The paper constitutes Part 2 of a study performing a first systematic
inter-model comparison of the atmospheric responses to stratospheric aerosol
injection (SAI) at various single latitudes in the tropics, as simulated by
three state-of-the-art Earth system models – CESM2-WACCM6, UKESM1.0, and
GISS-E2.1-G. Building on Part 1 (Visioni et al., 2023) we demonstrate
the role of biases in the climatological circulation and specific aspects of
the model microphysics in driving the inter-model differences in the
simulated sulfate distributions. We then characterize the simulated changes
in stratospheric and free-tropospheric temperatures, ozone, water vapor, and
large-scale circulation, elucidating the role of the above aspects in
the surface SAI responses discussed in Part 1.
We show that the differences in the aerosol spatial distribution can be
explained by the significantly faster shallow branches of the Brewer–Dobson
circulation in CESM2, a relatively isolated tropical pipe and older tropical
age of air in UKESM, and smaller aerosol sizes and relatively stronger
horizontal mixing (thus very young stratospheric age of air) in the two GISS
versions used. We also find a large spread in the magnitudes of the tropical
lower-stratospheric warming amongst the models, driven by microphysical,
chemical, and dynamical differences. These lead to large differences in
stratospheric water vapor responses, with significant increases in
stratospheric water vapor under SAI in CESM2 and GISS that were largely not
reproduced in UKESM. For ozone, good agreement was found in the tropical
stratosphere amongst the models with more complex microphysics, with lower
stratospheric ozone changes consistent with the SAI-induced modulation of
the large-scale circulation and the resulting changes in transport. In
contrast, we find a large inter-model spread in the Antarctic ozone
responses that can largely be explained by the differences in the simulated
latitudinal distributions of aerosols as well as the degree of
implementation of heterogeneous halogen chemistry on sulfate in the models.
The use of GISS runs with bulk microphysics demonstrates the importance of
more detailed treatment of aerosol processes, with contrastingly different
stratospheric SAI responses to the models using the two-moment aerosol
treatment; however, some problems in halogen chemistry in GISS are also
identified that require further attention. Overall, our results contribute
to an increased understanding of the underlying physical mechanisms as well
as identifying and narrowing the uncertainty in model projections of climate
impacts from SAI.
Introduction
Observations of the cooling produced by past explosive volcanic eruptions
(Robock, 2000) have prompted numerous investigations into the feasibility
and risks of artificially injecting SO2 in the lower stratosphere in
order to partially counteract the effect of rising greenhouse gases
(Crutzen, 2006); this is usually termed stratospheric aerosol injection
(SAI) or solar geoengineering. The formation of sulfate aerosols after
SO2 oxidation prevents a portion of the incoming sunlight from reaching
the troposphere, thus cooling the surface. However, this is not the only
effect that would be produced from sulfate aerosols in the Earth system
(e.g., Visioni et al., 2021). The localized heating of the lower stratosphere
would modify the local chemical composition of the atmosphere and
alter the large-scale atmospheric circulation. These side effects can thus
modify the direct response to SAI, further modulating the radiative balance
as well as impacting regional climate and ecosystems. Yet, past
investigations of these most often focused on one single model. For
instance, Ferraro et al. (2015) analyzed the impacts of a tropical sulfate
injection on stratospheric dynamics in the University of Reading
Intermediate General Circulation Model (IGCM). Tilmes et al. (2017, 2018b)
and Richter et al. (2017) analyzed the atmospheric response to injections at
different latitudes and/or altitudes in the Community Earth System Model 1
(CESM1) with the Whole Atmosphere Community Climate Model (WACCM) as its
atmospheric component (CESM1-WACCM) in order to understand the underlying
mechanisms in the climate response.
However, models are themselves imperfect, and hence model intercomparisons
are useful in understanding uncertainties in climate responses to SAI. Such
uncertainties arise from many sources, including the efficiency of SO2
to aerosol conversion, the extent to which sulfate aerosols will be
transported away from the injection locations by the large-scale circulation
and mixing processes, the removal of aerosols from the atmosphere
altogether, and the efficiency of the direct impacts of aerosols on the
radiative balance, as well as from uncertainties in any indirect impacts, for
instance on atmospheric circulations and clouds. Simulations with a number
of different models can thus help represent the uncertainty in real-world
climate responses to a hypothetical SAI deployment, whilst identifying and
attributing certain characteristics of individual model responses to
particular aspects of model design or features can help narrow this
real-world uncertainty. Several of such intercomparison studies were carried
out as a part of the Geoengineering Model Intercomparison Project (GeoMIP,
Kravitz et al., 2011, 2015). However, the implementation of the experimental
protocols often differed between the participating climate models, which
hindered confident attribution of drivers of the inter-model spread. Pitari
et al. (2014) found large differences in the simulated stratospheric ozone
responses in the GeoMIP G4 experiment, which were partially related to
different profiles of the latitudinal distribution of sulfate aerosols used
in various models. Tilmes et al. (2022) examined the impacts of SAI on the
future evolution of stratospheric ozone using the three Earth system models
(ESMs) with interactive chemistry participating in the GeoMIP G6 experiment.
However, only two out of the three models included an interactive aerosol
scheme, while the third model prescribed the aerosol optical depth (AOD) from
the G4SSA experiment (Tilmes et al., 2015). Moreover, even for these two
models, both the location of injections (0∘ at 25 km for CESM and
10∘ S–10∘ N at 18 km for UKESM) and the yearly amounts of
SO2 injected (the injection rates were modified to achieve an amount of
cooling corresponding to the model difference between the Shared
Socioeconomic Pathway, SSP, 5–8.5 and 2–4.5 scenarios; Meinshausen et al.,
2020) varied considerably.
Finally, rather than injecting fixed amounts of SO2, some recent
studies examined the climate response to SAI in CESM1-WACCM using a feedback
algorithm that injected varied amounts of SO2 at four off-equatorial
locations in order to control not only the global mean surface temperature
but also the Equator-to-pole and interhemispheric temperature gradients
(e.g., Tilmes et al., 2018a). This approach has been shown to result in a
more uniform surface cooling and thus fewer side effects than an
equatorial injection strategy (Kravitz et al., 2019). Providing the basis
for replicating this approach in other climate models is one of the goals of
the experiments described here, as detailed in the companion paper (Visioni
et al., 2023; hereafter Part 1). Such a multi-model comparison will provide
insights into the climatic impacts of a more complex, time-varying SAI
strategy aimed at reducing some of the surface side effects of a potential
deployment. However, understanding inter-model differences in the simulated
responses in such experiments could present its own challenges due to likely
different magnitudes and distributions of the simulated SO2 injections
amongst the models and thus differences in the stratospheric responses and
their contribution to the surface climate changes.
The study presented here avoids these issues by examining a set of carefully
designed sensitivity experiments with fixed point injections of SO2 in
the lower stratosphere at single latitudes in the tropics. We use three
comprehensive ESMs that were previously used to inform a range of past and
future climate studies and that participated in the CMIP6 intercomparison,
i.e., CESM version 2 with WACCM6 as its atmospheric component
(CESM2-WACCM6), the United Kingdom Earth System Model (UKESM1.0), and the NASA
Goddard Institute for Space Studies model (GISS-E2.1-G). By keeping the
simulated SO2 emissions in the model experiments as similar as
possible, we aim to robustly identify the similarities and differences in
the simulated responses amongst the models, as well as identify and
attribute the drivers of these differences. Such an exercise aims to improve
our understanding of the sources of uncertainty in climate model projections
of SAI and identify areas of future model improvements. In addition,
as mentioned above, characterizing model responses to fixed SO2
injections and origins of inter-model differences will also help our
understanding of the simulated responses in more complex scenarios of SAI
deployment employing a feedback algorithm.
Part 1 analyzed the simulated aerosol fields and their relationships to the
surface temperature and precipitation responses in these experiments. Here
we build on these findings by elucidating the contribution of biases in
model transport to the simulated sulfate distributions, in addition to further
illustrating aspects of aerosol microphysics. We then characterize the
simulated changes in stratospheric and free-tropospheric temperatures,
ozone, water vapor, and large-scale circulation, elucidating the role of
the above aspects in the surface SAI responses discussed in Part 1. We also
identify commonalities and differences in the simulated responses, and by
doing so we elucidate whether the main findings of Tilmes et al. (2018b) and
Richter et al. (2017) utilizing CESM1-WACCM can be reproduced in a
multi-model framework. Section 2 summarizes the model simulations performed.
In Sect. 3.1 we focus on the simulated sulfate aerosol distributions, and we
evaluate and discuss the role of biases in model transport in contributing
to the inter-model spread. We then discuss the associated SAI impacts on
stratospheric temperatures (Sect. 3.2), ozone, large-scale residual
circulation (Sect. 3.3), water vapor (Sect. 3.4), and zonal winds
(Sect. 3.5). Finally, Sect. 4 summarizes and discusses the main results.
MethodsExperimental description
A detailed description of the ESMs used and the simulations performed can be
found in Part 1. Briefly, we use CESM2-WACCM6 (Gettelman et al., 2019;
Danabasoglu et al., 2020, thereafter CESM2), UKESM1.0 (Sellar et al., 2019;
Archibald et al., 2020; thereafter UKESM), and GISS-E2.1-G (Kelley et al.,
2020). Both CESM2 and UKESM use modal two-moment aerosol microphysical
schemes that account for the evolution of both aerosol mass and size
distribution. For GISS-E2.1-G, we use two versions differing only in the
aerosol scheme, i.e., the two-moment MATRIX (Multiconfiguration Aerosol
TRacker of mIXing state) scheme with Aitken and accumulation aerosol modes
(Bauer et al., 2008, 2020; hereafter GISS-MATRIX) and the bulk aerosol OMA
(One-Moment Aerosol) scheme (Koch et al., 2006; hereafter GISS-OMA). The use
of three ESMs allows us to better constrain the uncertainty in the climate
response to SAI. The inclusion of simpler GISS-OMA simulations in
addition to GISS-MATRIX can be used as a benchmark that allows us to test
the importance of detailed representation of aerosol processes for the
simulated response. It is also more representative of models used in early
geoengineering studies (e.g., Robock et al., 2008; Pitari et al., 2014). With
each of the models, we perform five simulations under the CMIP6 SSP2–4.5
emission scenario (Meinshausen et al., 2020) with constant single-point
injections of 12 Tg SO2 yr-1 at 22 km altitude and either 30∘ S, 15∘ S, 0∘, 15∘ N, or 30∘ N
latitude.
The injections are initialized in January 2035 from the first member of the
SSP2–4.5 simulation for each model and extend through December 2044 (i.e., 10 years in total). Since the focus of this paper is on the simulated
atmospheric responses, we diagnose the responses using the last 8 years of
each simulation, i.e., slightly longer time period than in Part 1, in order to
reduce the contribution from interannual variability to the diagnosed
signals.
Diagnostic of sulfate surface aerosol density
In Sect. 3.1 we discuss sulfate surface aerosol density (SAD) simulated
in each run. Apart from providing a measure of the simulated sulfate burden,
the diagnostic is particularly relevant for ozone chemistry, as it is
directly related to the rates of heterogeneous reactions occurring on
aerosol surfaces. Since the SAD diagnostic was not available for the two
GISS model versions, we calculate SAD offline for all models from the
monthly mean sulfate mass mixing ratios (χi), number
concentrations (Ni), and number densities (ni) using the formula in
Eq. (1), with the mean radius (ri) in each of the aerosol modes
calculated as given by Eq. (2): (σi denotes the prescribed
geometric standard deviation for each mode and ρ the sulfate aerosol
density).
1SAD=∑4πri2niexp2ln2σi2ri3=34πexp(4.5ln2σi)⋅χiρNi
Note that the resulting offline-calculated SAD responses are somewhat
smaller in CESM2 and UKESM than the values obtained from the corresponding
online SAD diagnostics (Fig. S1 in the Supplement), but for consistency we compare
the offline-calculated values for all models.
Yearly mean changes in surface area density [10-7 cm2 cm-3] averaged over the last 8 years of the simulations
compared to the same period in the SSP2–4.5 run for CESM (column 1), UKESM
(column 2), GISS-MATRIX (column 3), and GISS-OMA (column 4). The SAD values
were calculated offline using monthly mean diagnostics; see text for
details. Stippling indicates regions where the response is not statistically
significant (here taken as smaller than ±2 standard errors of the
difference in means).
ResultsStratospheric sulfate aerosols and the role of transport
Figure 1 shows simulated changes in sulfate surface aerosol densities. For
off-equatorial injections, aerosols are primarily dispersed across the
hemisphere they were injected in, with little cross-over to the opposite
hemisphere. Such limited dispersion into the opposite hemisphere to that of
injection was also noted in simulations of explosive volcanic eruptions,
although for higher injection rates at higher altitudes more significant
cross-equatorial transport was noted (e.g., Jones et al., 2017). CESM2
simulates the largest sulfate SAD in the high latitudes out of the three
ESMs with two-moment aerosol microphysics; these highest SAD values also
correspond to the largest total sulfate loads in the middle and high latitudes
as shown in Part 1. This can be explained by the significantly faster shallow
branch of the Brewer–Dobson circulation (BDC) simulated in CESM2 in both
hemispheres compared to the other models (Fig. 2). The fast shallow branches
of the BDC, found in the lower stratosphere (below ∼30 hPa)
and active year-round, facilitate transport of sulfate from the injection
locations in the tropics to higher latitudes, resulting in significantly
elevated middle- and high-latitude sulfate loadings.
Climatological mass streamfunction of the residual circulation
averaged over 2035–2064 in the control SSP2–4.5 for each of the four
models.
Normalized age of air (AoA; years) at (a) 10∘ S–10∘ N, (b) 30∘ S–60∘ N, (c) 30∘ N–60∘ S, and (d) 21 km diagnosed from the UKESM (orange) and
GISS-OMA (light blue) CMIP6 historical integration. Note that the GISS-OMA
simulation uses prescribed sea surface temperatures (SSTs) and sea ice (unlike the SAI GISS-OMA simulations,
which include an interactive ocean module). Black lines show the corresponding
AoA derived from the MIPAS SF6 satellite observations (black; Stiller et
al., 2020). Both model and observed AoA values were averaged over the 7-year period
from May 2005 to April 2012 inclusive. Both model and observed AoA values were
normalized to be zero at the tropical tropopause by subtracting the values
calculated in each case for the tropical tropopause layer (here approximated
as a mean over 25∘ S–25∘ N, 16–17 km). CESM is not
included as no AoA diagnostic is available from its historical CMIP6
simulations.
Climatological transformed vertical residual velocity averaged
over 2035–2064 and 10∘ S–10∘ N in the control SSP2–4.5
for each of the four models. Error bars denote ±2 standard error of
the mean.
In the simulations with equatorial injections (third row in Fig. 1), the
highest SAD values are found in the tropics, with the largest variability across the
models in regions poleward of 30∘ latitude. UKESM shows the greatest
confinement of sulfate inside the tropical pipe out of the different models;
the stronger confinement in UKESM is also visible for other injection
locations. Comparison of the UKESM age of air (AoA) with MIPAS satellite
observations (Stiller et al., 2020) shows significantly older model AoA in
the tropics than observed (Fig. 3); this indicates a slow rate of transport
out of the tropics and is thus consistent with the high fraction of sulfate
aerosols found at low latitudes. In addition, UKESM simulates the fastest
vertical velocities in the tropics at the altitudes where sulfate aerosols
are injected (∼22 km, Fig. 4). This slows down the
gravitational settling of aerosols, thereby adding to their tropical
confinement. The effect is further amplified by the relatively smaller
aerosol sizes in UKESM than in CESM2 (as indicated by the locally higher SAD
in Fig. 1; see also Fig. 5 in Part 1), with a maximum effective radius of
∼0.3µm in UKESM compared to ∼0.6µm
in CESM2.
Both GISS-MATRIX and GISS-OMA show a relatively deeper aerosol layer. This
is partially because of the much smaller size of sulfate aerosols, resulting
in slower gravitational settling and increased lifetime of sulfate aerosols
in the stratosphere. As shown in Part 1, the maximum effective radius reaches
∼0.25µm in GISS-MATRIX (compared to ∼0.6µm in CESM2) but the value drops substantially near the injection
location, corresponding to locally very small aerosol particles and very high
SAD values (Fig. 1). The lack of an explicit aerosol nucleation model in
CESM2, wherein nucleating aerosols are directly transferred to the Aitken
mode, may help explain why such a drop is not present in CESM2 (see also
Weisenstein et al., 2022). In addition, the GISS model shows anomalously young
AoA throughout the depth of the tropical pipe when compared to observations
(Fig. 3) but relatively slow resolved upwelling (Fig. 4), thus suggesting
additional diffusion processes operating in the model that enhance transport
of air (and aerosols) to higher altitudes by dispersion and/or mixing.
Importantly, GISS-OMA shows substantially (i.e., a few times) larger sulfate
SAD than any other models with two-moment aerosol microphysics (Fig. 1,
rightmost column). The bulk aerosol scheme restricts the mean size of
aerosols (with the imposed dry radius of 0.15 µm), thereby preventing
their growth by coagulation and leading to the formation of a large number
of relatively small aerosols. These high sulfate concentrations are also
readily transported to higher latitudes since smaller particles have lower
gravitational settling velocities and increased atmospheric lifetimes. In
addition, horizontal mixing in GISS is likely very strong – this can be
inferred from the anomalously young model AoA simulated throughout the
stratosphere (Fig. 3
Figure 3 includes the result of a historical
GISS-OMA experiment with prescribed observed sea surface temperatures and
sea ice. While the presence of an interactive ocean component in the SAI
GISS-OMA integrations discussed in this work would have some impact on the
resulting AoA simulated by the model, no analogous historical run was
available for the model with interactive ocean.
). In general, AoA shows
combined effects of transport from the residual circulation and mixing
(Garny et al., 2014). Since the residual circulation simulated in the two
GISS models is generally comparable to that in UKESM and much weaker than in
CESM2 (Fig. 2), the relatively younger AoA in GISS is mostly likely the
result of much stronger mixing. This is further supported by the weaker
climatological zonal winds simulated in GISS in the stratosphere in both
hemispheres (Fig. S2) and thus weaker potential vorticity gradients that
control mixing efficiency (Abalos and de la Cámara, 2020). Neither of the two GISS
models is able to simulate the Quasi-Biennial Oscillation (QBO; see also
Sect. 3.5), which is known to be an important factor in controlling the
confinement of aerosols inside the tropical latitudes (Niemeier and Schmidt,
2017; Visioni et al., 2018).
Shading: yearly mean changes in temperature [K] averaged over the
last 8 years of the simulations compared to the same period in the SSP2–4.5
run for CESM (column 1), UKESM (column 2), GISS-MATRIX (column 3), and
GISS-OMA (column 4). Contours show the values in the control SSP2–4.5 run
for reference. Stippling as in Fig. 1.
Temperature
The absorption of incoming solar and outgoing terrestrial radiation by
sulfate aerosols increases temperatures in the tropical lower stratosphere
in the three models with two-moment aerosol microphysics (Fig. 5). These
changes in stratospheric temperatures, whilst far away from the surface, can
drive a dynamical response (Sect. 3.3 and 3.5) that alters stratospheric
composition (Sect. 3.3 and 3.4) and indirectly affects regional
surface climate (Part 1). In these simulations, while lower stratospheric
temperatures increase primarily in the tropics, both CESM2 and GISS-MATRIX
also show substantial temperature increases in the midlatitude lower
stratosphere in the hemisphere of injection. This is consistent with both
models showing significant aerosol levels outside the tropics (Sect. 3.1, Fig. 1; see also Part 1). In each model, the tropical lower
stratospheric warming is strongest for the equatorial injection case (Fig. 5; e.g., up to ∼4–6 K for off-equatorial injections and up to
∼8 K for the equatorial injection in CESM2).
The magnitude of the lower stratospheric warming is approximately a factor
of 2 smaller in UKESM than in CESM2. This can be partially understood by
the smaller average size of sulfate aerosols (as can be inferred from SAD
values in Fig. 1, see also Part 1), which are less effective at absorbing
terrestrial radiation (Laakso et al., 2022), and by the smaller total
sulfate aerosol load (Part 1). However, differences in the radiative codes
are likely still an important contributing factor (e.g., Boucher et al.,
1998; DeAngelis et al., 2015; Niemeier et al., 2020). For the equatorial
injection case in UKESM, the strong confinement of sulfate aerosols inside the
tropical pipe, as well as their uplift via the somewhat faster tropical velocities
(Fig. 3), leads to a greater vertical extent of the lower stratospheric
warming.
In GISS-MATRIX, the lower-stratospheric warming is comparable to CESM2 in
terms of the maximum amplitude but much more vertically spread for all
injection locations. As discussed in Sect. 3.1, this is related to a
greater depth of the aerosol layer in GISS-MATRIX, resulting from smaller
sulfate particle sizes and thus slower gravitational settling, a weaker shallow
branch of the BDC, and likely stronger diffusion. In addition, the associated
acceleration of tropical upwelling in the stratosphere from aerosol heating,
which acts to increase adiabatic cooling and thus opposes the diabatic
heating from aerosol absorption, is much smaller in GISS-MATRIX than in
CESM2 (Fig. 6).
Shading: yearly mean changes in transformed vertical velocity
[mm s-1] averaged over the last 8 years of the simulations compared to the
same period in the SSP2–4.5 run for CESM (column 1), UKESM (column 2),
GISS-MATRIX (column 3), and GISS-OMA (column 4). Positive values indicate
anomalous upwelling. Contours show the vertical velocities in the control
SSP2–4.5 run for reference (note that for GISS-MATRIX and GISS-OMA only the
0 contour is plotted for clarity). Stippling as in Fig. 1.
In contrast to the three models with two-moment microphysics, no lower-stratospheric warming is simulated in GISS-OMA (Fig. 5, rightmost column).
The use of a bulk aerosol scheme with fixed aerosol sizes results in much
smaller particles than for the models with more complex aerosol schemes; the
small aerosols are not as effective in absorbing radiation. In addition, the
simulations are associated with substantial reductions in lower-stratospheric ozone (Sect. 3.3), which otherwise contributes to the
shortwave heating there (Richter et al., 2017); these thus effectively
offset any warming tendency from aerosol absorption.
As expected, all models simulate tropospheric cooling as a result of the
reduction of the incoming solar radiation from SAI. In each model, the
strongest cooling is found in the hemisphere of injection, consistent with
the near-surface temperature changes discussed in Part 1. In each case the
cooling maximizes in the tropical upper troposphere; this is consistent with
changes produced by the strong radiative feedback from water vapor, the
tropospheric concentrations of which decrease when the surface is cooled
(Sect. 3.4). As the result, surface temperature signals tend to be
amplified in the upper troposphere; this is also the case under global
warming from rising greenhouse gases (Sherwood et al., 2010; Steiner et al.,
2020) and predicted by the moist adiabatic lapse rate theory (Stone and
Carlson, 1979). However, here the magnitude of the tropospheric cooling
varies substantially between the models, with the two GISS models showing the
strongest responses, consistent with the near-surface temperature changes
discussed in Part 1. A large difference in the upper tropospheric temperature
responses amongst models has also been observed under climate change
simulations (Minschwaner et al., 2006).
Ozone and large-scale circulationStratospheric ozone changes in models with two-moment microphysics
Changes in tropospheric and stratospheric temperatures, and hence the
large-scale transport as a result of SAI, drive changes in stratospheric
ozone. The absorption of incoming solar radiation by stratospheric ozone
plays a crucial role in shielding the Earth's surface from harmful UV
radiation, thus having direct impacts on human health and ecosystems. In
addition, the absorption of outgoing terrestrial radiation by ozone in the
troposphere and lower stratosphere contributes to the greenhouse effect.
Therefore, any ozone changes there can modulate the direct radiative
response from aerosol reflection, impacting the surface temperature
responses discussed in Part 1.
Shading: yearly mean changes in ozone [%] averaged over the
last 8 years of the simulations compared to the same period in the SSP2–4.5
run for CESM (column 1), UKESM (column 2), GISS-MATRIX (column 3), and
GISS-OMA (column 4). Contours show the ozone mixing ratios [ppmv] in the
control SSP2–4.5 run for reference. Stippling as in Fig. 1.
CESM2, UKESM, and GISS-MATRIX all show increased ozone in the tropical lower
stratosphere at ∼70 hPa (Fig. 7). The response results from
local deceleration of upwelling in the tropical troposphere (Fig. 6)
brought about by the increase in static stability associated with heating in
the lower stratosphere and cooling in the troposphere (Fig. 5). This
deceleration of tropospheric upwelling slows down the transport of
ozone-poor tropospheric air into the lower stratosphere, thus increasing
ozone in the region. The deceleration of tropical upwelling also reduces
precipitation (e.g., Simpson et al., 2019), thereby further modulating the
precipitation responses discussed in Part 1. For ozone, the differences in
the magnitudes of ozone responses amongst the three models with two-moment
aerosol microphysics are commensurate with the differences in the lower-stratospheric temperature responses, with larger ozone increases in
GISS-MATRIX and CESM2 and smaller in UKESM. We find a strong correlation
between the lower-stratospheric ozone and temperature responses across the
models (Fig. 8). This demonstrates that whilst differences remain in the
magnitudes of these responses amongst different models, such uncertainties
are coherently correlated within each model.
Correlation between yearly mean 30∘ S–30∘ N
changes in temperature at 50 hPa and ozone at 70 hPa between each of the SAI
experiments and SSP2–4.5. Colors indicate the models, and the dashed black
line shows a linear fit to all results for the four models together.
In the middle stratosphere, CESM2, UKESM, and GISS-MATRIX all show local
ozone reductions near the latitude of SAI, as well as further ozone
increases higher up (CESM and UKESM only). These changes can also be
explained by the associated changes in the large-scale residual circulation,
which redistributes ozone to and from its photochemical production region
(i.e., tropical middle stratosphere, where ozone mixing ratios maximize).
The acceleration of tropical upwelling in the stratosphere near the latitude
of injection brings more air with lower ozone mixing ratios from the lower
to middle stratosphere (and, conversely, more air with higher ozone mixing
ratios from the middle to upper stratosphere). Note that although the
simulated tropical and midlatitude ozone responses are primarily
dynamically driven (by changes in ozone transport), any associated changes
in chemistry (Tilmes et al., 2018b, 2021) do contribute to the simulated responses
(Tilmes et al., 2022). In contrast to CESM2 and UKESM, GISS-MATRIX shows a
small ozone decrease of a few percent in the upper stratosphere; the
response is consistent with the elevated ClO in the region (Fig. S3) and
suggests problems in the chemistry scheme in GISS that merit further
attention by the modeling teams.
Yearly mean (YM; left) and October mean (OCT; right) changes in
total column ozone [DU] averaged over the last 8 years of the simulations
compared to the same period in the SSP2–4.5 run for CESM (red), UKESM
(orange), GISS-MATRIX (dark blue), and GISS-OMA (light blue). Error bars
indicate ±2 standard errors of the difference in means.
When ozone responses are integrated vertically over the whole atmosphere
(Fig. 9), we find reasonably good agreement between the tropical column
ozone responses between CESM2 and UKESM. Both models show local decreases in
column ozone near the injection latitude of the order of ∼10 DU and small but statistically significant increases in tropical ozone
columns of a few DU further away. In general, these tropical ozone changes,
whilst small in absolute terms, can play a relatively important role given
the much lower climatological column ozone values found in the tropics than
at higher latitudes. A similar pattern of tropical column O3 responses
was also found in GISS-MATRIX, although the column O3 changes there
tend to be more negative, presumably because of the contribution of the
reductions in upper-stratospheric ozone (the origins of which in this model,
as discussed above, are not fully understood and suggest problems in the
chemistry scheme).
Antarctic stratosphere
Previous decades have seen significant reductions of ozone in the Southern
Hemisphere (SH) high latitudes brought about by accelerated heterogenous
halogen reactions inside the Antarctic polar vortex as a result of
anthropogenic emissions of ozone-depleting substances. Therefore, future
evolution and recovery of Antarctic ozone continue to be the focus of
significant scientific and political interest (WMO, 2018). CESM2 shows a
significant ozone decrease in the lower stratosphere as a result of SAI,
in particular for the SH injections (up to ∼35 % ozone
decrease in the polar lowermost stratosphere, Fig. 7, or up to
∼50 DU vertically averaged, Fig. 9). These yearly mean
changes are dominated by the response during austral spring (Figs. S5 and
9), i.e., when the impact of heterogenous halogen activation on polar
ozone maximizes. As discussed in Sect. 3.1, CESM2 has a very fast shallow
BDC, which effectively transports sulfate aerosols from the SO2
injection locations in the tropics to higher latitudes. The presence of
increased surface area densities in polar regions (Fig. 1) facilitates
heterogenous halogen reactions inside the cold polar vortex that convert
halogen species from their reservoir forms into active species like ClO or
BrO (Figs. S4 and S5); these then enhance catalytic ozone
destruction during austral spring.
A similar decrease in the SH lower stratospheric ozone is not reproduced in
UKESM in the yearly mean (Fig. 7). The model also does not show any
significant Antarctic ozone depletion during austral spring (Figs. 9 and
S5). First, UKESM shows greater confinement of sulfate aerosols inside the
tropical pipe and weaker shallow BDC than CESM2 (Sect. 3.1). Therefore, the
model simulates much lower aerosol concentrations at high latitudes (Fig. 1). Second, UKESM does not include the important heterogenous ClONO2+ HCl reaction on sulfate aerosols or any heterogenous bromine chemistry
on sulfate aerosols. Both effects significantly reduce the concentrations of
activated halogens simulated in the lower stratosphere under SAI (Figs. S3
and S4), which thus limits the amount of catalytic ozone depletion in
the Antarctic lower stratosphere.
We note that, in general, the magnitude of the chemical ozone response
depends on the background stratospheric halogen concentrations, which are
projected to decrease over the 21st century, and thus any
halogen-catalyzed ozone reduction from SAI would be lower in later parts of
the century (Tilmes et al., 2021). Similar considerations will also apply to
the impacts of SAI on the Arctic ozone; however, the short length of the
simulations (i.e., 8 years analyzed) does not allow us to assess this
confidently, as any changes in ozone in the NH high latitudes will be
dominated by natural interannual variability.
In comparison, the two-moment version of GISS also show decreases in
Antarctic ozone in the lower stratosphere coinciding with local increases in
ClO (Fig. S3). However, the coupled O3–ClO response is qualitatively
and quantitatively similar for all injection cases (despite large
differences in the high-latitude sulfate levels), suggesting that factors
other than the latitudinal distribution of sulfate and thus of anomalous
heterogenous halogen activation on aerosol surfaces could be an important
but erroneous contributing driver in the model.
Yearly mean changes in specific humidity [%] averaged over
the last 8 years of the simulations compared to the same period in the
SSP2–4.5 run for CESM (column 1), UKESM (column 2), GISS-MATRIX (column 3),
and GISS-OMA (column 4). Contours indicate the corresponding values in the
SSP2–4.5 experiment in the units of parts per million by volume (ppmv) for reference. Stippling as in Fig. 1.
Tropospheric ozone changes
In addition to acting as a greenhouse gas, in the troposphere ozone
constitutes an atmospheric pollutant, adversely impacting human health (e.g.,
Eastham et al., 2018), crop production (e.g., Xia et al., 2017), and ecosystems
(e.g., Zarnetske et al., 2021). Here we find significant reductions of
tropospheric ozone (up to ∼15 %) in GISS-MATRIX throughout
most of the troposphere. The response is likely related to the significantly
stronger tropospheric cooling (Fig. 5) and thus a stronger reduction in
tropospheric water vapor (Fig. 10), which plays an important role in the
tropospheric ozone budget. The reduction in tropospheric ozone in the
tropics is not reproduced in either CESM2 or UKESM, likely because of the
smaller level of tropospheric cooling in these models. In addition, the
CESM2 version used includes a chemistry scheme tailored for middle
atmosphere studies and thus does not include comprehensive tropospheric
chemistry; this factor thus likely played a role in determining the
tropospheric ozone response simulated in the model. The CESM2 model does,
however, show significant reductions in tropospheric ozone in the SH middle and
high latitudes, in particular for the SH injections. The response is likely
driven by the Antarctic lower-stratospheric ozone depletion (Sect. 3.3.2)
and the resulting reduction in stratosphere-to-troposphere ozone transport
(e.g., Xia et al., 2017).
Response in GISS model with bulk aerosol microphysics
In stark contrast to the ozone responses in the three models with more
detailed aerosol microphysics, the bulk version of GISS simulates
substantial reductions of lower stratospheric ozone throughout the globe
(locally up to 40 %–60 %, Fig. 7). In the tropics, the GISS-OMA results
constitute an outlier in the previously identified relationship between
lower-stratospheric temperature and ozone responses (Fig. 8). The different
ozone response in GISS-OMA is likely related to the number and size of
sulfate aerosols produced from the SO2 injections, i.e., the very
high concentrations of very small aerosols. Since smaller aerosols have
proportionally larger surface areas than their larger counterparts, this
leads to much higher sulfate SAD compared to the two-moment version of GISS
(Fig. 1). In addition, smaller aerosols have longer lifetimes and can thus
be transported rapidly by the presumed strong mixing in the model (Sect. 3.1). All of these factors lead to significantly elevated sulfate SAD
simulated in GISS-OMA throughout the lower stratosphere. These could in
principle enhance heterogenous halogen activation and thus explain the
substantial ozone depletion found in these runs. We note, however, that the
simulations do not show elevated active halogen concentrations in the lower
stratosphere (the simulated lower-stratospheric ClO and BrOx levels in fact
decrease under SAI in GISS-OMA, Figs. S3 and S4) but only spurious increases
in ClO at higher altitudes, highlighting problems in the chemistry scheme in
GISS that merit future attention.
Stratospheric water vapor
Figure 10 shows the associated changes in water vapor. As with ozone, the
absorption of outgoing terrestrial radiation by water vapor in the lower
stratosphere and the troposphere contributes to the greenhouse effect. Thus,
any SAI-induced changes in it can further modulate the radiative balance and
surface temperature responses discussed in Part 1. In addition, the
photolysis of stratospheric water vapor (SWV) constitutes the main source
of reactive HOx in the stratosphere, which acts to reduce stratospheric ozone
levels and thereby further modulate the ozone responses discussed in Sect. 3.3.
We find large differences in the SWV responses amongst the models, ranging
from +40 % to -15 % in the tropical lower stratosphere for the equatorial
injections. SWV increases in CESM2 and GISS-MATRIX for all injection
locations, consistent with the increase in cold-point tropopause
temperatures associated with the warming of the tropical lower stratosphere. The
increase in SWV is strongest in the simulations with equatorial injections
(up to 40 % and 25 % in the tropical lower stratosphere for CESM2 and
GISS-MATRIX, respectively), consistent with the strongest lower-stratospheric warming (Sect. 3.2, Fig. 5). However, while the increase in
SWV in CESM2 is simulated throughout the entire stratosphere, the
GISS-MATRIX simulations show negative SWV changes in the upper stratosphere,
especially at high latitudes; the latter may be related to its problems with
halogen chemistry there (see Sect. 3.3).
The significant increase in SWV under SAI is not reproduced in UKESM, which
does not show substantial changes in SWV in any of the experiments except
for the equatorial injection (up to 15 % in the tropical lower
stratosphere). In fact, instead of an increase in SWV seen in CESM2 and
GISS-MATRIX, there is a very small decrease in SWV for simulations with
injections at 30∘ S and 30∘ N. This may be related to
anomalously high climatological SWV in that model (Archibald et al., 2020).
The increase in SWV under SAI is also not reproduced in GISS-OMA, which
shows decreases in SWV (up to ∼-20 % in the high-latitude
upper stratosphere) consistent with the absence of warming in the lower
stratosphere, similar to the GISS response reported in Pitari et al. (2014).
Nonetheless, in all simulations carried out with the four models water
vapor decreases in the troposphere as a result of surface and
tropospheric cooling, with the largest changes found for GISS-OMA.
We note that apart from the differences in the SAI responses amongst the
models, we also find large differences in the climatological SWV values
(contours in Fig. 10). These differences are consistent with the large
inter-model spread in SWV reported amongst all CMIP6 models (Keeble et al.,
2021).
As in Fig. 5 but for zonal wind changes [m s-1]. Stippling as
in Fig. 1.
Zonal winds
Figure 11 shows changes in zonal winds resulting from SAI. Note that both
CESM2 and UKESM include an internally generated QBO, whilst the two GISS
versions do not. The equatorial SO2 injection in CESM2 and UKESM leads
to a westerly response in the tropical lower stratosphere and an easterly
response above. This pattern corresponds to a locking of the QBO in a
permanent westerly phase (Fig. S6; see also, e.g., Aquila et al., 2014; Jones
et al., 2022) and arises because of the acceleration of equatorial upwelling
under SAI (Fig. 6), inhibiting the downward propagation of the westerly QBO
shear (Franke et al., 2021). A similar response was also found for UKESM in
the G6 GeoMIP experiment (Jones et al., 2022). In general, the variability
in equatorial zonal winds has been linked to variability in tropical
tropospheric convection, subtropical and midlatitude tropospheric jets, and modes of high-latitude variability, e.g., the North Atlantic Oscillation
(Anstey et al., 2022). Therefore, any SAI impacts on the QBO, including its
locking in a permanent westerly phase under equatorial injections, have
potential to impact the circulation in regions outside the equatorial
stratosphere, although longer simulations would be needed to confidently
diagnose such teleconnections. In any case, the QBO locking is not
reproduced for the 15 and 30∘ injections in CESM2 and
UKESM (Fig. S6); this is because the acceleration of tropical upwelling
occurs off-equatorial near the injection latitudes (Fig. 6). The results
illustrate that off-equatorial injections successfully avoid QBO locking, in
agreement with Kravitz et al. (2019). Since the two GISS models do not
include any representation of the QBO, the zonal wind is always easterly in
the entire tropical stratosphere (Fig. S6).
In the extratropical stratosphere, CESM2, UKESM, and GISS-MATRIX all
simulate strengthening of stratospheric jets in both hemispheres, consistent
with geostrophic balance and the strengthening of the horizontal temperature
gradient brought about from heating in the lower stratosphere. The results
suggest impacts on the modes of high-latitude variability, including the
Northern Annular Mode and Southern Annular Mode (NAM and SAM, respectively), which would
influence regional middle- and high-latitude surface temperature and
precipitation patterns during dynamically active seasons (e.g., boreal winter in
the NH). However, here the derived responses are substantially affected by
interannual variability due to the short length of the integrations; this
prevents confident analysis of any inter-model differences or the dependence
of the stratospheric polar vortex response on the latitude of injection.
Unlike the three models with two-moment microphysics, GISS-OMA shows
weakening of zonal winds in the lower stratosphere and the free troposphere
below, consistent with the tropical tropospheric cooling simulated in the
model.
In the troposphere, however, i.e., where the interannual variability is
lower, all models suggest qualitatively consistent impacts on the
tropospheric jets, which are important for modulating midlatitude weather
patterns. In particular, the off-equatorial injection cases show an
equatorward shift of the tropospheric jet in the hemisphere of injection and
an opposite sign response in the other hemisphere. In the case of equatorial
injections, tropospheric jets weaken in both hemispheres. The qualitative
agreement between GISS-OMA, which does not show a warming in the tropical
lower stratosphere, and the other three models illustrates the role of
changes in meridional temperature gradients within the troposphere for the simulated changes in tropospheric jets.
Summary
This paper constitutes Part 2 of the study performing a first systematic
inter-model comparison of atmospheric responses to equatorial and
off-equatorial stratospheric SO2 injections. We used three
comprehensive Earth system models – CESM2-WACCM6, UKESM1.0, and GISS-E2.1-G.
For the latter we used two model versions, one with two-moment and one with
bulk aerosol microphysics, to illustrate the importance of a detailed
treatment of aerosol processes. We performed a set of five sensitivity
experiments with constant point injections of 12 Tg SO2 yr-1 in the lower
stratosphere at either 30∘ S, 15∘ S, 0∘,
15∘ N, or 30∘ N.
Building on Part 1 of this study, we demonstrated how inter-model
differences in the simulated sulfate aerosol fields relate to biases in the
climatological circulation and specific aspects of the model microphysics.
In particular, CESM2 was found to simulate larger concentrations of
sulfate aerosols in the high latitudes than the other two models with
two-moment microphysics. This could be understood in light of the
significantly faster climatological shallow branch of the Brewer–Dobson
circulation in CESM2, as well as a relatively isolated tropical pipe and older
tropical age of air in UKESM. The two GISS versions also simulated elevated
sulfate surface area densities at higher latitudes, consistent with smaller
aerosol particles and relatively stronger horizontal mixing (thus very young
stratospheric age of air).
We then characterized the simulated changes in stratospheric and
free-tropospheric temperatures, ozone, water vapor, and large-scale
circulation, elucidating the role of the above aspects in the surface SAI
responses discussed in Part 1. A large spread in the magnitudes of the
tropical lower-stratospheric warming was found amongst the models, and these
could partially be attributed to the differences in aerosol distributions
and their sizes. Whilst differences in radiative parameterizations certainly
also played an important role, those are harder to isolate and would require
further sensitivity experiments (e.g., with fixed size distribution and
specified chemistry). For each model, the strongest lower-stratospheric
warming was found for the equatorial injection case, in agreement with
previous studies (e.g., Kravitz et al., 2019). Regarding stratospheric
ozone, all models with two-moment aerosol microphysics agreed in the
tropical and subtropical regions and suggested local decreases in total
column ozone of ∼10 DU near the latitude of injection as well as
small increases in the tropical–subtropical regions further away. The ozone
responses there could be explained by the associated changes in upwelling
and the large-scale Brewer–Dobson circulation and were thus commensurate in
magnitude with the associated changes in lower-stratospheric temperatures
amongst the models.
In contrast to the relative agreement amongst the models regarding ozone
responses at low latitudes, we found a large inter-model spread in the
Antarctic ozone responses; these could largely be explained by the
differences in the simulated latitudinal distributions of sulfate noted
above as well as the degree of implementation of heterogeneous halogen
chemistry on sulfate amongst the models. In particular, CESM2 showed
elevated surface area densities in the high latitudes; these facilitated
heterogenous halogen reactions that accelerated catalytic springtime ozone
destruction in the Antarctic stratosphere. A similar response was not
simulated in UKESM, consistent with a stronger confinement of sulfate
aerosols inside the tropical pipe as well as an incomplete treatment of
heterogenous halogen chemistry on sulfate in the model version used.
For stratospheric water vapor, the study found substantial spread of the
model responses to sulfate injections, ranging from -15 % to +40 % in
the tropical lower stratosphere. CESM2 and GISS-MATRIX both showed
significant increases in stratospheric water vapor consistent with the
increases in the tropical cold-point temperatures. The response was not
reproduced in UKESM, which only showed a substantial (up to ∼15 %) increase in stratospheric water vapor in the equatorial injection
case, or in GISS-OMA, wherein stratospheric water vapor decreased as a
result of the absence of lower stratospheric warming.
In general, the sensitivity simulations with GISS using simple bulk aerosol
microphysics illustrate the importance of a more detailed treatment of
aerosol processes. In particular, the simulations showed very high sulfate
surface area densities as a result of the very small aerosol sizes; these
were in turn associated with changes in stratospheric ozone (including
substantial reductions in the lower stratosphere of up to ∼40 %–60 %), temperatures, water vapor, and zonal winds that contrasted strongly
with the models using two-moment aerosol microphysics. While problems in the
halogen chemistry were identified in GISS that require further assessment by
the modeling teams, the results point towards the importance of detailed
treatment of aerosol microphysics, including resolving the complex
relationships between the size distributions of aerosols and their physical
and chemical properties, for accurate modeling of climate impacts from SAI.
The importance of a detailed treatment of aerosol microphysics for the
simulated SAI responses was also recently highlighted by Laakso et al. (2022), wherein multiple injection scenarios were simulated with the same
model using two different microphysical schemes (in this case a modal and a
sectional scheme); the resulting differences in simulated aerosol size
distributions led to varying estimates of the overall radiative forcing
produced by SAI.
Yearly mean stratospheric responses simulated in each model in
each simulation expressed as (left) absolute changes and (right) changes
normalized with the corresponding global mean surface temperature decrease.
Rows top to bottom are for changes in tropical temperature at 50 hPa,
tropical ozone at 70 hPa, tropical water vapor at 70 hPa, and Antarctic
sulfate surface area densities at 150 hPa. Black points and whiskers denote
multi-model means of CESM2, UKESM, and GISS-MATRIX responses ±1
standard deviation.
We summarize the results of this work in Fig. 12, where we offer an overview
of relevant stratospheric changes due to SAI in two ways: in terms of both
absolute responses (left panels) and responses normalized by the associated
global mean changes in surface temperature (right panels) for each
experiment and model (similar to what is done in Part 1). Black points and
whiskers show the corresponding multi-model responses (note that GISS-OMA
results are excluded from the multi-model means). This allows us to
highlight some novel and interesting features.
As in Fig. 12 but for changes in global mean (top to bottom):
stratospheric aerosol optical depth (AOD), surface temperature, and
precipitation.
We find that the inter-model uncertainty in the absolute stratospheric
responses increases under off-equatorial injections compared to the
equatorial ones. This is in contrast to what one would expect based on the
uncertainty in AOD, which is largely driven by aerosol microphysical
properties and thus maximizes under equatorial injections (Fig. 13a); in that case,
a strong confinement of the aerosol cloud results in much larger differences
in the aerosol size distribution (Part 1, see also Visioni et al., 2018). In
the stratosphere, however, uncertainties are also significantly affected by
other drivers that, moreover, change depending on the injection location.
For equatorial injections, the main source of uncertainty is the differences
in tropical dynamics (and its interplay with aerosol microphysics). For
15∘ injections, uncertainties are larger because models
disagree over the strength and location of the tropical pipe edges. In that
case, UKESM results are characterized by the relatively large
confinement of the simulated aerosol cloud inside the tropical pipe,
resulting in a different response compared to models for which
15∘ lies outside it. Finally, for 30∘
injections uncertainties are driven mainly by differences in isentropic
mixing and the large-scale poleward transport.
In contrast, if the inter-model uncertainties are considered in terms of the
responses normalized with the associated global mean surface cooling, the
picture changes and the largest inter-model spread is observed for the
equatorial injections (right panels in Fig. 12). Overall, Fig. 12 highlights the fact
that determining how SAI-related uncertainties change with changing
injection location strongly depends on how these uncertainties are defined;
this is especially true if the efficacy of the produced global cooling is
considered the most relevant parameter and all other changes are defined
per a unit of it.
Conclusions and outlook
Our findings illustrate the importance of a detailed and adequate
representation of a range of microphysical, dynamical, and chemical processes
in models for accurately representing the potential impacts from SAI, both
directly in the stratosphere and lower down at the surface. By
demonstrating the role of biases in climatological circulation, our results
highlight the importance of not only model microphysics but also transport
processes for simulating the evolution of the aerosol plume. They also
highlight the large uncertainties in the representation of these processes
in current Earth system models and the need for realistic representation of
both aspects for determining the aerosol response and thus the potential
impacts of SAI on atmospheric radiative balance, composition, and
circulation. This thus suggests that a certain degree of caution is needed
in interpreting the results of studies conducted with single models and
that more work should be undertaken to improve the models and evaluate them
against the available observational data, e.g., from recent volcanic
eruptions to evaluate the model aerosol microphysics or using long-lived
tracers to evaluate model transport. For modeling intercomparisons,
understanding and attributing the reasons behind the inter-model spread
rather than focusing only on multi-model mean responses would help identify
which model responses are likely more trustworthy and representative of the
uncertainty in a hypothetical real-world SAI response and which arise from
spurious model features or problems with the code. This in turn would help
to identify the areas in need of potential future model development and
thus to narrow the uncertainties in future model projections of SAI
impacts. We have demonstrated here that our experiments provide a framework
to assess model skills in simulating SAI response and attribute some of the
sources of uncertainty and drivers of inter-model spread. As such, we would
like to suggest them as a possible test-bed experiment for GeoMIP to assess
model structural uncertainty, which would in turn help develop an
intercomparison of more comprehensive SAI strategies.
The results underscore the dependence of the dynamical response to SAI on
the latitude of SO2 injections. For example, CESM2 and UKESM both
showed that off-equatorial injections avoid locking of the QBO in a
perpetual westerly phase that was otherwise found for equatorial
injection. In the troposphere, all models suggested qualitatively similar
impacts on tropospheric jets, i.e., equatorward shift of the tropospheric
jet in the hemisphere of injection and an opposite sign response in the
other hemisphere. Given the short length of the simulations, detailed
analysis of the dynamical response in both the stratosphere and the
troposphere (e.g., impacts on the Northern and Southern Annular Modes) as well as
its dependence on the latitude of SAI alongside the underlying mechanisms is
beyond the scope of this study but will be explored in the future with
longer simulations (and multiple ensemble members).
Finally, our results further confirm the need to think of potential SAI
deployment considering multiple injection locations outside the Equator.
Injecting SO2 at the Equator gives rise to the lowest efficiency of
global cooling per AOD (Part 1) as a result of the confinement of sulfate
inside the tropical pipe (thereby reducing the AOD global coverage; Part 1
and Sect. 3.1 here) as well as leading to the largest increases in lower-stratospheric temperatures (Sect. 3.2). The latter leads to the strongest
increases in tropical lower-stratospheric water vapor (Sect. 3.4) and
ozone (Sect. 3.3), which act to partially offset the direct aerosol-induced
surface cooling and cause the strongest perturbations of
stratospheric and tropospheric circulation (Sect. 3.3 and 3.5), thereby
indirectly affecting the surface temperature and precipitation responses
discussed in detail in Part 1 and summarized in Fig. 13 here. In a
modeling framework, a feedback algorithm that adjusts injection rates
annually to achieve some specified climate goals (e.g., global mean surface
temperature and its interhemispheric and Equator-to-pole gradients, as in
Kravitz et al., 2019; Tilmes et al., 2018a) would compensate for some of the
differences in physical processes among the models (whether due to models
not matching observations in some respect or due to “true” uncertainty),
leading to more consistent large-scale surface climate outcomes (as also
discussed in detail in Part 1). While this uncertainty compensation may be
more representative of a hypothetical deployment of SAI, it would also
confound diagnosis of the mechanisms underlying inter-model differences by
creating a dependence between these underlying mechanisms and the amount and
distribution of injection rates across latitudes (see Fasullo and Richter,
2022, for an example). Our fixed-injection-rate intercomparison thus
constitutes an essential enabler to comparing simulations that incorporate
such a feedback algorithm.
Data availability
The output from model simulations is available at 10.7298/22cqmx33
(Visioni and Bednarz, 2022).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-23-687-2023-supplement.
Author contributions
EMB performed the analysis and wrote the paper. DV performed the CESM
simulations and helped with the discussion of the results and writing of the
paper. BK performed the GISS simulations. AJ performed the UKESM
simulations. All authors contributed to the discussion of the results and
writing of the paper.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Special issue statement
This article is part of the special issue “Resolving uncertainties in solar geoengineering through multi-model and large-ensemble simulations (ACP/ESD inter-journal SI)”. It is not associated with a conference.
Acknowledgements
The Community Earth System Model (CESM) project is supported primarily by
the National Science Foundation. We would like to acknowledge
high-performance computing support from Cheyenne (10.5065/D6RX99HX; Hart, 2017) provided by NCAR's Computational and Information Systems
Laboratory, sponsored by the National Science Foundation. The UKESM
simulations were carried out using MONSooN2, a collaborative
high-performance computing facility funded by the Met Office and the Natural
Environment Research Council.
Support for Ewa M. Bednarz, Daniele Visioni, and Douglas G. MacMartin was provided by the Atkinson Center for a
Sustainable Future at Cornell University through SilverLining's Safe Climate
Research Initiative and by the National Science Foundation through
agreement CBET-2038246 for Douglas G. MacMartin. Support for Ben Kravitz was provided in part by the
National Science Foundation through agreement CBET-1931641, the Indiana
University Environmental Resilience Institute, and the Prepared for
Environmental Change Grand Challenge initiative. Andy Jones and James M. Haywood were supported
by the Met Office Hadley Centre Climate Programme funded by BEIS and by
SilverLining through its Safe Climate Research Initiative. This material is
based upon work supported by the National Center for Atmospheric Research,
which is a major facility sponsored by the National Science Foundation under
cooperative agreement no. 1852977.
The authors thank Alan Robock and two other anonymous reviewers for helpful comments that improved the paper.
Financial support
This research has been supported by the Atkinson Center for a Sustainable
Future at Cornell University through SilverLining's Safe Climate
Research Initiative and by the National Science Foundation (grant nos. CBET-2038246 and CBET-1931641).
Review statement
This paper was edited by Anja Schmidt and reviewed by Alan Robock and two anonymous referees.
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