We investigate the differences between these models that led to such a
large variation in the predicted temperature response. We explore below a
number of possible sources of discrepancy.
Differences in simulated aerosol amounts and aerosol optical
depths
We first address the possibility that differences in the aerosol schemes
themselves led directly to very different aerosol loadings between the
models, despite the identical change in SO2 emissions applied. Figure 3
shows the change in column-integrated SO4 in each model as a result of
removing SO2 emissions from China. The models vary in both the
distribution and magnitude of SO4 reductions. In particular,
HadGEM3-GA4 has the reduction in SO4 burden fairly concentrated over
China. CESM1 and GISS-E2 simulate changes in SO4 which extend further
downwind from the source region, giving a larger spatial footprint, although
CESM1 still has large reductions over China as well.
Global changes in column-integrated SO4 burden due to removing
anthropogenic SO2 emissions from China for (a) GISS-E2,
(b) CESM1, and (c) HadGEM3-GA4. Differences are calculated
as perturbation simulation minus control simulation, averaged over
150 years.
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For GISS-E2 and HadGEM3-GA4, more detailed chemistry diagnostics were
available from a 5-year period of a HadGEM3-GA4 atmosphere-only control
simulation, and a 5-year period of the GISS-E2 coupled control simulation.
For these two models, the difference in spatial extent of the SO4 field
from Chinese SO2 emissions seems to be due to faster conversion of
SO2 to SO4 in HadGEM3-GA4, resulting in much more concentrated
changes in SO4 close to the source. The SO2 lifetime is around
1.8 times shorter in HadGEM3-GA4, associated with around 45 % higher wet
oxidation rates in this model. This difference is due in part to the
inclusion of an additional wet oxidation pathway in HadGEM3-GA4: whereas
GISS-E2 only includes wet oxidation of SO2 by H2O2 (around
730 kg(S) s-1 globally integrated), HadGEM3-GA4 includes wet oxidation
by both H2O2 and O3, each of which contribute similarly in
this model (around 540 and 520 kg(S) s-1, respectively).
Globally integrated, HadGEM3-GA4 and GISS-E2 simulate fairly similar
reductions in SO4 burden, at -0.070 and -0.076 Tg, respectively
(Table 2). This, combined with the more spread-out SO4 field in
GISS-E2, means that locally over eastern China HadGEM3-GA4 has a much more
intense reduction in SO4 burden, with 50 % of the global reduction
occurring over E. China in HadGEM3-GA4 (-0.035 Tg), compared with only
15 % (-0.011 Tg) in GISS-E2.
CESM1 includes the same oxidation pathways as HadGEM3-GA4, and in fact has a
slightly shorter SO2 lifetime still, and so the differences between
these two models have different origins. CESM1 in fact simulates almost
double the global change in SO4 burden as the other two models, with
-0.136 Tg. This means that although the SO4 reduction spreads further
from the source in CESM1 than in HadGEM3-GA4, CESM1 still has a similar
reduction to HadGEM3-GA4 locally over E. China as well (-0.039 Tg), which is
also evident in Fig. 3.
Given that HadGEM3-GA4 and GISS-E2 simulate a similar global reduction in
SO4, it is surprising that there is such a difference in the magnitude
of their climate responses. Also, given that CESM1 simulates a much larger
global reduction in SO4 than the other two models, it is similarly
surprising that this model does not have the largest response. A partial
explanation may be found by inspecting the change in total aerosol optical
depth (AOD), which is a more direct measure of the radiative properties of
the aerosol column. Unfortunately, the AOD diagnosed by the models is not
completely equivalent: HadGEM3-GA4 diagnosed clear-sky AOD, which is done in
this model by calculating the relative humidity in the cloud-free portion of
each grid box, and using this adjusted humidity to calculate the size of the
aerosol droplets in the optical depth calculation (Bellouin et al., 2007).
However, CESM1 uses the unadjusted grid box relative humidity to calculate
the droplet sizes in its optical depth calculation, thereby providing an
all-sky AOD calculation (Neale et al., 2012). GISS-E2 diagnosed both all-sky
and clear-sky AOD, and unless otherwise stated we compare here its clear-sky
AOD, as it is more directly comparable with satellite retrievals of AOD
(Kahn et al., 2010; Levy et al., 2013). Figure 4 shows these changes in AOD
at the 550 nm wavelength for the three models.
Change in AOD at 550 nm due to removing SO2 emissions from
China for (a) GISS-E2, (b) CESM1, and
(c) HadGEM3-GA4. For HadGEM3-GA4 and GISS-E2, AOD is calculated for
clear-sky conditions, whereas for CESM1, AOD is calculated for all-sky
conditions, which will generally result in higher values within each
simulation. Differences are calculated as the perturbation simulation minus
the control simulation, averaged over 150 years. The plot region focuses on
Asia as changes outside of this domain were minimal.
![]()
As with the radiative flux change, there is a large range in the magnitude
of local AOD reduction, with E. China AOD reductions ranging from 0.047 in
GISS-E2 to 0.287 in HadGEM3-GA4, i.e. about 6 times higher (Table 2). This
is comparable to the approximately 6-fold range of SW flux change found over
this region. Globally averaged, HadGEM3-GA4 also has a much larger AOD
reduction than GISS-E2: 0.0042 compared with an almost negligible 0.0003 in
GISS-E2, despite these two models having a similar change in global SO4
burden. The much lower globally averaged value in GISS is partly due to a
very small but quite zonally uniform compensating increase in nitrate
aerosol (absent in HadGEM3-GA4), which occurs across the Northern Hemisphere
(not shown). However, the global change in sulfate-only optical depth in
GISS-E2 is still only half that in HadGEM3-GA4 (not shown), and locally
around eastern China we find the increase in nitrate optical depth in
GISS-E2 is at least an order of magnitude smaller than the decrease in
sulfate optical depth, and so nitrate compensation does not substantially
contribute to the discrepancy in local AOD changes. We therefore still find
that HadGEM3-GA4 simulates a considerably larger change in sulfate optical
depth per unit change in SO4 burden at both global and local scales.
Having the largest change in AOD per unit change in aerosol burden (Table 2)
appears to be key to this model simulating the largest climate response.
Comparing the clear-sky and all-sky AOD for GISS-E2 (for which we have both
diagnostics), we find that the simulated reduction in E. China all-sky AOD
(-0.183) is much larger than the reduction in clear-sky AOD (-0.047). We
cannot be sure that the same would apply to CESM1, but it suggests that we
might expect the all-sky values we have for CESM1 to be larger than the
equivalent clear-sky values. Given this, it is all the more surprising to
find reductions of all-sky AOD in CESM1 for the E. China region of -0.076
and for the global mean of -0.0013 (Table 2), which lie in between the
clear-sky values of GISS-E2 and HadGEM3-GA4, even though CESM1 had the
largest change in SO4 burden both locally and globally.
The AOD changes per unit burden change are summarised in Table 2, and it is
clear that there is a large diversity between the models. The possible
contributors to diversity in the AOD per unit burden are extensive, and a
full analysis of them is beyond the scope of this paper. Host model effects,
such as different cloud climatologies and radiative transfer schemes, are
one likely contributor. Stier et al. (2013) suggests that one-third of total
diversity originates there. Relative humidity, which drives water uptake
(hygroscopic growth), is also diverse among models. For example, Pan et al. (2015) found that over India, boundary-layer RH is the main source of
diversity. At the more basic level, assumed composition and hygroscopic
growth curves also often differ between models – in this case, the aerosol
scheme used for HadGEM3-GA4 assumes that all sulfate is in the form of
ammonium sulfate, whereas CESM1 and GISS-E2 both assume a mixture of
ammonium sulfate and sulfuric acid, and additionally all three models use
different sources for their hygroscopic growth parameterisations (Bellouin
et al., 2011; Liu et al., 2012; Koch et al., 2011; and references therein).
The changes in SW radiative flux and the final climate response seem to
correlate with the changes in AOD much better than with the changes in
SO4 burden for HadGEM3-GA4 and GISS-E2, where over China there is a
6-fold difference both in AOD and in SW flux change between these two models.
For CESM1, the all-sky AOD change over E. China is about 1.6 times larger
than the clear-sky change in GISS-E2 (Table 2). If we used instead all-sky
AOD from GISS-E2 (not shown in Table 2), we find that the AOD change over E.
China is more than 2 times smaller in CESM1 than in GISS-E2. However, the
change in TOA SW over the same region is about 4.7 times larger in CESM1, and
so it seems that unlike the discrepancies between HadGEM3-GA4 and GISS-E2,
differences in the AOD response cannot explain the difference in the
magnitudes of radiative flux change between CESM1 and GISS-E2 (see
Sect. 4.2).
Validation of aerosol fields
To get an indication of whether the model-simulated AODs are realistic in
the region of interest, we compare the mean AOD from each model's control
run with station observations in Asia from the AERONET radiometer network
(Holben et al., 2001). Because of the limited number of stations in the
region with long data records, we use the observed AOD at 500 nm from all
AERONET stations able to provide an annual mean estimate for at least 1
year, averaged over all years for which an annual mean was available
(generally ranging between 1998 and 2014 in different stations), and compare
this with the annual mean AODs at 550 nm from the three models, masked to
the locations of the AERONET stations (Fig. S2). Focusing on
stations in E. China (eight in total), we find that HadGEM3-GA4 compares
best with AERONET in this region with a mean station bias of -22 %, whilst
both GISS-E2 and CESM1 appear to be biased lower in this part of the world,
with mean biases of -56 and -60 %, respectively.
We also calculate the area-weighted mean AOD as observed by the MODIS and
MISR satellite instruments. The MODIS (Moderate Resolution Imaging
Spectroradiometer) instrument is flown on both the Terra and Aqua
satellites, whilst MISR (Multi-angle Imaging Spectroradiometer) is flown on
Terra. For MODIS, we use the Collection 6 combined Deep Blue plus Dark Target
monthly AOD product at 550 nm (Levy et al., 2013) (available from
https://ladsweb.nascom.nasa.gov/), averaged from both Terra and Aqua
satellites, and take a 10-year average from 2003 to 2012 (2003 being the
earliest year that data from both satellites are available). For MISR, we use
the best estimate monthly AOD product (Kahn et al., 2010) version 31
(available from https://eosweb.larc.nasa.gov/) at 550 nm over a 15-year
averaging period from 2000–2014 (2000 being the earliest year MISR data are
available). For MODIS, the area-weighted E. China mean AOD is 0.51, whilst
for MISR it is 0.31, so regionally there is a considerable uncertainty in
these observations. HadGEM3-GA4 overestimates the AOD compared with both
instruments (though only slightly so when compared to MODIS), with a
regional average AOD of 0.58, whilst GISS-E2 and CESM1 underestimate it with
regionally averaged AODs of 0.23 for both models. Globally, the two
instruments are in better agreement, with MODIS giving a global average AOD
of 0.17 and MISR giving 0.15. Again, HadGEM3-GA4 overestimates global AOD
compared with both instruments (0.22) whilst GISS-E2 and CESM1 both
underestimate it (0.13 and 0.12). Given that CESM1 diagnosed all-sky AOD,
whereas satellite retrievals are only possible for clear-sky conditions, the
underestimate for this model is likely greater than these numbers suggest.
There is considerable variation in the observations as well as the models.
Globally averaged GISS-E2 seems to compare best against MODIS and MISR,
though tentatively HadGEM3-GA4 seems to have the more accurate AOD over
China, comparing best regionally with both AERONET and MODIS, though poorer
against MISR. This suggests that the more concentrated sulfate aerosol burden
and larger AOD reduction simulated by HadGEM3-GA4 over this region may be
more realistic. We note though that since these observations only measure
total AOD and cannot differentiate by species, the comparison cannot show for
certain that the higher sulfate optical depth specifically is more realistic
in HadGEM3-GA4. The AOD reduction over E. China due to removing Chinese
SO2 represents 50 % of the climatological total AOD in HadGEM3-GA4
over the region, compared with 34 % in CESM1 and only 20 % in
GISS-E2. Even if the total AOD in HadGEM3-GA4 is more realistic, there is
still considerable variation between the models as to what fraction of that
total AOD is due to Chinese SO2 emissions. This is illustrated further
for the two extreme cases, HadGEM3-GA4 and GISS-E2, in Fig. S3, which shows
that the fraction of climatological AOD made up by sulfate is consistently
higher across the E. China region in HadGEM3-GA4 than in GISS-E2. However,
the total non-sulfate AOD is fairly similar across the region in these two
models (Fig. S4), indicating that the stark difference in the fractional
contribution of sulfate comes primarily from HadGEM3-GA4 simulating much
greater sulfate AOD alone. Given that GISS-E2 appeared to underestimate total
AOD regionally, this would then suggest that either the higher sulfate AOD in
HadGEM3-GA4 is more realistic, or both models underestimate the non-sulfate
AOD.
To try and better constrain whether the sulfate content (rather than total
aerosol) is correct, we therefore also compared against the surface sulfate
observations conducted in China reported by Zhang et al. (2012) for
2006–2007 (Fig. S5). However, all three models performed extremely poorly,
with HadGEM3-GA4 having a mean bias of -71 % (-66 % if urban
stations are excluded), CESM1 a mean bias of -71 % (unchanged if urban
stations are excluded), and GISS-E2 a mean bias of -87 % (-86 %
when urban stations are excluded). Although HadGEM3-GA4 and CESM1 are
slightly closer to the observed values, the large underestimation despite the
relatively good column AOD in HadGEM3-GA4 suggests that at least this model
has difficulty representing the vertical profile of sulfate aerosol, and so
this comparison with surface measurements may not be particularly useful in
constraining the sulfate optical depth or column-integrated burdens. Large
underestimations of surface sulfate concentration over E. China have been
reported previously for two other models, MIROC and NICAM, by Goto et
al. (2015), suggesting that this is a problem common to many
current-generation models.
It seems plausible that any differences in the processing of sulfate aerosol
would apply to all polluted regions, and not just over China. Indeed, the
spatial pattern of the climatological sulfate burden over other major
emission regions such as the United States shows a similar characteristic to
that over China, with HadGEM3-GA4 and CESM1 having higher burdens close to
the emission source regions, whilst GISS-E2 has a more diffuse sulfate
distribution (Fig. S6). With this in mind, we also validated
the models against surface sulfate observations from the Interagency
Monitoring of Protected Visual Environments (IMPROVE) network in the United
States (Malm et al., 1994), a data set with a far more extensive record than
the Zhang et al. (2012) data set for China. Taking 61 IMPROVE stations which
have data for at least 6 years between 1995 and 2005, we find that over
the United States all three models are in fact biased high, with GISS-E2
performing relatively better with a mean bias of +10.1 %, but
HadGEM3-GA4 somewhat worse with +44.5 %, and CESM1 worse still with
+86 %. However, in the case of HadGEM3-GA4 we find that the larger mean
bias comes mainly from an incorrect spatial distribution (Fig. S7), with a high bias on the west coast but a pronounced low bias in surface
SO4 on the east coast. Consequently, this comparison would suggest that
HadGEM3-GA4 in fact has too little sulfate around the principal US emission
regions on the east coast, even though over that area HadGEM3-GA4 actually
has a larger column-integrated sulfate burden (Fig. S6) and a
larger AOD (not shown) than GISS-E2, as was the case for China. This
suggests that HadGEM3-GA4 again fails to capture the vertical profile of
sulfate, underestimating surface concentrations over this region despite
having a high column-integrated burden.
Validation with surface observations therefore seems insufficient to
constrain which model performs better with regard to the more
climate-relevant, column-integrated quantities of sulfate burden and AOD.
Returning to Asia, we therefore also tried evaluating the models against
column sulfur dioxide observations. We use the gridded, monthly mean Level 3 observations from the Ozone Monitoring Instrument (OMI) (Krotkov et al.,
2008) (available from http://disc.sci.gsfc.nasa.gov/Aura) which is flown on
the Aura satellite, averaged over 8 years from 2005 to 2012. Over the E.
China region, the mean OMI SO2 is 0.153 Dobson units (DU), and all three
models appear to overestimate this substantially, with very similar regional
mean SO2 columns of 0.282 DU for HadGEM3-GA4, 0.272 DU for GISS-E2, and
0.259 DU for CESM1. Spatially, all three models also appear to have more
diffuse SO2 fields than the OMI observations in which, by contrast, the
SO2 burden seems much more localised around sources (Fig. S8). This may be partly due to the coarse resolution of the models compared
with the 0.25∘ satellite product, and the fact that weaker
SO2 concentrations further from the source locations may fall below the
detection threshold of the satellite instrument. It could alternatively
indicate that the lifetimes for SO2 may be too long in all three
models, or transport processes too efficient. The surprisingly similar
column SO2 burdens in all three models suggests that, at least on
regional scales, column SO2 may not constrain SO4 burden that
well.
An alternative observational measure which to an extent reflects a
column-integrated quantity is the deposition rate, and for the two extreme
cases of HadGEM3-GA4 and GISS-E2 we therefore also try comparing against
observations of sulfate wet deposition. We use the 3-year mean wet deposition
data from 2000 to 2002 described in Vet et al. (2014) and provided by the
World Data Centre for Precipitation Chemistry (http://wdcpc.org), taking the
six stations located in China. We exclude the station in Guizhou province in
southern China where HadGEM3-GA4 has a bias of +590 % and GISS-E2 a
bias of +253 %. This station only provided data for 1 year and was
flagged as having a high uncertainty in the Vet et al. (2014) data set; it is
also located in a mountainous region and so it could equally be that the
models cannot resolve the specific local conditions. Removing this station
from the analysis, we find for the remaining five stations in China that
HadGEM3-GA4 performs well with a mean bias of -3.9 %, compared with
-64.8 % for GISS-E2. This gives an indication that HadGEM3-GA4 has more
realistic sulfate deposition directly over China (though the sample size is
very small), and supports the earlier findings from the comparison against
AERONET and MODIS. If we broaden the analysis to include all stations
described as being broadly in Asia – an additional 32 stations – then the
mean bias for HadGEM3-GA4 is worsened (-41.8 %), whilst the bias in
GISS-E2 is slightly improved (-54.1 %). HadGEM3-GA4 still performs
better over the Asian region as a whole, though less dramatically (Fig. S9).
This overall picture seems consistent with that of the other observational
measures looked at here, although it should be noted that wet deposition
rates are dependent not just on the column sulfate burden but also on the
amount and distribution of precipitation, and so biases in wet deposition
could also be due to incorrect precipitation distribution rather than
sulfate.
Still, overall HadGEM3-GA4 seems to compare slightly better than GISS-E2 and
CESM1 regionally over E. China against observations of total AOD, and better
than GISS-E2 regionally against surface sulfate as well as wet deposition
observations, although globally and over other regions this model is not
necessarily found to compare better in general. This might hint that at least
over China, HadGEM3-GA4 has more realistic sulfate optical depth, although
none of these comparisons is very conclusive in that respect. Moreover, given
that none of these observational measures directly constrains the sulfate
radiative forcing, there is also no guarantee that performance with respect
to these variables will necessarily translate to a more realistic climate
response (see also Sect. 4.3).
Differences in cloud effects
Sulfate aerosol exerts indirect radiative effects by modifying cloud
properties. The strength of these indirect effects is highly uncertain (e.g.
Boucher et al., 2013) and differs substantially between the models, having
been shown to contribute substantially to inter-model variation in
historical aerosol forcing (Wilcox et al., 2015). Differences in the
underlying climatologies of the models, particularly with regard to cloud
distributions, could also be important. For instance, the radiative effect
of sulfate aerosol is modulated by the reflectivity of the underlying
surface in the radiation scheme (Chýlek and Coakley, 1974; Chand et al.,
2009), which may often be a cloud top. The low contrast with a highly
reflective cloud surface means that sulfate aerosol above a cloud top will
have a reduced direct radiative forcing. Blocking of radiation by clouds
will also reduce the direct radiative effects of any aerosols within or
below them (e.g. Keil and Haywood, 2003). Additionally, aerosol indirect
effects can saturate in regions with a high level of background aerosol
(e.g. Verheggen et al., 2007; Carslaw et al., 2013), meaning that the
potential for indirect radiative forcing can also vary with the location of
clouds. On top of diversity in indirect effects, and in the climatological
distribution of clouds, different dynamical changes in cloud cover could
also alter the all-sky flux.
In our case, the good correspondence between higher (clear-sky) AOD change
in HadGEM3-GA4 and higher (all-sky) SW flux change in this model might
suggest that the cloud effects are not the root cause of the larger
radiative response in this model. However, the origin of this good
correspondence in fact appears to be somewhat dependent on how clouds modify
the radiative effects of sulfate aerosol.
For the extreme cases of HadGEM3-GA4 and GISS-E2, comparing the changes in
clear-sky TOA SW flux with the all-sky TOA SW flux anomalies (Table 2 and
Fig. S10) reveals that for clear-sky conditions there is in
fact a much smaller regional discrepancy between these two models: over the
E. China region GISS-E2 has a 4.1 W m-2 clear-sky SW flux change, whereas
HadGEM3-GA4 has a 5.1 W m-2 flux change. HadGEM3-GA4 still has the
larger radiative change, but nowhere near the 6-fold difference that is seen
in the all-sky flux (Sect. 3, and Table 2). This much-reduced difference
between GISS-E2 and HadGEM3-GA4 in the clear-sky compared with the all-sky
anomaly is hard to apportion quantitatively though, because compared with
the clear-sky change, the all-sky response incorporates all the contributing
factors described above: the additional radiative forcing due to aerosol
indirect effects, the screening of direct radiative effects due to clouds
blocking radiation and providing a high albedo background, and also any
dynamical changes in cloud cover.
In this case, GISS-E2 is found to simulate a small increase in cloudiness in
E. China due to dynamical changes when sulfate is removed (Fig. S11a).
Combined with the screening effect of clouds, this appears to almost
completely offset the direct forcing of reduced SO4, and results in a
far smaller all-sky flux change than clear-sky flux change over E. China
(0.9 W m-2 all-sky flux compared with 4.1 W m-2 clear-sky flux).
HadGEM3-GA4, by contrast, has very little difference between all-sky and
clear-sky flux changes (5.3 and 5.1 W m-2, respectively; Table 2). The
changes in cloud amount over E. China are somewhat more mixed (Fig. S11c),
although area-averaged, the overall cloud change is a small decrease, which
should enhance the all-sky flux change. However, spatially as well as in
magnitude, the HadGEM3-GA4 all-sky flux change is exceptionally similar to the
clear-sky flux change, and does not resemble the pattern of cloud changes
(comparing Figs. S10e, f, and S11c), which suggests that changes in aerosol
radiative effects are larger than the effect of the small cloud cover
changes, and still dominate the all-sky flux changes. Therefore, the very
similar regional all-sky and clear-sky SW flux changes in HadGEM3-GA4 imply
that unlike in GISS-E2, aerosol indirect effects in HadGEM3-GA4 probably
roughly compensated for the presence of clouds reducing the direct effect, so
that the change in all-sky combined direct and indirect forcing is similar to
the change in clear-sky direct forcing when sulfate is removed.
The picture is different again for CESM1. Comparing the clear-sky and
all-sky TOA SW flux changes for this model (Fig. S10c, d), we
find that regionally, the clear-sky changes are much smaller than the
all-sky flux changes – in fact, over China the clear-sky SW flux changes in
CESM1 are considerably smaller in magnitude than the clear-sky flux changes
in GISS-E2 (comparing Fig. S10a, c). Averaged over the E.
China region, the clear-sky flux change in CESM1 is only 2.2 W m-2,
compared with the 4.1 W m-2 clear-sky change in GISS-E2 (Table 2).
However, whereas in GISS-E2 the all-sky SW flux change (0.9 W m-2) was
then more than 4 times smaller than this clear-sky flux change, in CESM1
the all-sky SW flux change is instead almost 2 times larger than the
clear-sky flux change: 4.2 W m-2 regionally averaged.
This is partly again due to cloud changes – in this case CESM1
predominantly has reductions in cloud amount over E. China (Fig. S11b), which
will have the effect of increasing the all-sky radiative flux change relative
to the clear-sky changes. However, as with HadGEM3-GA4, these regional cloud
reductions in CESM1 do not match up spatially with the maximum changes in
all-sky SW flux seen in Figs. 1b and S10d. Instead, the maximum changes in
the all-sky SW flux change closely match the clear-sky SW flux changes
(Fig. S10c), which in turn correspond very well with the reduction in AOD
(Fig. 4b). Both all-sky and clear-sky SW flux changes are maximum around
where the AOD reduction is maximum, and in this location the all-sky flux
change is still substantially greater than the clear-sky change. This implies
that in CESM1 a large aerosol indirect effect, and/or effect of clouds
increasing aerosol particle size through hygroscopic growth, overall
amplifies the radiative effect of aerosols considerably in cloudy conditions,
resulting in the much greater regional change in all-sky flux when aerosol is
removed.
Between these three models, then, the way that clouds modify the radiative
balance is a major source of diversity over the E. China region in the
response to removing SO2 emissions from China. In GISS-E2, the
inclusion of clouds greatly reduces the radiative effect of a change in
sulfate aerosol. In HadGEM3-GA4, the effect of including clouds is small,
and does not change the clear-sky forcing substantially. Finally, in CESM1,
including clouds considerably amplifies an otherwise weak clear-sky
radiative flux change. We note though that clear-sky diagnostics will be
influenced by choices within the models of how aerosol water uptake is
determined under the artificial assumption of clear-sky conditions. The
all-sky SW flux change, which drives the final climate response, is
regionally still the most directly comparable quantity, reflecting the total
radiative effect of the aerosol change in the different models.
Differences in aerosol forcing efficiency
An additional source of discrepancy between the models lies in differences
in the aerosol radiative forcing efficiency – the direct forcing that
results from a given aerosol optical depth or burden (e.g. Samset et al.,
2013). A previous model intercomparison looking at radiative forcing as part
of the AeroCom Phase II study found that, on a global scale, there was a
large variation in the radiative forcing due to aerosol-radiation
interactions per unit AOD between different participating models (Myhre et
al., 2013a). As a result, whether a model simulates AOD changes correctly,
for instance, may not even particularly constrain the resultant direct forcing,
let alone the indirect forcing or eventual climate response.
Globally averaged, the changes in radiative flux and AOD are too small in
our experiments to calculate an accurate ratio, but instead we calculate
here a regional radiative efficiency by taking the change in clear-sky SW
flux over the 100–120∘ E, 20–40∘ N E. China region, and dividing by the AOD
change over the same region (Table 2). This is not directly comparable with
previous studies like Myhre et al. (2013a), as we use a regionally averaged
number instead of a globally averaged number, and for the numerator we use the change
in clear-sky TOA SW flux as the best available measure of aerosol direct
radiative effect, rather than the direct radiative forcing diagnosed either
from double radiation calls or simulations with fixed meteorology.
Consequently, we use this metric here mainly to qualitatively highlight
differences between the models.
As noted in Sect. 4.1 and 4.2, over the eastern China region, HadGEM3-GA4 has
a 6-fold larger mean AOD reduction (-0.29) compared with GISS-E2
(-0.047), but only slightly larger clear-sky SW change (5.1 W m-2
compared with 4.1 W m-2). As a result, the regional radiative
efficiency for HadGEM3-GA4 is much smaller than that of GISS-E2:
-17.6 W m-2 compared with -87.2 W m-2 per unit AOD change
(Table 2). If instead of AOD we normalise by the change in sulfate burden
integrated over the same region, we find a similar relationship: HadGEM3-GA4
has a smaller regional mean change in clear-sky SW flux per Tg sulfate than
GISS-E2 (-145 W m-2 Tg-1 compared with
-373 W m-2 Tg-1). Proportionally though, the discrepancy is not
as great when normalising by change in sulfate burden, due to the much larger
AOD per unit mass of sulfate simulated in HadGEM3-GA4. Curiously Myhre et
al. (2013a) reported results that were qualitatively the inverse of what we
show here, finding that the atmospheric component of GISS ModelE has a
smaller sulfate radiative forcing than that of HadGEM2 (HadGEM3's
predecessor, with a very similar aerosol scheme) when normalised by AOD,
although still larger when normalised by column-integrated sulfate burden.
The reason for the discrepancy is not clear, though the aforementioned fact
that we calculate our numbers for a specific region means that there may be
important local factors. The sulfate-specific forcing efficiencies in Myhre
et al. (2013a) are calculated
relative to all-sky direct radiative effect, and so local differences in
vertical profiles and cloud screening may therefore change the relationship
– however, they also evaluated clear-sky forcing normalised by AOD for all
aerosol species combined, and again found HadGEM2 to be higher than GISS
ModelE.
CESM1 seems to sit in the middle of the range, with a regional radiative
efficiency of -28.4 W m-2 per unit AOD change (Table 2) – though
again with the caveat that for CESM1, the AOD is an all-sky quantity, whereas
the values given for HadGEM3-GA4 and GISS-E2 (-17.6 and
-87.2 W m-2, respectively) were calculated using clear-sky AOD.
GISS-E2 provided both clear-sky and all-sky AOD diagnostics, and using
instead the all-sky AOD change from GISS-E2 gives a smaller value of
-22.4 W m-2 per unit AOD, which suggests that when compared
like-for-like, CESM1 (with -28.4 W m-2) may in fact have the greater
regional radiative efficiency. More directly comparable between all three
models is the regional clear-sky flux change normalised by regional change in
sulfate burden, which for CESM1 is -55.4 W m-2 Tg-1. This is
considerably lower than either HadGEM3-GA4 or GISS-E2, and indicates that
despite having at least average radiative efficiency per unit AOD, the very
small translation of sulfate burden to AOD in CESM1 is a dominant factor
which prevents this model from simulating a larger SW flux change and climate
response than it already does. As noted in the previous section though, this
small clear-sky flux change per unit sulfate change is compensated by a large
indirect effect as well as favourable regional cloud changes, meaning that
the all-sky flux change per unit AOD is by far the largest in CESM1
(Table 2), and the all-sky flux change per sulfate burden change is then
average in CESM1 (not shown, but readily calculated from Table 2). Similarly,
the exceptional reduction in aerosol radiative effects due to clouds in
GISS-E2 means that its all-sky flux change per unit AOD is almost exactly the
same as that of HadGEM3-GA4 (Table 2), despite the clear-sky regional
radiative efficiency being so much larger.
The Myhre et al. (2013a) AeroCom intercomparison found that globally, the
atmospheric component of CESM1 (CAM5.1) had a much higher sulfate radiative
efficiency than the atmosphere-only version of GISS-E2. In their case, they
found CAM5.1 to have approximately 2.25 times higher all-sky direct radiative
forcing per unit AOD than GISS-E2. However, the study also found that,
globally, the atmospheric component of HadGEM2 had a slightly larger forcing
efficiency than CAM5.1 both for sulfate (all-sky) and all aerosols
(clear-sky), unlike the somewhat smaller regional efficiencies found here for
HadGEM3-GA4 compared with CESM1. Given that our regional values from GISS-E2
and HadGEM3-GA4 also seem to conflict qualitatively with the global values
from the AeroCom study, this would suggest that either the global comparison
is not relevant on regional scales, or else the radiative efficiency is very
sensitive to changes in model configuration and version.
Differences in climate sensitivity
So far we have discussed mainly factors which influence the translation of a
change in aerosol precursor emissions to a radiative heating, and these
varied strongly between the models. There is a final step in arriving at the
climate response, which is the translation of a given radiative heating into
a surface temperature change. The climate sensitivity – the amount of
warming simulated per unit radiative forcing – is also well known to vary
considerably between models, globally (Flato et al., 2013) and regionally
(Voulgarakis and Shindell, 2010), and this will additionally impact the
strength of the final response. Climate sensitivity is typically estimated
from a 2× or 4 × global CO2 simulation, giving a large response and a
large forcing from which to calculate the ratio. For GISS-E2, a climate
sensitivity value of 0.6 K (W m-2)-1 was found in the IPCC AR5
report from a 4 × CO2 simulation (Flato et al., 2013) using the
regression method of Gregory et al. (2004) to estimate radiative forcing.
For CESM1, a value of 1.1 K (W m-2)-1 is obtained from values from
a 2 × CO2 simulation (Meehl et al., 2013), noting that in this case the
radiative forcing was calculated using the stratospheric adjustment method
(Hansen et al., 2005). For HadGEM3-GA4, we use a 100-year 2 × CO2
simulation that was performed separately as part of the Precipitation Driver
Response Model Intercomparison Project (Samset et al., 2016), which gives a
value of 1.1 K (W m-2)-1 based on the Gregory method.
While CESM1 and HadGEM3-GA4 both have very similar climate sensitivities, we
see that GISS-E2 has a particularly small sensitivity – in fact, the
smallest value of all the CMIP5 models reported in the AR5 report (Flato et
al., 2013). This presumably compounds the fact that GISS-E2 simulates the
smallest SW flux change of the three models, ensuring that the resulting
surface temperature response is comparatively smaller still. Differences in
climate sensitivity do not seem to explain any of the variation in the
magnitude of the response between CESM1 and HadGEM3-GA4, at least based on
these values. However, it is worth noting that the climate sensitivity
values that we report are derived from global CO2 forcings, whereas in
our case we are looking at the translation of a very regional forcing into a
global response. It is not trivial that the global-mean temperature response
to a regionally localised forcing is a function only of the resulting
globally averaged forcing, and in particular it may be that different models
are more or less sensitive to forcings in specific regions. Unfortunately, we
know of no study that has calculated climate sensitivity to regional
forcings in single- or multi-model frameworks. Shindell (2012) calculated
climate sensitivities to forcings imposed in different latitudinal bands for
the GISS-E2 model, finding that there is considerable variation relative to
the global climate sensitivity. In that study, estimates of the response to
forcings at different latitudes in three other global climate models, based
on the GISS-E2 sensitivities, are found to largely agree to within ±20 %
with the full simulations, however, suggesting that regional
sensitivities (relative to a model's global sensitivity) may not vary that
much between models.