Climate models simulate lower rates of North Atlantic
heat transport under greenhouse gas climates than at present due to a
reduction in the strength of the Atlantic Meridional Overturning
Circulation (AMOC). Solar geoengineering whereby surface temperatures are
cooled by reduction of incoming shortwave radiation may be expected to
ameliorate this effect. We investigate this using six Earth system models
running scenarios from GeoMIP (Geoengineering Model Intercomparison Project)
in the cases of (i) reduction in the solar constant, mimicking dimming of
the sun; (ii) sulfate aerosol injection into the lower equatorial
stratosphere; and (iii) brightening of the ocean regions, mimicking enhancing
tropospheric cloud amounts. We find that despite across-model differences,
AMOC decreases are attributable to reduced air–ocean temperature
differences and reduced September Arctic sea ice extent, with no
significant impact from changing surface winds or precipitation - evaporation.
Reversing the surface freshening of the North Atlantic overturning regions
caused by decreased summer sea ice sea helps to promote AMOC. When comparing the
geoengineering types after normalizing them for the differences in top-of-atmosphere radiative forcing, we find that solar dimming is more effective
than either marine cloud brightening or stratospheric aerosol injection.
Introduction
Geoengineering, i.e., the deliberate and large-scale manipulation of the
Earth's climate, has been proposed as a way to mitigate or offset some of
the impacts of anthropogenic global warming (Keith, 2000). Solar radiation
management (SRM) is one of the fundamental geoengineering methodologies,
increasing Earth's albedo to reduce the net solar irradiance reaching Earth,
thus balancing longwave greenhouse gas (GHG) forcing (Niemeier et al.,
2013). Stratospheric aerosol injection (SAI), whereby aerosols aloft reflect
incoming solar radiation, and marine cloud brightening (MCB), i.e.,
introducing aerosols into the marine boundary layer and thereby increasing
cloud droplet numbers and hence their reflectivity (Jones et al., 2011; Ahlm
et al., 2017), are the most commonly discussed methods. Another hypothesized
method of SRM is simply blocking some incoming solar radiation before it
reaches the Earth (Angel, 2006), known as solar dimming or sunshade
geoengineering, and it has proven useful because of the climate response insights
it provides. All three methods can cool global mean temperatures, but the
tropospheric marine injection in MCB produces greater disparity in regional
climate effects, such as on precipitation (Muri et al., 2018; Kravitz et
al., 2018). This is not necessarily an inherent disadvantage relative to SAI
since it is plausible that combining different SRM methods may deal with
regionally specific deleterious impacts of climate change better than any
one method alone (Cao et al., 2017).
The most comprehensive model simulations of climate under SRM scenarios to
date come from the GeoMIP (Geoengineering Model Intercomparison Project;
Kravitz et al., 2013, 2016). These experiments are highly idealized – for
example, offsetting of a sudden quadrupling of CO2 concentrations by
turning down the solar constant. The point of the experiments is to examine
the mechanistic behavior of the climate system when subjected to different
styles of SRM forcing in comparison to pure greenhouse gas (GHG) forcing.
The global nature of the scenarios allows for sufficient signal-to-noise ratio
to discern impacts on various parts of the climate system in a reasonable
simulation period with Earth system models (ESMs). There are still technical
barriers and risks to doing both MCB (Latham et al., 2012) and SAI (Smith
and Wagner, 2018), while doing sunshade SRM is well beyond the bounds of
likelihood (Angel, 2006). We are not advocating implementation anytime
soon. Instead, our aim with this paper is to use the GeoMIP experiments to
investigate the mechanistic effect that SRM and MCB has on an important
and unique climate subsystem: the Atlantic Meridional Overturning
Circulation (AMOC).
The AMOC describes an ocean circulation that is highly correlated with the
poleward transport of heat in the subtropical North Atlantic (Johns et al.,
2011). AMOC transports 90 % of the ocean meridional heat at
26.5∘ N (Johns et al., 2011). The upper branch of AMOC transports
warm surface freshwater from the tropics northward where it loses heat,
densifies and eventually descends in the North Atlantic deep convection
regions. AMOC releases about 1.25 PW of heat from the sea to the atmosphere
between 26 and 50∘ N, which warms the North Atlantic
region and northern Europe, while the deep branch transports cold salty deep
water southward that ultimately fills a large fraction of the global ocean
basins (Buckley and Marshall, 2016; Chen and Tung, 2018). AMOC is mainly
driven by global density gradients due to surface heat and freshwater fluxes
(more details are available in McCarthy et al., 2019). Its
potential for net northward heat transport is unique and plays an essential
role in global climate and the redistribution of heat. Changes to the heat
and salt fluxes carried by AMOC must produce various climatic effects, such
as changes in tropical cyclone number and intensity (and hence hurricanes
impacting its western boundaries) and changes in monsoonal rainfall in
Africa and India (Buckley and Marshall, 2016). Therefore, any effects that
SRM may have on AMOC have the potential to produce wide-ranging, societally
relevant consequences.
It has proven very difficult to observe the magnitude of AMOC directly
(McCarthy et al., 2019; Send et al., 2011), so the observational evidence
for AMOC strength remains limited. It has been possible to accurately
quantify the temporal variation of AMOC only since April 2004, when
continuous observations of AMOC began at 26.5∘ N by the Rapid
Climate Change–Meridional Overturning Circulation and Heat flux
Array–Western Boundary Time Series (RAPID–MOCHA–WBTS) project in the
North Atlantic (Smeed et al., 2018). The mean strength of AMOC from April 2004 to February 2017 was 17.0 Sv (sverdrup) with a standard deviation of 4.4 Sv
(Frajka-Williams et al., 2019). The 26.5∘ N array observations
provide information on the short-term interannual and seasonal variability
of AMOC. Annually, AMOC ranges in strength from 4 to 35 Sv and also has
seasonal characteristics (Frajka-Williams et al., 2019). AMOC intensity
decreased significantly during 2004–2012 but was then statistically
unchanged between 2012 and 2017 (Smeed et al., 2018). The decline is thought
to be related to the Atlantic Multidecadal Oscillation and not to the
long-term external climate forcing. The less than two-decade observational
record is insufficient to detect the effect of external climate stress on
AMOC (Roberts et al., 2014). Numerical climate models show a slight decline
of AMOC in the historical period and predict that AMOC will continue to
weaken in the 21st century (Cheng et al., 2013). Predicted AMOC decline is
stronger in more recent models than in earlier ones, with modern ensemble
mean estimates suggesting declines between 6 and 8 Sv (34 %–45 %) by 2100
(Weijer et al., 2020). Compared to the past 1500 years, AMOC has
experienced an exceptional weakening in the past 150 years (Thornalley et
al., 2018).
The external forcing factors that control AMOC intensity depend on the timescale being considered. On short timescales (monthly to seasonal), change
in wind stress can be the main factor affecting its intensity (Zhao and
Johns, 2014), but on long timescales (interannual to interdecadal) the
seawater density affected by freshwater flux and sea–air heat flux are the
main factors (Smeed et al., 2018).
To date, little research on the oceanic response at high northern latitudes
under SRM has been published (Malik et al., 2020; Muri et al., 2018; Smyth
et al., 2017). Some research has been done on AMOC under sunshade
geoengineering (Hong et al., 2017) and under SAI (Muri et al., 2018; Moore
et al., 2019; Tilmes et al., 2020). As with GHG forcing alone, these studies
found a weakening of AMOC relative to the present day under sunshade
geoengineering, mainly in response to the change of heat flux in the North
Atlantic, with little influence from the changes of freshwater flux and wind
stress (Hong et al., 2017). However, the AMOC is less weakened under
sunshade geoengineering than with GHG forcing alone (Hong et al., 2017).
Under SAI experiments, AMOC declines seen under greenhouse forcing are
consistently reversed (Moore et al., 2019; Tilmes et al., 2020; Muri et al.,
2018). All ESM simulation results agree that SAI mitigates weakening of the
AMOC as compared to the GHG control experiments. Hence AMOC is closer to
the present day with sunshade and SAI SRM than without, but very little
research on AMOC under MCB experiments has yet been published (Muri et al.,
2018).
Here, we evaluate and compare the potential for MCB to offset changes under
GHG forcing to AMOC and its effectiveness and mechanistic behavior relative
to SAI and sunshade geoengineering based on the same six ESMs (Table 1). We
focus on the response of northward ocean heat transport, freshwater flux,
sea–air heat flux, the AMOC strength, atmospheric wind stresses and Arctic
sea ice extent.
Data and methods
We analyze monthly output from all ESMs that participated in GeoMIP with
sufficient data fields available (Table 1). The G1 and G1oceanAlbedo
experiments are very idealized simulations where incoming solar radiation is
reduced to balance the longwave radiative forcing of quadrupled CO2
relative to preindustrial concentrations. The G4 and G4cdnc experiments
represent somewhat more real-world scenarios where the background greenhouse
concentration rises as specified by the RCP4.5 scenario (Representative Concentration Pathway), while SRM is
prescribed either by constant amounts for SAI (G4) or increased cloud
condensation nuclei over the ocean (G4cdnc; see Sect. 2.1 for more
information and Kravitz et al., 2011, 2013, for a full description of the
experiment design). Hence there are three control simulations: (i) the
standard piControl specifying preindustrial conditions, (ii) abrupt4×CO2 specifying the standard abrupt quadrupling of
CO2 and (iii) the RCP4.5 scenario specified under the Climate Model
Intercomparison Project Phase 5 (CMIP5; Taylor et al., 2012). Not all the
ESMs we use have every simulated climate field that we would like; some lack
heat and water flux data or sea ice extents (Table S1 in the Supplement).
Earth system models used in this study.
ModelReferenceOcean componentOcean lat × long × depthBNU-ESMJi et al. (2014)MOM4p1 (Griffies, 2010)(1/3∘∼ 1∘) × 1∘× L50CanESM2Yang and Saenko (2012)NCAR CSM Ocean Model (Gent et al., 1998)0.94∘× 1.41∘× L40HadGEM2-ESCollins et al. (2011)HadGEM2-O(1/3∘∼ 1∘) × 1∘× L40ISPL-CM5A-LRDufresne et al. (2013)NEMO1.875∘× 3.75∘× L39MIROC-ESMWatanabe et al. (2011)COCO3.4 (K-1 model developers, 2004)(0.5∘∼ 1.7∘) × 1.4∘× L44NorESM1-MBentsen et al. (2013); Iversen et al. (2012)a developed version of MICOM1∘× 1∘× L70
The response of the oceans is expected to be much slower than the
atmosphere. Typically, in the sunshade experiments which invoke abrupt and
strong forcing, the first decade of the simulations has not been included in
the analysis to mitigate this issue. It is of course unlikely that the deep
ocean would be close to a steady state within centuries of beginning
geoengineering experiments, but to be practical we assume that the scenario
responses after the first decade are sufficiently different from each other
to explore impacts. Most GeoMIP scenarios run for 50 years; while some
GHG and control scenarios run longer, we limit the analysis of all scenarios
to the same duration for statistical convenience. We test for significance
at the 95 % level using the nonparametric Wilcoxon signed-rank test.
Experiments
Schematic representations of the experiments are shown in Fig. 1 and Table 2.
G1oceanAlbedo is part of the phase-2 GeoMIP experiments (Kravitz et al.,
2013, 2015) and designed to mimic the G1 solar dimming experiment (Kravitz
et al., 2011). Both are based on the CMIP5 abrupt4×CO2
experiment and started from a stable preindustrial climate run, i.e., the
CMIP5 experiment piControl (Taylor et al., 2012). In the G1 experiment, the
radiative forcing from an abrupt quadrupling of CO2 concentrations
above preindustrial levels is offset by a uniform insolation reduction,
thereby mimicking sunshade geoengineering. In G1oceanAlbedo, the radiative
forcing from abrupt4×CO2 is instead compensated for by using a
uniform increase in albedo in the ESM ocean-covered grid cells (Fig. 1a).
The G4 experiment, by contrast, starts with the RCP4.5 scenario as a
baseline and then employs an injection rate of SAI (5 Tg of SO2 per year)
into the equatorial lower stratosphere between the years 2020 and 2069
(Fig. 1c). The G4cdnc scenario is similar, except that the stratospheric
aerosols are replaced by a 50 % increase in the cloud number droplet
concentration in low clouds over the global ice-free oceans. In both G4 and
G4cdnc, the amount of geoengineering is held fixed over time rather than
being adjusted to balance the radiative forcing due to GHGs.
A summary of the four experiments included in this
proposal.
In the following analysis, we make comparisons between G1oa and G1 and between G4
and G4cdnc separately as they do not use the same greenhouse gas forcing
backgrounds (Table 2). But we are also interested in comparing the different
geoengineering types, and doing this can be done with the ratios of their
response, e.g., (G4-RCP4.5)/(G1-Abrupt4×CO2). The different
ESMs also have different climate sensitivities, and we also account for this
by considering their top-of-atmosphere (TOA) radiative forcing.
Schematics of the four experiments outlined in this paper, based
on Kravitz et al. (2011, 2013). (a) G1 is started from a preindustrial
control run; longwave forcing (blue) from quadrupled GHG forcing is
compensated by a fixed reduction in the solar constant (red) to leave net
zero forcing (black); the experiment is for 50 years duration. (b) In
G1oceanAlbedo the equivalent balance is obtained by an increase in ocean
albedo. (c) G4 is started from 2020 and ends in 2069, branching from RCP4.5
with 5 Tg yr-1 SO2 injected into the equatorial lower
stratosphere. (d) In G4cdnc the shortwave forcing comes from a constant
50 % increase in cloud droplet number concentration in oceanic low clouds.
AMOC index
The AMOC index (Cheng et al., 2013) is defined as the annual-mean maximum
volume of the transport stream function at 30∘ N in the North
Atlantic (in sverdrup (Sv)). The transport stream function is described by
the integral of the meridional transport from the surface to the bottom
depth at the given latitude (here 30∘ N):
Ψ(z,lat)=∫z0∫λEλWVcos(lat)dxdz,
where Ψ is the overall transport stream function, z is the bottom
depth, lat is latitude, and λE and λW represent the eastern
and western meridians, respectively. V is the meridional ocean velocity.
Northward heat transport
In this study, we use the ocean potential temperature and the ocean
meridional velocity to calculate the northward heat transport, H (Stouffer
et al., 2017):
Hlat=Cp⋅∮lat∫z0ρ⋅T⋅Vdzd(long),
where H(lat) is the ocean heat transport in the latitude, Cp is the ocean specific
heat capacity, ρ is the ocean potential density, long is longitude and
T is ocean potential temperature.
AMOC response and its impactExperiments
Differences in average AMOC index, upward heat flux (W m-2),
September sea ice extent (106 km2) and top-of-atmosphere
radiation (W m-2) over the 40-year analysis period. Bold entries denote
differences significant at the 95 % level in the Wilcoxon signed-rank
test. G1oa refers to G1oceanAlbedo, and piC refers to piControl. Individual
ESM results are shown in Tables S2–S5.
The 11-year running annual means simulated by the six ESMs and the
multimodel ensemble mean (black curve) of the AMOC strength (Sv) over the
40-year analysis period under (a) piControl, (b) abrupt4×CO2
and (c) RCP4.5. The gray band in (a) is the range of AMOC intensity (17.0 ± 4.4 Sv) measured by the RAPID- MOCHA (Frajka-Williams et al., 2019).
Panels (d)–(f) show AMOC anomalies (Sv), and panels (g)–(i) show the percentage
changes relative to the other scenarios: left column (d, g) relative to
piControl; middle (e, h) relative to global warming scenarios (RCP4.5 and
abrupt4×CO2); right (f, i) relative to other geoengineering
scenarios (G1oa-G1; G4cdnc-G4). Colored bands in panels (d)–(i) represent the
across-ESM spread. G1oa refers to G1oceanAlbedo, and piC refers to
piControl.
Under the piControl scenario, the six ESM ensemble mean AMOC index is about
17.9 Sv, which is consistent with the average AMOC strength (17.7 ± 0.3 Sv) from the RAPID- MOCHA array (Weijer et al., 2020) (Fig. 2a). Under RCP4.5, the AMOC intensity decreases by about 2.4 Sv from 2020 to 2069 (Fig. 2c; Table 3), consistent with previously published ESM simulation results
(Cheng et al., 2013; Weijer et al., 2020; Muri et al., 2018). Compared to piControl, the AMOC intensity in the 50th year of abrupt4×CO2 decreased by about 7.9 Sv (42 %) compared to a 15 %
reduction under RCP4.5 (Fig. 2g), which is consistent with the lower GHG
forcing under RCP4.5.
Under G1 and G1oceanAlbedo scenarios, the average AMOC strength over the
40-year analysis period increased by about 5.3 and 4.6 Sv relative to
abrupt4×CO2 (Table 3). Compared to abrupt4×CO2, the AMOC intensity in the 50th year of G1 and G1oceanAlbedo
increased by about 7.2 Sv (41 %) and 6.2 Sv (35 %) (Fig. 2e, h). The
average AMOC intensity is insignificantly weaker under G1 but statistically
significant and lower by 1.4 Sv under G1oceanAlbedo (p<0.05; Table 3)
than under piControl. MIROC-ESM simulated a slightly stronger AMOC under
G1oceanAlbedo than under G1 (Table S2), but the other five ESMs and the
ensemble mean agree that AMOC under the G1 scenario is stronger than that
under G1oceanAlbedo. Even though G1oceanAlbedo is designed to produce
radiative forcing over ice-free oceans, it is significantly less effective
at restoring AMOC to piControl levels than the global forcing applied under
G1.
Both G4cdnc and G4 apply constant reductions to shortwave solar radiation,
but in contrast with the abrupt4×CO2 scenario, the GHG
concentrations continue to rise in these scenarios as specified by RCP4.5.
Under the G4 and G4cdnc scenarios, the average AMOC strength over the
40-year analysis period increased by about 0.9 and 1.3 Sv relative to
RCP4.5 (Table 3), both significantly different from RCP4.5 (Table 3). Five
ESMs and the ensemble mean agree that the ocean-only forcing under G4cdnc is
more effective than the global G4 forcing for restoring AMOC to present-day
strength (Table S2).
We may thus conclude that the four geoengineering experiments mitigate AMOC
weakening caused by the forcing of GHG, but the mitigation efficacies are
different. Generally, mitigation of AMOC weakening under G4cdnc is more than
with G4 but weaker than G1 solar dimming. G1oceanAlbedo is more effective
than G4cdnc, but these scenarios were not designed to have identical
forcing, so we shall discuss their relative efficacy later in the
Discussion section.
Northward heat transport response
AMOC transports heat from low latitudes to high latitudes at the upper
levels of the ocean. How will the northward heat transport change with the
change of AMOC intensity under different styles of SRM?
Meridional distribution of the average northward heat transport
(PW) over the 40-year analysis period at Atlantic Ocean (depth 0–700 m)
under (a) piControl, (b) abrupt4×CO2, and (c) RCP4.5. Panels
(d)–(f) show northward heat transport anomalies relative to the other
scenarios: left column (d) relative to piControl; middle (e) relative to
global warming scenarios; right (f) relative to other geoengineering
scenarios. Colored bands in panels (d)–(f) represent the across-ESM spread.
Under piControl, the six ESMs ensemble mean northward heat transport at
26.5∘ N in the Atlantic Ocean is about 1.27 PW (Fig. 3a), which is
consistent with the estimate by Johns et al. (2011) of 1.25 PW for meridional heat
transport in the Atlantic Ocean from 2004 to 2007.
Under the two global warming scenarios (RCP4.5 and 4×CO2),
the northward heat transport at the Atlantic basin to the south of
60∘ N decreases significantly relative to piControl, particularly
between 30 and 50∘ N, and increases between
60 and 70∘ N (Fig 3d). Under the abrupt4×CO2 and RCP4.5 scenarios, AMOC weakening reduces the heat transported
northward by about 0.51 and 0.07 PW between 30 and
50∘ N relative to piControl.
Under G1 and G1oceanAlbedo scenarios, the northward heat transport increased
by about 0.45 and 0.4 PW between 30 and 50∘ N
relative to abrupt4×CO2 (Fig. 3e). The northward heat
transport weakening between 30 and 50∘ N caused by
GHGs is significantly mitigated by G1 and G1oceanAlbedo, but the northward
heat transport between 30 and 50∘ N is still weaker
by about 0.06 and 0.12 PW under G1 and G1oceanAlbedo than under piControl
(Fig. 3d). The mitigation of northward heat transport weakening is
consistent with the mitigation of AMOC weakening under G1 and G1oceanAlbedo.
Northward heat transport weakening between 30 with
50∘ N caused by abrupt4×CO2 is more balanced under
G1 than with G1oceanAlbedo, consistent with their relative AMOC performance.
Both G4 and G4cdnc significantly mitigate the reduction of northward heat
transport between 30 and 50∘ N in the North Atlantic
basin under RCP4.5 (Fig. 3e). Compared to the RCP4.5 scenario, the G4 and
G4cdnc scenarios increase the northward heat transport by about 0.1 and
0.08 PW between 30 and 50∘ N. The mitigation of
northward heat transport weakening between 30 and 50∘ N is stronger under G4 than G4cdnc, although differences between G4 and
G4cdnc are generally not significant at the 95 % level. Changes in
northward heat transport are thus more complex than their AMOC responses
summarized in Table 3.
Drivers of changes in AMOC
Three drivers of AMOC intensity change have been proposed: (i) wind stress at
monthly to seasonal periods (Zhao and Johns, 2014) and at annual and
decadal scales, (ii) changes in seawater density due to varying freshwater
flux, and also (iii) changes in ocean–air heat exchange (Smeed et al., 2018).
We consider each of these in relation to the different SRM experiments. We
also look at how model dependent the drivers are.
Near-surface wind speed
We used six ESMs to calculate near-surface wind speed and wind direction
under different scenarios. Under the abrupt4×CO2 scenario,
the global wind speed has obvious changes compared to other scenarios,
especially in the Southern Ocean subpolar westerlies (Fig. 4a). But there is
no significant change of wind speed under other scenarios in the Atlantic
high latitudes.
Spatial distribution of six ESMs and ensemble mean 1000 hPa wind speed
and wind direction (arrows) changes under different scenarios (11–50 years).
Blue colors indicate decreased wind speed; the length of arrow in each
panel's bottom right represents speeds of 1 m s-1. Translucent white
overlay indicates regions where differences are not significant at the
95 % level according to the Wilcoxon signed-rank test.
There is a significant correlation between wind and AMOC when all models and
scenarios except for HadGEM2-ES are selected (Fig. 5). AMOC intensity is
significantly related to wind speed within the same scenario, as clearly
shown for abrupt4×CO2 in red in Fig. 5, which lies on a
relation parallel to, but above, the other scenarios. Similarly, for G1 and
piControl points lie on a relation parallel to (but lower than) the mean
regression. Similar results were obtained for winds only over the deep
convection regions and for just the Atlantic north of 45∘ N (Fig. S2). This suggests that the wind speed is correlated with scenario as well
as AMOC, but this analysis does not address causal relation between wind and
AMOC. This is consistent with the observation that while wind stress clearly
affects AMOC on short timescales it is not the main factor affecting AMOC
intensity one long timescales (Zhao and Johns, 2014).
ESM mean of wind speed (m s-1) over the 40-year analysis
period in the whole North Atlantic (north of 30∘ S). All ESMs
except HadGEM2-ES show a high correlation between near-surface wind speed
and AMOC intensity. The solid line is the linear regression line of AMOC
intensity and wind speed (area average of the whole North Atlantic) over the
40-year analysis period in the five ESMs excluding HadGEM2-ES.
Upward heat flux
AMOC transports heat from the tropics to high latitudes, releases heat in
the deep convective regions of the North Atlantic, and then the surface water
density increases and sinks to form cold water flowing southward. Under
abrupt4×CO2 and RCP4.5 scenarios, GHG forcing reduces the
temperature difference between ocean and atmosphere at high latitudes (Fig. 6b, c), resulting in reduced heat transfer from the ocean to the atmosphere
in the deep convective regions (Fig. 7d). The reduction of upward heat flux
impedes the release of heat from the seawater, weakening the densification
process and the rate of sinking in the deep convective region and thus weakening
AMOC.
Upward heat flux change (W m-2) in different
scenarios (11–50 years). The red boxes mark the three deep convective regions in
the northern North Atlantic (from left to right: Labrador Sea, Irminger Sea and
Nordic Seas (often referred to as the Greenland–Iceland–Norwegian (GIN)
Seas). Yellow to orange colors represent an increase in heat flux from the
ocean to the atmosphere. Stippling indicates regions where differences are
not significant at the 95 % level according to the Wilcoxon signed-rank
test.
The 11-year running annual means simulated by the six ESMs and their
ensemble mean of the upward heat flux (W m-2) over the 40-year
analysis period in the three deep convective regions outlined in Fig. 10
under (a) piControl, (b) abrupt4×CO2 and (c) RCP4.5. Panels
(d)–(f) show AMOC anomalies, and panels (g)–(i) show the changes relative to the other
scenarios: left column (d, g) relative to piControl; middle (e, h) relative to
global warming scenarios; right (f, i) relative to other geoengineering
scenarios. Colored bands in panels (d)–(i) represent the across-ESM spread.
G1oa refers to G1oceanAlbedo, and piC refers to piControl.
Model mean of upward heat flux (W m-2) over the 40-year
analysis period in the three deep convective regions outlined in Fig. 6.
Only heat flux data from BNU-ESM, MIROC-ESM and NorESM1-M are available. All
three ESMs show a high correlation between upward heat flux (area average of
the deep convective regions) and AMOC intensity. The solid line is the
linear regression trend line of AMOC intensity and upward heat flux (area
average of the deep convective regions) over the 40-year analysis period.
Under G1 and G1oceanAlbdedo, the upward heat flux is increased in the deep
convective regions relative to abrupt4×CO2, but it is not as
high as under the piControl scenario. The changes of upward heat flux in the
deep convective region are consistent with those changes in AMOC intensity.
AMOC intensity shows a significant correlation with upward heat flux in the
three deep convective regions (Fig. 8), although only three models have data
fields available. These results show that the change in upward heat flux
caused by the modified ocean–atmosphere temperature difference is an
important contributor in all ESMs to the change in AMOC intensity in the
geoengineering scenarios.
Freshwater flux (precipitation - evaporation)
In the output data from ESM, the total freshwater flux into the North Atlantic
includes precipitation, evaporation, runoff from river and freshwater flux
caused by sea ice thermal dynamic change. Due to the lack of data of runoff
and freshwater fluxes caused by sea ice and melting of the Greenland ice
sheet, we separately analyze the impact of freshwater flux change caused by
precipitation minus evaporation (P-E) on AMOC (Shu et al., 2017).
Relative to piControl, P-E increases by 134 and 51 mm yr-1
under abrupt4×CO2 and RCP4.5 scenarios, respectively. P-E
under the four geoengineering scenarios we studied are decreased compared
to their reference GHG forcing scenarios. Geoengineering methods mitigate
the increase in P-E under GHG forcing. AMOC intensity has no significant
correlation with freshwater flux in the three deep convective regions (Fig. 9), so P-E is not the main driver of AMOC change under the four
geoengineering scenarios we studied.
Model mean of P-E (mm yr-1) over the 40-year analysis period
in the three deep convective regions (outlined in Fig. 6). The dotted line
is the linear regression trend line of AMOC intensity and upward heat flux
(area average of the deep convective regions) over the 40-year analysis
period in all six ESMs.
September sea ice extent
Arctic surface temperatures have risen 2–3 times faster than the global
average level, leading to loss of Arctic sea ice (Dai et el., 2019). Stronger
seasonal sea ice melting weakens the AMOC intensity by injecting freshwater
and increasing its storage in the North Atlantic (Li and Fedorov, 2021). But
sea ice cover also reduces heat transport from the sea to the air and hence
inhibits ocean convection, which may weaken AMOC (Drijfhout et al., 2012).
Therefore, we can analyze the relationship between AMOC intensity and the
freshwater flux changes caused by Arctic sea ice thermodynamics via Arctic
sea ice extent changes under the four geoengineering scenarios.
There is almost no September sea ice in the northern North Atlantic in any
scenario (Fig. 10). The sea ice extent changes most in the northern seas
along the Eurasian Arctic coast. Under both abrupt4×CO2 and
RCP4.5 scenarios, Arctic sea ice area is greatly reduced (Fig. 10b, c).
Six ESMs and ensemble mean Arctic minimum sea ice fraction percentage
changes (defined as the limit of the 15 % ice concentration region) in
September in different scenarios (11–50 years). Stippling indicates regions
where differences are not significant at the 95 % level according to the
Wilcoxon signed-rank test.
Model mean Arctic September sea ice area (106 km2) over
the 40-year analysis period (defined as the limit of 15 % ice
concentration region). The lines are the linear regression trend lines of
AMOC intensity and Arctic September Sea ice area over the 40-year analysis
period for each ESM. Those significant at the 95 % level are shown as
heavier lines, and the ESMs are labeled in bold in the legend.
Under the G1 scenario, the spatial pattern of September sea ice is changed
relative to piControl, with extent changes of up to 20 % regionally and
in total reduced by 3 % in an eight-model ensemble (Moore et al., 2014). In
our study, relative to piControl, the ensemble mean of six ESMs sea ice area is
decreased by about 9 % under G1 and 25 % under G1oceanAlbedo, which is
consistent with their AMOC changes. Indeed, the sea ice of the Norwegian Sea
is slightly increased under G1 and G1oceanAlbedo relative to piControl (Fig. 10b, c). Compared to abrupt4×CO2, September sea ice
increases significantly under G1 by about 80 % and by about 64 % by
G1oceanAlbedo. G1oceanAlbedo produces about 16 % less sea ice area than G1
(Table 3). This shows that G1oceanAlbedo and G1 can mitigate the reduction
of Arctic September sea ice caused by GHG forcing, and the mitigation effect
under G1 is stronger than G1oceanAlbedo.
Under G4 and G4cdnc, the September sea ice significantly increased by about
22 % and 28 % compared to RCP4.5. G4cdnc generally increases sea ice
area by 6 % more than G4 (Table 3), consistent with AMOC behavior under
the two scenarios. All six ESMs agree that the mitigation on the reduction
of Arctic September sea ice area under solar dimming is stronger than with
MCB and SAI. Mitigation of sea ice loss under G1oceanAlbedo is stronger than
G4cdnc, which is all consistent with AMOC behavior under the four scenarios.
To examine the dependence of sea ice extent on AMOC, we plot the 40-year mean
values for all models and all scenarios (Fig. 11). Such plots can determine
how linear relations are, as well as their across-model differences. In four of the
ESMs, AMOC is significantly correlated (p<0.05) with Arctic sea ice
area, HadGem2-ES and MIROC-ESM being the ESM without a significant
relationship. Although most ESMs do have significant relationships between
minimum sea ice extent and AMOC strength, the models themselves show large
differences in strength of the response, in part due to their AMOC strength
in the control simulations but also in the changes in September ice extent
across the scenarios as seen by the slope of the regression lines in Fig. 11.
There is no significant correlation between AMOC and P-E but significant
correlation between AMOC and Arctic September sea ice area. This also shows
that the freshwater changes caused by Arctic September sea ice is a key
factor in AMOC changes under the four geoengineering experiments. The slopes
of the regression lines in Fig. 11 are positive, meaning that greater AMOC
strength is correlated with greater ice extent. However, Fig. 3e also shows
that heat transport anomalies under the geoengineering scenarios change sign
at about 60∘ N, with reductions in heat transport in the south
coinciding with increases to the north of 60∘ N. But correlations
of heat transport across 60∘ N with sea ice extent for separate
ESM across scenarios are all insignificant and vary in sign (Fig. S3), in
stark contrast to the regression lines in Fig. 11. For individual scenarios,
there are significant anticorrelations only for the RCP4.5, G4 and G4cdnc
scenarios (Fig. S4). In this respect, the behavior is similar, although
less robust, as for wind forcing in Fig. 5, where scenario impacts as
expected, but a consistent relation between scenarios simulated by each ESM
is not present. The stronger sea ice correlation with increased AMOC
suggests that sea ice may be driving changes in AMOC through the change in
freshwater budget.
Discussion
The mitigation of AMOC intensity, northward heat flux and sea ice extent
changes caused by global warming under G1 is significantly stronger than
under G1oceanAlbedo. The mitigation under G4cdnc is slightly stronger than
G4. The radiative fluxes of the abrupt4×CO2 experiments are
7–8 times greater than those under the RCP4.5 scenarios, as are changes
induced in mean and extreme temperatures (Ji et al., 2018). The relative
change in AMOC under G4 compared to G1 relative to appropriate controls is
similar at about 15 %. This compares with about 25 % effectivity for
G4cdnc relative to G1 and 33 % relative to G1oceanAlbedo (Table S3). Heat
content effectivity for G4 relative to G1 is around 25 %, and for G4cdnc
the ensemble effectiveness is over 40 % of G1oceanAlbedo (Table S3). The
changes in September sea ice extent effectiveness under G4 are about 30 %
of those under G1 and 50 % for G4cdnc relative to G1oceanAlbedo. This
might mean that specific measures under G4cdnc appear more effective than
those simulated under G4 stratospheric aerosol injection, but the forcing
applied under G4cdnc was not specifically designed to match the net
radiative forcing of the G4 SAI.
We want to examine the differences in response to type of SRM as defined in
the GeoMIP experiments we analyze. The ESMs have different sensitivities to
climate forcing, so we normalize the model fields with top-of-atmosphere
radiative forcing (TOA), e.g.,
(G4-RCP4.5)AMOC/(G4-RCP4.5)TOA,
which represents AMOC changes per unit change of the corresponding TOA radiation
flux changes.
Because of the large differences in forcing magnitude between, for example,
Abrupt4×CO2 and RCP4.5, we cannot simply look at anomalies,
but instead we can compare the responses as a ratio; for example,
(G4-RCP4.5)/(G1-Abrupt4×CO2)
compares the SAI and the solar dimming anomalies.
Then we compare the efficacy of different mitigation experiments by the
ratio of their sensitivity parameters; for example, the measure of efficacy
in the example of comparing the SAI and the solar dimming anomalies above
becomes
(G4-RCP4.5)AMOC/(G4-RCP4.5)TOA(G1-Abrupt4×CO2)AMOC/(G1-Abrupt4×CO2)TOA,
which we can calculate for upward heat flux and September sea ice extent in
addition to AMOC, as well as for ratios indicative of the relative responses of MCB
to solar dimming and SAI to MCB. The ensemble means indicate the typical
differences in efficacy between type of geoengineering (Table 4).
Across-ESM ensemble mean relative efficacy ratios of geoengineering
compared to their efficacy in changing TOA radiation. Where individual ESM
have no data, the ensemble mean was used.
Table 4 shows that changes to AMOC and upward heat flux are less than for
overall climate sensitivity measured as TOA since the ratios are all less
than 1. The relative efficacy by SAI and MCB for AMOC and upward heat
fluxes are about half those of solar dimming. Comparing MCB and SAI shows
smaller differences, with relative efficacies closer to unity. Arctic
September sea ice extent indicates larger differences between type of
geoengineering, with SAI being more effective than MCB in the experiments
analyzed here. Different ESMs have different responses to MCB and SAI,
especially in the comparison between G4cdnc and G4. Individual model results
are shown in Tables S6–S8.
Five out of six ESMs agree that SAI is more effective than MCB for AMOC (Table S6),
with the outlier being HadGEM2-ES. This model is also the only one with greater
AMOC intensity under the RCP4.5 and G4 scenarios than in piControl.
HadGEM2-ES is also unique in displaying no correlation between wind speed
and AMOC (Fig. 5), and along with MIROC-ESM it shows an insignificant relation
between AMOC and September sea ice extent (Fig. 11).
There is lower consensus on which of SAI or MCB is more effective, with the ESM split three against three. In the case of upward heat
flux (Table S7), the ESMs generally agree that solar dimming is more
effective than either SAI or MCB with little to choose between SAI and MCB.
For September sea ice (Table S8), SAI clearly outscores MCB in all but
one (BNU-ESM) of the models and experiments we analyzed, while SAI and solar dimming are
fairly similar in effectiveness across the ESMs.
The proximate factor from our analysis of the main drivers of changes under
the different scenarios is the change in heat flux transported from ocean
to atmosphere caused by the air–sea temperature difference changes in deep
convective regions of the North Atlantic (Fig. 7). This is consistent with
the analysis of the G1 experiment (Hong et al., 2017). All the
geoengineering scenarios produce surface cooling, which partially restores
the ocean–atmosphere temperature contrast altered by GHG forcing and thus
increases the heat flux from ocean to atmosphere. In the three deep
convection regions of the northern North Atlantic (Fig. 6), the ocean
temperature is usually higher than the near-surface temperature, and the
surface seawater originating from the tropics can release heat to the
atmosphere and cool down. Global warming increases the near-surface air
temperature, reducing the air–sea heat exchange. While geoengineering might
be expected to ameliorate this problem by cooling the atmosphere, we found
that the surface air temperatures in the deep convection regions of the
North Atlantic remained higher than in piControl, even in scenarios in which
the global radiative budget is balanced. Thus, the geoengineering scenarios
had an intermediate amount of AMOC weakening, in between the preindustrial
state and unmitigated global warming. The decrease of surface seawater
density in the northern North Atlantic caused by the increase in surface
seawater temperature weakens the surface seawater subsidence in this area
under the four SRM geoengineering scenarios as compared to piControl.
While changes in upward heat flux over the three convective regions play a
prominent role, the freshwater flux changes caused by Arctic sea ice
melting may also affect AMOC changes under geoengineering. Sea ice melting
releases large amounts of freshwater into the North Atlantic. In the deep
convection regions of the North Atlantic, the injection of a large amount of
freshwater reduces the density of surface seawater, hindering surface
water sinking and weakening AMOC. Although the wintertime formation of
Arctic sea ice increases the density of the surface water in the Arctic,
promoting surface water sinking in the deep convection regions of the North
Atlantic, the sustained decline in Arctic sea ice and strengthened seasonal
cycle produces a gradual freshening of the upper Arctic Ocean (Li and
Federov, 2021). Wang et al. (2019) note that sea ice decline is likely to
have a remarkable influence on the ocean environment and that sea ice decline
impacts on dynamical processes should be considered. Climate model
sensitivity studies perturbing both sea ice and radiative forcing (Sévellec
et al., 2017; Liu et al., 2018; Liu and Fedorov, 2019) elucidate how buoyancy
anomalies may escape the Arctic into ocean deep convection regions, weakening
the AMOC. The four geoengineering experiments may thus mitigate the AMOC
weakening caused by GHG forcing through increasing the September Arctic sea
ice area and reducing sea ice seasonality. The changes of Arctic sea ice
area and AMOC are interactive, and the changes of Arctic September sea ice
area are significantly correlated under different scenarios (except for
HadGEM2-ES). However, the response is ESM dependent as the relationship
between AMOC changes and Arctic sea ice area changes are different. A key
uncertainty when it comes to the AMOC in the future is the melting of the
Greenland ice sheet. More realistic modeling of the ice sheet,
sensitivity studies (e.g., Swingedouw et al., 2015) or indeed interactive ice
sheet modeling would be needed to address this, which is beyond the scope
of this study.
Changes in near-surface wind speed are known to alter the speed of northward
surface water transport and hence AMOC. The effects of wind speed appear on
short timescales (Yang et al., 2016). Near-surface wind speed changes over
North Atlantic are correlated with AMOC under many of the different
scenarios, but they are not significantly correlated across scenarios
simulated by each ESM. Thus, scenario impacts wind speed as expected, but a
consistent relation between scenarios simulated by each ESM is not evident.
Hence, near-surface wind speed is not the main factor of AMOC changes under
different scenarios.
Conclusions
GHG forcing weakens AMOC intensity, reducing northward ocean heat transport.
The three SRM methods we studied, solar dimming (G1), MCB (G1oceanAlbedo and
G4cdnc) and SAI (G4) mitigate the AMOC weakening caused by GHG forcing. The
mitigation effects of AMOC weakening under MCB are similar to SAI, but both
are relatively less effective than solar dimming in these experiments. All four geoengineering scenarios demonstrate weakened AMOC compared to
the piControl scenario. The drivers producing the changes in AMOC are
dominated by the differences in surface air–ocean temperatures, with the
radiative cooling produced by the SRM tending to reverse the GHG changes. We
found no relationship between freshwater flux due to river flow or imbalance
in precipitation - evaporation and changes in AMOC, but there is a significant
correlation between September sea ice extent and AMOC intensity. The bigger
the decline in sea ice extent, the stronger the reduction in AMOC intensity. The
strong statistical relationship for most models across scenarios suggests
that AMOC is not directly driving sea ice reduction since a lower AMOC means
less ocean heat transport. Instead, it supports modeling studies that
indicate freshening mechanisms in the deep convection regions associated
with greater sea ice seasonality that may act to reduce AMOC as summer sea ice is
removed.
Data availability
All data used in this study, except for parts of the data of NorESM1-M and IPSL-CM5A-LR, are available from the Coupled Model Intercomparison Project 5 (CMIP5) network https://esgf-node.llnl.gov/search/cmip5/, established by the World Climate Research Programme (WCRP) the Working Group on Coupled Modelling (WGCM), 2022). The NorESM1-M and IPSL-CM5A-LR data are archived by the modeling team.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-22-4581-2022-supplement.
Author contributions
JCM and MX conceived and designed the analysis. MX mainly collected all the data and performed the analysis. MX
wrote the paper, and JCM, LZ, MW and HM provided critical suggestions and revised the paper. All authors
contributed to the discussion.
Competing interests
The contact author has declared that neither they nor their co-authors have 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
This study is supported by the National Key Research and Development Program of China (grant nos. 2021YFB3900105, 2018YFC1406104), the National Natural Science Foundation of China (grant no. 41941006), and Finnish Academy COLD Consortium (grant no. 322430). We thank two anonymous reviewers that made many suggestions to help improve the paper.
Financial support
This research has been supported by the National Key Research and Development Program of China (grant nos. 2021YFB3900105, 2018YFC1406104), the National Natural Science Foundation of China (grant no. 41941006), and Finnish Academy COLD Consortium (grant no. 322430).
Review statement
This paper was edited by Hailong Wang and reviewed by two anonymous referees.
ReferencesAhlm, L., Jones, A., Stjern, C. W., Muri, H., Kravitz, B., and Kristjánsson, J. E.: Marine cloud brightening – as effective without clouds, Atmos. Chem. Phys., 17, 13071–13087, 10.5194/acp-17-13071-2017, 2017.Angel, R.: Feasibility of cooling the Earth with a cloud of small spacecraft
near the inner Lagrange point (L1), P. Natl. Acad. Sci. USA., 103,
17184–17189, 2006.Bentsen, M., Bethke, I., Debernard, J. B., Iversen, T., Kirkevåg, A., Seland, Ø., Drange, H., Roelandt, C., Seierstad, I. A., Hoose, C., and Kristjánsson, J. E.: The Norwegian Earth System Model, NorESM1-M – Part 1: Description and basic evaluation of the physical climate, Geosci. Model Dev., 6, 687–720, 10.5194/gmd-6-687-2013, 2013.Buckley, M. W. and Marshall, J.: Observations, inferences, and mechanisms
of the Atlantic Meridional Overturning Circulation: A review, Rev. Geophys.,
54, 5–63, 10.1002/2015RG000493, 2016.Cao, L., Duan, L., Bala, G., and Caldeira, K.: Simultaneous stabilization of
global temperature and precipitation through cocktail geoengineering,
Geophys. Res. Lett., 44, 7429–7437, 10.1002/2017GL074281, 2017.Chen, X. and Tung, K. K.: Global surface warming enhanced by weak Atlantic
overturning circulation, Nature, 559, 387–391, 10.1038/s41586-018-0320-y, 2018.Cheng, W., Chiang, J. C. H., and Zhang, D.: Atlantic Meridional Overturning
Circulation (AMOC) in CMIP5 Models: RCP and Historical Simulations, J.
Climate, 26, 7187–7197, 10.1175/JCLI-D-12-00496.1, 2013.Collins, W. J., Bellouin, N., Doutriaux-Boucher, M., Gedney, N., Halloran, P., Hinton, T., Hughes, J., Jones, C. D., Joshi, M., Liddicoat, S., Martin, G., O'Connor, F., Rae, J., Senior, C., Sitch, S., Totterdell, I., Wiltshire, A., and Woodward, S.: Development and evaluation of an Earth-System model – HadGEM2, Geosci. Model Dev., 4, 1051–1075, 10.5194/gmd-4-1051-2011, 2011.Dai, A., Luo, D., Song, M., and Liu, J.: Arctic amplification is caused by
sea-ice loss under increasing CO2, Nature Commun., 10, 1–13,
10.1038/s41467-018-07954-9, 2019.Drijfhout, S., Oldenborgh, G. T., and Cimatoribus, A.: Is a Decline of AMOC
Causing the Warming Hole above the North Atlantic in Observed and Modeled
Warming Patterns?, J. Climate, 25, 8373–8379, 10.1175/JCLI-D-12-00490.1, 2012.Dufresne, J.-L., Foujols, M.-A., Denvil, S., Caubel, A., Marti, O., Aumont,
O., Balkanski, Y., Bekki, S., Bellenger, H., Benshila, R., Bony, S., Bopp,
L., Braconnot, P., Brockmann, P., Cadule, P., Cheruy, F., Codron, F., Cozic,
A., Cugnet, D., de Noblet, N., Duvel, J.-P., Ethe, C., Fairhead, L.,
Fichefet, T., Flavoni, S., Friedlingstein, P., Grandpeix, J.-Y., Guez, L.,
Guilyardi, E., Hauglustaine, D., Hourdin, F., Idelkadi, A., Ghattas, J.,
Joussaume, S., Kageyama, M., Krinner, G., Labetoulle, S., Lahellec, A.,
Lefebvre, M.-P., Lefevre, F., Levy, C., Li, Z. X., Lloyd, J., Lott, F.,
Madec, G., Mancip, M., Marchand, M., Masson, S., Meurdesoif, Y., Mignot, J.,
Musat, I., Parouty, S., Polcher, J., Rio, C., Schulz, M., Swingedouw, D.,
Szopa, S., Talandier, C., Terray, P., Viovy, N., and Vuichard, N.: Climate
change projections using the IPSL-CM5 Earth System Model: from CMIP3 to
CMIP5, Clim. Dynam., 40, 2123–2165, 10.1007/s00382-012-1636-1, 2013.Frajka-Williams, E., Ansorge, I. J., Baehr, J., Bryden, H. L., Chidichimo,
M. P., Cunningham, S. A., Danabasoglu, G., Dong, S., Donohue, K. A., Elipot,
S., Heimbach, P., Holliday, N. P., Hummels, R., Jackson, L. C., Karstensen,
J., Lankhorst, M., Le Bras, I. A., Lozier, M. S., McDonagh, E. L., Meinen,
C. S., Mercier, H., Moat, B. I., Perez, R. C., Piecuch, C. G., Rhein, M.,
Srokosz, M. A., Trenberth, K. E., Bacon, S., Forget, G., Goni, G., Kieke,
D., Koelling, J., Lamont, T., McCarthy, G. D., Mertens, C., Send, U., Smeed,
D. A., Speich, S., van den Berg, M., Volkov, D., and Wilson, C.: Atlantic
Meridional Overturning Circulation: Observed Transport and Variability,
Front. Mar. Sci., 6, 260, 10.3389/fmars.2019.00260, 2019.Gent, P. R., Bryan, F. O., Danabasoglu, G., Doney, S. C., Holland, W. R.,
Large, W. G., and McWilliams, J. C.: The NCAR Climate System Model Global
Ocean Component, J. Climate, 11, 1287–1306, 10.1175/1520-0442(1998)011<1287:TNCSMG>2.0.CO;2, 1998.Griffies, S. M.: Elements of MOM4p1, GFDL Ocean Group Technical Report No.
6, NOAA/Geophysical Fluid Dynamics Laboratory, 444 pp., 2010.Hong, Y., Moore, J. C., Jevrejeva, S., Ji, D., Phipps, S. J., Lenton, A.,
Tilmes, S., Watanabe, S., and Zhao, L.: Impact of the GeoMIP G1 sunshade
geoengineering experiment on the Atlantic meridional overturning
circulation, Environ. Res. Lett., 12, 034009, 10.1088/1748-9326/aa5fb8, 2017.Iversen, T., Bentsen, M., Bethke, I., Debernard, J. B., Kirkevåg, A., Seland, Ø., Drange, H., Kristjansson, J. E., Medhaug, I., Sand, M., and Seierstad, I. A.: The Norwegian Earth System Model, NorESM1-M – Part 2: Climate response and scenario projections, Geosci. Model Dev., 6, 389–415, 10.5194/gmd-6-389-2013, 2013.Ji, D., Wang, L., Feng, J., Wu, Q., Cheng, H., Zhang, Q., Yang, J., Dong, W., Dai, Y., Gong, D., Zhang, R.-H., Wang, X., Liu, J., Moore, J. C., Chen, D., and Zhou, M.: Description and basic evaluation of Beijing Normal University Earth System Model (BNU-ESM) version 1, Geosci. Model Dev., 7, 2039–2064, 10.5194/gmd-7-2039-2014, 2014.Ji, D., Fang, S., Curry, C. L., Kashimura, H., Watanabe, S., Cole, J. N. S., Lenton, A., Muri, H., Kravitz, B., and Moore, J. C.: Extreme temperature and precipitation response to solar dimming and stratospheric aerosol geoengineering, Atmos. Chem. Phys., 18, 10133–10156, 10.5194/acp-18-10133-2018, 2018.Johns, W. E., Baringer, M. O., Beal, L. M., Cunningham, S. A., Kanzow, T.,
Bryden, H. L., Hirschi, J. J. M., Marotzke, J., Meinen, C. S., Shaw, B., and
Curry, R.: Continuous, Array-Based Estimates of Atlantic Ocean Heat
Transport at 26.5∘ N, J. Climate, 24, 2429–2449,
10.1175/2010JCLI3997.1, 2011.Jones, A., Haywood, J., and Boucher, O.: A comparison of the climate impacts
of geoengineering by stratospheric SO2 injection and by brightening of
marine stratocumulus cloud, Atmos. Sci. Let., 12, 176–183, 10.1002/asl.291, 2011.K-1 Model Developers: K-1 Coupled GCM (MIROC) description, K-1 Tech Report
No. 1. Center for Climate System Research, University of Tokyo, National
Institute for Environmental Studies, Frontier Research Center for Global
Change, edited by: edited by Hasumi, H., and Emori, S., https://ccsr.aori.u-tokyo.ac.jp/~hasumi/miroc_description.pdf#:~:text=The%20Model%20for%20Interdisciplinary%20Research%20on%20Climate%20%28MIROC%29%2C,interacts%20with%20the%20land%20and%20sea%20ice%20components (last access: 4 April 2022), 2004.Keith, D. W.: Geoengineering the climate: History and prospect, Annu.
Rev. Energ. Env., 25, 245–284, 10.1146/annurev.energy.25.1.245, 2000.Kravitz, B., Robock, A., Boucher, O., Schmidt, H., Taylor, K. E.,
Stenchikov, G., and Schulz, M.: The Geoengineering Model Intercomparison
Project (GeoMIP), Atmos. Sci. Let., 12, 162–167, 10.1002/asl.316,
2011.Kravitz, B., Forster, P. M., Jones, A., Robock, A., Alterskjær, K.,
Boucher, O., Jenkins, A. K. L., Korhonen, H., Kristjánsson, J. E., Muri,
H., Niemeier, U., Partanen, A. I., Rasch, P. J., Wang, H., and Watanabe, S.:
Sea spray geoengineering experiments in the geoengineering model
intercomparison project (GeoMIP): Experimental design and preliminary
results, J. Geophys. Res.-Atmos., 118, 11175–11186, 10.1002/jgrd.50856, 2013.Kravitz, B., Robock, A., Tilmes, S., Boucher, O., English, J. M., Irvine, P. J., Jones, A., Lawrence, M. G., MacCracken, M., Muri, H., Moore, J. C., Niemeier, U., Phipps, S. J., Sillmann, J., Storelvmo, T., Wang, H., and Watanabe, S.: The Geoengineering Model Intercomparison Project Phase 6 (GeoMIP6): simulation design and preliminary results, Geosci. Model Dev., 8, 3379–3392, 10.5194/gmd-8-3379-2015, 2015.Kravitz, B., MacMartin, D. G., Wang, H., and Rasch, P. J.: Geoengineering as a design problem, Earth Syst. Dynam., 7, 469–497, 10.5194/esd-7-469-2016, 2016.Kravitz, B., Rasch, P. J., Wang, H., Robock, A., Gabriel, C., Boucher, O., Cole, J. N. S., Haywood, J., Ji, D., Jones, A., Lenton, A., Moore, J. C., Muri, H., Niemeier, U., Phipps, S., Schmidt, H., Watanabe, S., Yang, S., and Yoon, J.-H.: The climate effects of increasing ocean albedo: an idealized representation of solar geoengineering, Atmos. Chem. Phys., 18, 13097–13113, 10.5194/acp-18-13097-2018, 2018.Latham, J., Bower, K., Choularton, T., Coe, H., Connolly, P., Cooper, G.,
Craft, T., Foster, J., Gadian, A., Galbraith, L., Iacovides, H., Johnston,
D., Launder, B., Leslie, B., Meyer, J., Neukermans, A., Ormond, B., Parkes,
B., Rasch, P., Rush, J., Salter, S., Stevenson, T., Wang, H., Wang, Q., and
Wood, R.: Marine cloud brightening, Philos. T. R. Soc. A., 370,
4217–4262, 10.1098/rsta.2012.0086, 2012.Li, H. and Fedorov, A. V.: Persistent freshening of the Arctic Ocean and changes
in the North Atlantic salinity caused by Arctic sea ice decline, Clim. Dynam.,
57, 2995–3013, 10.1007/s00382-021-05850-5, 2021.Liu, W. and Fedorov, A. V.: Global impacts of Arctic sea ice loss mediated by the
atlantic meridional overturning circulation, Geophys. Res. Lett., 46,
944–952, 10.1029/2018GL080602, 2019.Liu, W., Fedorov, A., and Sevellec, F.: The mechanisms of the Atlantic Meridional
Overturning Circulation slowdown induced by Arctic sea ice decline, J.
Climate, 32, 977–996, 10.1175/JCLI-D-18-0231.1, 2018.Malik, A., Nowack, P. J., Haigh, J. D., Cao, L., Atique, L., and Plancherel, Y.: Tropical Pacific climate variability under solar geoengineering: impacts on ENSO extremes, Atmos. Chem. Phys., 20, 15461–15485, 10.5194/acp-20-15461-2020, 2020.McCarthy, G. D., Brown, P. J., Flagg, C. N., Goni, G., Houpert, L., Hughes,
C. W., Hummels, R., Inall, M., Jochumsen, K., Larsen, K. M. H., Lherminier,
P., Meinen, C. S., Moat, B. I., Rayner, D., Rhein, M., Roessler, A., Schmid,
C., and Smeed, D. A.: Sustainable Observations of the AMOC: Methodology and
Technology, Rev. Geophys., 58, e2019RG000654, 10.1029/2019RG000654, 2019.Moore, J. C., Rinke, A., Yu, X., Ji, D., Cui, X., Li, Y., Alterskjær,
K., Kristjánsson, J. E., Muri, H., Boucher, O., Huneeus, N., Kravitz,
B., Robock, A., Niemeier, U., Schulz, M., Tilmes, S., Watanabe, S., Yang,
S.: Arctic sea ice and atmospheric circulation under the GeoMIP G1 scenario,
J. Geophys. Res.-Atmos., 119, 567–583, 10.1002/2013JD021060, 2014.Moore, J. C., Yue, C., Zhao, L., Guo, X., Watanabe, S., and Ji, D.:
Greenland Ice Sheet Response to Stratospheric Aerosol Injection
Geoengineering, Earth's Future., 7, 1451–1463, 10.1029/2019EF001393, 2019.Muri, H., Tjiputra, J., Otterå, O. H., Adakudlu, M., Lauvset, S. K.,
Grini, A., Schulz, M., Niemeier, U., and Kristjánsson, J. E.: Climate
response to aerosol geoengineering: A multimethod comparison, J.
Climate, 31, 6319–6340, 10.1175/JCLI-D-17-0620.1, 2018.Niemeier, U., Schmidt, H., Alterskjær, K., and Kristjánsson, J. E.:
Solar irradiance reduction via climate engineering: Impact of different
techniques on the energy balance and the hydrological cycle, J. Geophys.
Res.-Atmos., 118, 11905–11917, 10.1002/2013JD020445, 2013.Roberts, C. D., Jackson, L., and McNeall, D.: Is the 2004–2012 reduction of
the Atlantic meridional overturning circulation significant?, Geophys. Res.
Lett., 41, 3204–3210, 10.1002/2014GL059473, 2014.Send, U., Lankhorst, M., and Kanzow, T.: Observation of decadal change in
the Atlantic meridional overturning circulation using 10 years of continuous
transport data, Geophys. Res. Lett., 38, L24606, 10.1029/2011GL049801,
2011.Sévellec, F., Fedorov, A. V., and Liu, W.: Arctic sea-ice decline weakens the
Atlantic Meridional Overturning Circulation, Nat. Clim. Change, 7,
604–610, 10.1038/nclimate3353, 2017.Shu, Q., Qiao, F., Song, Z., and Xiao, B.: Effect of increasing Arctic river
runoff on the Atlantic meridional overturning circulation: a model study,
Acta Oceanol. Sin., 36, 1–7, 10.1007/s13131-017-1009-z, 2017.Smeed, D. A., Josey, S. A., Beaulieu, C., Johns, W. E., Moat, B. I.,
Frajka-Williams, E., Rayner, D., Meinen, C. S., Baringer, M. O., Bryden, H.
L., and McCarthy, G. D.: The North Atlantic Ocean Is in a State of Reduced
Overturning, Geophys. Res. Lett., 45, 1527–1533, 10.1002/2017GL076350, 2018.Smith, W. and Wagner, G.: Stratospheric aerosol injection tactics and costs
in the first 15 years of deployment, Environ. Res. Lett., 13, 124001,
10.1088/1748-9326/aae98d, 2018.Smyth, J. E., Russotto, R. D., and Storelvmo, T.: Thermodynamic and dynamic responses of the hydrological cycle to solar dimming, Atmos. Chem. Phys., 17, 6439–6453, 10.5194/acp-17-6439-2017, 2017.Stouffer, R. J., Eyring, V., Meehl, G. A., Bony, S., Senior, C., Stevens,
B., and Taylor, K. E.: CMIP5 scientific gaps and recommendations for CMIP6,
B. Am. Meteorol. Soc., 98, 95–105, 10.1175/BAMS-D-15-00013.1,
2017.Swingedouw, D., Rodehacke, C. B., Olsen, S. M., Menary, M., Gao, Y.,
Mikolajewicz, U., and Mignot, J.: On the reduced sensitivity of the Atlantic
overturning to Greenland ice sheet melting in projections: a multi-model
assessment, Clim. Dynam., 44, 3261–3279, 10.1007/s00382-014-2270-x,
2015.Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An overview of CMIP5 and
the experiment design, B. Am. Meteorol. Soc., 93, 485–498, 10.1175/BAMS-D-11-00094.1, 2012.
Thornalley, D. J. R., Oppo, D. W., Ortega, P., Robson, J. I., Brierley, C.
M., Davis, R., Hall, I. R., Moffa-Sanchez, P., Rose, N. L., Spooner, P. T.,
Yashayaev, I., and Keigwin, L. D.: Anomalously weak Labrador Sea convection
and Atlantic overturning during the past 150 years, Nature, 556,
227–230, 10.1038/s41586-018-0007-4, 2018.Tilmes, S., MacMartin, D. G., Lenaerts, J. T. M., van Kampenhout, L., Muntjewerf, L., Xia, L., Harrison, C. S., Krumhardt, K. M., Mills, M. J., Kravitz, B., and Robock, A.: Reaching 1.5 and 2.0 °C global surface temperature targets using stratospheric aerosol geoengineering, Earth Syst. Dynam., 11, 579–601, 10.5194/esd-11-579-2020, 2020.Watanabe, S., Hajima, T., Sudo, K., Nagashima, T., Takemura, T., Okajima, H., Nozawa, T., Kawase, H., Abe, M., Yokohata, T., Ise, T., Sato, H., Kato, E., Takata, K., Emori, S., and Kawamiya, M.: MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments, Geosci. Model Dev., 4, 845–872, 10.5194/gmd-4-845-2011, 2011.Wang, Q., Wekerle, C., Danilov, S., Sidorenko, D., Koldunov, N., Sein, D.,
Rabe, B., and Jung, T.: Recent Sea Ice Decline Did Not Significantly
Increase the Total Liquid Freshwater Content of the Arctic Ocean, J.
Climate, 32, 15–32, 2019.Weijer, W., Cheng, W., Garuba, O. A., Hu, A., and Nadiga, B. T.: CMIP6
Models Predict Significant 21st Century Decline of the Atlantic Meridional
Overturning Circulation, Geophys. Res. Lett., 47, e2019GL086075, 10.1029/2019GL086075, 2020.World Climate Research Programme (WCRP) the Working Group on Coupled Modelling (WGCM): Coupled Model Intercomparison Project 5 (CMIP5) network, https://esgf-node.llnl.gov/search/cmip5/, last access: 4 April 2022.Yang, D. and Saenko, O. A.: Ocean heat transport and its projected change
in CanESM2, J. Climate, 25, 8148–8163, 10.1175/JCLI-D-11-00715.1, 2012.Yang, H., Wang, K., Dai, H., Wang, Y., and Li, Q.: Wind effect on the
Atlantic meridional overturning circulation via sea ice and vertical
diffusion, Clim. Dynam., 46, 3387–3403, 10.1007/s00382-015-2774-z, 2016.Zhao, J. and Johns, W.: Wind-forced interannual variability of the Atlantic
meridional overturning circulation at 26.5∘ N, J. Geophys. Res.-Oceans, 119, 2403–2419, 10.1002/2013JC009407, 2014.