For short-lived climate forcers (SLCFs), the impact of emissions depends on
where and when the emissions take place. Comprehensive new calculations of
various emission metrics for SLCFs are presented based on radiative forcing
(RF) values calculated in four different (chemical-transport or
coupled chemistry–climate) models. We distinguish between emissions during
summer (May–October) and winter (November–April) for emissions in Europe
and East Asia, as well as from the global shipping sector and global emissions.
The species included in this study are aerosols and aerosol precursors (BC,
OC, SO
For the aerosols, the emission metric values are larger in magnitude for emissions in Europe than East Asia and for summer than winter. A variation is also observed for the ozone precursors, with largest values for emissions in East Asia and winter for CO and in Europe and summer for VOCs. In general, the variations between the emission metrics derived from different models are larger than the variations between regions and seasons, but the regional and seasonal variations for the best estimate also hold for most of the models individually. Further, the estimated climate impact of an illustrative mitigation policy package is robust even when accounting for the fact that the magnitude of emission metrics for different species in a given model is correlated. For the ramping emission metrics, the values are generally larger than for pulse or sustained emissions, which holds for all SLCFs. For SLCFs mitigation policies, the dependency of metric values on the region and season of emission should be considered.
Climate is impacted by various emitted gases and particles with a range of
radiative efficiencies, lifetimes, and climate efficacies
(e.g., Myhre et al., 2013).
Emissions of CO
The impact of emissions of different SLCFs may be measured with the use of
emission metrics which quantify an idealized climate impact per unit mass of
emissions of a given species. Various applications exist (Fuglestvedt et
al., 2003; Tanaka et al., 2010; Aamaas et al., 2013), the main ones are to (1) provide an “exchange rate” between different emitted species used in
mitigation policies, (2) compare different activities and technologies that
emit a range of species over time such as in life cycle assessment (LCA),
and (3) compare in a simplified manner the climate responses of various
emissions to gain and communicate scientific understanding. The most common
emission metrics are time integrated radiative forcing (absolute global
warming potential, AGWP) (IPCC, 1990) and temperature perturbation
(absolute global temperature change potential, AGTP)
(Shine et al., 2005; Shine et al., 2007), which,
when normalized to CO
Emissions metrics have normally been calculated for global emissions. However, for SLCFs, due to their short lifetimes compared to large-scale atmospheric mixing times, and because the chemistry and radiative effects on climate depends on the regional physical conditions, even the global mean radiative forcing depends on the region of emissions (Fuglestvedt et al., 1999; Wild et al., 2001; e.g., Berntsen et al., 2005; Naik et al., 2005). Then, the emission metric values will vary for different emission locations (Fuglestvedt et al., 2010). In addition, distinct patterns in the temperature response appear from all forcings (Boer and Yu, 2003; Shindell et al., 2010). A growing literature investigates how the weights of the emission metrics change as emissions from different regions of the world are considered. Collins et al. (2013) assessed variations in emission metrics for four different regions (East Asia, Europe, North America, and South Asia) for aerosols and ozone precursors, based on radiative forcings from consistent multimodel experiments from the Hemispheric Transport of Air Pollution (HTAP) experiments given by Yu et al. (2013) and Fry et al. (2012). Collins et al. (2010) also investigated how emission metric values differ between regions, including vegetation responses. Bond et al. (2011) quantified differences in RFs for BC and OC emissions from different locations and types of emissions.
For SLCFs, the impact also depends upon the season of emissions. As the chemistry and radiative effects vary between summer and winter, the RF per unit emissions will differ between the seasons. An additional factor is that the magnitude of emissions fluctuates between the seasons, which can also be the case for LLGHGs. For example, emissions of certain species from wood burning for domestic heating will be much larger in winter than summer (Streets et al., 2003).
Bellouin et al. (2016) detail a comprehensive set of
dedicated RF calculations with four models (ECHAM6-HAMMOZ, HadGEM3-GLOMAP,
NorESM, and OsloCTM2) for emission perturbations in different regions
(Europe, East Asia, shipping, and global) and seasons (Northern Hemisphere (NH) summer
(May–October) and winter (November–April)) for various SLCFs or their precursors (BC,
OC, SO
An overview of the four different coupled chemistry–climate models or
chemical-transport models presented by Bellouin et al. (2016), their resolution and species investigated (SO
General circulation models (GCMs) and chemical transport models (CTMs) used to calculate radiative forcing in this study. Resolution shows the horizontal resolution and the number of vertical layers. Radiative forcing has been calculated for emissions of these gases and particles by Bellouin et al. (2016).
The calculations are based on different processes that affect RF (see
Bellouin et al., 2016). For aerosols and aerosol precursors,
three of the four models calculate the aerosol direct and first indirect
(cloud-albedo) effect, with ECHAM6 only diagnosing direct RF. For BC,
OsloCTM2 estimated in addition the RF from BC deposition on the snow and
semi-direct effect. Only a few previous studies, such as Bond et al. (2013), have included the semi-direct effect in emission metrics. For the
ozone precursors and CH
The best estimate of a species' RF is given as the sum of all the processes,
in which the average across the models is used for each process. ECHAM6 is
not included in the best estimate for BC, OC, and SO
For the high and low estimate, we sum the highest and lowest values, respectively, for each individual process.
These global-mean RFs of various species were calculated for emissions in
different regions. The three regions, following tier 1 HTAP regions, are
Europe (western and eastern Europe up to 66
In this study, we use the emission metrics GWP and GTP with varying time
horizons. In all perturbations, RF is annually and globally averaged; thus,
the responses are also annually averaged. AGWP for species
The AGTP is given as
To calculate the time-varying RF for a pulse emission of CO
Pulse, sustained, and ramping emission profiles. The ramping period can vary.
Emission metrics for pulse emissions are in principle the most useful
metrics, even though emissions follow a given temporal profile. A pulse can
be seen as an instantaneous emission, or constant emission during a short
period (
Emission metrics normalized to the corresponding absolute emission metric
for ramping emissions of CO
Note that since the pulse metrics are given by region and season, so are the
ramping metrics (
For policymakers to apply this concept to compare different (
First, we present the best estimate of emission metric values for pulse emissions; see Table 2 for GTP(20) values. Additional values for GWP and for other selected time horizons are given in Table S1 in the Supplement. Due to space constraints, we can only present values for a few time horizons. The choice of emission metric and time horizon depends on the application, and a range of different justified choices are possible (e.g., Aamaas et al., 2013). If the focus is on temperature change in the next few decades, GTP(20) is appropriate. In Fig. 2, GTP(20) values are given for the different species, decomposed by a range of processes. Figure 3 presents results for GWP(100) for the ozone precursors. We first focus on a few selected time horizons, but Sect. 3.1.5 shows how emission metrics evolve for a range of time horizons.
GTP(20) values for the species, for all regions and seasons,
decomposed by processes. The regions included are Europe (EUR), East Asia
(EAS), shipping (SHP), and global (GLB), all for both NH summer (s),
May–October, and NH winter (w), November–April. How the best estimate
of the net effect is calculated is given in Sect. 2.1. The uncertainty bars
show the range across models, which is not given for shipping as the best
estimate is based on only two models for that sector. For CH
GWP(100) values for the ozone precursors, for all regions and seasons, decomposed by processes. The regions included are Europe (EUR), East Asia (EAS), shipping (SHP), and global (GLB), all for both NH summer (s), May–October, and NH winter (w), November–April. How the best estimate of the net effect is calculated is given in Sect. 2.1. The uncertainty bars show the range across models, which is not given for shipping as the best estimate is based on only two models for that sector.
The best estimate given for GTP(20) values. The component of each species which the mass emission refers to is shown in brackets. The regions are Europe (EUR), East Asia (EAS), shipping (SHP), and global (GLB), for emissions occurring in NH summer (s), May–October, and NH winter (w), November–April.
The uncertainties in Figs. 2 and 3 are given as the range across all contributing models. The uncertainty is in general larger than the variation between different regions and seasons. Thus, when including the uncertainty, it is less clear which region and season give the largest and smallest emission metric values. However, we will show in Sects. 3.1.3 and 3.1.4 that the best estimate is more robust than the uncertainty bars indicate.
The emission metric values for the shipping sector are based on only two models (OsloCTM2 and NorESM). We do not provide uncertainty ranges for shipping due to the low numbers of models. Further, the robustness of these values presented is lower than for the other regions for the same reason.
We find distinct differences between regions and seasons for all species.
For the aerosols BC, OC, and SO
For BC, the elevated aerosol effect in summer is partially canceled out by
a cooling effect by the semi-direct effect (see Fig. 2). The semi-direct
effect is due to the absorption of solar radiation of particles, which
affects the atmospheric static stability, and impacts on clouds. The impact
of BC deposition on snow is largest for emissions during winter and larger
for Europe than East Asia. The BC surface albedo effect is governed by the
extent of snow- and ice-covered surface area but also depends on the
availability of solar radiation where the BC is deposited. For Europe, the
snow effect is 54 % of the direct effect in winter and 2.61 % in summer,
while the corresponding percentages are 22 and 1.1 % for East Asia.
The shares are similar for the shipping and global, with lowest shares for
global emissions. As explained by Bellouin et al. (2016),
this is due to atmospheric transport: according to the models, European
emissions of BC are preferentially transported to the Arctic, where they
modify the albedo of snow. Seasonality is driven by snow cover, which is
larger in winter and early spring. In Europe, the semi-direct effect is
For the ozone precursors, the variability between regions and seasons is
smallest for CO. For CO, GTP(20) values are higher for winter than summer.
Due to the longer lifetime of CO during winter, a large fraction of the CO
emitted during winter will undergo long-range transport and will be oxidized
in relatively clean low-NO
For aerosols emissions and the major aerosols precursors, the relative
ratios between the different regions and seasons are constant while varying
the emission metric and time horizon applied. On the other hand, the
relative ratios between different emission metric values for the ozone
precursors differ with varying emission metrics and time horizons. The
ratios for the aerosols are fixed since the aerosols have little effect on
perturbations of atmospheric composition and components with long adjustment
times. By contrast, the ozone precursors affect processes with longer time
constants. By causing a change in OH levels, methane with an adjustment time
of about 10 years is perturbed. Hence, we also show GWP(100) values for the
ozone precursors (Fig. 3), while similar figures for the other species are
provided in the Supplement (Fig. S1). For the ozone precursors, the aerosol direct and
indirect effect and the short-lived ozone effect are given relatively more
weight for GWP(100) than GTP(20) than the methane effect and methane-induced
ozone effect, since GWP integrates the RF up to the time horizon, while GTP
is an end-point indicator. As the time horizon increases, the relative
contribution from methane and methane-induced ozone increases and the
contribution from aerosols and short-lived ozone decreases. The overall
picture presented here for GTP(20) and GWP(100) is mostly similar. However, for
NO
We provide only one global emission metric value for CH
As already noted, the variations with respect to regional emissions for emission metric values are in line with Collins et al. (2013). Fuglestvedt et al. (2010) also presented emission metrics with respect to regional emissions based on earlier calculations in the literature, but with some conflicting results between available studies. Due to this spread, our findings are partly in line with Fuglestvedt et al. (2010). In general, the specific emission metric values are also comparable with Collins et al. (2013). However, a complete comparison is not possible as we have included the effect of aerosols for the ozone precursors and the semi-direct and deposition on snow effect for BC. The findings are also generally similar to previous estimates for emission metrics of global emissions (e.g., Fuglestvedt et al., 2010), with some discrepancies we will discuss here. A comparison of modeled GWP and GTP values with a selection from the literature for some selected time horizons is given in Table S1.
For BC, Bond et al. (2013, 2011) presented about 20–40 %
higher emission metric values (GTP and GWP), while other studies
(Fuglestvedt et al., 2010; Collins et al., 2013) are in line with or up to
40 % lower than this study and Hodnebrog et al. (2014) give
significant lower values. As discussed in Hodnebrog et al. (2014), the atmospheric lifetime of BC may be shorter and the BC emissions
may be larger than previously thought (e.g.,
Fuglestvedt et al., 2010), leading to emission metric values almost halved
compared to previous estimates (
The differences in the emission metric values between the emission regions
and seasons of emissions seen for the best estimate holds generally in each
model, which strengthens our confidence in the modeled variations between
regions and seasons. For emissions of aerosols and their precursors, the
magnitude of GTP(20) values is higher in summer than winter in 86 % of
the model cases. The consistency between the individual models and our best
estimate based on the models is 100 % for SO
For the ozone precursors, the variation in GTP(20) values observed for the
best estimate also holds for most of the models. For both regional and
seasonal variability, 83 % of the model cases agree with the best
estimate. For CO, all cases agree that the GTP(20) values are larger for
East Asian emissions than European emissions and for winter than summer,
even though the relative differences in GTP(20) values between Europe and
East Asia in summer and winter are relatively small. The difference may
occur since the East Asia region is located closer to the Equator. The
findings for NO
Emission metrics are used to quantify the climate impacts of different sets
of emission changes following either mitigation policies or changes caused
by some other mechanisms (e.g., technological development). However, the
uncertainties given by the model ranges for individual regions, seasons, and
species shown in Figs. 2 and 3 do not provide a good indication for the
robustness of the
Scatter plot of the normalized variability of the model estimates
(NV
Models with more efficient vertical transport and/or slow removal of
aerosols by wet scavenging will tend to give longer lifetimes for the
aerosols and thus stronger RF per unit emission for all aerosol species, and
thus emission metric values for the individual species and seasons would be
correlated. This means that the ranking of measures and the net impact of
measures that lead to reduction in emissions of co-emitted species that
cause a cooling effect could be more robust. Similar effects can be expected
across ozone precursors due to non-linear chemistry effects and removal
efficiencies; for instance, such correlations across models were observed
for the climate effect of NO
The values of NV
Figure 4 clearly shows the correlation between the species for the individual model emission metrics. For the aerosols, HadGEM and NorESM tend to give higher (in absolute terms, i.e., more negative for cooling agents) emission metric values compared to the best estimate, while ECHAM gives much lower values. For the ozone precursors, the picture is the opposite, with NorESM being lower than the BE while the OsloCTM is higher. This indicates that for both aerosols and ozone precursors there are generic features in the models related to representation of key processes (e.g., vertical mixing, wet scavenging, ozone production efficiency) that systematically affects the emission metric values.
Emission metric-based estimate of change in global mean
temperature by 10 % reduction in emissions of all SLCFs based on 2008
global emissions with positive best estimate AGTP(20) values (BC, CO, and
VOCs, labeled B), and 10 % global reduction in all SLCFs (also including
OC, SO
These correlations between the estimates for the individual species have to
be taken into account when the uncertainty in the net effect of a
multi-component mitigation policy is estimated. Since different SLCFs are
often co-emitted, most mitigation options will affect emissions of several
species at the same time. The uncertainty in the estimate of the net effect
depends on the composition of the mitigation, i.e., mix of species, regions,
and sectors. To be useful for policymaking, the emission metrics should be
robust enough so that there is trust in the sign of the net effect of a
mitigation measure and that the uncertainty in the emission metrics does not
hinder a ranking of different measures when cost efficiency is considered.
Figure 5 shows the estimates of the net effect (here in terms of temperature
change after 20 years, i.e., using AGTP(20) for pulse emissions) when using
the sets of emission metrics from the individual models. First, we consider
a global mitigation of a 10 % reduction in emissions of all SLCFs for
which the best estimate is positive for the AGTP(20) (BC, OC, and VOCs –
labeled B in Fig. 5), and then a 10 % global reduction in all SLCFs (an
extreme case of also reducing the co-emitted cooling species OC, SO
A comparison of GTP values, as a function of time horizon, for summer emissions in Europe (left) and East Asia (right).
The black bars in Fig. 5 give the uncertainty in the net global temperature effect assuming all the metric values are independent. This gives a similar or narrower uncertainty interval than the spread of the estimates using the individual model metrics, again showing that there is considerable correlation in the model estimates. However, if the difference between the models were 100 % systematic (i.e., one model always giving the lowest estimates by magnitude and another model giving the highest), then the model-based interval would be given by the blue bar in Fig. 5. From this analysis, we conclude that the uncertainty for an estimate of the net temperature effect of multi-component emission change is enhanced due to the correlations; however, for mitigation measures that mainly change emissions of species with positive GTPs, the sign of the global temperature signal is robust.
Since not all processes are included in all the models, the average of all
models in Fig. 5 will differ from the best estimate. This deviation is
observed in both scenarios but is clearest for a mitigation scenario including
both warming and cooling SLCFs, as the net climate impact is a sum of large
positive and negative numbers. The processes not included are dominated by
cooling. Three out of four models do not include the cooling from the
semi-direct effect of BC, nor do they include what is mainly cooling from nitrate for the
ozone precursors and SO
Our findings show that the robustness is largest for individual species, i.e., what region and season of emissions to mitigate for an individual species. Next follows a subgroup of species that correlates, such as aerosols. Lowest robustness is given for mitigation for all SLCFs. However, we observe that all models agree whether two hypothetical mitigation scenarios give warming or cooling.
GTPs (top) and GWPs (bottom) for BC (left) and NO
We have until now presented emission metric values at certain fixed time
horizons; however, these values vary greatly with time horizon, which is
partially controlled by CO
The global temperature response 10, 20, 50, and 100 years after
regional and seasonal emissions in 2008. The regions from top to bottom are
Europe, East Asia, the global shipping sector, and global. NH summer season
(May–October) is to the left and NH winter season (November–April) to the
right. Note that the
We have applied the emission metrics on an emission dataset for year 2008
(Klimont et al., 2016). The variability
discussed in the previous section is also found in the global temperature
response for regional and seasonal emissions (Fig. 8). A seasonal profile is
included in the emissions, with typically largest emissions in the winter
season, but the temperatures should be taken as being annual mean values.
The temperature response drops rapidly off due to the short lifetimes of the
SLCFs. The response of the SO
We have calculated emission metrics for pulse emissions, which is the
standard method. However, changes in emissions are often gradual in real
life. In this section, we present how the emission metric values differ
based on a gradual implementation of mitigation policy (see Fig. 9), which
is calculated by convolution as given in Eq. (6). We show results only for a
ramping period of 15 years, but we have also looked at other implementation
rates. The emission metric values presented here are for Europe in the
summer season, with the exception of CH
The emission metric values for different types of emission
profiles for European emissions in summer, with GTP to the left and GWP to the
right. The ramping period is set to 15 years. We include NO
For species that have a shorter influence on the climate system than
CO
The emission metric values for ramping scenario emissions. GTP
(top) and GWP (bottom) are given for BC (left) and NO
We also present the temporal evolution for all the regions and seasons for
BC and NO
The other significant difference between emission metrics based on pulse and
ramping emissions is the sign switch for NO
Emission metrics can be applied as an “exchange rate” between different
emissions in climate polices, such as for different LLGHGs in the Kyoto
Protocol. While the calculations of how emissions impact the climate build
on scientific knowledge, how the emission metrics should be used is given by
political choices. There is no particular reason why there should be one and
only one goal for our climate policy (Fuglestvedt et al., 2000; Rypdal et
al., 2005; Daniel et al., 2012; Sarofim, 2012; Jackson, 2009; Victor and Kennel,
2014). In particular there may be harmful impacts of exceeding a long-term
temperature constraint (e.g., 2
A general difference between LLGHGs and SLCFs is that the location of the LLGHG emissions does not matter, while we have shown that different locations, as well as timing of emissions, will cause different impacts of SLCFs (Fuglestvedt et al., 1999; Naik et al., 2005; Berntsen et al., 2006; Shindell and Faluvegi, 2009; Berntsen et al., 2005). In addition to differences in the total global response, the spatial distribution of the impact depends on the location and timing of the SLCFs emissions. Further, we have shown that individual models may give significantly different emission metric values than other models.
We have presented emission metrics for regional emissions of several SLCFs
(BC, OC, SO
We have also calculated emission metrics for transient scenarios where we
consider a ramping of the emission over time. This emission metric will
better represent the effect of imposing a mitigation measure (i.e., a new
technology standard) that is known to give a long-term change in emissions.
For species that have a shorter influence on the atmosphere than CO
We observe variability in the emission metrics between different regions and seasons, but with varying robustness between the models. As the certainties in the numbers increases, the regional and seasonal differences may be accounted for in mitigation policies, agreements, and potential trading schemes involving SLCFs. One robust finding in our study is that, per unit mass of emissions, emissions of aerosols and their precursors in Europe should likely be given more weight than emissions in East Asia, as well as emissions in summer likely more weight than in winter. When emission metrics are applied, the selection of the specific emission metric and time horizon is of significance. The emission metric values for SLCFs drop quickly with time horizon. For the ozone precursors, the ranking between different regions and seasons can vary with different time horizon. Thus, emission metrics must be used based on careful consideration of these factors.
The RF data applied are given by Bellouin et al. (2016).
The authors would like to acknowledge the support from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 282688 – ECLIPSE, as well as funding by the Norwegian Research Council within the projects “Climate and health impacts of Short-Lived Atmospheric Components (SLAC)” and “the Role of Short-Lived Climate Forcers in the Global Climate Regime” (project no. 235548). We thank Øivind Hodnebrog and Dirk Olivié for providing radiative forcing data and Glen Peters for comments. We thank the two anonymous referees and the editor for valuable comments that improved the paper.Edited by: K. Tsigaridis