The amount of solar constant reduction required to offset the
global warming from an increase in atmospheric CO2 concentration is
an interesting question with implications for assessing the feasibility of
solar geoengineering scenarios and for improving our theoretical
understanding of Earth's climate response to greenhouse gas and solar
forcings. This study investigates this question by analyzing the results of
11 coupled atmosphere–ocean global climate models running experiment G1 of
the Geoengineering Model Intercomparison Project, in which CO2
concentrations are abruptly quadrupled and the solar constant is
simultaneously reduced by an amount tuned to maintain the top-of-atmosphere
energy balance and pre-industrial global mean temperature. The required solar
constant reduction in G1 is between 3.2 % and 5.0 %, depending on the
model, and is uncorrelated with the models' equilibrium climate sensitivity,
while a formula from the experiment specifications based on the models'
effective CO2 forcing and planetary albedo is well correlated with
but consistently underpredicts the required solar reduction. We propose an
explanation for the required solar reduction based on CO2
instantaneous forcing and the sum of radiative adjustments to the combined
CO2 and solar forcings. We quantify these radiative adjustments in
G1 using established methods and explore changes in atmospheric temperature,
humidity, and cloud fraction in order to understand the causes of these
radiative adjustments.
The zonal mean temperature response in G1 exhibits cooling in the tropics and
warming in high latitudes at the surface; greater cooling in the upper
troposphere at all latitudes; and stratospheric cooling which is mainly due
to the CO2 increase. Tropospheric specific humidity decreases due
to the temperature decrease, while stratospheric humidity may increase or
decrease depending on the model's temperature change in the tropical
tropopause layer. Low cloud fraction decreases in all models in G1, an effect
that is robust and widespread across ocean and vegetated land areas. We
attribute this to a reduction in boundary layer inversion strength over the
ocean, and a reduction in the release of water from plants due to the
increased CO2. High cloud fraction increases in the global mean in
most models. The low cloud fraction reduction and atmospheric temperature
decrease have strong warming effects on the planet, due to reduced reflection
of shortwave radiation and reduced emission of longwave radiation,
respectively. About 50 % to 75 % of the temperature effect is caused
by the stratospheric cooling, while the reduction in atmospheric humidity
results in increased outgoing longwave radiation that roughly offsets the
tropospheric temperature effect. The longwave (LW) effect of the cloud changes is small
in the global mean, despite the increase in high cloud fraction. Taken
together, the sum of the diagnosed radiative adjustments and the
CO2 instantaneous forcing explains the required solar forcing in G1
to within about 6 %. The cloud fraction response to the G1 experiment
raises interesting questions about cloud rapid adjustments and feedbacks
under solar versus greenhouse forcings, which would be best explored in a
model intercomparison framework with a solar-forcing-only experiment.
Introduction
In light of the warming of Earth in response to anthropogenic greenhouse gas
emissions , and continued lack of progress in curbing those
emissions , some e.g., have argued
for serious consideration of solar geoengineering, or reflecting sunlight to
artificially cool the Earth, as a means of reducing harms from climate
change. The Geoengineering Model Intercomparison Project (GeoMIP;
) was created to study the climate impacts of solar
geoengineering schemes. GeoMIP consists of a set of standardized experiments
for global climate models (GCMs) that include both an increase in
CO2 and some compensating effect, such as a reduction in the solar
constant or an increase in stratospheric aerosol concentration. In experiment
G1, the simplest of the GeoMIP experiments, the CO2 concentration
is abruptly quadrupled relative to pre-industrial levels, as in the
abrupt4xCO2
experiment from the Coupled Model Intercomparison Project, phase 5 (CMIP5;
), and at the same time the solar constant is abruptly
reduced by an amount tuned to maintain top-of-atmosphere (TOA) energy balance
and therefore keep the global mean temperature approximately at pre-industrial
levels. Besides providing an important theoretical underpinning to the
consideration of solar geoengineering scenarios, the G1 experiment is helpful
for improving our fundamental understanding of how the climate responds
differently to solar forcings, which operate in the shortwave (SW) part of
the radiative spectrum versus greenhouse gas forcings, which operate in the
longwave (LW), and how linear the response is to combinations of SW and LW
forcings. This can help us understand paleoclimates in which the Sun was
weaker , attribution of climate change to anthropogenic as
opposed to solar forcings , and the response of the
climate to non-solar SW forcings such as aerosol forcings .
Models included in this study, with references, institutions, solar
constant reduction in the G1 experiment (ΔS0), and global mean
surface air temperature change in G1 – piControl (ΔT). All models have a
full dynamical ocean coupled to the atmosphere.
ModelReferenceInstitutionΔS0ΔT (K)BNU-ESMBeijing Normal University4.4 %0.025CanESM-2Canadian Centre for Climate Modeling and Analysis4.0 %-0.013CCSM4National Center for Atmospheric Research4.1 %0.233CESM-CAM5.1-FVNational Center for Atmospheric Research4.7 %-0.157CSIRO-Mk3L-1-2Commonwealth Scientific and Industrial Research3.2 %0.034Organization/Bureau of MeteorologyGISS-E2-RNASA Goddard Institute for Space Studies4.5 %-0.292HadGEM2-ESMet Office Hadley Centre3.9 %0.241IPSL-CM5A-LRInstitut Pierre Simon Laplace3.5 %0.109MIROC-ESMAtmosphere and Ocean Research Institute (The University5.0 %-0.065of Tokyo), National Institute for Environmental Studies,and Japan Agency for Marine-Earth Science and TechnologyMPI-ESM-LRMax Planck Institute for Meteorology4.7 %-0.011NorESM1Bjerknes Centre for Climate Research, Norwegian4.0 %-0.044Meteorological Institute
For BNU-ESM, we are using a new realization, r3i1p1, that has a
greater solar constant reduction and better compensates global mean
temperature than the original. Two models that originally participated in G1,
EC-Earth and HadCM3, are excluded from our analysis because many of the
output fields necessary for this study were not available.
Percent solar constant reduction for models running the G1
experiment versus (a) solar constant reduction predicted by
Eq. (), based on effective radiative forcing values
from and pre-industrial planetary albedo values from
, and (b) equilibrium climate
sensitivity in the models, from . CESM-CAM5.1-FV and
CSIRO-Mk3L-1-2 are excluded from this figure because these models were not
included in .
An interesting question related to G1 is what amount of solar constant
reduction |ΔS0| is required to compensate for the CO2
increase. (For convenience, we hereafter drop the absolute value symbol and
use ΔS0 to refer to the solar constant reduction, keeping in mind
that the sign of the change is always negative in this context.) This
quantity varies between about 3 % and 5 % depending on the model; the
values for each model, which in every case achieved a global mean surface air
temperature within 0.3 K of that in the CMIP5 pre-industrial control
(piControl) experiment, are listed in Table . Because of its
implications for the scale of the solar geoengineering intervention that
would be required, it is important to understand what determines this
quantity. We start our investigation of this question by plotting in
Fig. a the required values of ΔS0 versus the
values predicted by a simple formula based on matching the reduction in
outgoing LW radiation (OLR) from the CO2 increase with a reduction
in the absorbed SW radiation:
ΔS0=4×F4xCO2,eff1-α,
or, in percentage terms,
ΔS0(%)=4×F4xCO2,eff1-α×100%S0,
where S0 is the solar constant (about 1361 W m-2), α is
the model's planetary albedo, and F4xCO2,eff is the effective
radiative forcing from a CO2 quadrupling, calculated by regressing
net TOA radiative flux against global mean temperature change in abrupt4xCO2
and taking the intercept .
Figure a shows a strong correlation (correlation
coefficient r=0.86) between the value of ΔS0 predicted by
Eq. () and the value that actually achieved the
experiment's objectives, indicating that CO2 forcing and planetary
albedo determine ΔS0 to a first order (primarily forcing, since it
varies much more between models than albedo does). However, for every model,
the actual ΔS0 is greater than those predicted by this theory, as
has been noted by for a subset of four models. This
underprediction is relevant from a scenario modeling standpoint, since
Eq. () was used by the modeling groups to create an
initial guess for ΔS0. Obtaining the correct value required running successive
10-year tuning runs of the GCMs, readjusting the solar reduction until a net TOA radiation imbalance of less than 0.1 W m-2 was
achieved.
One factor not accounted for by the initial guess formula is climate
feedbacks. We can get a sense for how feedbacks might affect the required ΔS0 by plotting it against
equilibrium climate sensitivity (ECS), or the amount of global mean warming
that occurs due to a doubling of CO2, the inter-model spread in
which is primarily determined by
feedbacks . Figure b shows that there is
no correlation (correlation coefficient r=0.02) between these quantities.
This makes sense because feedbacks are defined based on global mean
temperature changes, which are zero by design (and close to zero in practice)
in G1, and because the strengths of feedbacks are, at least to a first order,
similar for different types of forcings . These results from GeoMIP corroborate those of
, who found that the required geoengineering
forcing is independent of climate sensitivity in experiments with an ocean
GCM coupled to a single-layer atmosphere.
If neither radiative forcings nor feedbacks can fully explain the variation
in the required ΔS0, then we must turn to radiative adjustments
that do not depend on global mean temperature changes. The effective
CO2 radiative forcing in Eq. ()
incorporates rapid adjustments of the atmosphere's temperature and humidity
profiles, cloud properties, and surface albedo to the CO2 increase.
However, such adjustments to the solar forcing are not accounted for.
Effectively, Eq. () calculates the solar constant
reduction that would balance the instantaneous CO2 increase if
atmospheric properties were allowed to adjust to the CO2 increase
but not to the solar constant reduction. The consistent underestimation of
the required ΔS0 by Eq. () indicates
that atmospheric and surface adjustments in response to the combined
CO2 and solar instantaneous forcings have a greater net warming
effect on the climate than such adjustments to the CO2 forcing
alone, requiring a greater reduction in the solar constant to restore the
global mean temperature to pre-industrial conditions.
While we cannot calculate rapid adjustments to the solar forcing alone
without a set of model runs in which only the solar constant is changed, we
can use the G1 output to calculate radiative adjustments to the combined
CO2 and solar forcings, using existing analysis tools including the
approximate partial radiation perturbation (APRP) method
and radiative kernels . Assuming energy is
conserved in the models and the analysis methods are reasonably accurate, it
should be possible to use these calculated radiative adjustments to explain the
required solar constant reduction in G1, as expressed in the following
equation:
ΔS0=4×F4xCO2,inst+∑ΔRX1-α,
where F4xCO2,inst is the instantaneous radiative forcing from the
CO2 quadrupling, which is the change in OLR from the CO2
increase when all atmospheric and surface properties are held constant
, and ΔRX represents the global mean TOA
radiative adjustments to the combined forcings associated with various
physical properties X, following the notation of .
Since there is no global mean temperature change in G1 by design (and
approximately none in practice), we refer to the changes in TOA radiative
balance resulting from changes in various physical properties of the
atmosphere and surface as “adjustments” and not “feedbacks”. Note,
however, that the changes in TOA radiation are in many ways dependent on
local surface temperature changes, as discussed later.
This study examines changes in atmospheric temperature, specific humidity,
cloud fraction, and surface albedo in G1, and quantifies the radiative
effects of these changes in order to understand what determines the required
ΔS0 and why it is greater than that predicted using effective
CO2 forcing. We also explore the physical reasons for the changes
in atmospheric properties, particularly cloud properties, which have been
found to strongly affect meridional energy transport changes in G1, with
implications for regional temperature and precipitation responses
. The changes in atmospheric properties, including
clouds, are plotted and discussed in Sect. 2. Section 3 quantifies the
radiative effects of these surface and atmospheric adjustments to the G1
forcing. Section 4 examines the global means of these adjustments to see
which are most important in determining the required ΔS0 according
to Eq. (). In Sect. 5, we summarize our
results and discuss implications for future research on geoengineering and
solar climate forcings.
Zonal mean temperature change for G1 – piControl in each model as a
function of pressure.
Changes in the physical state of the atmosphere
To understand the physical basis for the radiative adjustments calculated in
later sections, in this section, we show changes in atmospheric temperature,
specific humidity, and cloud fraction that occur in the G1 experiment
relative to pre-industrial conditions. Throughout the paper, we show averages
over 40-year time periods: years 11–50 of the G1 simulation, to avoid
incorporating transient effects that occur in the first 10 years into
averages, and years 1–40 of the piControl simulation, except where otherwise
noted. Averaging over years 11–50 is standard procedure for analysis of the
GeoMIP experiments e.g.,; a longer
averaging period would not be possible since most models stopped the
experiment after 50 years. We treat years 11–50 mean as equilibrium
response, which seems appropriate since all components of the surface energy
budget show little to no drift after the first 10 years
. We also plotted time series of
the SW radiative adjustments calculated in Sect. 3 (Fig. S13 in the
Supplement) and found no appreciable drift that would have extended beyond
50 years in any of the models.
Figure shows the zonal mean temperature change for
G1 – piControl in each of the 11 models listed in Table . Several features
common to all models are apparent. First, while the global mean surface air
temperatures are all within 0.3 K of pre-industrial levels
(Table ), all of the models exhibit surface cooling in the
tropics and warming in the polar regions. This phenomenon has long been noted
as a feature of climate model experiments with the G1 setup
e.g., and is due to the
imposition of a net negative forcing in the tropics and a net positive
forcing at the poles . However, cooling dominates
when considering the atmosphere as a whole. The tropical mid- to upper
troposphere cools more than the surface does, because the tropical
temperature profile tends to follow a moist adiabat
e.g.,, so that slight cooling at the surface
leads to greater cooling aloft. The cooling of the tropical upper troposphere
mirrors the effect that happens in global warming, where the upper
troposphere warms more than the surface and emits more LW radiation, leading
to a negative climate feedback known as the lapse rate feedback. In the case
of G1, reduced LW emission from the atmospheric cooling has a warming effect
on the planet; we quantify this effect using radiative kernels in
Sect. .
Zonal mean change in the natural log of specific humidity for G1 – piControl in each model as a function of
pressure.
Most models have an area of reduced cooling or even warming in the tropics
near 100 hPa. This corresponds to the location of the tropical tropopause
layer (TTL), an area in the tropics between about 70 and 150 hPa with
properties of both the troposphere and stratosphere
. The detailed vertical structure of temperature
changes here may have to do with complex interactions between local
temperature, humidity, and cloud properties. Another notable feature of the
temperature change is the cooling of the stratosphere. An increase in carbon
dioxide concentration cools the stratosphere, due to increased emission of LW
radiation to space , and a decrease in the solar
constant also cools the stratosphere because it reduces the amount of
ultraviolet radiation absorbed by ozone. The stratospheric cooling effect
from the solar constant reduction is about an order of magnitude smaller than
that from the CO2 quadrupling .
Zonal mean change in cloud fraction for G1 – piControl in each
model as a function of pressure or height for HadGEM2-ES. To help
comparisons with other models, the vertical axis for HadGEM2-ES is scaled
according to e-z/8000m (where z is height), which is
approximately proportional to pressure.
Figure shows the change in
the natural log of specific humidity
between G1 and piControl in each model. We use a log scale because it makes
it easier to visualize changes in specific humidity that occur over multiple
orders of magnitude, and because log humidity changes are used in the water
vapor radiative kernel calculations described in Sect. .
Most of the troposphere becomes drier in G1 in all models, consistent with
the large-scale cooling given similar relative humidity. Since water vapor is a strong
greenhouse gas, this drying has a cooling effect on the planet, which we
quantify in Sect. . Most models show moistening in the
polar regions at low altitudes, consistent with the warming there, although
the moistening is typically confined to smaller areas than the warming,
indicative of a slight decrease in relative humidity at the poles
(see Fig. 5
of ). Interestingly, stratospheric water vapor decreases
in most models, but it increases in the three models (BNU-ESM,
CSIRO-Mk3L-1-2, and IPSL-CM5A-LR) that have warming in the TTL (albeit this
moistening is mostly confined to the Northern Hemisphere in the IPSL model).
This is consistent with stratospheric humidity being set by temperatures in
the TTL, through which air travels to reach the stratosphere as part of the
Brewer–Dobson circulation e.g.,.
Figure shows the zonal mean changes in cloud
fraction in each of the models for G1 – piControl. Unlike atmospheric
temperature and humidity, cloud fraction model output in CMIP5 and GeoMIP was
archived on the native model vertical grid instead of a set of standardized
pressure levels. Most of the GeoMIP models use hybrid sigma pressure
coordinates, with the exceptions of GISS-E2-R, which uses pressure
coordinates, and HadGEM2-ES, which uses hybrid sigma height coordinates. To
enable direct comparisons with the temperature and humidity changes and
radiative kernel calculations, we have regridded the cloud fraction output to
the standard CMIP5 pressure levels or to a fixed height grid for HadGEM2-ES.
Conversion from hybrid sigma to pressure or height coordinates was done using
a Python function (see “Code and data availability” below) based on the
algorithm used in the “convert_sigma_to_pres” Matlab function by
, available at
http://www.aos.wisc.edu/~dvimont/matlab/. Since surface pressure output
(required for the hybrid sigma pressure regridding) was only available for
the last 50 years of the piControl simulation for CSIRO-Mk3L-1-2, we have
used the last 40 years of this simulation as the control case for cloud
fraction for this model, instead of the first 40 years.
In their study of four models running G1, noted that
all four had a reduction in low cloud fraction, while high clouds had an
inconsistent change. Figure shows that an overall
reduction of low cloud fraction occurs in all 11 models included in this
study. For high clouds, we also find an inconsistent response, but overall
high cloud fraction increases in most models. Some models, especially those
in which the TTL warms (Fig. ), have a decrease in
high cloud fraction in the TTL, and two of them, CSIRO-Mk3L-1-2 and
IPSL-CM5A-LR, have an overall decrease in high cloud fraction. Since low
clouds primarily have a cooling effect on the climate due to their strong SW
reflection, a reduction in low clouds would result in a warming effect that
would partially offset the cooling from solar geoengineering. An increase in
high cloud fraction would also be expected to have a warming effect on the
planet by reducing LW emission to space, although other variables, such as
cloud height, are more important to the LW effect of cloud changes in global
warming simulations . We quantify the TOA SW and LW
effects of the changes in cloud properties in Sect.
and , respectively. In many models, there is an increase in
clouds in the stratosphere over Antarctica, likely due to the stratospheric
cooling. Two models, HadGEM2-ES and MIROC-ESM, have a dipole in cloud
fraction changes in the upper troposphere, corresponding to northward and
southward shifts, respectively, of the intertropical convergence zone (ITCZ)
in these models .
To get a sense of the zonally asymmetric spatial patterns of cloud fraction
changes and to better understand areas of inter-model consensus and
disagreement, we plot in Fig. maps of the
multi-model mean changes in low, middle, and high cloud fraction for G1 –
piControl. Within the ranges for low, middle, and high clouds, we assume
random overlap between adjacent layers of the common pressure grid. We use
680 hPa as the boundary between low and middle clouds and 440 hPa as the
boundary between middle and high clouds, following the standards for the
International Satellite Cloud Climatology Project (ISCCP; see Fig. 2 of
), or 3250 and 6500 m in the case of
HadGEM2-ES, which roughly correspond to these pressure levels in the 1976
Standard Atmosphere . These plots, and all subsequent
multi-model mean maps, show stippling where fewer than all but two of the
included models agree on the sign of the change, so that unstippled areas
indicate robust changes. Since this agreement could happen by chance in
isolated areas, we focus on areas with apparent spatial structure or a
physical reason why we might expect a change. For all multi-model mean maps,
corresponding maps for each of the individual models are available in the
Supplement. Global mean cloud fraction changes for the individual models are
shown in Table .
Multi-model mean changes in low (a), middle (b),
and high (c) cloud fraction for G1 – piControl. Hatching indicates
areas where fewer than 9 of the 11 models agree on the sign of the
change.
Global mean changes in low, middle, and high cloud fraction in G1 – piControl.
The reduction in low cloud fraction (Fig. a) is
widespread, occurring over most ocean areas, except for regions close to the
Equator and poles, and over most non-desert land areas. Middle clouds
(Fig. b) have fewer areas with robust changes, but
there is a reduction in the cloud fraction on either side of the Equator over
the Atlantic and Pacific and over the equatorial Indian Ocean. This may be
related to a narrowing of the annual mean tropical precipitation maximum
see Fig. 5 of, which may be due in part to a
reduction in the seasonal migration of the ITCZ . For high
clouds (Fig. c), there are few areas with robust
changes, but there is a notable increase in high clouds over the Equator, in
some subtropical regions (around 30∘ N and S), and over the poles,
particularly Antarctica. Figure shows that the high
cloud increases over the poles are mostly in the stratosphere.
Without additional experiments varying potential drivers of cloud changes, it
is difficult to prove definitively the causes for the changes in cloud
fraction. However, it is possible to gain some insight into the reasons for
changes in low cloud fraction over the ocean by plotting several variables
that are correlated with low cloud fraction in observations. These include
lower-tropospheric stability (LTS), defined as the difference in potential
temperature between 700 hPa and the surface , and
estimated inversion strength (EIS), a metric of the temperature inversion at
the top of the marine boundary layer. EIS is defined as Eq. 4EIS=LTS-Γm850z700-LCL,
where Γm850 is the moist adiabatic lapse rate at 850 hPa,
z700 is the height of the 700 hPa surface, and LCL is the lifting
condensation level.
Figure shows the changes in EIS (Fig. a) and LTS (Fig. b) for G1 – piControl.
Both of these quantities generally decrease across most of the ocean, except
for some regions centered near 15∘ N and S. The reduction in EIS is
generally smaller than the reduction in LTS (due to the correction for the
moist adiabatic temperature profile) but is still widespread. A reduction of
the strength of the inversion at the top of the boundary layer would be
expected to reduce low cloud fraction by encouraging mixing of dry air into
the boundary layer, so the reduction in EIS over the ocean is a likely
explanation for the reduction in low cloud fraction there. Stability metrics
are included in low cloud fraction schemes in many models, and those that use
the scheme, such as CCSM4 and NorESM1-M, have an explicit
dependence of low cloud fraction on stability. However, the robustness of the
reduction in low cloud fraction in G1 indicates that it is not the result of
the idiosyncrasies of any one cloud fraction scheme.
Multi-model mean changes in EIS (a) and LTS (b)
for G1 – piControl. Hatching indicates areas where fewer than seven of nine models
agree on the sign of the change. CSIRO-Mk3L-1-2 and MPI-ESM-LR models are
excluded from this plot because near-surface specific humidity output, which
is required to calculate EIS, was not available.
Besides changes in stability metrics, other factors that have been suggested
as explaining changes in marine stratocumulus cloud fraction under global
warming conditions in large-eddy simulation models include reduced LW
radiative cooling from cloud tops due to increased CO2 and H2O
concentrations, decreased subsidence above the boundary layer, and increased
sea surface temperatures . analyzed
changes in marine stratiform cloud fraction in CMIP3 and CMIP5 global warming
experiments, and found a reduced low cloud fraction in most models, which
they attributed to an increase in sea surface temperature (SST). While EIS
increased in the global warming experiments, which would promote increased
cloud fraction, this was not enough to compensate for the SST increase. In
G1, SST changes little (and in fact decreases slightly in the tropics and
subtropics; Fig. 1), leaving EIS to dominate changes in
low cloud fraction over the ocean.
It does not appear that cloud top radiation or subsidence could be
responsible for the widespread low cloud reduction for the following
reasons. The mechanism of reduced LW radiative cooling from cloud tops would
be much weaker for G1 than for global warming if at all present because,
while CO2 concentrations have increased, water vapor concentrations
have decreased; also, the reduction in insolation further reduces the net
radiative cooling rate via its direct SW effect. We have not tried to
quantify how these fluxes have changed in G1 since LW radiative fluxes at the
top of the boundary layer were not included in the GeoMIP model output
archive. We might expect that subsidence would change due to the effects of
the combined CO2 and solar forcings on the atmospheric radiative
cooling profile. However, meridional stream function anomaly plots for G1 – piControl show that while some areas
have anomalous subsidence, others have anomalous rising motion, and these
regions are not consistent between models or with the regions of low cloud
fraction decrease. Large-eddy simulation experiments involving a
CO2 increase and insolation reduction could help better understand
what role, if any, these processes play in the changes in low cloud fraction
in the G1 scenario, as well as the role of any changes in boundary layer or
free troposphere relative humidity not associated with any of the processes
discussed here.
attribute the increase in EIS in global warming experiments
to greater surface warming over the continents and the tropical western
Pacific warm pool relative to the rest of the ocean; the warmed air is then
advected over the tops of the marine stratocumulus fields. However, a reverse
version of this mechanism does not seem to be at work in G1 because cooling
is more robust over the ocean than over land
Fig. 2. It is also important to keep in
mind that there are different metrics of stability that are useful for
different parts of the atmosphere and for different types of clouds.
argued that any cloud cover
changes in G1 would be due in part to increases in atmospheric stability, but
in our study it appears to be a decrease in stability that is most relevant
to the low cloud reduction. Another metric of stability, the rate of increase
of equivalent potential temperature θe with height, does in fact
increase in G1 relative to piControl, as shown in Fig. 8 of
. So, even as the atmosphere has gotten less
stable in G1 with respect to boundary layer turbulence, it has gotten more
stable with respect to deep convection, at least to the extent to which
∂θe/∂z is a predictor of changes in deep
convection, as assumed by . To better
understand the reasons for the changes in clouds, it would be useful to
further investigate the effects of CO2 and solar forcings on
potential and equivalent potential temperature profiles.
Over land, existing research suggests that the reduction in low cloud
fraction in G1 is a result of the physiological responses of plants to
increased CO2, as represented in the models' dynamic vegetation
schemes. ran GCM simulations in which the CO2
concentrations experienced by plants were doubled while the radiative fluxes
were held constant, and found that low cloud fraction decreased in many
vegetated land areas (see their Fig. 1, central panel). The low cloud
fraction decrease in the Cao et al. study is strongest in South America,
eastern North America, southeast Asia, southeast Africa, and western Europe,
which are the same areas of reduced low cloud cover in G1. The mechanism is
that, when CO2 concentrations are higher, plants' stomata do not
need to open as much to take in the same amount of CO2, leading to
less transpiration of water from the plants . This causes
a reduction in near-surface relative humidity over land, seen in both
Fig. 2 and G1 Fig. 5, which reduces
the cloud fraction. In addition to plant physiology, it is possible that some
of the reduction in relative humidity and cloud fraction over land in G1 is
due to a reduction in evaporation directly caused by the reduction in surface
SW radiation. The balance between these two quantities explains the reduction
in global mean precipitation in G1
, since precipitation must balance
evaporation, suggesting that a similar mechanism may affect cloud fraction.
Over the ocean, however, near-surface relative humidity increases in G1 in
most areas, despite the reduction in evaporation ,
implying that evaporation changes are not the reason for the low cloud
changes there.
Global mean radiative adjustments in G1 – piControl, and excess and
total solar forcing in G1, in W m-2. Positive values indicate a warming
effect (increase in absorbed SW radiation or decrease in OLR), except for
solar forcing where positive values represent a cooling. SW adjustments
correspond to multi-model means plotted in Fig. . LW
adjustments correspond to multi-model means plotted in
Figs. and , with the sign flipped
for Fig. . “Sum” is the sum of all the SW and LW
adjustments. Fexcess is calculated using
Eq. () and represents the actual instantaneous solar
forcing (Fsolar) in G1 minus that which would match the
CO2 effective or instantaneous forcing. Fsolar
represents the total instantaneous solar forcing calculated from theory
(Eq. ) or actually used in G1
(Eq. ).
To calculate the SW radiative effects of changes in clouds and other
atmospheric and surface properties, we use the APRP method introduced by
, which is based on a single-layer radiative transfer
model of the atmosphere that can be expressed analytically and requires as
inputs only the monthly mean surface and TOA radiative fluxes and total
column cloud fraction outputs from the GCMs. APRP shows the radiative effects
of physical changes in clouds, accounting for cloud masking effects, in which
the differences between clear-sky and all-sky fluxes change in response to
forcing without changes in the clouds themselves. The calculations shown here
have previously been used as inputs to energy balance model simulations to
understand the effects of changes in clouds and surface albedo on atmospheric
energy transport in G1 .
Figure shows the multi-model mean change in net downward SW
radiative flux at the TOA due to changes in clouds (Fig. a),
non-cloud atmospheric scattering and absorption (Fig. b), and
surface albedo (Fig. c), calculated using APRP. Global mean
radiative adjustments for the individual models in the SW and LW are shown in
Table , which will be referred to in the discussion
of the required solar forcing in G1 in Sect. . Clouds
generally have a robust and widespread warming effect in the SW, in locations
that closely correspond to the areas of reduced low cloud fraction shown in
Fig. a. The non-cloud atmosphere effects are very
weak by comparison in the multi-model mean, but there are several models with
appreciable positive values for this adjustment. Maps of this adjustment for
the individual models (Fig. S7) show that for HadGEM2-ES, it appears to be
related to a reduction in atmospheric dust, since most of the warming effect
occurs over and downwind of deserts; in IPSL-CM5A-LR, the effect is
relatively spatially uniform but slightly stronger in higher latitudes. For
surface albedo, there are warming effects in high latitudes from decreases in
sea ice and snow cover associated with the residual polar warming in G1.
There are also some warming effects in lower latitudes near desert regions,
such as in the Sahel region; this may have to do with vegetation effects.
There are several small regions, such as Tibet, with increases in surface
albedo, presumably due to increased snow cover as a result of surface cooling
there (see Fig. 2 of ). Surface
albedo effects are strong in some locations, such as the Sea of Okhotsk, but
the relatively small area over which surface albedo changes can occur limits
their importance in the global mean.
Multi-model mean change in net downward SW radiation at the TOA in
G1 – piControl due to changes in cloud properties (a), non-cloud
atmospheric absorption and scattering (b), and surface
albedo (c), calculated using APRP method .
Hatching indicates areas where fewer than seven of nine models agree on the sign of
the change. CSIRO-Mk3L-1-2 and GISS-E2-R models are excluded because not all
fields necessary for APRP were correctly archived.
LW radiative effects
The technique of radiative kernels was developed to quantify LW radiative adjustments and feedbacks
using standard monthly mean climate model output. These kernels consist of
matrices of the partial derivatives of OLR with respect to changes in surface
temperature, atmospheric temperature, specific humidity, and greenhouse gas
concentration as a function of latitude, longitude, month, and (where
applicable) pressure, calculated using offline calculations with a particular
GCM's radiative transfer code. Radiative kernels have been developed based on
a variety of GCMs, including GFDL AM2 , CAM3
, MPI-ESM-LR , and CESM-CAM5
.
We have applied the radiative kernels to the G1 ensemble.
The choice of model used to generate the kernels has been shown to have
little effect on the results . After regridding the
kernels to the latitude and longitude grid of each GCM, we multiplied them by
the changes in temperature and the log of specific humidity, normalized by
the standard anomaly used to compute the kernels (1 K for the surface and
atmospheric temperature kernels, and the change in log specific humidity
associated with a 1 K warming at constant relative humidity for the water
vapor kernel), in order to compute the change in OLR associated with the
changes in each of these quantities for G1 – piControl. We summed the OLR
changes from each vertical level in order to get overall radiative
adjustments from column temperature and water vapor changes, and we used the
annual mean of the monthly results for our analysis.
Figure shows multi-model mean changes in OLR for G1
– piControl calculated from the atmospheric temperature
(Fig. a), surface temperature
(Fig. b), and water vapor
(Fig. c) kernels. Global means for the individual
models are shown in Table . For the atmospheric
temperature kernel, there is a strong decrease in OLR that is widespread
across the globe and robust across models. This is associated with the
cooling of the atmosphere (see Fig. ) and reduced longwave emission. The
reduction in OLR is stronger in the tropics than in the polar regions and is
due to some combination of upper tropospheric and stratospheric cooling. We
discuss the contribution of the stratospheric component in the next section.
Surface temperature changes have little effect on the TOA LW radiation
balance, but there is a reduction in OLR in the tropics and subtropics and an
increase in the polar regions that is consistent across models, due to the
patterns of tropical cooling and polar warming at the surface. The OLR change
from the surface temperature kernel is much smaller than that for atmospheric
temperature because the atmosphere is not very transparent to LW radiation in
most wavelengths, and because temperature changes are smaller at the surface
than in the upper troposphere and stratosphere. Changes in water vapor
concentration cause a robust increase in OLR that partially offsets the
decrease in OLR from the atmospheric temperature kernel. The water vapor
concentration decreases in the upper troposphere
(Fig. ), which increases LW emission to space by
lowering the effective altitude of emission.
Multi-model mean change in OLR in G1 – piControl due to changes in
atmospheric temperature (a), surface temperature (b), and
specific humidity (c), calculated using radiative kernels
. Hatching indicates areas where fewer than 9 of 11 models
agree on the sign of the change.
In addition to the quantities plotted in Fig. ,
radiative kernels can also be used to calculate the effect of changes in
cloud properties on OLR. This is often measured according to the change in
the cloud radiative effect (CRE), which is the difference in OLR in clear-sky
minus all-sky averages. However, changes in the cloud radiative effect may
include cloud masking effects. We can correct the change in LW CRE for the
effects of existing clouds on clear-sky fluxes using the difference in flux
changes calculated according to clear-sky and all-sky kernels, following
:
ΔLWCREadjusted=LWCREG1-LWCREpiControl+ΔOLRk,T-ΔOLRk,T,clear+ΔOLRk,Ts-ΔOLRk,Ts,clear+ΔOLRk,q-ΔOLRk,q,clear+ΔOLRk,CO2-ΔOLRk,CO2,clear,
where, in the subscripts, k denotes a change in OLR calculated using a
kernel, clear denotes quantities calculated using the clear-sky
instead of all-sky kernels, T is atmospheric temperature, Ts is surface
temperature, and q is specific humidity. Since the CO2 forcing kernels were for a doubling of CO2, we
doubled these kernels to obtain the radiative flux changes for a
CO2 quadrupling.
Figure shows the multi-model mean change in LW CRE
calculated using Eq. (). There is a modest cooling
effect in the global, multi-model mean (see also
Table ), but there are some places where there is a
robust warming effect. The strongest warming effects occur near the eastern
equatorial oceans, where the increase in high cloud fraction is greatest,
while the strongest cooling effects occur in two belts in the eastern
Pacific, which are associated with robust decreases in low and middle cloud
fraction (see Fig. ). There are also
widespread cooling effects over the midlatitude oceans, where low cloud
fraction decreases. Generally, an increase in high cloud fraction would be
expected to result in a warming effect, because high clouds are much cooler
than the surface and are more effective at trapping LW radiation. However, in
the case of G1, it appears that the LW effect of the decrease in low cloud
fraction compensates for this, despite the cloud temperature being closer to
the surface temperature, because the low cloud reduction occurs over a wide
area. The spatial correspondence of areas of strong cooling effects in
Fig. to areas of strong low cloud fraction decrease in
Fig. a supports this view. Besides cloud fraction,
LW radiation is also sensitive to changes in cloud height and cloud optical
depth . It may be that the global mean increase in
high cloud fraction that occurs in most models has a limited effect on OLR
because the new clouds being formed are optically thin; we would especially
expect this in the case of polar stratospheric clouds. The radiative effects
of changes in cloud optical thickness are difficult to assess from the GeoMIP
output currently available. These effects have been quantified in global
warming simulations using cloud radiative kernels ,
but the use of these requires cloud fraction statistics binned by optical
depth and cloud top height produced by the ISCCP satellite simulator
that is part of the Cloud Feedback Model
Intercomparison Project CFMIP; Observation
Simulator Package . The simulator must be run
inline with each GCM or else requires instantaneous cloud fraction output
(rather than monthly means) in order to be run retrospectively. The necessary
outputs for cloud radiative kernels were saved in CFMIP but not in GeoMIP. It
would be useful to follow the CFMIP protocols in future GeoMIP experiments in
order to allow further quantitative analysis of the changes in clouds that
occur under combined SW and LW forcings.
Multi-model mean change in LW cloud radiative effect in G1 –
piControl, corrected for cloud masking of LW air temperature, surface
temperature, and water vapor adjustments and CO2 forcing. Positive
values indicate a decrease in OLR, i.e., a warming effect. Hatching indicates
areas where fewer than 9 of 11 models agree on the sign of the
change.
Excess required solar radiative forcing in G1 versus that expected from
effective CO2 forcing (black bar), global mean SW and LW radiative
adjustments (colored bars), and sum of all the radiative adjustments (gray
bar), in models for which all of these quantities were calculated. For all
except Fexcess, positive values indicate a warming effect
(increase in absorbed SW radiation or reduction in OLR). The first three
colored bars correspond to the SW radiative adjustments calculated using APRP
(multi-model mean maps shown in Fig. ). The three blue and
green bars correspond to the LW radiative adjustments calculated using
radiative kernels (multi-model mean maps shown in
Fig. ). The magenta bar corresponds to the change in
LW cloud radiative effect, corrected for cloud masking effects using
radiative kernels (multi-model mean map shown in
Fig. ).
Connections between radiative effects and required solar reduction
Having quantified the radiative effects of changes in the physical properties
of the atmosphere and surface in G1, we now revisit the question of the
amount of solar constant reduction required to offset the quadrupling of
CO2. The solar constant reduction predicted based on effective
CO2 radiative forcing (Eq. )
systematically underestimated the actual reduction required
(Fig. b). In this section, we attempt to account for this
discrepancy by comparing the amount of extra solar forcing needed with the
global means of the radiative adjustments calculated in Sect. . This comparison is shown in
Fig. for the eight models for which effective radiative
forcing values from were available and all of the
radiative adjustments could be calculated. The excess required solar
reduction, Fexcess, shown in black, is calculated according to
Fexcess=ΔS0(%)actual-ΔS0(%)predicted×1361Wm-2100%×1-α4,
where ΔS0(%)actual is listed in
Table and ΔS0(%)predicted is
calculated using Eq. (). In terms of radiative
forcing, Fexcess is the difference between the actual solar
forcing required in G1 and the effective forcing from the CO2
quadrupling.
The relative sizes of the bars in Fig. are fairly
similar across models. The strongest warming effect is generally from the LW
atmospheric temperature adjustment, followed by the SW cloud adjustment. The
only consistent cooling effect comes from the LW water vapor adjustment.
Surface albedo effects are generally small, as is the SW clear-sky adjustment,
with the exceptions discussed in Sect. . The LW surface
temperature adjustment is practically negligible in all models, while the LW
cloud adjustment is also small but has an inter-model range of about 1 W m-2. The model with the greatest cooling effect from the LW cloud
adjustment, IPSL-CM5A-LR, is the model with the greatest global mean decrease
in high cloud fraction, whereas most other models have an increase in high
cloud fraction (Table ).
As in Fig. but with excess solar forcing
calculated using instantaneous instead of effective CO2 radiative
forcing. The navy blue bar indicates the reduction in OLR due to stratospheric
temperature adjustment from CO2 quadrupling given by
, to illustrate the portion of the atmospheric
temperature adjustment to G1 attributable to stratospheric
cooling.
Comparing the black and gray bars in Fig. shows that
the sum of all the global mean radiative adjustments more than accounts for
the additional solar constant reduction required to balance the CO2
quadrupling, compared to the amount predicted by Eq. (). The fact that the sum of the radiative
adjustments consistently overestimates Fexcess points to the fact
that this is not really a fair comparison. Rapid adjustments to a
CO2 quadrupling by itself, which were included in the calculation
of effective CO2 radiative forcing, are being double counted,
because they also show up in the radiative adjustments to the G1 combined
forcing, to the extent that they are not canceled by the solar reduction.
To account for this, we plot in Fig. the same
quantities as in Fig. but where the black bars are
calculated using instantaneous rather than effective CO2 forcing
for the predicted solar constant reduction (i.e., using
F4xCO2,inst rather than F4xCO2,eff in Eq. and then substituting into Eq. ). Expressed mathematically, the comparison done in
Fig. is
ΔS0(%)actual×1361Wm-2100%-4×F4xCO2,inst1-α×1-α4=?∑ΔRX.
The black bars in Fig. show the left-hand side of the
Eq. (), while the gray bars show the right-hand
side. If the two bars are the same size, that means that the actual solar
constant reduction matches that from Eq. (3).
Instantaneous forcing, unlike effective forcing, cannot be calculated from
monthly mean model output through a simple linear regression of TOA flux
changes against surface temperature; instead it requires running each GCM's
radiative transfer code offline with standard and quadrupled CO2
concentrations. For this reason, estimates of instantaneous CO2
forcing are available for fewer models than for effective forcing. We used
the “double call” instantaneous forcing estimates from the CMIP5 archive
shown in for the CanESM-2 and IPSL-CM5A-LR models. For
three other models (CCSM4, HadGEM2-ES, and NorESM1-M), we use estimates of
instantaneous CO2 forcing given by based on
residuals between total TOA flux changes and radiative responses calculated
with radiative kernels.
In Fig. , the black and gray bars match to within
about 10 %, indicating that the theory expressed in Eq. () works well for explaining the amount of solar
constant reduction required to balance a CO2 increase. Since the
equation must be true given energy conservation, this agreement demonstrates
that the approximate methods used to calculate the radiative adjustments work
well in the context of G1. In evaluating this agreement, it is useful to
express Eq. () in terms of total
instantaneous solar forcing rather than solar constant reduction:
Fsolar,theory=F4xCO2,inst+∑ΔRX,
and compare it to the actual solar forcing in G1:
Fsolar,actual=ΔS0,actual(%)×1361Wm-2100%×1-α4.
These values are listed in the last two columns of
Table . The errors in the total solar forcing in G1
obtained from Eq. () are all within 0.5 W m-2 or within about 6 % of the total, indicating that the
instantaneous solar forcing required to balance an abrupt CO2
increase is well explained by the sum of the instantaneous CO2
forcing and the radiative adjustments to the combined forcings.
The two largest radiative adjustments to the G1 forcing scenario are the LW
atmospheric temperature adjustment and the SW cloud adjustment. Since the
temperature adjustment contains effects of both stratospheric and
tropospheric temperature changes, it is worth trying to understand the
partitioning between these effects. We have overlaid the OLR reduction due to
the stratospheric cooling in abrupt4xCO2 given by onto
the ΔT (atmosphere) bar in Fig. . This OLR
reduction is the stratospheric adjustment to the CO2 forcing, shown
in Fig. 2b of . The overlay shows that between about
50 % to 75 %, depending on the model, of the atmospheric temperature
radiative adjustment in G1 is due to cooling of the stratosphere by the
increase in CO2. The rest is due to a combination of the additional
cooling of the stratosphere from the reduction in insolation and the cooling
of the upper troposphere which arises from the surface cooling in the
tropics. The water vapor adjustment roughly compensates for the tropospheric
component of the temperature adjustment, and these effects are physically
linked because a cooler atmosphere emits less LW radiation but also contains
less water vapor to absorb radiation from below. Therefore, the main reasons
why the instantaneous solar forcing must be greater than the instantaneous
CO2 forcing in order to maintain energy balance are the failure to
undo the stratospheric cooling and the reduction in low cloud fraction.
Conclusions
This paper characterizes the physical responses of the atmosphere and surface
to the GeoMIP G1 scenario and quantifies their radiative effects, with the
goal of explaining what determines the solar constant reduction required to
balance the CO2 increase. At the surface, the tropics cool and the
poles warm while global mean temperature remains at pre-industrial conditions. The upper
troposphere experiences cooling at all latitudes, with the tropical upper
troposphere cooling more than the surface. The stratosphere cools more than
anywhere else in the atmosphere, due primarily to the CO2 increase
. The tropospheric temperature effect is a reversal
of the negative lapse rate feedback that happens in global warming
simulations, in which the tropical upper troposphere warms more than the
surface; in G1, because the tropics cool and the tropical temperature profile
tends to follow a moist adiabat, the upper troposphere also cools, which has
a warming effect on the climate by reducing OLR. Atmospheric specific
humidity is reduced in the upper troposphere, which makes the atmosphere less
opaque to LW radiation and largely offsets the radiative effect of the
tropospheric cooling. Low cloud fraction exhibits a widespread decrease over
the ocean and vegetated land areas in all models, which we attribute to
decreases in boundary layer inversion strength over the ocean and reduced
evaporation from plants due to the physiological response to increased
CO2 over land. The low cloud fraction reduction has a strong
surface warming effect due to reduced reflection of sunlight by the clouds.
High cloud fraction increases in the global mean in most models, but the LW
radiative effect of cloud changes in G1 is slightly negative in the global,
multi-model mean. When all the global mean radiative adjustments in G1 are
added together, the results account, to within 10 %, for the difference
between the solar constant reduction that would match the instantaneous
CO2 forcing and the tuned solar constant reduction that met the TOA
energy balance threshold required by the G1 experiment protocol.
For future model runs of the G1 experiment, such as those being prepared for
the next phase of GeoMIP corresponding to CMIP6 ,
it would be useful to have a better initial guess for the solar constant
reduction in order to reduce the necessary amount of tuning. Using Eq. () for this purpose would be tricky because the
radiative responses to the combined CO2 and solar forcings would be
unknown before actually running the model. However, one could simply
substitute an empirical value of about 4 W m-2, a typical value for the
sum of the radiative adjustments in G1 (Fig. ), for
∑ΔRX in Eq. (). Then, tuning
would only need to account for model-specific deviations from this number. If
instantaneous CO2 forcing was not available for a particular model,
the modelers could add a correction of about 2.5 to 3 W m-2, a typical
value for the black bars in Fig. , to the effective
CO2 forcing in Eq. ().
Our analysis of the G1 experiment provides some insights into how the climate
responds differently to CO2 and solar forcings, but more work is
necessary to better understand this question. The sums of the radiative
adjustments in G1 (gray bars of Fig. ) are about 2 W m-2 larger than the difference between effective and instantaneous
forcing in abrupt4xCO2 e.g., Table 1 of. This must be
due to some combination of the solar forcing enhancing or imperfectly
canceling CO2-induced radiative adjustments that warm the planet
(such as the stratospheric cooling), and the solar forcing overcompensating
for adjustments that cool the planet (such as the tropospheric lapse rate
adjustment). Going beyond showing the stratospheric adjustment from
abrupt4xCO2 in Fig. to separate the contributions of
the CO2 and solar forcings to the radiative adjustments in G1 would
be nontrivial. Regressing the APRP- and kernel-derived radiative responses in
the abrupt4xCO2 experiment against global mean temperature change to obtain
the rapid adjustments to the CO2 quadrupling would run into issues
with accuracy due to nonlinearity of feedbacks with temperature increases
that would skew the location of the intercept , so an
analysis of GCM runs with increased CO2 and fixed SSTs would be
necessary. Furthermore, it may not be the case that the rapid adjustments to
the two forcings add together linearly. While some variables, such as global
mean temperature, respond linearly to different combinations of CO2
and solar forcings , other aspects of the
climate system are inherently nonlinear. LW emission goes with the fourth
power of temperature, and specific humidity rises exponentially with
temperature, a relationship that affects atmospheric energy transport and the
meridional temperature gradient . The
interactions between the exponential dependence of specific humidity and the
fourth power dependence of LW emission on temperature may affect the extent to
which the water vapor and tropospheric temperature adjustments compensate for
each other, as they seem to roughly cancel in G1 but the water vapor feedback
exceeds the lapse rate feedback in global warming simulations
. The water vapor and lapse rate
adjustments are dependent on the pattern of tropical cooling and polar
warming which might not occur if a latitudinal distribution of solar
reflection was targeted to cool the poles more .
It would be very interesting to study how cloud rapid adjustments and
feedbacks differ under solar versus CO2 forcing in a model
intercomparison framework. The cloud fraction changes in G1 imply that rapid
cloud responses to CO2 and solar forcings are different, but this
requires further study with GCM runs that perturb only the solar constant and
not CO2. Since the global mean temperature does not change, the G1
experiment tells us very little about cloud feedbacks, which are temperature
dependent. An attempt was made to study cloud rapid
adjustments and feedbacks under solar forcings by subtracting the G1
experiment from the abrupt4xCO2 experiment, but this approach is bound to
produce similar feedback parameters for this “solar” forcing versus the
abrupt4xCO2 – piControl CO2 forcing because, while there are two
different baselines, there is only one perturbation run, abrupt4xCO2, that
has a global mean temperature change onto which radiative flux changes can be
regressed. Some studies have included solar-only GCM runs
e.g.,, but
these have included only one or two models, and while some, such as
, have looked at cloud radiative effects and cloud
fraction, none have used methods that account for cloud masking to isolate
the radiative effects of physical cloud changes. There is no solar equivalent
of abrupt4xCO2 in CMIP5 or any of its associated projects; the closest
analogue is probably the aerosol-forcing-only historical runs from the CMIP5
“historicalMisc” collection, analyzed, e.g., by
. The Precipitation Driver and Response Model
Intercomparison Project includes a solar constant
increase experiment, and the CFMIP component of CMIP6 will include abrupt
solar constant increase and decrease runs . These ensembles
will provide good opportunities to further explore cloud and other changes
under solar forcings.
If we were thinking about actually doing solar geoengineering, using Eq. () to predict the necessary solar reflection
would be hampered by the fact that we would not know the radiative responses
to the intervention a priori. Estimates of these adjustments from
models would be subject to uncertainty (note the inter-model spread of 2 W m-2 in the gray bars of Fig. ), and various
aspects of the current anthropogenic radiative forcing, particularly aerosol
forcing, also have large uncertainty . A smaller-scale
geoengineering test that would impose a measurable change in the global mean
radiation balance, which might require about 1/10 the radiative forcing
of a full deployment and last about a decade , could provide a better estimate of these quantities. Such a
test would pose ethical questions related to justice, compensation and
informed consent similar to those for a full deployment
. Another option would be to actively control the
global mean temperature by adjusting the amount of solar reflection every
year in response to observations . If solar
geoengineering was attempting to actually cool the planet from its
temperature at the start of deployment (e.g., back to pre-industrial conditions
or reversing an overshoot of some temperature target), instead of simply
preventing future warming under increasing CO2, then
temperature-dependent feedbacks on the solar forcing, which are not captured
by the G1 experiment, may affect the amount of solar geoengineering required.
While the lack of correlation with ECS in Fig. b suggests
that the feedbacks would work just as well for cooling as warming, the
inertia in the system caused by ocean heat storage would affect the rate at
which feedbacks could operate, and we should be cautious about extending
arguments based on an assumption of equilibrium to such transient situations.
Analysis of other GeoMIP experiments, such as G4, that do impose a global
mean temperature change from the solar forcing, could help illustrate these
issues. If solar geoengineering was to be done using stratospheric aerosols,
then an additional layer of uncertainty regarding microphysical and chemical
effects would impact the amount of aerosol injection required to achieve the
desired forcing, as summarized by .
Besides their effects on the required solar forcing, the changes in
atmospheric physical properties that occur in G1 are interesting in their own
right and may have policy implications if they translated to a real
geoengineering deployment. If low cloud fraction were actually reduced by
solar geoengineering, it could result in increased solar energy production,
and could enhance vegetation growth in sunlight-limited regimes like the
Amazon . On the other hand, a reduction in low clouds
over the ocean would make it more difficult to do marine cloud brightening at
the same time as other forms of solar geoengineering. Changes in cirrus
clouds are also relevant in the context of research on the effects of
sedimentation of injected stratospheric aerosols on high clouds
and proposals to intentionally thin
cirrus clouds with nucleation-inducing aerosols in order to cool the Earth by
increased LW emission . The increase in high clouds
in most models in G1 indicates that thermodynamic and radiative adjustments
to the forcing scenario can have effects on high clouds that may counteract
unintentional or intentional microphysical effects. Our analysis of G1 also
illustrates that stratospheric ozone could be affected by changes in
stratospheric water vapor resulting from TTL temperature changes. In model
runs with actual injection of sulfate aerosols, LW absorption of these
particles warms the tropical tropopause and increases stratospheric water
vapor, which results in decreased ozone concentrations
. suggest that this risk could be
mitigated by instead injecting calcite aerosols, which would absorb much less
LW radiation than sulfates, but the inconsistency between models in
stratospheric water vapor responses to the G1 experiment, which includes no
aerosol injection in G1, shows that much uncertainty remains in this area.
Taken together, these issues emphasize the importance of continuing to
perform and analyze geoengineering simulations, both in highly idealized
scenarios like G1 and more realistic ones like G4 or G4SSA
, in order to better understand the climate responses to
geoengineering schemes and the different roles played by thermodynamics,
radiation, microphysics, and chemistry in these responses.
All scripts used to analyze data and create plots are available at
https://zenodo.org/record/1328272.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-18-11905-2018-supplement.
RDR analyzed the GCM output, produced the figures, and wrote the bulk of the paper.
TPA provided general guidance and assisted with the preparation of the manuscript.
The authors declare that they have no conflict of
interest.
This article is part of the special issue “The Geoengineering
Model Intercomparison Project (GeoMIP): Simulations of solar radiation
reduction methods (ACP/GMD inter-journal SI)”. It is not associated with a
conference.
Acknowledgements
Three anonymous reviewers provided constructive comments that helped to improve the paper.
This work was supported by a grant to JISAO from the Fund for Innovative Climate and Energy Research.
For their roles in producing, coordinating, and making available the CMIP5 and GeoMIP model output,
we acknowledge the climate modeling groups (listed in Table of this paper),
the World Climate Research Programme's (WCRP) Working Group on Coupled Modelling (WGCM),
and the Global Organization for Earth System Science Portals (GO-ESSP).
We are grateful to Duoying Ji, Ben Kravitz, Helene Muri, Ulrike Niemeier, Stephen Phipps, and
Jin-Ho Yoon for helping to provide access to GeoMIP output that was
not available through online repositories; to Karen Shell for helping to explain how to use the
radiative kernels; and to Eui-Seok Chung for providing numbers for the double-call instantaneous
forcing plotted in .
We thank Dargan Frierson, Blaž Gasparini, Cristian Proistosescu, Brian Rose,
and Robert Wood for discussions and comments that helped to influence the paper.
Edited by: Ben Kravitz
Reviewed by: three anonymous referees
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