Aerosol radiative forcing can influence climate both
locally and far outside the emission region. Here we investigate black
carbon (BC) aerosols emitted in four major emission areas and evaluate the
importance of emission location and magnitude as well as the concept of the
absolute regional temperature-change potentials (ARTP). We perform
simulations with a climate model (NorESM) with a fully coupled ocean and
with fixed sea surface temperatures. BC emissions for year 2000 are
increased by factors of 10 and 20 in South Asia, North America, and Europe,
respectively, and by 5 and 10 in East Asia (due to higher emissions there).
The perturbed simulations and a reference simulation are run for 100 years
with three ensemble members each. We find strikingly similar regional surface
temperature responses and geographical patterns per unit BC emission in
Europe and North America but somewhat lower temperature sensitivities for
East Asian emissions. BC emitted in South Asia shows a different
geographical pattern in surface temperatures, by changing the Indian monsoon
and cooling the surface. We find that the ARTP approach rather accurately
reproduces the fully coupled temperature response of NorESM. Choosing the
highest emission rate results in lower surface temperature change per
emission unit compared to the lowest rate, but the difference is generally
not statistically significant except for the Arctic. An advantage of
high-perturbation simulations is the clearer emergence of regional signals.
Our results show that the linearity of normalized temperature effects of BC
is fairly well preserved despite the relatively large perturbations but
that regional temperature coefficients calculated from high perturbations
may be a conservative estimate. Regardless of emission region, BC causes a
northward shift of the ITCZ, and this shift is apparent both with a fully
coupled ocean and with fixed sea surface temperatures. For these regional BC
emission perturbations, we find that the effective radiative forcing is not
a good measure of the climate response. A limitation of this study is the
uncertainties in BC–cloud interactions and the amount of BC absorption, both of
which are model dependent.
Introduction
There has been a growing interest in reducing black carbon (BC) emissions
to slow global warming and improve air quality (Jacobson, 2002; Shindell et
al., 2012; Quinn et al., 2015; Sims et al., 2015). However, estimating the
total climate impact from BC is complicated because BC absorbs solar
radiation and therefore rapidly influences heating rates, humidity, and clouds
in the atmosphere (Bond et al., 2013; Hansen et al., 1997; Cook and Highwood,
2004). These effects are often called “rapid adjustments” and are distinct
from the direct radiative forcing (DRF), i.e. scattering and absorption of
sunlight by BC, and the indirect radiative forcing, i.e. changing the
microphysical properties of clouds (Twomey, 1977; Haywood and Boucher,
2000). The surface warming by BC is highly sensitive to these rapid
adjustments and is dependent on the altitude of the BC layer and the
co-location of clouds (Ban-Weiss et al., 2012; Koch and Del Genio, 2010;
Johnson et al., 2004; Jacobson, 2002). For instance in Johnson et al. (2004)
using large-eddy simulations, it was found that BC within the boundary layer
heated the air, decreased the liquid water path (LWP), and thinned the marine
stratocumulus clouds, while BC above the boundary layer increased the LWP,
leading to a shallower and more moist boundary layer. Ramanathan et al. (2001)
showed that BC can also reduce the surface solar radiation and increase
static stability, lowering the surface moisture fluxes. Using prescribed BC
in a general circulation model coupled to a mixed-layer ocean, Ban-Weiss et al. (2012) showed that BC near the surface caused surface warming and
increased precipitation, while BC near the tropopause and in the
stratosphere, on the other hand, decreased surface temperatures and
decreased precipitation. BC forcing from outside the region can contribute
to the surface temperature response via transport of heat (Shindell, 2007;
Sand et al., 2013; Menon et al., 2002).
Coupled Earth system models (ESMs) now include most of the relevant
processes to study the complex feedbacks mentioned above and could in
principle be used to analyse and compare the effects of different
regional/sectoral BC mitigation options of interest to policy makers.
However, the change in emissions for these mitigation options are often too
small (e.g. agricultural waste in Europe) to get a statistically significant
signal in an ESM without running it for thousands of years, which makes it
nearly impossible with state-of-the-art supercomputer clusters. As an
alternative, climate metrics provide an easy way to compare emission
perturbations, forcing, and response. The absolute regional
temperature-change potentials (ARTPs) derived by Shindell and Faluvegi
(2009) provide a relationship between forcing in one region and a surface
temperature response in another region. The relation between forcing and
surface temperature response was calculated for four different latitude
bands (the Arctic, mid-latitudes, tropics, and Southern Hemisphere (SH)),
using a fully coupled climate model (GISS ModelE). Shindell and Faluvegi
(2009) highlighted the importance of remote forcing in certain regions and
found that the Arctic and mid-latitudes were especially sensitive to the
location of the forcing. For instance, the radiative forcing of BC at mid-latitudes strongly influenced the surface temperature in the Arctic. In a
follow-up study Shindell et al. (2010) evaluated the method using transient
historical simulations in four climate models and found good agreement. The
ARTP coefficients have been used in many studies to estimate the surface
temperature response to different forcing perturbations derived from
chemistry transport models (Sand et al., 2015a; Collins et al., 2013). For
instance, Sand et al. (2015a) estimated the Arctic surface temperature
response to emissions of BC, SO2, and OC from a wide range of sectors
and regions by calculating the direct radiative forcing (DRF) in four CTMs
and applying the ARTP coefficients.
The ARTP method is quick and efficient; however, there are important
simplifications underlying the calculations. As these coefficients cannot
easily be validated by observations and have not been calculated in a
multi-model experiment, there are considerable uncertainties in these
coefficients. Also, the emission rates are calculated for broad latitude
bands, and any longitudinal variations in the response are not represented.
Lewinschal et al. (2019) evaluated the surface temperature change in
response to SO2 emission perturbations in four major emission regions
using a different climate model (NorESM) and compared it to the temperature
response estimated by using the ARTPs. The ARTP method predicted a similar
latitudinal temperature response to those estimated by NorESM. Here we
perform simulations of the response to regional BC emissions with the fully
coupled climate model (NorESM) following the set-up used by Lewinschal for
SO2 emissions. We investigate the importance of emission location and
magnitude and test the ARTP concept. We calculate emission-to-temperature
responses by estimating the regional surface temperature response to BC
emissions from four major emission regions: North America, Europe, South
Asia, and East Asia. To get a signal of a small emission perturbation in a
coupled (atmosphere–ocean) climate model, it is often necessary to scale up
the emissions. We evaluate this assumed linearity by perturbing the BC
emissions with two different emission rates.
MethodsNorESM
We have used the Norwegian Earth System model (NorESM1) (Bentsen et al.,
2013; Iversen et al., 2013), which is largely based on the CCSM4.0 framework
(Gent et al., 2011) with special features for aerosols and their interaction
with radiation and warm cloud microphysics (Kirkevåg et al.,
2013; Seland et al., 2008). The model uses the finite-volume dynamical core
for transport, with a horizontal resolution of 1.9∘ latitude by
2.5∘ longitude and 26 levels in the vertical. Unlike CCSM4.0,
NorESM1 is run with an elaborated version of the Miami Isopycnic Community
Ocean Model (MICOM).
Overview of the model simulations in this study and the global BC
emissions in each simulation (in Tg BC yr-1).
ControlYear 2000 control run 3×100 years fully coupled equilibrium simulations7.710× EuropeSame as control but with BC emissions in Europe multiplied by 1012.820× EuropeSame as control but with BC emissions in Europe multiplied by 2017.910× North AmericaSame as control but with BC emissions in North America multiplied by 1011.920× North AmericaSame as control but with BC emissions in North America multiplied by 2016.110× South AsiaSame as control but with BC emissions in South Asia multiplied by 1013.520× South AsiaSame as control but with BC emissions in South Asia multiplied by 2019.35× East AsiaSame as control but with BC emissions in East Asia multiplied by 515.010× East AsiaSame as control but with BC emissions in East Asia multiplied by 1022.4
The aerosol life-cycle scheme calculates mass concentrations of SO2,
BC, organic matter, sea salt, and mineral dust in up to four size modes
(nucleation, Aitken, accumulation, and coarse modes). BC is emitted in the
nucleation, Aitken, and accumulation modes and in the internally mixed Aitken
mode with organic matter. BC primary particles are assumed to be externally mixed.
BC is internally mixed with organic matter and SO4, the latter through
either condensation or coagulation. The mass of internally mixed coating
species is used in the calculations of the optical properties and cloud
droplet number concentration, but it does not alter wet deposition rates.
Water is mixed into the aerosols based on the hygroscopicity and ambient
relative humidity. The calculated gas-phase components are DMS and SO2.
The aerosol concentrations are tagged according to different processes. The
processes include gas-phase and aqueous-phase chemical production, gas to
particle nucleation, condensation on pre-existing aerosol surfaces, and
coagulation of smaller particles onto pre-existing Aitken-, accumulation-, and
coarse-mode particles. The process-tagged aerosol mass concentrations and
relative humidity are given as input to look-up tables that estimate the
optical and physical properties of the aerosols. The aerosols can also act
as cloud condensation nuclei (CCN) based on their size and composition. BC
that is mixed with other components and has become hydroscopic can
contribute to the number of CCN. Cloud droplet number concentration (CDNC)
and liquid water path are prognostically calculated. CCN activation is
estimated based on supersaturations calculated from Köhler theory
(Abdul-Razzak and Ghan, 2000) using a sub-grid-scale vertical velocity. The
rate of activation depends on the size distribution and the hygroscopicity
of the particles. Any cloud “burn-off” effects due to absorption by BC
within individual cloud droplets are not included. Deposited aerosols on
snow cover and bare sea ice are calculated prognostically and include
hydrophobic and hydrophilic black carbon and dust. While the effects of
deposition of these light-absorbing aerosols are taken into account, we have
not calculated the radiative transfer of BC in snow explicitly. A
comprehensive assessment of the mean model state is available in Bentsen et al. (2013). They have shown that the global mean energy fluxes and
associated cloud forcing are close to or within the observation range but
that the cloud cover is underestimated. The aerosol fields are evaluated in
Kirkevåg et al. (2013). Here it is shown that global BC concentrations
are underestimated by -18 % compared to observations. The global
anthropogenic aerosol DRF is -0.08 W m-2 and the indirect radiative
forcing is -1.2 W m-2.
Experimental set-up
Coupled atmosphere–ocean simulations with NorESM have been performed for a
set of emission perturbations. BC emissions have been increased in four
areas: South Asia, East Asia, North America, and Europe. In these regions the
emissions have been perturbed by factors of 10 and 20, except East Asia that
has been perturbed by 5 and 10 (due to the relatively higher emissions in this
region). The magnitude of these rates was chosen to get a large enough
signal and at the same time keep global DRF below ∼1 W m-2. Figure S1 in the Supplement shows the increase in total global emissions compared
to a baseline simulation for each region. The baseline is run with year 2000
annually repeating emissions of aerosols and precursors, land use conditions,
and greenhouse gas concentrations. The aerosol emissions (and their
precursors) are the historical emissions of CMIP5 described in Lamarque et al. (2010). The global mean BC emissions for the baseline
simulation are 7.7 Tg yr-1. Table 1 provides an overview of all the simulations. For each
perturbation coupled simulations for 100 years with three ensemble members have
been run, i.e. a total of 300 years of simulation per emission perturbation.
Furthermore, we calculate the DRF as the instantaneous change in energy flux
at the top of the atmosphere with a double call in the radiation routine.
The calculations of the DRF do not include the BC-on-snow forcing. In
separate simulations with prescribed sea surface temperatures and sea-ice
cover we calculate the effective radiative forcing (ERF) which includes rapid
tropospheric adjustments, often related to humidity and clouds that may
exhibit fast adjustments to the radiative forcing and any changes in CCN. We
have calculated the ERF as the annual mean change in the net radiation at
TOA after the atmosphere is allowed to respond to the forcing (i.e. rapid
adjustments in temperature, relative humidity, and clouds) over the last 10
years of a total of 15 years of simulations. The surface temperature response per
unit of DRF, ERF, and emission change is then compared for the four
emission areas. All numbers reported are annual means from the last 80 years
of the simulations (i.e. 80×3 years for each perturbation
experiment). Twenty-year spin-up was sufficient for equilibrium to occur for BC
perturbations (this is also shown in Sand et al., 2015b).
Results
Figure 1 shows the surface temperature change for the perturbed emission
runs compared to the baseline. The broad patterns are similar to the well-known
warming pattern due to CO2 forcing, with larger warming over land and in the
northern areas. The geographical pattern of warming is comparable for BC
emitted in North America, Europe and East Asia, with only small longitudinal
differences between those perturbations. For South Asia, however, there is a
significant local cooling effect over India. This cooling is linked to a
strong decrease in downwelling shortwave radiation (SW) radiation over India caused by the
absorption and scattering by BC aerosols, less upward motion, and an increase
in low-level clouds. Northern Hemisphere BC emissions cause a northward
displacement of the ITCZ that is most pronounced for emissions in South Asia
and a corresponding shift in the Indian monsoon. The tropospheric jet
moves northward to adjust for a northward shift in the meridional
temperature gradient (not shown). This type of displacement caused by a
hemispherically asymmetric heating is a robust feature found in several
climate model studies (Chung and Seinfeld, 2005; Jones et al., 2007; Meehl
et al., 2008; Allen et al., 2012; Voigt et al., 2017; Kovilakam and Mahajan,
2015).
Surface temperature change (in K) for the perturbed runs minus the
baseline. The emission region is given in each panel (from top: Europe,
North America, South Asia, East Asia). In the right-hand side column the emission
rate is doubled compared to the left-hand side column. The stippled areas
represent statistically significant changes (p<0.05).
Surface temperature change per global BC emissions (in K per Tg yr-1). Bold numbers represent statistically significant differences
between the emission rates (using 10 vs. 20 or 5 vs. 10 for East Asia).
ArcticMid-latsTropicsSH10× Europe0.100.080.020.0120× Europe0.060.060.010.0010× North America0.100.060.010.0120× North America0.080.060.010.0010× South Asia0.080.040.010.0120× South Asia0.040.040.010.015× East Asia0.080.040.010.0110× East Asia0.050.040.010.01
Figure 2 shows the regional surface temperature change in four latitude
bands (Arctic (60–90∘ N), mid-latitude
(28–60∘ N), tropics (28∘ S–28∘ N),
and SH (90–28∘ S)) normalized by the enhancement in
the BC emissions. Table 2 provides the corresponding numbers. For all cases
the Arctic region shows the highest sensitivities, followed by the mid-latitudes. The temperature sensitivities in the tropics and SH are lower
compared to the mid-latitudes and the Arctic, but these two regions are also
much larger regions compared to the mid-latitudes and the Arctic. These
zonal mean temperature sensitivities are similar regardless of emission
location, especially for BC emitted in North America and Europe. The
temperature sensitivities are slightly lower in the Arctic and at mid-latitudes
for BC emitted in Asia, in particular South Asia. Temperatures in the Arctic
increase by 0.04–0.1 K per Tg yr-1 BC emitted. At the mid-latitudes the
temperature increases by 0.04–0.08 K per Tg yr-1 BC emitted.
Regional surface temperature response per global BC emissions
(K per Tg yr-1). The emission location (Europe, North America, South Asia,
East Asia) is given at the top of each plot. The response region (latitude band)
is given on the x axis. The emission rate is doubled in the right-hand side bars
(darker colours) compared to the left-hand side bars (lighter colours). The error
bars represent the standard error of the mean.
The two twin bars in Fig. 2 given for each emission region represent the
linearity test. The lighter coloured bars are the perturbations with the
lowest emission rates, while the darker coloured bars are the runs with the
highest rates. In general, the sensitivity decreases with increasing
emissions, most pronounced for the response in the Arctic (compare e.g.
20×EU vs. 10×EU, i.e. the right and left columns in Fig. 2 or numbers in Table 2). The decrease in Arctic sensitivities for the
highest emissions are significant for BC emitted in Europe and Asia, but not
North America (p<0.05). For the mid-latitude sensitivities, the
changes are small, and only European emissions show significant changes
(again, with weaker sensitivities for 20× emissions compared to
10× emissions).
Regional BC burden change per global BC emissions
(Tg per Tg yr-1). The emission location (Europe, North America, South Asia,
East Asia) is given at the top of each plot. The response region (latitude band)
is given on the x axis. The emission rate is doubled in the right-hand side bars
(darker colours) compared to the left-hand side bars (lighter colours). The error
bars represent the standard error of the mean.
Regional surface temperature response per global BC (a) DRF and
(b) ERF (in K per W m-2). The emission location (Europe, North America,
South Asia, East Asia) is given at the top of each plot. The response region
(latitude band) is given on the x axis. The emission rate is doubled on the
right-hand side bars (darker colours) compared to the left-hand side bars (lighter
colours). The error bars represent the standard error of the mean.
To understand the regional differences and the non-linearities, we first
investigate the relation between emissions and concentrations. Figure 3
shows the change in regional BC burden normalized to global BC emissions. As
expected, the burden changes per global emissions are largest in the
latitude band where the emissions are located. The regional burden change
normalized to emissions is slightly higher for the highest emission rates.
These changes are statistically significant using a 0.05 level of
significance (and also using a 0.005 level), except for the changes in the
tropics and SH for North American emissions. The same results are also
obtained when we look at total global burden change (not shown). For example, in
Europe, emitting 20× emissions results in a 117 % increase in
global BC burden compared to 10× emissions. The rapid adjustments
of BC cause a local warming of the atmosphere and an increased local
convection pushing the BC layers higher up in the atmosphere where the BC is
less likely to be scavenged. For the simulations with the high emission
rate, the relative concentration increase normalized to global emissions is
largest at high altitudes above 200 hPa (25 %–55 % increase shown in zonal
mean plots of concentrations normalized to global emissions per altitude in
Figs. S3–S6). This effect has also been shown in Sand et al. (2015b) for BC
perturbed by increasing the BC emission rate compared to increasing the BC
concentrations. As in this study, increasing the emission rate resulted in a
relative increase in BC at higher altitudes compared to lower altitudes and
a longer BC residence time. This effect may explain why the temperature
increase per emissions is largest for the small emission rate. Although BC
perturbations at higher altitudes cause a larger DRF per unit burden change
(Samset and Myhre, 2011), this is outweighed by a smaller temperature
response per unit forcing. As BC is transported higher up in the atmosphere
the surface temperature response decreases, as shown in Ban-Weiss et al. (2012). In the Arctic the effect of high-altitude BC is particularly strong,
even causing a cooling at the surface for positive regional DRF, because of
the strong vertical stability in this area (Flanner, 2013; Sand et al.,
2013). A similar weaker temperature efficiency with stronger emission
perturbations has also been found in Yang et al. (2019), but for much higher
emission perturbations.
Figure 4a shows the climate sensitivity in terms of regional surface air
temperature change per global DRF, i.e. a similar concept to the ARTPs (but
for global forcing). In general, the temperature sensitivities for BC
emitted in Europe and North America are similar and 50 % lower for BC
emitted in East Asia and South Asia. In the Arctic the temperature increases
by 0.8–1.7 K per W m-2, while at the mid-latitudes the temperature
increases by 1 K per W m-2. The Arctic region shows a higher climate
sensitivity for the lower emission rate compared to the higher emission rate
(the difference is not significant for North American emissions). The ERF is
defined as the net SW minus the net longwave radiation (LW) for fixed SST perturbations. The
temperature sensitivities when using ERF instead for DRF (Fig. 4b) do not
show the same consistent pattern. The rapid adjustments of BC are strongly
negative in our model and partly offset the DRF, resulting in lower ERF
(-0.0004–0.2 W m-2) compared to DRF (0.3–1.3 W m-2). The small
value of ERF makes the sensitivities (K per W m-2) large and uncertain (maps
of ERF are shown in S02). For 20× South Asian emissions, the
(global mean) ERF is close to 0 (which makes the climate sensitivity defined
here indefinitely large). Even though ERF may not be a good predictor of the
surface temperature response, only using DRF will hide some of the
uncertainties related to atmospheric absorption. The rapid adjustments of BC
cause a shift in the large-scale circulation patterns in the atmosphere.
This shift is apparent both when using a coupled ocean and fixed SSTs. As an
example, the net TOA SW and LW fluxes are plotted for 10× East Asia
for both ocean set-ups in Fig. 5. The ERF is positive close to (all) the
emission perturbations but shows negative values linked to the northward
shift in the ITCZ.
Change in delta TOA net SW and LW radiative fluxes (sum of
downward and upward) for the 10×BC in East Asia perturbation minus
the baseline. The top row shows the runs with fixed SSTs, and the bottom row shows
the runs with a fully coupled ocean. The stippled areas represent
statistically significant changes (p<0.05).
The rapid adjustments to BC perturbations include changes in cloud cover.
Figure 6 shows the global maps of changes in high clouds and low clouds. High
cloud cover is slightly reduced in all runs and is surprisingly consistent
between the runs. Low cloud cover increases globally for all simulations,
especially for marine stratocumulus cloud regions. This increase in low
cloud cover outside the western coast of North America is largest for the Asian
emission increases and is correlated with surface cooling. In the emission
areas local low cloud cover decreases, except for South Asian emissions, where
local low clouds increase. As in most other models, BC is not included as
ice nuclei in NorESM. This might have an impact on the monsoon response and
clouds. The version of NorESM we have used is known to have a relatively
large convective transport of aerosols (Allen and Landuyt, 2014), which may
cause an overestimation of BC in the upper troposphere.
Response in high clouds (located above 400 hPa) (a) and low clouds
(surface up to 700 hPa) (b) in
%. The emission location (Europe, North
America, South Asia, East Asia) is given at the top of each plot. In the
right-hand side column the emission rate is doubled compared to the left-hand side
column.
Surface temperature change per global direct radiative forcing (in
K per W m-2). Bold numbers represent statistically significant differences
between the emission rates (using 10 vs. 20 or 5 vs. 10 for East Asia).
ArcticMid-latsTropicsSH10× Europe1.741.390.310.2320× Europe1.201.180.260.0610× North America1.571.040.190.1520× North America1.360.960.210.0810× South Asia0.770.380.130.0820× South Asia0.390.390.110.085× East Asia1.200.650.190.1510× East Asia0.810.600.170.09
Simulating the temperature response with coupled ESM requires large computer
resources (here we have used 300 model year simulations for each experiment)
due to the internal variability of the model. Alternatively, one can
estimate the temperature response in broad latitude bands by using the ARTP
approach. We compare our annual mean surface temperature estimates from the
coupled simulations with estimates using the much simpler ARTP-based
approach based on the regional response coefficients (in K per W m-2) from
Shindell and Faluvegi (2009). This is done by calculating the temperature
responses in each band by using their response coefficients multiplied by
our forcing estimates (i.e. the DRF); see Collins et al. (2013). Please note
that the coefficients from Shindell and Faluvegi (2009) are normalized by
DRF, and the rapid adjustments in the atmosphere are thus included in the
regional response coefficients (as calculated in the coupled runs by
Shindell and Faluvegi, 2009). Figure 7 shows the comparison (temperature
sensitivities in K per Tg (BC)yr-1 per latitude band) between the
ARTP-based method and our coupled model estimates. For the ARTP calculations
in the Arctic response region, we have used numbers from Sand et al. (2015a),
which includes vertically resolved forcing and sea-ice/snow forcing. This is
not included in the other latitude bands (as we did not have it available).
GISS ModelE and NorESM have the same equilibrium climate sensitivity (2.9 K). By reconstructing the temperature response, we obtain a fairly
comparable response, even though the ARTP-constructed response varies more
between the emission regions in each latitude band. Reasons for this might
be differences in the vertical distributions of BC and indirect effects.
Lewinschal et al. (2019) calculated the same emission-to-temperature
responses using NorESM, but for SO2 emissions. They found that the
temperature response was independent of emission location and as a global
average equal to -0.006 K per Tg S yr-1. As in this study, the Arctic was
found to have the largest temperature response in all simulations. Here, we
find global surface temperature responses per unit BC emitted to vary
between the emission regions, with a systematic north–south gradient: 0.029
(or 0.021 with a high emission rate) K per Tg yr-1 for European emissions;
0.025 (0.023) K per Tg yr-1 for North American emissions; 0.019 (0.016) K per Tg yr-1 for East Asian emissions; and 0.017 (0.015) K per Tg yr-1
for South Asian emissions.
A comparison between ARTP-calculated and NorESM-calculated regional
surface temperature response per unit BC emissions (K per Tg yr-1). The
lowest emission perturbation is shown here (10× Europe,
10× North America, 10× South Asia, 5× East Asia).
The response region (latitude band) is given on the x axis. The error bars
for NorESM represent the standard error of the mean.
Summary and conclusion
We have estimated the temperature responses for BC emitted in four major
emission regions in the Northern Hemisphere. The largest temperature
response was found in the Arctic, independent of emission region. Generally,
the response is similar whether BC is emitted in North America or Europe,
and to some extent also East Asia (but with a slightly lower response in the
Arctic). For BC emitted in South Asia the response is weaker due to a strong
decrease in downwelling solar radiation and local surface cooling due to a
displacement of the Indian monsoon. Regardless of emission region, BC causes
a northward shift in the ITCZ. This is apparent both when using a coupled
ocean and with fixed SSTs.
The Arctic temperature change per unit emissions depends on the magnitude of
the forcing. The higher the emission rate, the lower the temperature
sensitivity. This non-linearity is partly because enhanced absorption in the
highest emission cases increases vertical mixing, so that of BC is transported
higher up in the atmosphere, which decreases the surface temperature
response, a feature also shown in Ban-Weiss et al. (2012). For considerably
lower emissions, the sensitivity could be higher. This result implies that
the regional temperature coefficients calculated from high emission
perturbations may be a conservative estimate but in general that the
linearity of normalized temperature effects of BC is fairly well preserved
in our model.
When comparing our global temperature responses per unit BC emitted to the
response to SO2 emissions for the same regions also using the NorESM
model (Lewinschal et al., 2019), we find that the BC sensitivities are 3–5
times larger compared to SO2. The global temperature responses to BC
also vary between the emission regions with a systematic north–south
gradient.
By reconstructing the temperature response using the much simpler ARTP
method, we find that the ARTP method works quite well but that there are
regional differences within the latitude bands, especially linked to
circulation changes and the Indian monsoon.
Code and data availability
The NorESM model data that support the findings of this study are openly available at 10.11582/2020.00012 (Sand, 2020). The source code for NorESM is available at https://noresm-docs.readthedocs.io/en/latest/ and described in more detail in Bentsen et al. (2013), Iversen et al. (2013), and Kirkevåg et al. (2013).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-3079-2020-supplement.
Author contributions
MS performed the simulations and figures/statistics and led the writing of
the paper. All the authors contributed to the design of the study, the analysis
of the results, and the writing of the paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This work has been supported by the Research Council of Norway through
projects EVA (229771), AC/BC (240372), and BlackArc (240921). The
simulations were performed on resources provided by UNINETT Sigma2 – the
National Infrastructure for High Performance Computing and Data Storage in
Norway (nn9188k).
Financial support
This research has been supported by the Norwegian Research Council (grant nos. 229771, 240921, and 240372).
Review statement
This paper was edited by Alma Hodzic and reviewed by three anonymous referees.
ReferencesAbdul‐Razzak, H. and Ghan, S. J.: A parameterization of aerosol activation: 2. Multiple aerosol types, J. Geophys. Res., 105, 6837–6844, 10.1029/1999JD901161, 2000.Allen, R. J. and Landuyt, W.: The vertical distribution of black carbon in
CMIP5 models: Comparison to observations and the importance of convective
transport, J. Geophys. Res., 119, 4808–4835, 10.1002/2014jd021595, 2014.
Allen, R. J., Sherwood, S. C., Norris, J. R., and Zender, C. S.: Recent
Northern Hemisphere tropical expansion primarily driven by black carbon and
tropospheric ozone, Nature, 485, 350–354, 2012.Ban-Weiss, G., Cao, L., Bala, G., and Caldeira, K.: Dependence of climate
forcing and response on the altitude of black carbon aerosols, Clim. Dynam.,
38, 897–911, 10.1007/s00382-011-1052-y, 2012.Bentsen, M., Bethke, I., Debernard, J. B., Iversen, T., Kirkevåg, A., Seland, Ø., Drange, H., Roelandt, C., Seierstad, I. A., Hoose, C., and Kristjánsson, J. E.: The Norwegian Earth System Model, NorESM1-M – Part 1: Description and basic evaluation of the physical climate, Geosci. Model Dev., 6, 687–720, 10.5194/gmd-6-687-2013, 2013.Bond, T. C., Doherty, S. J., Fahey, D. W., Forster, P. M., Berntsen, T.,
DeAngelo, B. J., Flanner, M. G., Ghan, S., Kärcher, B., Koch, D., Kinne,
S., Kondo, Y., Quinn, P. K., Sarofim, M. C., Schultz, M. G., Schulz, M.,
Venkataraman, C., Zhang, H., Zhang, S., Bellouin, N., Guttikunda, S. K.,
Hopke, P. K., Jacobson, M. Z., Kaiser, J. W., Klimont, Z., Lohmann, U.,
Schwarz, J. P., Shindell, D., Storelvmo, T., Warren, S. G., and Zender, C.
S.: Bounding the role of black carbon in the climate system: A scientific
assessment, J. Geophys. Res., 118, 5380–5552, 10.1002/jgrd.50171, 2013.Chung, S. H. and Seinfeld, J. H.: Climate response of direct radiative
forcing of anthropogenic black carbon, J. Geophys. Res., 110, D11102, 10.1029/2004JD005441, 2005.Collins, W. J., Fry, M. M., Yu, H., Fuglestvedt, J. S., Shindell, D. T., and West, J. J.: Global and regional temperature-change potentials for near-term climate forcers, Atmos. Chem. Phys., 13, 2471–2485, 10.5194/acp-13-2471-2013, 2013.Cook, J. and Highwood, E. J.: Climate response to tropospheric absorbing
aerosols in an intermediate general-circulation model, Q. J. Roy. Meteor.
Soc., 130, 175–191, 10.1256/qj.03.64, 2004.Flanner, M. G.: Arctic climate sensitivity to local black carbon, J.
Geophys. Res., 118, 1–12, 10.1002/jgrd.50176, 2013.Gent, P. R., Danabasoglu, G., Donner, L. J., Holland, M. M., Hunke, E. C.,
Jayne, S. R., Lawrence, D. M., Neale, R., Rasch, P. J., Vertenstein, M.,
Worley, P., Yang, Z.-L., and Zhang, M.: The Community Climate System Model
Version 4, J. Climate, 24, 4973–4991, 10.1175/2011JCLI4083.1, 2011.
Hansen, J., Sato, M., and Ruedy, R.: Radiative forcing and climate response,
J. Geophys. Res, 102, 6831–6864, 1997.Haywood, J. and Boucher, O.: Estimates of the direct and indirect radiative
forcing due to tropospheric aerosols: A review, Rev. Geophys., 38,
513–543, 10.1029/1999rg000078, 2000.Iversen, T., Bentsen, M., Bethke, I., Debernard, J. B., Kirkevåg, A., Seland, Ø., Drange, H., Kristjansson, J. E., Medhaug, I., Sand, M., and Seierstad, I. A.: The Norwegian Earth System Model, NorESM1-M – Part 2: Climate response and scenario projections, Geosci. Model Dev., 6, 389–415, 10.5194/gmd-6-389-2013, 2013.Jacobson, M. Z.: Control of fossil-fuel particulate black carbon and organic
matter, possibly the most effective method of slowing global warming, J.
Geophys. Res., 107, 4410, 10.1029/2001JD001376, 2002.Johnson, B. T., Shine, K. P., and Forster, P. M.: The semi-direct aerosol
effect: Impact of absorbing aerosols on marine stratocumulus, Q. J. Roy.
Meteor. Soc., 130, 1407–1422, 10.1256/qj.03.61, 2004.Jones, A., Haywood, J. M., and Boucher, O.: Aerosol forcing, climate
response and climate sensitivity in the Hadley Centre climate model, J.
Geophys. Res., 112, D20211, 10.1029/2007JD008688, 2007.Kirkevåg, A., Iversen, T., Seland, Ø., Hoose, C., Kristjánsson, J. E., Struthers, H., Ekman, A. M. L., Ghan, S., Griesfeller, J., Nilsson, E. D., and Schulz, M.: Aerosol–climate interactions in the Norwegian Earth System Model – NorESM1-M, Geosci. Model Dev., 6, 207–244, 10.5194/gmd-6-207-2013, 2013.Koch, D. and Del Genio, A. D.: Black carbon semi-direct effects on cloud cover: review and synthesis, Atmos. Chem. Phys., 10, 7685–7696, 10.5194/acp-10-7685-2010, 2010.Kovilakam, M. and Mahajan, S.: Black carbon aerosol-induced Northern
Hemisphere tropical expansion, Geophys. Res. Lett., 42, 4964–4972,
10.1002/2015gl064559, 2015.Lamarque, J.-F., Bond, T. C., Eyring, V., Granier, C., Heil, A., Klimont, Z., Lee, D., Liousse, C., Mieville, A., Owen, B., Schultz, M. G., Shindell, D., Smith, S. J., Stehfest, E., Van Aardenne, J., Cooper, O. R., Kainuma, M., Mahowald, N., McConnell, J. R., Naik, V., Riahi, K., and van Vuuren, D. P.: Historical (1850–2000) gridded anthropogenic and biomass burning emissions of reactive gases and aerosols: methodology and application, Atmos. Chem. Phys., 10, 7017–7039, 10.5194/acp-10-7017-2010, 2010.Lewinschal, A., Ekman, A. M. L., Hansson, H.-C., Sand, M., Berntsen, T. K., and Langner, J.: Local and remote temperature response of regional SO2 emissions, Atmos. Chem. Phys., 19, 2385–2403, 10.5194/acp-19-2385-2019, 2019.Meehl, G. A., Arblaster, J. M., and Collins, W. D.: Effects of Black Carbon
Aerosols on the Indian Monsoon, J. Climate, 21, 2869–2882,
10.1175/2007jcli1777.1, 2008.
Menon, S., Hansen, J., Nazarenko, L., and Luo, Y.: Climate effects of black
carbon aerosols in China and India, Science, 297, 2250–2253, 2002.
Quinn, P. K., Stohl, A., Arnold, S., Baklanov, A., Berntsen, T. K.,
Christensen, J. H., Eckhardt, S., Flanner, M., Klimont, Z., Korsholm, U. S.,
Kupiainen, K., Langner, J., Law, K., Monks, S., van Salzen, K., Sand, M.,
Schmale, J., and Vestreng, V.: AMAP Assessment 2015: Black carbon and ozone
as Arctic climate forcers, Arctic Monitoring and Assessment Programme (AMAP), Oslo, Norway, vii + 116 pp., 2015.Ramanathan, V., Crutzen, P. J., Kiehl, J. T., and Rosenfeld, D.: Aerosols,
Climate, and the Hydrological Cycle, Science, 294, 2119–2124,
10.1126/science.1064034, 2001.Samset, B. H. and Myhre, G.: Vertical dependence of black carbon, sulphate
and biomass burning aerosol radiative forcing, Geophys. Res. Lett., 38,
L24802, 10.1029/2011GL049697, 2011.Sand, M.: Climate model output NorESM, 10.11582/2020.00012, 2020.Sand, M., Berntsen, T. K., Kay, J. E., Lamarque, J. F., Seland, Ø., and Kirkevåg, A.: The Arctic response to remote and local forcing of black carbon, Atmos. Chem. Phys., 13, 211–224, 10.5194/acp-13-211-2013, 2013.Sand, M., Berntsen, T. K., von Salzen, K., Flanner, M. G., Langner, J., and
Victor, D. G.: Response of Arctic temperature to changes in emissions of
short-lived climate forcers, Nature Clim. Change, 6, 286,
10.1038/nclimate2880, 2015a.Sand, M., Iversen, T., Bohlinger, P., Kirkevåg, A., Seierstad, I.,
Seland, Ø., and Sorteberg, A.: A Standardized Global Climate Model Study
Showing Unique Properties for the Climate Response to Black Carbon Aerosols,
J. Climate, 28, 2512–2526, 10.1175/jcli-d-14-00050.1, 2015b.Seland, Ø., Iversen, T., KirkevÅG, A., and Storelvmo, T.:
Aerosol-climate interactions in the CAM-Oslo atmospheric GCM and
investigation of associated basic shortcomings, Tellus A, 60, 459–491,
10.1111/j.1600-0870.2008.00318.x, 2008.Shindell, D.: Local and remote contributions to Arctic warming, Geophys.
Res. Lett., 34, L14704, 10.1029/2007GL030221, 2007.Shindell, D. and Faluvegi, G.: Climate response to regional radiative
forcing during the twentieth century, Nat. Geosci., 2, 294–300,
10.1038/ngeo473, 2009.Shindell, D., Schulz, M., Ming, Y., Takemura, T., Faluvegi, G., and
Ramaswamy, V.: Spatial scales of climate response to inhomogeneous radiative
forcing, J. Geophys. Res., 115, D19110, 10.1029/2010jd014108, 2010.Shindell, D., Kuylenstierna, J. C. I., Vignati, E., van Dingenen, R., Amann,
M., Klimont, Z., Anenberg, S. C., Muller, N., Janssens-Maenhout, G., Raes,
F., Schwartz, J., Faluvegi, G., Pozzoli, L., Kupiainen, K.,
Höglund-Isaksson, L., Emberson, L., Streets, D., Ramanathan, V., Hicks,
K., Oanh, N. T. K., Milly, G., Williams, M., Demkine, V., and Fowler, D.:
Simultaneously Mitigating Near-Term Climate Change and Improving Human
Health and Food Security, Science, 335, 183–189, 10.1126/science.1210026,
2012.
Sims, R., Gorsevski, V., and Anenberg, S.: Black Carbon Mitigation and the
Role of the Global Environment Facility: A STAP Advisory Document, Global
Environment Facility, Washington, D.C., 2015.
Twomey, S.: The influence of pollution on the shortwave albedo of clouds, J.
Atmos. Sci, 34, 1149–1152, 1977.Voigt, A., Pincus, R., Stevens, B., Bony, S., Boucher, O., Bellouin, N.,
Lewinschal, A., Medeiros, B., Wang, Z., and Zhang, H.: Fast and slow shifts
of the zonal-mean intertropical convergence zone in response to an idealized
anthropogenic aerosol, J. Adv. Model. Earth Sys., 9,
870–892, 10.1002/2016ms000902, 2017.Yang, Y., Smith, S. J., Wang, H., Mills, C. M., and Rasch, P. J.: Variability, timescales, and nonlinearity in climate responses to black carbon emissions, Atmos. Chem. Phys., 19, 2405–2420, 10.5194/acp-19-2405-2019, 2019.