Atmospheric Environment 43 (2009) 6260–6270
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Atmospheric Environment
journal homepage: www.elsevier.com/locate/atmosenv
Global temperature change from the transport sectors: Historical
development and future scenarios
Ragnhild Bieltvedt Skeie, Jan Fuglestvedt*, Terje Berntsen, Marianne Tronstad Lund,
Gunnar Myhre, Kristin Rypdal
CICERO – Center for International Climate and Environmental Research – Oslo, P.O. Box 1129 Blindern, N-0318 Oslo, Norway
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 30 January 2009
Received in revised form
13 May 2009
Accepted 14 May 2009
Transport affects climate directly and indirectly through mechanisms that operate on very different
timescales and cause both warming and cooling. We calculate contributions to the historical development in global mean temperature for the main transport sectors (road transport, aviation, shipping and
rail) based on estimates of historical emissions and by applying knowledge about the various forcing
mechanisms from detailed studies. We also calculate the development in future global mean temperature for four transport scenarios consistent with the IPCC SRES scenarios, one mitigation scenario and
one sensitivity test scenario. There are large differences between the transport sectors in terms of sign
and magnitude of temperature effects and with respect to the contributions from the long- and shortlived components. Since pre-industrial times, we calculate that transport in total has contributed 9% of
total net man-made warming in the year 2000. The dominating contributor to warming is CO2, followed
by tropospheric O3. By sector, road transport is the largest contributor; 11% of the warming in 2000 is due
to this sector. Likewise, aviation has contributed 4% and rail w1%. Shipping, on the other hand, has
caused a net cooling up to year 2000, with a contribution of 7%, due to the effects of SO2 and NOx
emissions. The total net contribution from the transport sectors to total man-made warming is w15% in
2050, and reaches 20% in 2100 in the A1 and B1 scenarios. For all scenarios and throughout the century,
road transport is the dominating contributor to warming. Due to the anticipated reduction in sulphur
content of fuels, the net effect of shipping changes from cooling to warming by the end of the century.
Significant uncertainties are related to the estimates of historical and future net warming mainly due to
cirrus, contrails and aerosol effects, as well as uncertainty in climate sensitivity.
Ó 2009 Elsevier Ltd. All rights reserved.
Keywords:
Transport sectors
Radiative forcing
Temperature
Historical emissions
Scenarios
1. Introduction
The impact of emissions from the transport sectors on climate
has recently received increasing attention. Initially, the focus was
on the aviation sector because its emissions are deposited at high
altitudes initiating complex mechanisms in a chemically sensitive
part of the atmosphere (Schumann, 1990; Friedl, 1997; Minnis et al.,
1998, 1999; IPCC, 1999; Myhre and Stordal, 2001; Travis et al., 2002;
Stevenson et al., 2004; Sausen et al., 2005; Stuber et al., 2006).
Attention then turned toward the shipping sector (Corbett and
Fischbeck, 1997; Capaldo et al., 1999; Lawrence and Crutzen, 1999;
Endresen et al., 2003, 2007; Eyring et al., 2005, 2007). Many of the
studies of impacts from various transport sectors focus on the
chemical responses and changes in atmospheric burdens (Granier
* Corresponding author.
E-mail address: j.s.fuglestvedt@cicero.uio.no (J. Fuglestvedt).
1352-2310/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved.
doi:10.1016/j.atmosenv.2009.05.025
and Brasseur, 2003; Niemeier et al., 2006), and some have
considered the radiative forcing resulting from these burden
changes (Eyring et al., 2007; Lauer et al., 2007; Fuglestvedt et al.,
2008). In this paper we calculate the change in global mean
temperature due to emissions from all the main transport sectors
(road transport, aviation, shipping and rail) and we consider both
emissions and responses from pre-industrial times up to present
and scenarios for future emissions.
There are four main mechanisms by which emissions from
transport affect climate: (i) by emission of direct greenhouse gases,
mainly CO2, (ii) by emission of indirect greenhouse gases, i.e.
precursors of tropospheric O3 or gases affecting the oxidation
capacity of the atmosphere, such as NOx, CO and VOC, (iii) by the
direct effect of emission of aerosols or aerosol precursors, in
particular black carbon (BC), organic carbon (OC), and sulphur
compounds, and (iv) by the indirect effect of aerosols, which trigger
changes in the distribution and properties of clouds. In addition,
water vapour from aircraft engines triggers formation of contrails
R.B. Skeie et al. / Atmospheric Environment 43 (2009) 6260–6270
and cirrus clouds, with the latter effect being strongly dependent
on atmospheric conditions. Thus, there is not only a broad mix of
species emitted from the transport sectors, but also a broad set of
mechanisms by which climate is affected. These mechanisms
operate on very different timescales (hours to centuries) and cause
both warming and cooling.
Previous studies focusing on radiative forcing from historical
and current emissions have shown that (i) the shipping sector has
had a cooling effect on global climate (Lauer et al., 2007; Fuglestvedt et al., 2008), that (ii) aviation causes a stronger warming effect
than might be expected from its total emissions as a result of the
many additional potentially strong effects (e.g. formation of
contrails and cirrus) (Penner et al., 1999; Sausen et al., 2005), and
that (iii) road transport is the dominating sector in terms of total
net forcing (Fuglestvedt et al., 2008).
In this paper we aim to integrate and apply knowledge about the
various forcing mechanisms from detailed studies in order to
calculate the historical development in global mean temperature
based on estimates of historical emissions of a suite of gases,
aerosols and aerosol precursors. We also calculate the development
in future global mean temperature for four transport scenarios
consistent with the IPCC SRES scenarios (both excluding and
including future effects of historical emissions), one mitigation
scenario for aviation (ACARE) and a sensitivity test scenario. We
calculate the total net contribution from transport as a whole to
historical, current, and future warming. The contributions from the
various emissions and radiative forcing mechanisms are also
quantified, as are the effects of uncertainties in the chain from
emissions to temperature response.
2. Data and methods
2.1. Emissions
Current and future emissions for road transport, rail, shipping
and aviation are based on the QUANTIFY (www.pa.op.dlr.de/
quantify) emission inventory and scenarios, while the historical
emissions are supplemented by results from previous studies (see
Table 1 in supporting information (SI) for a summary of the references for the transport emissions). Future emissions until 2100
were compiled consistent with the four SRES storylines A1, B1, A2
and B2 (Nakicenovic et al., 2000).
The emissions scenarios take into account growth in activity,
changes in emission factors/fuel efficiency, and vehicle/fleet turnover consistently with each of the storylines. The modelling has been
more rigorously carried out for 2050 compared to 2100 and shortterm projections reflect current legislations. Exact technologies and
fuel mix for 2100 are difficult to assess for the given storylines and
the estimates rely on a number of assumptions concerning the
development and implementation of new technologies and the
future fuel mix. Consistently with the SRES thinking, none of
the scenarios assumes explicit mitigation to reach a climate target.
2.1.1. Emission references and methodology
Current (year 2000) emissions from road transport are described
in Borken et al. (2007). Past road CO2 emissions are taken from
Fuglestvedt et al. (2008). Emissions of non-CO2 gases are estimated
from their emission factors for pre-control technologies and
historical CO2 emissions. Future emissions from road transport are
taken from Borken-Kleefeld et al. (in preparation).
Current and future emissions from aviation are taken from
Owen et al. (2006). CO2 emissions prior to 2000 are estimated from
energy statistics from the International Energy Agency consistent
with Fuglestvedt et al. (2008). Emissions of non-CO2 gases before
1990 are from Lee et al. (2009).
6261
The historical and current emissions from shipping are taken
from Endresen et al. (2007). The methodology used to estimate
future shipping emissions is described in Eide et al. (2007). Emissions from inland waterways (Borken-Kleefeld et al., in preparation)
have been added to the maritime shipping emissions. It is assumed
that the past trend in emissions from inland waterways follows the
maritime shipping emission trends.
Emissions from rail are taken from Borken-Kleefeld et al. (in
preparation) and Borken-Kleefeld (personal communication,
2009). Prior to 1970, the emissions are scaled with the CO2 emission
from rail from Fuglestvedt et al. (2008).
Emissions from electricity generation are included in the railinventory. Current emissions were estimated consistent with
EDGAR (Olivier and Berdowski, 2001). The emissions are scaled
with the electricity consumption for past years, which is identical
to the data used in Fuglestvedt et al. (2008). Year 2000 emissions
from electricity production in Olivier and Berdowski (2001) are
scaled to 2050 and 2100 based on Nakicenovic et al. (2000). For
both years we calculate the fraction of total regional electricity
production used by rail and apply this fraction to the power
generation emissions in 2050 and 2100. Finally we correct for
energy efficiency and technology improvements based on specific
model data from the IMAGE model (Bouwman et al., 2006).
A scenario is developed which is consistent with the strategic
research agenda of the Advisory Council for Aeronautics Research
in Europe (ACARE) (www.acare4europe.org) targets for greening of
air transport. It is described in SI Section 1 and some results are
presented in Section 4.2.3.
Future transport is expected to introduce biofuels, hydrogen fuel
cells and electric powered vehicles. While direct emissions from
these technologies/fuels are small or zero, emissions at the
production stage still remain and can be higher than those from the
use of fossil fuels in the vehicles. SI Section 2 describes the quantification of the indirect emissions. The climate impact of these
indirect emissions is calculated in addition to the scenarios.
2.1.2. Historical and future development of transport emissions
Fig. 1 shows the past and future development of emissions of
CO2, NOx and SO2 for the different transport sectors and SRES
scenarios. CO2 emissions from the transport sectors increased
during the 20th century (Fig. 1A), and road transport contributed
w16% to the total anthropogenic CO2 emissions (excluding land use
change) in 2000 (Fig. 1B). The shipping and aviation sector each
contributed w3% to the man-made CO2 emissions in 2000, while
the contribution from rail was only 1%.
Future CO2 emissions
In all scenarios, future CO2 emissions from road transport will
remain higher than from the other transport sectors. Emissions
from road transport are highest in A1 where they more than
double from 2000 to 2050, while the growth in the other
scenarios is smaller (Fig. 1A). The emissions decline from 2050
to 2100 in all scenarios except A2 where the growth is 6%. The
development in emissions is a result of the interplay of several
of factors; the development in economy and population under
the different SRES storylines leading to changes in transport
volume, the mix of high- and low-carbon fuels used, the emission indices, and the different temporal and regional development for these factors.
The aviation sector is assumed to remain dependent on fossil
fuels until 2100, except in the B1 scenario where it is assumed
that, by the end of the 21st century, 35% of the fuel used will be
non-fossil or produced from hydrogen. CO2 emissions from
aviation increase substantially from 2000 in all scenarios
(Fig. 1A). Under A2, B1 and B2 emissions double until 2050,
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R.B. Skeie et al. / Atmospheric Environment 43 (2009) 6260–6270
Road
B 30
3
Rail
2.5
2
Ship
1.5
Avia
1
0.5
0
1900
1950
2000
2050
CO2 emission, % of total
CO2 emission, Pg C/year
A 3.5
25
Rail
20
Ship
15
Avia
10
5
0
1900
2100
Road
1950
Year
Road
10
Rail
8
Ship
6
Avia
4
2
1950
2000
2050
2100
2050
2100
Year
D6
SO2 emission,Tg S/year
NOx emission, Tg N/year
C 12
0
1900
2000
Year
Road
5
Rail
4
Ship
3
Avia
2
1
0
1900
1950
2000
2050
2100
Year
Fig. 1. Emission (1900–2100) of CO2 (A), NOx (C) and SO2 (D) from road transport, rail, shipping and aviation. For future emissions scenarios: A1 (solid), A2 (dash-dot), B1 (dashed)
and B2 (dotted). Fig. 1B shows emission of CO2 from the transport sectors relative to total man-made CO2 emissions (excluding land use change (LUC)).
while for A1 the increase is higher. In 2100 the growth in B1 and
B2 has slowed down (emissions in B1 even decline slightly from
2050) while A1 and A2 emissions more than double from 2050.
In the shipping sector, oil is assumed to still be the predominant
fuel in 2050 and 2100 in all scenarios. However, biofuels and gas
in internal combustion engines and fuel cells are introduced.
Shipping also utilizes non-emitting technologies such as wind,
nuclear and solar cells. In all scenarios, the fuel consumption
increases from 2000 to 2100. As a result CO2 emissions more
than double from 2000 to 2050; in A1 the increase is almost
four-fold (Fig. 1A). This increase continues, and in 2100 emissions in A1 are eight times higher than in 2000, while emissions
in the other scenarios are about five times higher. (The emergence of new shipping routes is included in the scenarios).
Emissions from the rail sector are small relative to the other
transport modes. The freight transport volumes and emission
factors are higher, while the electrification rate and introduction
of energy efficiency improvements are lower in A2 than in A1.
Hence, A2 has the highest CO2 emissions in this sector. Emissions increase throughout the century in A1 and A2, while in B1
and B2 there is a slight decrease.
Fig. 1B shows the percentage contribution from transport to
total man-made CO2 emissions. With the exception of road
transport in scenario A2 and B2 and of rail, the contribution will
increase or stay stable.
only be a small fraction of the 2000 emission level. This is due to
already available technology for conventional vehicles in the
road sector, prospects for further development and the introduction of zero emitting vehicles and fuels. The penetration of
new technologies is also quicker in the road sector compared to
shipping and aviation due to the generally shorter vehicle lifetimes. For both the aviation sector and the shipping sector, NOx
emissions will increase throughout the century (except for
aviation in B1), but the increase is much lower than for CO2 since
the increase in activity is assumed to be compensated by technology improvements. In the rail sector, emissions are highest
and increasing in A2, while there is a decrease in the other
scenarios.
The future SO2 emissions remain substantially higher for shipping than for the other transport sectors. In scenario B1 and B2
the rapid reduction in SO2 emissions starts early in the 21st
century, and in 2050 the SO2 emissions are 1/3 of the emission
level in 2000. The decline in SO2 emissions in scenarios A1 and
A2 starts after 2050, and in 2100 the SO2 emissions in all four
scenarios are less than 1/10 of the emission level in 2000
(Fig. 1D). The scenarios span a range of possible developments
and capture the effects of the expected sulphur reductions
according to the regulations adopted by the International
Maritime Organization (IMO).
2.2. Models and methods
Future non-CO2 emissions
In the scenarios, emissions of non-CO2 gases decrease
substantially throughout the 21st century in the road transport
sector, for example as seen for NOx (Fig. 1C), and will in 2100
To calculate the global temperature change from the transport
sectors a simple climate model (SCM) is used (Fuglestvedt and
Berntsen, 1999). The model has been applied in several studies of
R.B. Skeie et al. / Atmospheric Environment 43 (2009) 6260–6270
contributions to global warming and responses to emission change
(Fuglestvedt et al., 1999, 2000, 2008; den Elzen et al., 2005; Rive
et al., 2007). The SCM calculates global mean concentrations from
emissions of 24 gases and the radiative forcing for 30 components
(see SI Table 2) based on detailed input. The global mean temperature change is calculated by an energy-balance climate/up-welling
diffusion ocean model developed by Schlesinger et al. (1992). In the
SCM the atmosphere is represented by a single layer separated into
a Northern and Southern hemisphere, while the ocean under the
surface layer is split into 40 vertical layers in addition to the north/
south separation. The climate response is governed by the
prescribed climate sensitivity, which encompasses the processes,
including feedbacks, involved in the response of the climate system
to a radiative forcing, and parameters which control the uptake of
heat by the oceans (Harvey et al., 1997). The input parameters are
based on output from more detailed GCM experiments. In this work
we have used a climate sensitivity of 0.8 K (W m2)1, but have also
tested the effects of other climate sensitivities and other input
parameters; see Section 4.2.1 and SI Section 3.
The development in global concentration of CO2 is calculated
using a scheme based on Joos et al. (1996). The CO2 module uses an
ocean mixed-layer pulse response function that characterizes the
surface to deep ocean mixing in combination with a separate
equation describing the air-sea exchange (Siegenthaler and Joos,
1992). It also includes changes in CO2 uptake by terrestrial vegetation due to CO2 fertilization. A feedback between atmospheric
CO2 levels and CO2 uptake via changes in oceanic pH is included,
while the feedback via ocean temperatures is not. For the other
gases, standard values for lifetime/adjustment time are used.
Indirect effects of CH4 on tropospheric O3 and stratospheric H2O as
well as effects on its own adjustment time are taken into account.
Radiative forcings for the well-mixed gases are parameterized
using updated concentration-forcing relations from IPCC AR4
(Forster et al., 2007), while for short-lived components we use RF
results based on detailed CTM and forcing calculations (see below).
To calculate the climate impact of transport, we use the common
method of removing all emissions from one transport sector at
a time and then calculating the difference between this perturbed
case and the reference simulation with all anthropogenic emissions
(Penner et al., 1999; den Elzen et al., 2005; Fuglestvedt et al., 2008;
Marais et al., 2008). The difference in the climate response is then
a measure of the impact of that sector. For long-lived greenhouse
gases (CO2, CH4, N2O) we subtract the emission trajectories for the
transport sectors. For short-lived components we subtract precalculated radiative forcing (RF) over time for the transport sectors.
Transport emissions have a spatial and vertical distribution that
differs from that of the total global emissions. This distribution must
be accounted for when atmospheric burden and RF of short-lived
components from the transport sectors are calculated. To establish
the global mean RF development, RF(t), for short-lived components
from the transport sectors, reference radiative forcings (RFref) and
emissions (EMref) for the year 2000 from more detailed studies
(Sausen et al., 2005; Fuglestvedt et al., 2008) are used. These studies
use global 3D chemistry transport models (CTMs) and detailed
radiative transfer models to calculate the RF. SI Table 3 gives the RFref
and EMref used in this study. The reference RF and emissions are
scaled with emission trajectories (EM(t)) for the transport sectors:
RFðtÞ ¼
RFref
EMðtÞ
EMref
(1)
For SO4 direct and indirect forcing we scale with the SO2
emission trajectories and for BC and OC we scale with BC and OC
emissions, respectively. For the short-term ozone forcing, mainly
due to NOx, CO and VOC changes, we scale with NOx emissions.
6263
Studies of impact of transport emissions on O3 and CH4 indicate
that such linear scalings only introduce inaccuracies of less than
a few percent (Hoor et al., 2009).
NOx emissions reduce the lifetime of CH4 (and thus the
concentration), which gives a negative RF (Fuglestvedt et al., 1996;
Wild et al., 2001; Berntsen et al., 2005). Since the RFref for the CH4lifetime effect from more detailed studies is due to all NOx
emissions up to 2000, the RF development cannot be calculated by
using Eq. (1). Instead Eq. (2), which takes into account the effect of
NOx on CH4 over time, is used:
RFðtÞ ¼
0
tX
¼t
NOxðt 0 Þ
RFref ð1 expð1=sÞÞexpð ðt t 0 Þ=sÞ
NOxref
t0 ¼ 0
(2)
where s is the methane adjustment time (12 years), NOxref is the
reference NOx emission in 2000 and RFref is the RF-CH4 at steady
state corresponding to NOxref. The term RFref ð1 expð1=sÞÞ gives
the RF after one year and the term expððt t 0 Þ=sÞ accounts for the
decaying effect over time. By summing up these contributions we
capture the effect of historical and future emissions of NOx on CH4.
This implicitly assumes that CO and VOC changes are parallel to NOx.
The O3 perturbation consists of two components. In addition to
the short-term forcing mentioned above, there is also a longer-term
perturbation, called primary mode (PM) O3 forcing, which results
from changes in CH4 due to NOx emissions (Wild et al., 2001;
Berntsen et al., 2005). The development for RF-O3PM is calculated
in a similar way as for the CH4 effect and the RFref is calculated as in
Berntsen et al. (2005). The changes in CH4 lifetime are from
Fuglestvedt et al. (2008) and Sausen et al. (2005). For the sensitivity
of O3 to changes in methane we use results from Ehhalt and Prather
(2001).
For aviation, the RF in 2000 for H2O and contrails (Sausen et al.,
2005) are scaled by the CO2 emissions to give the RF over time. In
the case of the RF from cirrus, we have accounted for the increasing
overlap of aviation induced cirrus when air traffic increases. We
follow the method by Mannstein and Schumann (2005) to calculate
cirrus cover using air traffic data on a 1 1 grid for the year 2000
from Owen et al. (2006):
d
c ¼ cpot 1 ed*
(3)
where c is the aviation induced cirrus coverage, cpot is the fraction of
potential cirrus coverage, d is the air-traffic density and d* is the airtraffic density leading to full cirrus coverage. The value chosen for
cpot is 0.2 and d* ¼ 0.5 km (km2 h1) (Mannstein and Schumann,
2005). The air-traffic density on a 1 1 grid is scaled with the CO2
scenario emissions, and future cirrus coverage on a 1 1 grid is
calculated. The RF for the year 2000 for cirrus (Stordal et al., 2005) is
scaled by the global mean cirrus coverage to give the future RF. The
difference in cirrus forcing between this method and a linear scaling
with CO2 is up to 6% in the latter part of the 21st century.
3. Radiative forcing
To calculate the temperature change due to transport, the
historical and future RFs are established for each component and
each sector following the description above.
Fig. 2 shows global mean RF since pre-industrial times by
substance, transport sector and scenario for 2050. Both historical
and future transport emissions are taken into account in this figure
(see SI Fig. 1 for the results for 2000 and 2025).
For all substances, the RF from the rail sector is much smaller
than from the other transport modes, and will not be described
further in the following section.
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R.B. Skeie et al. / Atmospheric Environment 43 (2009) 6260–6270
Radiative Forcing Year 2050
700
Road
600
Rail
Aviation
Ship
500
mW/m
2
400
300
200
100
0
-200
A1
A2
B1
B2
A1
A2
B1
B2
A1
A2
B1
B2
A1
A2
B1
B2
A1
A2
B1
B2
A1
A2
B1
B2
A1
A2
B1
B2
A1
A2
B1
B2
A1
A2
B1
B2
A1
A2
B1
B2
A1
A2
B1
B2
A1
A2
B1
B2
-100
CO2
CH4
N2O
SO4 dir SO4 indir
O3
O3 LL
BC
OC
H2O
Contrail
Cirrus
Fig. 2. Global mean radiative forcing (mW m2) from pre-industrial times by substance, transport sector and scenario for 2050. Transport emission period: 1900–2050.
CO2 has the largest RF, with road transport being the dominating
sector. In 2000, the RF-CO2 from transport as a whole was
w200 mW m2, and in 2050 it reaches 470–630 mW m2
depending on scenario.
The RF from CH4 in Fig. 2 includes both the direct effect of
methane and the indirect effect of NOx, CO and VOC on methane. The
direct effect is small, less than 1 mW m2, due to low CH4 emissions
from transport. The indirect effect is the reduced lifetime of CH4 due
to the NOx induced enhancement of OH levels. This results in
a negative forcing. As can be seen in Fig. 2, shipping is the largest
contributor to the CH4 forcing, followed by aviation. For road transport the low effect of NOx on methane is due to the high background
levels of NOx and the opposing effect of VOC and CO. The contribution
from the aviation sector to RF-CH4 increases from 2000 to 2050 and
reaches 19 to 33 mW m2, depending on scenario, in 2050.
The RF from N2O is small. In 2050 it is w2 mW m2 for road
transport and less than 1 mW m2 for the other sectors.
As mentioned, the emission of NOx leads to a strong, short-lived
positive RF-O3, but also to a negative, long-lived forcing through
changes in CH4; RF-O3PM. Fig. 2 shows that the RF-O3 is, in absolute
magnitude, larger than the RF-O3PM. The aviation and shipping
sectors are the main contributors to the positive short-lived RF from
ozone. From 2000 to 2050 the forcing from aviation increases from
30 mW m2 to 38–82 mW m2, while the forcing from shipping
increases from 33 mW m2 to 36–62 mW m2. Similarly to the
indirect CH4 effect, the RF-O3 from the road transport sector is greatly
reduced from 2000 to 2050 due to a rapid reduction in NOx emissions.
The RF-O3PM is larger for shipping than aviation. This is due to the
larger change in the ozone primary mode lifetime for ship emissions
compared to aviation; 5.2% and 1.3% respectively (Fuglestvedt
et al., 2008).
Sulphate aerosols are formed from emissions of SO2. The aerosols
can affect the climate directly through scattering of solar radiation or
indirectly through changing cloud properties, both having a cooling
effect on climate. Shipping is the transport sector with the largest SO2
emissions, and is the dominating contributor to direct and indirect
SO4 forcing. In scenarios B1 and B2, the SO2 emissions from shipping
decrease rapidly in the beginning of the 21st century (Fig. 1D). Both
the direct and indirect RF from SO4 in 2050 are therefore 22% (in B1)
and 32% (in B2) of the corresponding year 2000 RF. In scenarios A1
and A2, the emission of SO2 has increased since 2000, and hence the
direct and indirect RF is enhanced. Aircraft SO2 emissions increase up
to 2050 in all scenarios, and the SO4 direct forcing from this sector is
enhanced. In the B1 and B2 scenarios the aviation sector dominates
the SO4 direct forcing in 2050.
The RF from BC and OC is largest at the beginning of the century.
In 2000 the RF–BC from transport was w16 mW m2. Strong
reductions in BC emissions from the transport sectors after 2000
lead to the strong decrease in RF.
The RF from water vapour in Fig. 2 is the net RF due to reduced
methane concentration in the troposphere, which, in turn, reduces
methane oxidation in the stratosphere and the resulting water
vapour, and the direct emission of H2O from aviation. The contribution is largest for the shipping sector (11 to 7 mW m2
depending on the scenarios) due to the high NOx emissions. For
aviation the two different effects (direct emission and reduced
methane oxidation) compensate each other.
The high-altitude emission of water vapour from the aviation
sector leads to RF mechanisms in addition to the increase in
stratospheric water vapour, namely contrails and cirrus formation.
The activity in the aviation sector increases in all four scenarios, and
the RF from cirrus and contrails increases from 2000 to 2050. The
RF from contrails increases from 10 mW m2 to 20–36 mW m2
and the forcing from cirrus increases from 30 mW m2 to
60–105 mW m2. The RF in 2050 from these mechanisms is 3–4
times larger for A1 and twofold larger in the other scenarios
compared to the year 2000 value. The uncertainty in the cirrus and
contrails forcing is large. IPCC (Forster et al., 2007) gives a 90%
confidence interval ranging from 0.003 to 0.03 mW m2 for contrail
distribution in 2005. Stordal et al. (2005) gave an interval ranging
from 10 mW m2 to 80 mW m2 for the RF from cirrus. The
uncertainty in RF calculation is taken into account when the
temperature change due to the transport sectors is calculated.
The effect of aircraft soot on cirrus was recently investigated in
two studies. Penner et al. (2009) found a change in the ice number
concentration due to aircraft soot emissions leading to a large net
negative RF of 160 to 120 mW m2. However, the effects of the
additional soot on cloud occurrence and cloud fraction was not
included. This was investigated in a study by Liu et al. (2009). Here,
aircraft soot was found to give a net negative cloud forcing only if soot
acts as an ice nuclei (IN) at a threshold RHi of 120–130%. When
a higher threshold RHi is required, the change in cloud forcing was
positive. The results from these two studies have the potential to
significantly change the overall picture of radiative forcing of aviation.
More studies are needed on this topic and due to the large uncertainties related to the role of soot in ice nucleation and the very
differing results in these first studies, we do not include these effects
in our estimates.
CO2 has a long adjustment time and the forcing is thus largely
dependent on the emission history. If the emission prior to 2000 is
R.B. Skeie et al. / Atmospheric Environment 43 (2009) 6260–6270
excluded from the analysis, the RF for CO2 from transport in 2050 is
111–119 mW m2 less, depending on scenario, than when the
historical CO2 emissions are included. For the other components
with shorter lifetimes, emissions prior to 2000 do not affect the RF
in 2050.
6265
included. However, here we want to make comparison across the
sectors on an equal basis. Therefore we also calculate the global mean
temperature change due to only future emissions (i.e. perturbation
start year 2000 and end year 2100).
4.1. Temperature change due to historical and future emissions
4. Development of global mean temperature
Fig. 3A shows the temperature increase for the control simulation for the four SRES scenarios. We calculate a total net temperature increase of 0.76 C from pre-industrial times to 2000. In 2100,
the total temperature change since pre-industrial times is between
2.5 and 4.5 C using the best estimate from IPCC of 0.8 K (W m2)1
for the climate sensitivity. The best estimate for global mean
temperature increase from 1980–1999 to 2090–2099 in IPCC AR4
(Meehl et al., 2007) varies between 1.8 and 3.4 C for the four
scenarios. Different time periods are considered in the above estimates and our results are in good agreement with the IPCC estimates when a temperature increase from pre-industrial times to
present day of w0.75 C is accounted for.
Fig. 3B and C show absolute and percentage contributions from
the transport sectors to total net man-made temperature change.
Road transport contributes most to historical temperature change. In
2000 the temperature change due to road transport is 0.08 C, corresponding to 11% of total anthropogenic temperature change. Even
though the CO2 emissions from road transport have increased
In this work we present two different perspectives on the evaluation of global mean temperature change due to transport. First we
calculate the temperature change due to historical and future
transport emissions (perturbation start year 1900 and end year 2100).
This gives the complete picture of how transport contributes to global
mean temperature change over time. However, including historical
emissions when calculating the contribution to future climate change
from transport is less relevant for policy making than considering
future emissions only. The transport sectors have very different
emission histories and past emissions thus affect the future
temperature change to a varying degree. For example, road transport
has a long history and large CO2 emissions which strongly impact the
future contribution to temperature change. Aviation, on the other
hand, is a relatively ‘‘young’’ sector with more short-lived emissions,
and the historical emissions thus impact the future temperature
change much less. As long as one compares different scenarios or
mitigation measures within one sector the full history can be
4.5
A1
A2
B1
B2
4
3.5
B
3
2.5
2
1.5
0.4
0.3
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0
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20
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0
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−10
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0
−15
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2100
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Temperature, % of total
C
2050
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1950
2000
2050
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2100
Year
Fig. 3. A: Total temperature change due to historical emissions and future emissions following the four SRES scenarios: A1 (solid), A2 (dash-dot), B1 (dashed) and B2 (dotted).
B: Temperature change due to emissions from transport sectors in the period from 1900 to 2100. C: Temperature change due to transport sectors relative to the total temperature
change. D: Absolute and percentage contribution from the transport sector as a whole to total net man-made temperature change. Climate sensitivity used in simulations:
0.8 K (W m2)1.
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R.B. Skeie et al. / Atmospheric Environment 43 (2009) 6260–6270
relative to the total man-made CO2 emissions (excluding land use
change, Fig. 1B) in the 20th century, the proportion of temperature
change due to road transport relative to total temperature change
declines in the latter part of the 20th century in our simulations. This
is due to the increasing effect of other sectors than transport.
Furthermore, the shipping sector contributes to cooling in the
20th century, mainly due to SO2 emissions. The contribution to
temperature change in 2000 from shipping is 0.05 C. Emissions
from the aviation sector started later than those from the shipping
and road transport sectors, and contributed 4% to the total
anthropogenic temperature change in 2000 (i.e. 0.03 C). The
contribution from the rail sector was 1%.
Fig. 3D shows both the absolute and the percentage contribution
from transport as a whole to total net man-made temperature
change. The transport sector contributed to global warming in the
20th century, with the exception of a period from 1900 to 1950
where a small negative temperature change is seen, mainly due to
shipping. Relative to total temperature change, transport contributed an increasing fraction to the warming in the latter part of the
century, to 9%, or 0.07 C, of the total temperature change in 2000.
According to our calculations and given the emission scenarios
used, transport will continue to contribute to global warming in the
future (Fig. 3B), and road transport remains the largest contributor
in all scenarios. The temperature change due to aviation increases
strongly in the A1 scenario. This scenario assumes very rapid
economic growth, low population growth and rapid introduction of
new and more efficient technology. However, for aviation the high
growth in demand outweighs the technological improvements, and
emissions are highest in the A1 scenario (Fig. 1A). The cooling from
shipping decreases in the future due to reduced SO2 emissions. In
scenarios B1 and B2 the temperature change due to the shipping
sector will be positive in the mid 21st century (Fig. 3B). In A1 and A2
the reduction in SO2 emissions from shipping is introduced later
(Fig. 1D), and the reduction in negative contribution to temperature
change takes place close to 2100. The temperature change from rail
traffic remains small.
In 2050, the transport sector as a whole contributes about 14% to
the total temperature change in scenarios A1 and B2, 16% in A2 and
10% in B1. At the end of the century, the contribution is between 9%
and 20% for the different scenarios, showing a growth in scenarios
A1 and B1 (Fig. 3D).
4.2. Temperature change due to future emissions
In Section 4.1 the whole emission history is included when
contributions from the transport sectors to temperature change are
calculated. This section shows the contribution to temperature
change in the 21st century due to future transport emissions alone in
order to facilitate a comparison across sectors without the influence
of very different emission histories (see beginning of Section 4). The
start of the emission perturbations is now year 2000.
Fig. 4A and B show the temperature change in 2050 and 2100
due to emissions from the transport sectors in the period 2000–
2100 for the four scenarios. The best estimates for road transport
are 0.13–0.18 C in 2050 and 0.19–0.35 C in 2100 depending on
scenario. For aviation the best estimates of temperature change are
0.07–0.10 C in 2050 and 0.10–0.28 C in 2100. For the rail sector,
the future temperature change is small, w0.007 C in 2050 and
w0.015 C in 2100 in scenarios A1 and A2. The road sector is the
largest contributor to temperature change in all scenarios, but in
the A2 scenario the contribution to temperature change from
aviation is nearly equal at the end of the century. In the A2 scenario,
the aviation sector undergoes a rapid growth and there are no
technological improvements assumed after 2020, while the
temperature change due to road transport has a slower rate of
increase. This is due to low growth in the road sector following low
GDP growth and large improvements in fuel economy.
Shipping makes a negative contribution to temperature change at
the beginning of the century in all scenarios due to the SO2 emissions. The contribution to temperature change still switches from
negative to positive in all scenarios except A2, but this occurs later
than when the whole history is included. This is due to the lower
contribution from CO2 when historical emissions are removed.
Excluding historical emissions gives 22–28% (i.e. w0.05 C),
depending on scenario, less temperature increase from road transport in 2050 than when the whole emission history is included. For
the aviation sector, the temperature change is reduced by 7–11% (i.e.
w0.008 C). In 2100 the difference is 11–16% for road transport and
2–7% for aviation. This shows the effect of pre-2000 emissions on
post-2000 temperatures and how the magnitude of this effect varies
between the different transport modes.
4.2.1. Uncertainties
In Fig. 4, which shows the temperature change in the years 2050
and 2100 for the transport sectors for the scenarios, the uncertainties at one standard deviation level is included. The establishment of the uncertainties in the RF calculation follows the method
in Fuglestvedt et al. (2008). The uncertainties in atmospheric
modelling (atmospheric dispersal, removal, and RF), with references, are given in SI Table 4. Uncertainties in emissions are not
included, since the emission scenarios themselves span a range of
possible developments and magnitudes.
The uncertainties in RF are combined with the uncertainty in the
climate sensitivity, as shown in Fig. 4A and B. IPCC 2007 stated that
the equilibrium climate sensitivity (ECS), i.e. the global average
surface warming following a doubling of carbon dioxide concentrations, is likely to be in the range 2–4.5 C, with a best estimate of
about 3 C. However, RF from transport is likely to continue to
increase through the coming century. It may therefore be more
relevant to use the ‘transient climate response’ (TCR, defined as the
globally averaged surface air temperature change at the time of CO2
doubling in the 1% yr1 transient CO2 increase experiment) in the
establishment of the uncertainty in the climate sensitivity used in
our simulations. IPCC gives a 90% confidence interval ranging from
1 to 3 C, with a best estimate of 2 C, for the TCR based on climate
modelling (Meehl et al., 2007). We use this confidence interval to
calculate the standard deviation for the climate sensitivity, which is
then 30% of the best estimate of the TCR.
The uncertainty in the contribution to temperature change is
assessed by combining the uncertainty in RF for different components and the uncertainty in the climate sensitivity by using
a Monte-Carlo approach; for further description see SI Section 5.
Fig. 4A and B show the absolute temperature change due to the
transport sectors with total uncertainty (i.e. RF and climate sensitivity) for 2050 and 2100, respectively. The uncertainty in the
temperature increase due to the transport sectors is larger than
the difference between the emission scenarios. However, ranking
the transport sectors according to their temperature impact is not
possible based on Fig. 4A and B since the uncertainty in the climate
sensitivity is not independent across all sectors. To be able to
perform a ranking, we also calculate the uncertainties in temperature change due to uncertainty in RF alone, i.e. assuming equal
climate sensitivity for all sectors (Fig. 4C and D).
From comparison of the two top figures with the two bottom
ones it can be seen that the uncertainty in RF dominates for aviation. For road transport on the other hand, the uncertainty in
temperature increase is mainly related to the uncertainty in climate
sensitivity. This is due to the fact that the well known RF from CO2
contributes most to temperature increase from road transport,
while for aviation temperature change has large contributions from
R.B. Skeie et al. / Atmospheric Environment 43 (2009) 6260–6270
A
0.50
B
Temperature change (K) Year 2050
Uncertainty in climate sensitivity and RF
6267
Temperature change (K) Year 2100
Uncertainty in climate sensitivity and RF
0.50
A1
0.40
A2
0.30
B1
B2
0.20
A1
0.40
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B2
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0.10
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0.00
0.00
-0.10
-0.10
-0.20
A2
0.30
-0.20
Road
Aviation
Rail
Ship
Temperature change (K) Year 2050
Uncertainty in RF
C
0.50
Road
D
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Rail
Ship
Temperature change (K) Year 2100
Uncertainty in RF
0.50
A1
0.40
0.30
0.40
B1
0.30
B2
0.20
A1
A2
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B2
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0.10
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0.00
0.00
-0.10
-0.10
-0.20
A2
-0.20
Road
Aviation
Rail
Ship
Road
Aviation
Rail
Ship
Fig. 4. Temperature change due to future emissions from the transport sectors (perturbation start year 2000) in 2050 (A) and 2100 (B) with uncertainty in both climate sensitivity
and RF, and in 2050 (C) and 2100 (D) with uncertainty in RF only. Climate sensitivity used in simulations: 0.8 K (W m2)1.
the uncertain forcing from contrails and cirrus. For shipping,
uncertainty in RF dominates in 2050, while uncertainty in climate
sensitivity becomes more important in 2100. In one of the scenarios
the uncertainty range includes both positive and negative values
for the temperature change (scenario B1 in 2050 and A2 in 2100).
When only the uncertainty in RF is included (Fig. 4C and D), the
temperature increase in 2050 from aviation, even with the
maximum value in the uncertainty range, remains smaller than
that from road transport. In 2100, the temperature increase from
aviation can be larger than from road transport in scenarios A1 and
A2. Correlations between the sectors exist also for the uncertainty
in RF, but this is likely of less importance for these two sectors since
the contribution from cirrus and contrails to temperature change
from aviation is not present for road transport.
To illustrate the contribution to temperature change from some
forcing mechanisms with large uncertainty, several sensitivity tests
are performed. For example, the temperature increase in 2050 due
to aviation is reduced by w47% if the cirrus effect is excluded.
A high uncertainty is also related to the indirect aerosol effect.
Lauer et al. (2007) suggested a substantially higher indirect RF from
shipping than the reference study used in this paper (Fuglestvedt
et al., 2008). Lauer et al. (2007) calculated the indirect RF from
shipping using three different emission datasets. Furthermore, they
used a general circulation model (GCM) and included both the first
and second indirect aerosol effect, whereas the study by
Fuglestvedt et al. (2008) only considered the first. The results in
Lauer et al. (2007) vary between 0.19 and 0.6 W m2 for the year
2000. If we use their sensitivity (RFref/EMref) for the SO4 direct and
indirect effects, the temperature change from shipping in 2050 is
0.04 to 0.33 C compared to 0.01 to 0.08 C with the sensitivity from the reference study.
4.2.2. Indirect emissions
In addition to the emissions directly from vehicles during
operation and propulsion, emissions are generated during the fuel
production and distribution stage. We calculate the temperature
change due to these indirect emissions in addition to the scenarios,
using the same method as described in Section 2.2. In 2050, the
contribution to temperature change from indirect emissions from
road transport is between 5% and 11% of the contribution from the
direct emissions depending on scenario. For shipping and aviation
the contribution to temperature change from indirect emissions is
small compared to that from the direct emissions. For the rail sector
the contribution is minor since we have chosen to consider electricity generation as part of the direct emissions.
4.2.3. Effect of mitigation
The transport emission scenarios, as well as the SRES
scenarios, do not take mitigation of climate change into account.
In our study we use the ACARE scenario for aviation to test how
a strong mitigation will affect the contribution to climate change
for this sector. The ACARE scenario assumes ‘‘excellent fuel efficiency’’ and larger NOx reductions than in scenario B1. The
ACARE target is achieved in 2020 and continuing improvements
beyond 2020 are assumed. The ACARE aviation scenario results in
a 0.05 C temperature increase in 2050, 20% less than the
temperature increase in the B1 scenario (SI Fig. 2).
4.2.4. A scenario sensitivity test
Starting between 2010 and 2030, depending on the scenario,
NOx and other air pollutant emissions from road transport decrease
strongly (Fig. 1C). This decline is justified by an increasing level of
road transport emission regulation in all world regions driven by
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current and future European and US legislation. However, there is
uncertainty related to the enforcement of regulations as well as to
the development and deployment of future technologies. Here,
a ‘‘delayed reduction’’ scenario for NOx, CO, NMVOC, BC and OC
emissions from road transport is introduced. All emissions after
2030 are a factor 1.5 higher than in the original scenario and the
decline is less abrupt.
Using this emission scenario, the temperature increase due to
road transport is increased by 4% (0.007 C) in A1 in 2050 and 0.5%
(0.001 C) in 2100 relative to the original road perturbation.
The emissions in this ‘‘delayed reduction’’ scenario differ less from
the original scenarios towards 2100, and the difference in
temperature increase is smaller in 2100 than in mid-century.
Furthermore, the components affected are short-lived forcing
agents with RFs of opposite signs and the early perturbation
therefore has little influence on the temperature in 2100. The
exception here is the negative RF from the CH4-lifetime effect. CO2
is not changed in this test, since the emission scenarios applied
here span a range of possible developments for CO2 emissions
through the 21st century.
5. Discussion
There are a number of non-linear relations in the climate
system. Some of these are accounted for in our model framework
by using results from more sophisticated and complex studies.
There is however no single correct way to handle non-linear
behaviour since this will depend on the chosen perspective. If
one is interested in the total effect of a sector, then this can be
studied based on the assumption that all other sectors and
emissions remain unchanged. One may also be interested in
changes at the margin, which would be relevant for assessment
of small and medium scale emission reductions. Because nonlinear processes control some substances (e.g. O3 and OH), the RF
of ozone and methane cannot be scaled exactly to obtain the
effect of marginal changes reflecting realistic short-term mitigation measures.
Many of the short-lived components have effects that depend
strongly on the location of emissions, e.g. NOx (Fuglestvedt et al.,
1999; Wild et al., 2001; Berntsen et al., 2005; Derwent et al., 2008).
For the NOx–O3 relation for road transport we have scaled the O3
forcing for this sector from Fuglestvedt et al. (2008). Ozone
production also depends on the CO and VOC emissions. We have
scaled the RF with global emissions, which can be done if
the geographical distribution of the emissions is assumed to remain
unchanged. This represents an approximation since the distribution is likely to change in the future.
For cirrus and contrails the calculation of RF includes scaling
with fuel use (i.e. CO2 emissions). Additionally, the cirrus and
contrail formation depends on the meteorological conditions. If
there are changes in flight paths the RF can be different.
Many of the RF agents studied here are controlled by processes
that are sensitive to changes in climate (e.g. water vapour or
temperatures); for example, a change in OH levels will affect
chemically active climate gases such as O3 and CH4. We have
assumed a constant background atmosphere and climate. The
couplings between chemistry and climate are potentially important
and need to be considered in more detailed studies. Due to
important feedback processes in the climate system, such as
increased atmospheric CO2 due to rising ocean temperatures and
the release of CH4 from melting permafrost, current conditions and
assumptions may not remain valid in the future, which might affect
our results. A larger fraction of anthropogenic CO2 will stay airborne
if climate change is accounted for. Friedlingstein et al. (2006) found
that by the end of the twenty-first century, this increase in CO2
varied between 50 and 100 ppmv for the majority of the models
applied in the study (and 20 and 200 ppmv for two extreme
models). The higher CO2 levels led to an additional climate
warming ranging between 0.1 and 1.5 C. Accounting for this effect
would increase the climate impacts of transport emissions, but is
not included in this study.
In our calculations we have not included efficacies for the various
forcing agents. Efficacy is defined as the ratio of the climate sensitivity parameter for a given forcing agent to the climate sensitivity
parameter for CO2 changes. In other words, different RF mechanisms
will yield different temperature responses. Both the geographical
and vertical distribution of the forcing can have significant effects on
efficacy (Forster et al., 2007). Ponater et al. (2005), Grewe et al.
(2007) and Grewe and Stenke (2008) presented efficacies for aircraft
RFs from model studies. Efficacies specific to other transport sectors
are not established. There is also a large spread in efficacies presented in IPCC AR4 (Forster et al., 2007), especially for BC. Differences
in efficacies between the various forcing agents may modify the
calculated contributions of the various components and the sectors.
However, Marais et al. (2008) performed a sensitivity analysis for the
effect of different efficacy values on the integrated temperature
response and found only a relatively small effect.
The transport sector contributes to climate change by means of
several forcing agents not covered by the Kyoto Protocol, most
notably tropospheric O3 driven by NOx, CO, and VOC. The negative
forcings caused by SO2, OC, and ozone precursors are also
excluded. Therefore, a ‘‘Kyoto perspective’’ does not capture the
full climate impact of transport, especially for the shipping sector
due to its large contribution to SO2 and NOx emissions. Furthermore, the Kyoto Protocol does not cover emissions from international aviation and shipping. If we look at the effect of including
only the Kyoto gases in the perturbations (i.e. only CO2 since the
contribution from N2O and CH4 is negligible), the temperature
increase due to road transport remains similar to the case in which
all emissions are included, only w4% and w0.5% smaller in 2050
and 2100, respectively. This is due to the large reduction of nonKyoto gases at the beginning of the century in this sector and the
dominating contribution from CO2. For the aviation sector, the
difference is larger; excluding non-Kyoto components significantly
reduces the calculated temperature increase. This is the case even if
the cirrus effect is excluded from the standard aviation perturbation. In line with earlier studies, the temperature response from
shipping is positive when only the Kyoto gases are included and is
of the same magnitude as the effect of aviation.
In addition to the RF effects considered in this study, other
indirect processes have been proposed. These include reduction of
the albedo of snow and ice by BC deposition (Warren and
Wiscombe, 1980; Hansen and Nazarenko, 2004; Flanner et al.,
2007), the so-called semi direct effect of BC on clouds (Ackerman
et al., 2000), and indirect effects on cirrus clouds by particles from
aviation (Penner et al., 1999). All these effects are currently given
a low or very low level of scientific understanding by the IPCC
(Forster et al., 2007).
Emissions of CFCs and HFCs from mobile air-conditioning (MAC)
are not taken into account in this study. CFC-12 (from MAC) has
lead to a direct forcing of 170 mW m2 in 2005 and an indirect RF of
34 mW m2 due to stratospheric ozone depletion while the RF
from HFC-134a was 5.5 mW m2 (Uherek et al., submitted for
publication).
6. Conclusion
This work has shown that there are large differences between
the transport sectors in terms of sign and magnitude of their effect
on climate, and also with respect to the contributions from the
R.B. Skeie et al. / Atmospheric Environment 43 (2009) 6260–6270
long- and short-lived components and thus the temporal characteristics of the climate response. Although we have analysed the
contributions both historically and for future scenarios to both RF
and global mean temperature, the results agree with the general
conclusions obtained by Fuglestvedt et al. (2008) and Berntsen and
Fuglestvedt (2008) for RF and temperature response to historical
and current emissions.
We find that, since pre-industrial times, the transport sector as
a whole has contributed 9% to the total net man-made warming in
2000. The dominating contributor to warming is CO2, followed by
tropospheric O3. Emissions of SO2, mainly from shipping have, on
the other hand, had a cooling effect. By sector, road transport is the
largest contributor to warming (11%). Aviation has contributed 4%
and rail 1% to the total net anthropogenic warming in 2000. Shipping has caused a net cooling up to 2000, contributing 7% to the
net man-made temperature change.
We have also calculated future effects of transport for four
scenarios consistent with IPCC SRES scenarios. In 2050 we find that
the net contribution from the transport sector as a whole to total
man-made warming is approximately 10% in scenario A2, 14% in A1
and B2 and 16% in B1 when historical emissions are included. In
2100 the contribution reaches w20% in scenarios A1 and B1.
In order to facilitate a comparison across the transport sectors
without the influence of their very different emission histories, we
calculate the future temperature increase due to only post-2000
emissions. In all scenarios road transport is the dominating
contributor to warming. The temperature increase resulting from
this sector is between 0.13 C (A2) and 0.18 C (A1) in 2050 for
a climate sensitivity of 0.8 K (W m2)1. Furthermore, the contribution from rail is 0.006–0.008 C in 2050, and the net effect of
emissions from the shipping sector is negative, ranging from
0.01 C to 0.08 C. The contribution from aviation varies
between 0.07 C and 0.1 C. The absolute values of temperature
change depend strongly on the chosen climate sensitivity. The
relative temperature change, however, does not, and will remain
approximately the same for different choices of climate sensitivity.
Throughout the 21st century road transport remains the
dominating sector, followed by aviation, and the warming from
these transport modes increases up to 2100. The contribution from
rail remains small. The net effect of emissions from the shipping
sector switches from cooling to warming towards the end of the
century in three of the scenarios. This is due to anticipated reductions in SO2 emissions from shipping.
Significant uncertainties are related to our estimates of both
historical and future net warming from the transport sectors,
mainly due to cirrus, contrails, and direct and indirect aerosol
effects. However, we find that the conclusions that (i) road transport provides the largest contribution to warming with CO2 and O3
as the dominating warming agents, and that (ii) the response from
shipping switches from initial cooling to warming on a longer time
scale, remain robust.
Acknowledgements
We thank Lynn Nygaard, Jens Borken-Kleefeld and colleagues in
QUANTIFY for valuable comments and discussions. This research
was supported by the European Union’s Sixth Framework Integrated Project QUANTIFY Contract No 003893 and the Norwegian
Research Council.
Appendix. Supplementary data
Supplementary data associated with this article can be found, in
the online version, at doi:10.1016/j.atmosenv.2009.05.025.
6269
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