tion,
em
piric
al fi
re
mod
el
no h
uman
impa
ct
16 G
CM
s, 52
G
CM
-sce
nario
co
mbi
natio
ns
Sign
ifica
nt d
ecre
ase
in n
orth
-eas
tern
, inc
reas
e in
w
este
rn E
urop
e, It
aly
and
Gre
ece,
mix
ed re
sults
fo
r Spa
in
Klo
ster
et a
l. (2
012)
ca
rbon
em
issi
ons
Glo
be
CLM
pr
oces
s bas
ed m
odel
M
PI a
nd C
CM
G
CM
s, SR
ES
A1B
, fa
ctor
ial
expe
rimen
ts
+116
% (M
PI) o
r +10
3% (C
CM
) bet
wee
n 19
85-
2009
and
207
5-20
99,
incr
ease
mos
tly in
sou
th-c
entra
l and
eas
tern
Eu
rope
, dec
reas
e in
Med
iterr
anea
n
Mig
liava
cca
et a
l. (2
013)
ca
rbon
em
issi
ons
Euro
pe, p
arts
of
Tur
key
and
Nor
th A
fric
a
CLM
ad
apte
d fo
r Eur
ope
5 R
CM
s fr
om 1
960-
1990
to 2
070-
2100
+63
% fo
r Ibe
ria
and
+87%
for r
est o
f sou
ther
n Eu
rope
, inc
reas
e in
fuel
load
A
mat
ulli
et a
l. (2
013)
bu
rned
are
a Po
rtuga
l, Sp
ain,
Fre
nch
Med
iterr
anea
n,
Italy
, Gre
ece
CFW
I com
bine
d w
ith se
vera
l st
atis
tical
mod
els,
diff
eren
t CFW
I cod
es a
nd
stat
istic
al m
odel
s by
coun
try
Sing
le R
CM
, SR
ES A
2, B
2 B
etw
een
1985
-200
4 an
d 20
71-2
100
+60%
for
Euro
pe a
nd +
500%
for S
pain
(B2)
, or +
140%
for
Euro
pe a
nd +
860%
for S
pain
Bed
ia e
t al.
(201
4)
SSR
of
CFW
I So
uthe
rn
Euro
pe, N
orth
A
fric
a
CFW
I m
eteo
rolo
gy o
nly
6 G
CM
-RC
M
com
bina
tions
SR
ES A
1B
Sign
ifica
nt in
crea
se fr
om 1
971-
2000
to 2
041-
2070
for P
ortu
gal,
Spai
n, It
aly,
Gre
ece
and
Turk
ey, t
o 20
71-2
100
the
sam
e pl
us F
renc
h M
edite
rran
ean
and
Bal
kans
K
norr
et a
l. (2
015)
ca
rbon
em
issi
ons
Glo
be
LPJ-
GU
ESS-
SIM
FIR
E pr
oces
s-ba
sed
vege
tatio
n,
sem
i-em
piric
al fi
re m
odel
8 G
CM
s, R
CP4
.5 a
nd
8.5
scen
ario
s
Dur
ing
21st c
entu
ry la
rge
incr
ease
due
to
popu
latio
n de
clin
e co
mbi
ned
with
incr
ease
d bu
rned
are
a dr
iven
by
clim
ate
war
min
g, w
hile
fu
el lo
ad is
dec
reas
ing;
sign
ifica
nt in
crea
ses i
n ce
ntra
l, ea
ster
n, so
uthe
rn E
urop
e W
u et
al.
(in p
ress
) bu
rned
are
a Eu
rope
LP
J-G
UES
S-SI
MFI
RE,
LP
J-SP
ITFI
RE
proc
ess-
base
d ve
geta
tion
and
fire
mod
els
4 G
CM
s, R
CP2
.6 a
nd
8.5
scen
ario
s
+88%
(SIM
FIR
E) o
r +28
5% (S
PITF
IRE)
from
19
71-2
000
to 2
071-
2100
for R
CP8
.5, e
spec
ially
in
eas
tern
Eur
ope
due
popu
latio
n de
clin
e (S
IMFI
RE)
or c
limat
e (S
PITF
IRE)
C
FWI:
Can
adia
n Fi
re W
eath
er In
dex;
CLM
: Com
mun
ity L
and
Mod
el; G
CM
: Gen
eral
Circ
ulat
ion
Mod
el; R
CM
: Reg
iona
l Clim
ate
Mod
el;
SRES
: Spe
cial
Rep
ort o
n Em
issi
ons S
cena
rios;
RC
P: R
epre
sent
ativ
e C
once
ntra
tion
Path
way
; SSR
: Sea
sona
l Sev
erity
Rat
ing;
Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2015-1000, 2016Manuscript under review for journal Atmos. Chem. Phys.Published: 16 February 2016c© Author(s) 2016. CC-BY 3.0 License.
37/50
Table 2: Total anthropogenic emissions for European study area. 849 Data set Species 2010 2030 2050 2090
ECLIPSE CLE
CO 37,689 30,183 22,720 16,970 PM2.5 2,712 2,370 2,031 1,581 BC 465 399 224 165 NOx 9,581 7,929 4,207 3,130 SO2 10,680 7,380 3,697 2,815
PEGASOS BL-CLE
CO 32,011 18,870 17,573 8,479 BC 525 153 99 29 NOx 8,253 3,775 2,936 2,596 SO2 10,533 3,419 3,150 2,837
ECLIPSE MFR
CO 11,538 11,732 5,866 PM2.5
567 552 276
BC
55 50 33 NOx
1,519 1,478 1,020
SO2 1,560 1,443 1,042
PEGASOS MFR-KZN
CO 30,575 12,587 10,824 4,977 BC 521 125 64 27 NOx 7,848 1,881 1,382 1,291 SO2 10,160 1,824 1,291 900
PEGASOS 450-MFR-
KZN
CO 30,575 11,653 9,074 4,735 BC 521 101 42 23 NOx 7,848 1,585 1,074 889 SO2 10,160 1,298 680 395
Emissions in Tg / yr; CLE: Current legislation; BL-CLE: baseline CLE, no change in emission factors after 2030; MFR: Maximum feasible reductions; MFR-KZN: growth domestic product driven decline in emission factors towards 2100; 450-MFR-KZN: as MFR-KZN with climate targe at 450 ppm atmospheric CO2. Number in italics: extrapolation by the authors.
850
Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2015-1000, 2016Manuscript under review for journal Atmos. Chem. Phys.Published: 16 February 2016c© Author(s) 2016. CC-BY 3.0 License.
38/5
0
85
1 Ta
ble
3: C
hang
es in
sim
ulat
ed P
M2.
5 em
issi
ons
for
regi
ons
used
in th
e an
alys
is.
852
Cou
ntry
/regi
on
GFE
D4.
1s m
ean
Sim
ulat
ed e
mis
sion
cha
nges
201
0 to
205
0 [%
] Si
mul
ated
em
issi
on c
hang
es 2
010
to 2
090
[%]
19
97-2
014
emis
sion
s R
CP4
.5 e
nsem
ble
RC
P8.5
ens
embl
e R
CP4
.5 e
nsem
ble
RC
P8.5
ens
embl
e
[Gg/
yr]
[g/(h
a yr
)]
min
. m
ean
max
. m
in.
mea
n m
ax.
min
. m
ean
max
. m
in.
mea
n m
ax.
Aus
tria
3 0.
5 -1
5 15
51
-4
32
77
-3
47
14
6 -1
6 81
21
3 B
elar
us
232
18.4
0
19
51
-1
20
43
-4
27
60
2 56
15
5 B
eNeL
ux
13
2.6
-43
27
164
-28
45
235
-71
120
537
-49
209
828
Bul
garia
96
12
.2
-8
27
47
6 32
68
12
44
75
32
82
15
6 C
zech
Rep
ublic
7
1.0
-8
55
138
-21
57
212
16
182
611
-2
212
800
Den
mar
k 1
0.3
-32
27
180
-34
13
73
-64
26
132
-49
44
197
Esto
nia
9 5.
2 -1
7 4
28
-35
-1
37
-26
4 40
-2
7 18
84
Fi
nlan
d 8
0.4
0 8
21
-5
5 16
-1
10
21
-1
6 -1
28
Fr
ance
15
4 4.
2 -1
3 15
62
0
26
59
-16
23
90
2 69
16
9 G
erm
any
44
1.7
4 45
12
1 18
62
13
8 7
126
426
30
201
657
Gre
ece
277
20.9
-1
3 30
76
-1
1 25
80
-9
31
77
20
78
21
1 H
unga
ry
8 2.
2 -1
2 14
46
-2
0 19
91
-2
1 48
16
1 -2
6 67
17
0 Ir
elan
d 1
1.1
-21
5 32
-7
20
56
-3
0 29
10
7 -6
54
15
7 Ita
ly
425
14.6
-4
41
97
-2
9 46
17
9 -1
4 70
19
7 -7
12
4 30
1 La
tvia
9
5.0
-1
20
66
5 26
61
-1
3 23
48
15
49
11
4 Li
thua
nia
4 4.
1 -5
20
11
0 -2
5 22
73
-2
2 22
84
-1
0 38
16
3 N
orw
ay
4 0.
3 8
21
40
6 26
42
11
29
46
10
42
82
Po
land
21
1.
3 21
32
46
6
36
61
34
61
115
39
99
178
Portu
gal
1706
18
2.2
0 23
42
2
34
68
2 41
85
50
93
14
3 R
oman
ia
37
5.3
14
48
83
10
61
144
38
103
231
55
140
303
Rus
sia
(wes
t of 4
0°E)
12
76
31.7
0
9 19
-1
1 5
24
-14
8 22
-1
6 13
52
Sl
ovak
ia
4 2.
7 -1
8 30
10
6 0
45
127
8 10
4 25
6 -1
14
0 41
5 Sp
ain
987
24.3
3
18
38
4 20
46
11
36
70
33
68
11
9 Sw
eden
35
0.
9 -4
11
27
-3
10
33
-6
15
41
-3
20
45
Sw
itzer
land
2
1.0
-18
42
152
-20
71
218
-16
140
390
-20
256
833
Ukr
aine
33
9 9.
3 2
29
62
-17
33
98
-5
41
120
24
80
215
Uni
ted
Kin
gdom
10
1.
6 -1
1 20
94
-1
0 22
82
-1
5 35
12
4 8
67
167
Yug
osla
via
& A
lban
ia
581
25.4
-4
34
79
5
38
80
14
57
131
38
95
185
Euro
pe
6297
14
.1
10
17
32
7 18
30
12
27
48
17
46
85
853
Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2015-1000, 2016Manuscript under review for journal Atmos. Chem. Phys.Published: 16 February 2016c© Author(s) 2016. CC-BY 3.0 License.
39/5
0
Figu
res
854
85
5
Figu
re 1
: Cur
rent
ant
hrop
ogen
ic (s
olid
line
s) a
nd w
ildfir
e em
issi
ons
(das
hed
lines
) for
Eur
ope
by r
ange
of p
opul
atio
n de
nsity
for
vari
ous
856
pollu
tant
s. A
nthr
opog
enic
em
issi
ons
are
for
2010
and
wild
fire
emis
sion
s av
erag
e 19
97-2
014.
85
7
00.
10.
31
310
3010
030
0>3
0010
ï�
10ï�
10ï�
10ï�
10ï�
10ï�
10ï�
10ï�
100
101
Popu
latio
n de
nsity
rang
e [in
habi
tant
s/km
� ]
Emissions [Tg/yr]
CO 30���
BC NOx
62�
Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2015-1000, 2016Manuscript under review for journal Atmos. Chem. Phys.Published: 16 February 2016c© Author(s) 2016. CC-BY 3.0 License.
40/5
0
85
8 Fi
gure
2: E
mis
sion
s of C
O (a
, b) a
nd P
M2.
5 (d
, e) f
rom
ant
hrop
ogen
ic so
urce
s (a,
d) a
nd w
ildfir
es (b
, e) d
urin
g pe
ak m
onth
of f
ire
seas
on (c
). 85
9 (f)
Tot
al w
ildfir
e em
issi
ons c
limat
olog
y 19
97-2
014
in d
ry m
ass p
er m
onth
dur
ing
the
fire
seas
on fo
r the
Eur
opea
n st
udy.
Whi
te: z
ero
emis
sion
s. 86
0
86
1
10-7
10-6
10-5
10-4
0.01
0.1
110
10-3
100
g / (
m2 m
onth
)
(a)
(b)
(d)
(e)
(c)
0°15
°E30
°E
45°N
60°N
June
July
Septem
ber
Octob
er
August
June
July
Augu
stSe
ptem
ber
Oct
ober
0
0.51
1.52
2.53
Emissions [Tg DM / month]
(f)
COCO
PM2.
5PM
2.5
Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2015-1000, 2016Manuscript under review for journal Atmos. Chem. Phys.Published: 16 February 2016c© Author(s) 2016. CC-BY 3.0 License.
41/5
0
86
2 Fi
gure
3: E
nsem
ble
mea
ns a
nd ra
nges
of s
imul
ated
PM
2.5
emis
sion
s for
all
Euro
pean
regi
ons f
or R
CP4
.5 (a
) and
RC
P8.5
(b).
His
tori
cal
863
popu
latio
n da
ta is
use
d fo
r 190
1 to
200
5, d
iffer
ent S
SP p
opul
atio
n sc
enar
ios f
or th
e re
mai
ning
per
iod.
86
4
865
1900
1950
2000
2050
2100
0.4
0.6
0.81
1.2
1.4
1.6
1.82
Wildfire PM2.5 emissions [Tg / yr]
Ense
mbl
e ra
nge
all s
imul
atio
nsEn
sem
ble
rang
e of
SSP
2 sc
enar
iohi
stor
ical
& S
SP5
popu
latio
nhi
stor
ical
& S
SP2
popu
latio
nhi
stor
ical
& S
SP3
popu
latio
n
1900
1950
2000
2050
2100
a)
b)
Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2015-1000, 2016Manuscript under review for journal Atmos. Chem. Phys.Published: 16 February 2016c© Author(s) 2016. CC-BY 3.0 License.
42/5
0
866
Figu
re 4
: Mon
thly
ant
hrop
ogen
ic (s
olid
line
s, c
ross
es) a
nd w
ildfir
e em
issi
ons
of s
elec
ted
pollu
tant
s (d
ashe
d lin
es, c
ircl
es) f
or E
urop
e du
ring
86
7 pe
ak fi
re s
easo
n by
ran
ge o
f pop
ulat
ion
dens
ity fo
r di
ffere
nt ti
me
win
dow
s an
d th
e SS
P5 p
opul
atio
n sc
enar
io. a
), RC
4.5
with
cur
rent
legi
slat
ion
868
anth
ropo
geni
c em
issi
ons.
b) R
CP8
.5 w
ith m
axim
um fe
asib
le r
educ
tions
ant
hrop
ogen
ic e
mis
sion
s.
869
00.
10.
31
310
3010
030
0����
PM2.
5
2010
2030
2050
2090
a) b)Emissions [Tg/yr] Emissions [Tg/yr]
00.
10.
31
310
3010
030
0
CO
00.
10.
31
310
3010
030
010
ï�
10ï�
10ï�
10ï�
10ï�
10ï�
10ï�
10ï�
100
BC
popu
latio
n de
nsity
rang
epo
pula
tion
dens
ity ra
nge
popu
latio
n de
nsity
rang
e0
0.1
0.3
13
1030
100
300
10ï�
10ï�
10ï�
10ï�
10ï�
10ï�
10ï�
10ï�
100
BC
00.
10.
31
310
3010
030
0
CO
00.
10.
31
310
3010
030
0In
f
PM2.
5
Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2015-1000, 2016Manuscript under review for journal Atmos. Chem. Phys.Published: 16 February 2016c© Author(s) 2016. CC-BY 3.0 License.
43/5
0
87
0
Figu
re 5
: Are
as w
here
wild
fire
emis
sion
s ex
ceed
ant
hrop
ogen
ic e
mis
sion
s in
203
0, 2
050
or 2
090
on a
nnua
l bas
is (a
) or
duri
ng p
eak
fire
seas
on
871
(b) (
mon
th o
f max
imum
wild
fire
emis
sion
s va
ryin
g by
gri
d ce
ll), a
ssum
ing
RCP8
.5 c
limat
e, S
SP5
popu
latio
n an
d m
axim
um fe
asib
le r
educ
tion
872
anth
ropo
geni
c em
issi
ons.
87
3
Wildfires)dom
inate)from
)2090)
Wildfires)dom
inate)from
)2050)
a))
b))
CO)
PM2.5)
CO)
PM2.5)
Wildfires)dom
inate)from
)2030)
Wildfires)dom
inate)2030)only)
Wildfires)dom
inate)2050)only)
Wildfires)dom
inate)2030)and
)2090)
Wildfires)dom
inate)2030)and
)2050)
BC)
BC)
Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2015-1000, 2016Manuscript under review for journal Atmos. Chem. Phys.Published: 16 February 2016c© Author(s) 2016. CC-BY 3.0 License.
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0
87
4
Figu
re 6
: Wild
fire
PM2.
5 em
issi
ons
duri
ng p
eak
fire
seas
on d
ispl
ayed
on
linea
r sc
ale,
in g
/ (m
2 mon
th).
a) c
urre
nt; b
) 209
0.
875
876
a)#
b)#
0#0.1#
0.2#
0.3#
0.4#
0.5#
0.6#
0.7#
Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2015-1000, 2016Manuscript under review for journal Atmos. Chem. Phys.Published: 16 February 2016c© Author(s) 2016. CC-BY 3.0 License.
45/5
0
App
endi
x 87
7
87
8
879
88
0
Figu
re A
1: C
urre
nt (2
010)
pop
ulat
ion
dens
ity [
inha
bita
nts
/ km
2 ] in
Eur
ope
by r
ange
s co
nsid
ered
in th
e an
alys
is. D
eriv
ed fr
om g
ridd
ed
881
obse
rved
200
5 va
lues
ext
rapo
late
d to
201
0 us
ing
SSP2
. 88
2
0"…"<0.1"
0.1"…"<0.3"
0.3"…"<1"
1"…"<3"
3"…"<10"
10"…"<30"
30"…"<100"
100"…"<300"
≥"300"
Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2015-1000, 2016Manuscript under review for journal Atmos. Chem. Phys.Published: 16 February 2016c© Author(s) 2016. CC-BY 3.0 License.
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0
88
3
Figu
re A
2: P
roje
cted
pop
ulat
ion
dens
ity [
inha
bita
nts
/ km
2 ] in
Eur
ope.
a) S
SP3;
b) S
SP5.
88
4
0"…"<0.1"
0.1"…"<0.3"
0.3"…"<1"
1"…"<3"
3"…"<10"
10"…"<30"
30"…"<100"
100"…"<300"
≥"300"
2030"
2050"
2090"
2030"
2050"
2090"
a)"
b)"
Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2015-1000, 2016Manuscript under review for journal Atmos. Chem. Phys.Published: 16 February 2016c© Author(s) 2016. CC-BY 3.0 License.
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0
88
5
Figu
re A
3: W
ildfir
e ca
rbon
em
issi
ons
for
all E
urop
ean
regi
ons
with
the
stan
dard
SIM
FIRE
par
amet
eris
atio
n co
mpa
red
to r
uns
usin
g SI
MFI
RE
886
optim
ised
aga
inst
MC
D45
glo
bal b
urne
d ar
ea, f
or tw
o RC
P sc
enar
ios
and
sim
ulat
ions
usi
ng th
e M
PI g
loba
l clim
ate
mod
el.
887
888
1900
1950
2000
2050
2100
1234567 Emissions [TgC / yr]
RC
P4.5
RC
P8.5
RC
P4.5
MC
D45
RC
P8.5
MC
D45
Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2015-1000, 2016Manuscript under review for journal Atmos. Chem. Phys.Published: 16 February 2016c© Author(s) 2016. CC-BY 3.0 License.
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0
88
9
Figu
re A
4: A
nnua
l ant
hrop
ogen
ic (s
olid
line
s, c
ross
es) a
nd w
ildfir
e em
issi
ons
(das
hed
lines
, cir
cles
) for
Eur
ope
by r
ange
of p
opul
atio
n de
nsity
89
0 fo
r se
lect
ed p
ollu
tant
s an
d tim
e w
indo
ws.
a) R
CP4
.5 c
limat
e, S
SP5
popu
latio
n an
d cu
rren
t leg
isla
tion
(CLE
) for
ant
hrop
ogen
ic e
mis
sion
s. b
) 89
1 RC
P8.5
clim
ate,
SSP
3 po
pula
tion
and
max
imum
feas
ible
red
uctio
n (M
FR) f
or a
nthr
opog
enic
em
issi
ons.
89
2
a) b)Emissions [Tg/yr] Emissions [Tg/yr]
popu
latio
n de
nsity
rang
epo
pula
tion
dens
ity ra
nge
popu
latio
n de
nsity
rang
e
00.
10.
31
310
3010
030
010
ï�
10ï�
10ï�
10ï�
10ï�
10ï�
10ï�
10ï�
100
101
BC
00.
10.
31
310
3010
030
0
CO
00.
10.
31
310
3010
030
0In
f
30���
����
����
����
����
00.
10.
31
310
3010
030
010
ï�
10ï�
10ï�
10ï�
10ï�
10ï�
10ï�
10ï�
100
101
BC
00.
10.
31
310
3010
030
0
CO
00.
10.
31
310
3010
030
0In
f
30���
Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2015-1000, 2016Manuscript under review for journal Atmos. Chem. Phys.Published: 16 February 2016c© Author(s) 2016. CC-BY 3.0 License.
49/5
0
89
3 Fi
gure
A5:
Com
pari
son
of a
nnua
l ant
hrop
ogen
ic C
O a
nd B
C e
mis
sion
s for
209
0 . a
, c) 5
0% o
f EC
LIPS
E G
AIN
S 4a
MFR
for 2
050
as a
ssum
ed
894
for 2
090
in p
rese
nt st
udy;
b, d
) PEG
ASO
S PB
L v2
MFR
-KZN
. 89
5
896
897
10-7
10-6
10-5
10-4
0.01
0.1
110
10-3
100
g / (
m2 m
onth
)
(a)
(b)
(c)
(d)
COCO
BCBC
Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2015-1000, 2016Manuscript under review for journal Atmos. Chem. Phys.Published: 16 February 2016c© Author(s) 2016. CC-BY 3.0 License.
50/5
0
Tabl
e A1
: Sen
sitiv
ity o
f pre
dict
ed e
mis
sion
s ch
ange
s to
SIM
FIRE
par
amet
eris
atio
n.
898
Cou
ntry
/regi
on
Ense
mbl
e em
issi
on c
hang
es 2
010
to 2
050
[%]
Ense
mbl
e em
issi
on c
hang
es 2
010
to 2
090
[%]
R
CP4
.5
RC
P8.5
R
CP4
.5
RC
P8.5
std.
(1)
MC
D45
(2)
std.
M
CD
45
std.
M
CD
45
std.
M
CD
45
Aus
tria
-6
-37
6 -7
26
2
45
26
Bel
arus
18
6
18
5 35
17
45
33
B
enel
ux
30
29
20
19
61
46
129
107
Bul
garia
50
35
21
20
75
56
14
6 73
C
zech
Rep
ublic
11
45
15
19
69
12
8 58
10
8 D
enm
ark
-7
-3
44
57
33
18
81
43
Esto
nia
-11
-21
-35
-2
-15
15
-18
-8
Finl
and
6 27
-3
-9
2
13
-13
-17
Fran
ce
-1
7 27
22
8
21
78
77
Ger
man
y 21
14
50
30
96
60
15
5 10
7 G
reec
e 85
35
-3
52
35
56
20
9 35
0 H
unga
ry
41
38
36
4 92
69
98
56
Ir
elan
d -7
-1
6 10
-9
-1
7 -2
1 38
8
Italy
72
93
73
45
77
11
1 16
5 14
6 La
tvia
23
23
25
36
23
23
16
36
Li
thua
nia
-2
-12
12
-9
28
4 26
25
N
orw
ay
6 11
2
9 23
24
15
38
Po
land
35
22
28
33
10
6 67
87
57
Po
rtuga
l 10
4 89
94
19
3 12
8 11
5 21
8 16
4 R
oman
ia
70
34
68
25
117
55
166
131
Rus
sia
5 7
-2
-1
-1
6 7
11
Slov
akia
27
9
42
57
129
79
133
115
Spai
n 30
26
34
90
82
10
0 13
4 15
7 Sw
eden
1
-2
3 2
16
8 13
10
Sw
itzer
land
58
31
10
1 44
20
2 71
31
0 16
8 U
krai
ne
28
18
32
20
55
39
79
56
Uni
ted
Kin
gdom
12
14
45
35
24
32
70
65
Y
ugos
lavi
a &
Alb
ania
71
47
35
24
11
4 71
11
6 69
Eu
rope
21
19
19
28
40
41
65
64
(1
) SIM
FIR
E st
anda
rd p
aram
eter
isat
ion
with
MPI
clim
ate
mod
el o
utpu
t. (2
) SIM
FIR
E op
timis
ed a
gain
st M
CD
45 g
loba
l bur
ned
area
pro
duct
, als
o w
ith M
PI c
limat
e m
odel
out
put.
89
9
Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2015-1000, 2016Manuscript under review for journal Atmos. Chem. Phys.Published: 16 February 2016c© Author(s) 2016. CC-BY 3.0 License.