Introduction
Geoengineering (also called “climate intervention”) describes
a set of technological approaches to reduce the effects of climate change by
deliberately intervening in the climate system e.g.,.
There are two broad categories of geoengineering that are commonly discussed:
solar geoengineering modifying the amount of shortwave radiation
incident at the surface; and carbon dioxide removal
. There are also proposals, such as cirrus cloud thinning
that do not fit neatly into either of these two
categories. In all subsequent discussions in this paper, we only discuss
solar geoengineering methods.
Two of the most commonly proposed methods of global geoengineering are
stratospheric sulfate aerosol geoengineering and marine cloud brightening
(MCB). Comparison of the different climate effects of these two methods
e.g., reveals that, among other things, the
spatial distribution of the applied forcing strongly affects the climate
effects. Many of the effects of sulfate geoengineering can be reasonably well
approximated by a uniform reduction in shortwave radiative flux reaching the
surface . Conversely, MCB targets low clouds over oceans
, which are not ubiquitous. In addition, there are
higher-order effects due to the altitude at which the shortwave scattering
occurs, including multiple scattering effects, infrared absorption of
shortwave and longwave radiative flux by sulfate aerosols or cloud particles,
and absorption of shortwave radiative flux by atmospheric CO2 and
water vapor e.g.,.
Idealized simulations of solar geoengineering are useful in the context of
multi-model intercomparisons, in that they capture many of the effects of
more complicated methods of representing geoengineering, yet can be performed
by a wide variety of models. In simulations conducted under the
Geoengineering Model Intercomparison Project GeoMIP;,
an idealized method of representing stratospheric sulfate aerosol
geoengineering is via reductions in total solar irradiance. As an example of
this representation, experiment G1 involved offsetting the global radiative
flux imbalance from a quadrupling of the CO2 concentration via
solar reduction. Thus far, 15 models have participated in this simulation,
providing information about model commonalities and differences in the global
climate response, including effects on temperature, the hydrological cycle,
cryosphere, terrestrial biosphere, and extreme events among numerous
other
studies.
The GeoMIP website
(http://climate.envsci.rutgers.edu/GeoMIP/, last access: 11 September 2018) provides an up-to-date list of publications using GeoMIP model
output.
While total solar irradiance reductions are straightforward to simulate in
all models, this idealization is not a good approximation of MCB, nor of
near-surface solar geoengineering approaches over the ocean in general. The
dominant effect of MCB would be an increase in albedo of marine low clouds
through aerosol effects. More generally, changes in the albedo near the
marine surface such as in the G4Foam experiment; can
produce different signatures from reductions in energy input at the top of
the atmosphere, particularly in terms of spatial distribution. While some
forms of albedo modification like stratospheric sulfate aerosol
geoengineering operate over broad areas (on a hemispheric or larger scale),
albedo changes produced by near-surface marine geoengineering would likely
operate on smaller spatial scales and be concentrated over particular oceanic
regions.
In this study, we investigate the climate effects of using ocean albedo
increases to offset CO2 warming and compare those effects with
those of total solar irradiance reduction (experiments are described in more
detail in the following section). All simulations were conducted under the
auspices of GeoMIP, allowing us to characterize a range of model responses to
these different idealized methods of representing solar geoengineering.
Methodology and description
Our analyses focus on four simulations: (1) a preindustrial control
simulation (piControl), (2) a simulation in which the CO2
concentration is abruptly quadrupled from its preindustrial value
(abrupt4xCO2), (3) a simulation in which the net radiative flux imbalance in
abrupt4xCO2 is offset by a reduction in total solar irradiance (G1), and
(4) a simulation in which the net top-of-atmosphere radiative flux imbalance
in abrupt4xCO2 is offset by an increase in ocean albedo everywhere by a
uniform factor (G1ocean-albedo). piControl and abrupt4xCO2 are standard
experiments in the Coupled Model Intercomparison Project phase 5
CMIP5;. G1 is described further by ,
and many of the gross features of the results are described by
. All models participating in experiment G1 needed to
reduce model total solar irradiance by 3.5 %–5.0 % to offset the
radiative forcing from a quadrupling of the CO2 concentration. In
G1ocean-albedo, the ocean surface albedo was increased abruptly at the start
of the simulation such that net top-of-atmosphere radiative flux perturbation
was within ±0.1 W m-2 of the piControl value in an average over
years 21–30 of simulation. Based on preliminary simulations described by
, it took approximately 20 years for the climate to
reach steady state after an abrupt simultaneous change in the CO2
concentration and the ocean albedo. As will be shown in subsequent sections,
once the appropriate value of ocean albedo increase is found and imposed, the
climate system adjusts rapidly, requiring at most a few years to reach a
steady state in global mean temperature as was the case in experiment
G1;. Table lists the models
participating in this study, including relevant references and the required
change in albedo to meet the objectives of experiment G1ocean-albedo. A
similar table for experiment G1 is given by . One of
the advantages of G1ocean-albedo is that, like G1, all models can conduct
this simulation fairly easily. Table S1 in the Supplement quantifies how well
each model achieved radiative balance in the G1 and G1ocean-albedo
experiments.
Description of the 11 models participating in this study. Column 1
gives the standard model name. Columns 2 and 3 give the default and perturbed
surface ocean albedo, defined as upward shortwave divided by downward
shortwave radiative flux at the surface, both averaged over ocean regions and
over years 11–50 of simulation. Column 4 is the ratio of column 3 to column
2 (calculated prior to rounding the values in columns 2 and 3). Column 5
gives the factor (δ) by which the model default ocean albedo was
multiplied to achieve negligible top-of-atmosphere radiative flux changes
under an abrupt4xCO2 simulation described in greater detail
by. The differences between ratio and δ are caused in
part by cloud responses. Column 6 gives a relevant reference for each model.
All values are rounded to two decimal places.
Model name
piControl
G1ocean-albedo
Ratio
δ
Reference
ocean albedo
ocean albedo
BNU-ESM
0.12
0.17
1.48
2.50
CanESM2
0.11
0.19
1.73
2.45
CESM-CAM5.1-FV
0.10
0.18
1.79
2.70
CSIRO-Mk3L-1.2
0.12
0.19
1.61
2.04
EC-Earth
0.10
0.19
1.97
3.17
GISS-E2-R
0.08
0.16
1.95
2.53
HadGEM2-ES
0.10
0.17
1.83
2.44
IPSL-CM5A-LR
0.10
0.17
1.78
2.33
MIROC-ESM
0.10
0.20
2.00
3.10
MPI-ESM-LR
0.09
0.23
2.40
5.42
NorESM1-M
0.09
0.18
1.95
2.77
Table S2 in the Supplement quantifies temperature trends in each
participating model over years 11–50 of simulation. The mean model trend
over this period is approximately 0 K decade-1 (to four decimal
places), and with little exception, the trends in G1 and G1ocean-albedo are
an order of magnitude smaller than the trends in the abrupt4xCO2 simulation.
As such, for the purpose of analysis, we assume that “slow responses”,
i.e., responses operating on timescales longer than a few years
e.g.,, are negligible in the G1 and
G1ocean-albedo simulations. We do not separate results into rapid adjustment
and slow response timescales, and with the exception of time series plots,
all figures show averages over the years 11–50 of simulation, which we take
as a sufficient indication of the dominant climate response after the
transient response has been resolved.
Except where indicated, all plots show the mean model response. All values in
the text are reported as mean (min to max), where mean indicates the
all-model mean for that particular quantity, min is the lower bound of the
range of model responses, and max is the upper bound of the range of model
responses. In all maps, stippling indicates where fewer than 75 % of the
models agree on the sign of the response. All models in
Table were able to provide output for all variables
except for cloud radiative forcing. The models included in cloud forcing
analyses are BNU-ESM, CanESM2, CESM-CAM5.1-FV, HadGEM2-ES, IPSL-CM5A-LR, and
MPI-ESM-LR. Tables S1–S15 in the Supplement provide more quantitative
information for all of the analyses presented in this study.
Results
Albedo and temperature
Figure shows the change in albedo at the
top of the atmosphere and at the surface for the abrupt4xCO2, G1, and
G1ocean-albedo simulations, where albedo is defined as the ratio of upwelling
to downwelling all-sky shortwave radiative flux. Quantitative values are
given in Tables S3 and S4 in the Supplement. Results for abrupt4xCO2 and G1 are
consistent with known responses of an increase in absorbed shortwave by
increased CO2, reduced cloud cover, and reduced snow and sea ice
cover e.g.,. These result in a broad
decrease in albedo at the top of the atmosphere and a decrease in surface albedo
in many regions with substantial snow and ice cover. G1ocean-albedo retains
many of these local high-latitude features, but with large albedo increases
over ocean, consistent with the experimental design and imposed forcing.
Top-of-atmosphere (TOA) and surface albedo differences (relative to
piControl) for the abrupt4xCO2, G1, and G1ocean-albedo experiments. Albedo
here is calculated as the ratio of upwelling to downwelling all-sky shortwave
radiative flux, either at TOA or at the surface. Values are averages over
years 11–50 of simulation. Stippling indicates where fewer than 8 out of 11
models agree on the sign of the response.
Figures and expand upon this
picture by showing changes in shortwave and longwave cloud forcing and clear
sky flux in G1 and G1ocean-albedo. In Fig. , cloud
forcing is defined as all-sky minus clear-sky radiative flux measured at the
top of the atmosphere. Positive shortwave values and negative longwave values
in Fig. are indicative of less cloud cover. In
Fig. , values indicate changes in top-of-atmosphere net
clear-sky flux, where net is defined as downward minus upward. Positive
values indicate less upward flux in the perturbed experiment (G1 or
G1ocean-albedo), and negative values indicate more upward flux.
Shortwave (a, b) and longwave (c, d) cloud forcing
changes due to the G1 (a, c) and
G1ocean-albedo (b, d) perturbations. Cloud forcing is defined as all-sky minus
clear-sky radiative flux at the top of the atmosphere, with positive values
indicating more net downward flux.
showed that cloud cover in G1 tends to be reduced,
which is consistent with what is depicted in Fig.
over broad swaths of the globe. For G1ocean-albedo, cloud cover is reduced
over most ocean regions and large portions of land. Exceptions include
negative shortwave and positive longwave values over the Arctic, much of
Africa, South Asia, Australia, and the leeward side of the Andes. The results
of Fig. are consistent with an increase in the
CO2 concentration, with more absorption of shortwave and more
outgoing longwave radiative flux. Exceptions are many of the same regions as
in Fig. , which show negative (or less positive)
shortwave values and less negative longwave values. Thus, over most regions
of the globe, the results are consistent with a combination of increased
CO2 and less cloud cover. Over the other regions (named
previously), Fig. would indicate that cloud cover
increases, which would result in less shortwave absorption and less outgoing
longwave radiative flux, consistent with the results in
Fig. . These changes in cloudiness have implications for
the hydrologic cycle, which we revisit in Sect. .
Shortwave (a, b) and longwave (c, d) net (downward
minus upward) clear-sky radiative flux changes at the top of the atmosphere due
to the G1 (a, c) and G1ocean-albedo perturbations, with positive
values indicating more net downward flux. Positive values indicate that
upward clear-sky flux decreased in the perturbed (G1 or G1ocean-albedo)
experiments, and negative values indicate that upward clear-sky flux
increased in the perturbed experiments.
Figures and admittedly only
report the first-order explanations of the radiative flux changes in G1 and
G1ocean-albedo. Second-order effects could include additional shortwave
absorption by clouds or feedbacks on water vapor flux due to reduced
evaporation. Additional work is needed to better understand the role of
individual flux changes and processes on clouds and circulation patterns.
Figure shows changes in global mean, land mean, and
ocean mean surface air temperature for the G1 and G1ocean-albedo multi-model
ensembles. Quantitative values are provided in Table S5 in the Supplement.
Whereas the G1 simulation largely offsets global temperature changes due to
increased CO2 concentration, G1ocean-albedo is approximately
0.36 K (-0.12 to 1.20) warmer than the control simulation. This is
predominantly due to warming over land by 1.14 K (0.41 to 1.83). The
temperature results in Fig. indicate that the
temperature change happens within approximately the first year, and while
some models show a slight trend in temperature over the 50-year
G1ocean-albedo simulation (Table S2 in the Supplement), in general, any such trends
are small, especially as compared to the warming in the abrupt4xCO2
simulation. This lack of substantial transient behavior after an initial fast
response indicates that G1ocean-albedo has entered a new approximate steady
state.
Global (a), land (b), and ocean
(c) average temperature change for the G1 (blue) and G1ocean-albedo
(red) simulations. Lines show the all-model ensemble mean, and shading shows
model spread (smallest to largest values).
Figure shows spatial patterns of change in temperature
and top-of-atmosphere net radiative flux. (Also see Tables S5 and
S6 in the Supplement.) The temperature changes are broadly consistent with the net radiative
flux changes in the respective experiments. As was discussed by
, G1 results in an “overcooling” of the tropics and
an “undercooling” of the poles, consistent with offsetting the ubiquitous
longwave forcing from CO2 with a latitudinally dependent reduction
in shortwave. G1ocean-albedo shows warming at high latitudes, over land
regions, and in some ocean regions near or downwind of large continents, with
the remaining ocean regions generally showing cooling. This warming pattern
downwind of large continents does not have a seasonal component, although
some individual models show more warming than others (not shown).
While the warming over land is easily explainable from first principles, the
temperature response over the ocean is heterogeneous (likely due to clouds;
see above), and it is perhaps somewhat counterintuitive that on average
temperatures over the global oceans do not decrease. Because net
top-of-atmosphere radiative flux is approximately zero in G1ocean-albedo, the
global warming cannot be the result of energy being added to or subtracted
from the climate system, and instead must be the result of energy
redistribution. Three hypotheses for why these temperature change patterns
look the way they do (which will be tested in subsequent sections) include
the following:
Based on energy balance arguments, G1ocean-albedo should experience global average warming.
Most warming over oceanic regions is due to transport of heat from land to
ocean.
Any contributions to temperature or radiative flux changes from changes in
ocean heat content are small on the timescales being evaluated here.
Hypothesis 1: energy balance
The Earth system can be considered as a simple surface-atmosphere energy
budget model:
S(1-A)4=(1-ϵ/2)σTs4,
where S is total solar irradiance at the top of the atmosphere (i.e., the
solar “constant”), A is albedo of the Earth, ϵ is the longwave
emissivity of the atmosphere, and Ts is surface temperature. In
this model, Ts=21/4Ta, where Ta is
atmospheric temperature.
Taking the total differential yields
dS(1-A)4-SdA4=1-ϵ24σTs3dTs-dϵ2σTs4.
Isolating dTs yields
dTs=dS(1-A)4-SdA4+dϵ2σTs4/1-ϵ24σTs3.
Simplifying,
dTs=dS(1-A)16σTs3(1-ϵ/2)-SdA16σTs3(1-ϵ/2)+dϵ/21-ϵ/2Ts4.
From Eq. () and using Ts=286.491 K (the
average piControl value from the Earth system models), S=1366 W m-2,
and A=0.3, it follows that ϵ=0.748.
Surface air temperature (a, c; K) and TOA net
radiative flux (c, d; W m-2) changes for experiments
G1 (a, c) and G1ocean-albedo (b, d). Values are averages
over years 11–50 of simulation. Stippling indicates where fewer than 8 out
of 11 models agree on the sign of the response.
Equation () can be augmented to consider changes in land and
ocean components separately. Fℓ and Fo are the
land and ocean fractions, 0.3 and 0.7, respectively, such that
Fℓ+Fo=1. The land and ocean albedos are
Aℓ and Ao, respectively. Greenhouse gases are
assumed to be well mixed (i.e., dϵ is the same over land
and ocean). Then, after solving for change in ocean temperature
dTs,o, Eq. () becomes
dTs,o=1Fo(FℓdSℓ+FodSo)(1-A)16σTs3(1-ϵ/2)-S(FℓdAℓ+FodAo)16σTs3(1-ϵ/2)+dϵ/21-ϵ/2Ts4-FℓdTs,ℓ.
Nearly all of the variables on the right sides of Eqs. () and
() can be solved from values provided in the Supplement, values
provided above, and dS/S=-0.042 . The
only variable that is difficult to solve for in this idealized context is
dϵ, representing changes in emissivity. Such changes can
occur due to changes in the CO2 concentration (or other greenhouse
gases), changes in water vapor, or changes in cloud cover. Estimating this
quantity using the abrupt4xCO2 scenario would correctly capture changes in
emissivity due to CO2 changes under the G1ocean-albedo simulation,
but it would likely overestimate contributions due to water vapor because of
tropospheric warming. As such, estimates of dϵ under
G1ocean-albedo will be calculated using G1, which will capture changes in
emissivity from the CO2 changes but without large changes in
atmospheric water vapor. Admittedly, water vapor and cloud cover will likely
differ between G1 and G1ocean-albedo, rendering this estimate imperfect.
However, we think this process yields a more appropriate result than using
abrupt4xCO2.
Using Eq. () and substituting dTs=0 K,
dS=1366⋅(-0.042) W m-2, A=0.3,
Ts=286.491 K, ϵ=0.748, and dA=-0.007
(Table S3 in the Supplement) yields dϵ=0.0401. For G1, each of the
three terms on the right side of Eq. () are then -3.01, 0.72,
and 2.29 K, respectively. The first of these terms corresponds to solar
changes, the second term is for planetary albedo changes, and the third term
is for emissivity (greenhouse gas) changes.
Annual mean change in land–ocean energy transport
(Sect. ; W m-2) from piControl. See
Eq. () for a formal definition.
From the Supplement tables, for G1ocean-albedo,
dAo=0.023, dAℓ=-0.004,
dSℓ=dSo=0, and
dTs,ℓ=1.14 K. Then substituting into
Eq. () yields dTs,o=0.61 K, which is
higher than the Earth system model ensemble average of 0.03 K. For
comparison with the values from G1, the term corresponding to changes in
solar input is 0 K, the term corresponding to changes in albedo is
-1.52 K, and the term for changes in emissivity is 2.29 K. By
Eq. (), these values yield a global mean temperature change of
0.77 K, which is higher than the Earth system model ensemble average of
0.36 K.
This simple energy balance formulation clearly cannot incorporate all of the
feedbacks and complex behaviors of the Earth system models. Nevertheless,
based on energy balance constraints, G1ocean-albedo results in both land and
ocean warming. However, the values recovered by the energy balance model are
not consistent with the results of the Earth system models for
G1ocean-albedo. To account for these differences, we turn to circulation
changes, which are described in the following section.
Hypothesis 2: the role of land–ocean energy transport (LOET)
Although the air over the ocean warms somewhat in G1ocean-albedo, it does not
warm uniformly. Figure shows that much of the warming
over the ocean is in areas near land, indicating the potential for some of
the heating energy over land to be transported to ocean regions. Indeed, the
oceans far from land experience cooling, which is consistent with
expectations for a large increase in albedo (Table ).
Transport of heating energy from land to ocean can be quantified via
calculating what call horizontal energy transport, and
which we call land–ocean energy transport (LOET), as it represents an
aggregate transport of energy from the atmosphere over the land (averaged
over all land regions) to the atmosphere over the ocean (averaged over all
ocean regions). provide a more detailed description,
calculation, and validation of this concept using a three-box energy balance
model that can be fitted to changes in land–ocean temperature and TOA energy
imbalance such that the model captures the relevant energy transport
dynamics; we repeat here only the calculations germane to our discussions.
Annual mean time series of all-model mean surface fluxes (terms in
Eq. ) for global averages (a), land averages
(b), and ocean averages (c). All fluxes are positive in the
downward direction.
describe a method of estimating adjusted radiative
forcing and the aggregate strength of global feedbacks via linear regression
of the net global, annual mean TOA radiative flux imbalance (ΔR)
against the global, annual mean temperature change (ΔT) in response
to a forcing. The y intercept of the regression line gives an estimate of
adjusted radiative forcing (F), and the negative of the slope of
the regression line gives the feedback parameter (λ). Similarly, one
can perform regression just over land-averaged quantities (denoted with the
subscript ℓ) or just over ocean quantities (subscript o). Feedback
parameter values are provided in Table .
Feedback parameters (Sect. ; units
W m-2 K-1) for global, land, and ocean averages, calculated via
the “Gregory method” , where annual mean
top-of-atmosphere net radiative flux is regressed against annual mean
temperature.
Global feedback
Land feedback
Ocean feedback
parameter
parameter
parameter
(λg)
(λℓ)
(λo)
BNU-ESM
0.9019
0.7181
0.9838
CanESM2
1.1539
1.1898
1.1260
CESM-CAM5.1-FV
1.1435
1.0357
1.1591
CSIRO-Mk3L-1.2
1.0192
0.9300
0.8034
EC-Earth
1.2124
1.1937
1.3155
GISS-E2-R
2.2440
1.9751
2.3560
HadGEM2-ES
0.8411
0.8363
0.8351
IPSL-CM5A-LR
0.8367
1.2891
0.5894
MIROC-ESM
1.0378
0.8736
1.0383
MPI-ESM-LR
1.3701
1.0573
1.3986
NorESM1-M
1.4285
1.8828
1.6063
In addition, as is derived in detail by , one can regress
ΔTℓ against ΔTo to obtain the equation
ΔTℓ=αo/Fℓλℓ+αℓ/FℓδTo+Fλℓ+αℓ/Fℓ,
where αℓ is the land heat transport parameter (units of
W m-2 K-1), αo is the ocean heat transport
parameter, and Fℓ is the land fraction (approximately 0.3). If
one solves this equation for αℓ and αo,
then one can define
ΔQ=αℓΔTℓ-αoΔTo.
The quantity ΔQ is the time-dependent LOET (units of W m-2).
Figure provides calculations of LOET for the simulations
presented here. See Table S7 in the Supplement for more details on individual model
values. In the abrupt4xCO2 simulation, changes in LOET are positive with
respect to piControl (indicating an increase in heat transport from the land
to the ocean) and decrease in magnitude steadily over the course of the
simulation; these results are discussed in more detail by
.
In experiment G1, LOET increases by a model-dependent constant value and
remains relatively unchanged over the course of the simulation. Although the
air temperature over land in G1 increases slightly, and the air temperature
over ocean decreases slightly , the temperature
changes in G1 are more latitude dependent than representative of a clear
land–ocean contrast (Fig. ), so it is perhaps not
unexpected that LOET would be small.
Experiment G1ocean-albedo exhibits a strong land–ocean contrast in
temperature (Fig. ), and the response is in steady state
after a few years. As such, consistent with the behavior of other fluxes,
LOET in G1ocean-albedo does not show transient behavior. LOET in
G1ocean-albedo is approximately 2.20 (1.35 to 3.21) W m-2, which is
larger than in the other experiments examined here.
Hypothesis 3: atmospheric column energetics and net energy flux into the oceans
An additional potential source of energy to the atmosphere is a reduction in
net ocean heat uptake. Calculating changes in ocean heat uptake are
challenging and not particularly revealing in this study for three reasons:
It is possible that the models used in simulating G1ocean-albedo were not
entirely spun up to steady state. As such, any remaining imbalances could
manifest as changes in ocean heat content. In principle, one could subtract
the preindustrial control value, which likely has a similar trend in
ocean heat content arising from spinup. However, this would not remove the
influence of nonlinearities (state dependence), so there is no way to
guarantee that the signal is entirely due to the G1ocean-albedo forcing.
As is seen in Table S1 in the Supplement, not all models were able to achieve
top-of-atmosphere net radiative flux balance over the course of the
simulation. These small changes can lead to large changes in ocean heat
content over the course of a 50-year simulation, consistent with CMIP5 models
. For example, a 0.1 W m-2 imbalance over a 50-year
period can lead to an additional 5.5×1022 J of energy incident at
the ocean surface. As such, we are unable to properly assess the degree to
which ocean heat content changes may be due to small imbalances.
Ocean heat content can be (and is often) calculated up to a certain depth,
meaning calculations of it can be sensitive to redistribution of heat to/from
lower depths, obscuring the signal of the forcing.
As an alternative, we calculate net energy exchange across the surface in
terms of changes in radiative and turbulent fluxes.
calculated energetics changes in the entire atmospheric column. However,
because we are only interested in net surface fluxes, we calculate
ΔB=ΔRsurf+ΔSH+ΔLH,
where ΔRsurf is the change in net surface radiative flux
(shortwave and longwave), ΔSH is change in sensible heat
flux from the atmosphere to the surface, and ΔLH is change
in latent heat flux from the atmosphere to the surface. By convention, all
fluxes are positive downward unless specifically noted. Calculations of
individual terms in this budget, as well as of ΔB, are provided in
Tables S8–S12 in the Supplement. Because these calculations are performed at the
surface, no advection term (e.g., LOET) is needed, and ΔB is well
defined as a land or ocean average.
Annual mean time series of hydrological cycle changes (all in
mm day-1). Green lines show precipitation changes, red lines show
evaporation changes, and black lines show precipitation minus evaporation. In
the first column, green lines are difficult to see because they are largely
overlaid by red lines. In panel (i), the green line has
values below -0.2 for all years.
Figure shows the all-model mean for all of the terms in
Eq. (). Several clear conclusions emerge. The first is
that ΔB is approximately zero globally, over land, and over ocean for
nearly the entire 50-year period, after an initial rapid adjustment that
resolves within a few years. With the exception of latent heat over land, all
fluxes for G1ocean-albedo reach a steady state after a few years
(Fig. ), and even latent heat flux over land reaches an
approximate steady state within 10 years. If ΔB indeed serves as a
useful proxy for global net energy flux into or out of the ocean, then these
results indicate that there is no sizable contribution to atmospheric
energetics by changes in global mean ocean heat content. Moreover, even if
ΔB were not zero over ocean, global mean ocean heat content changes
would still be an insufficient explanation for global mean temperature
changes due to incongruent timescales. The oceanic mixed layer operates on an
approximately decadal timescale, but all transient behavior in these
simulations is resolved well before 10 years. The transient response is much
more consistent with a land surface timescale, which is on the order of
1–3 years. As such, it seems plausible that the temperature changes over
ocean in G1ocean-albedo are due to land processes and land surface feedbacks
rather than ocean heat content changes. This is not to say that the ocean
plays no role in the observed temperature changes. Rather, given the
discussions in this section and the two previous sections, the role of global
mean ocean heat content in causing temperature changes over the ocean in
G1ocean-albedo (over the timescales being analyzed here) is likely small.
Because forcings and feedbacks are likely to be realized heterogeneously,
there may be roles for local changes or for changes in patterns of
circulation (e.g., the Atlantic meridional overturning circulation) in
altering oceanic heat content. However, such analyses are beyond the scope of
the present work.
The remainder of the results in Fig. are consistent
with the applied forcing. There is a large sensible heat flux increase from
the land to the atmosphere of 2.87 (-0.99 to 6.00) W m-2, with a
comparatively smaller sensible heat flux decrease from the ocean to the
atmosphere of 1.47 (0.34 to 2.20) W m-2. Over the ocean, latent heat
flux from the surface to the atmosphere is 6.71 (4.95 to 7.89) W m-2
lower in G1ocean-albedo than in the preindustrial control simulation. These
results indicate a greater shift of energy away from evaporating water and
toward increasing land temperature. Large differences in flux magnitude
between G1 and G1ocean-albedo can be found over land for net shortwave flux
and latent heat flux, and differences in sign can be found over land for
total radiative flux. These features are consistent with the applied forcing
being different over land and ocean.
Hydrological cycle changes
Introducing a strong land–ocean energy and temperature gradient, as in
G1ocean-albedo, will undoubtedly impact the hydrological cycle. Although the
G1ocean-albedo simulation is idealized, more realistic representations of MCB
have shown important hydrological cycle impacts, including secondary
circulation patterns that shift precipitation onto land in the tropics and
extratropics and changes in the Walker
circulation . Here, we evaluate the large-scale
hydrological cycle changes in G1ocean-albedo, with possible applicability to
other realizations of MCB.
Figure shows global, land, and ocean averaged
precipitation, evaporation, and precipitation minus evaporation (P-E) for all
of the simulations considered in this paper; quantitative descriptions
are given in Tables S13–15. The abrupt4xCO2 simulation is the only one with
a distinct rapid adjustment and slow response. Over both land and ocean, G1
shows decreases in precipitation and evaporation of approximately equal
magnitude, resulting in net changes in P-E of 0.02 (-0.05 to
0.11) mm day-1 over land and -0.01 (-0.04 to 0.01) mm day-1
over ocean. In G1ocean-albedo, global precipitation and evaporation both
decrease by approximately 0.19 (0.11 to 0.26) mm day-1 to yield little
net change in P-E. However, this net small change is due to differential
effects over land and ocean. Over land, precipitation remains relatively
unchanged, but evaporation decreases, resulting in a net change in P-E by
0.09 (-0.18 to 0.18) mm day-1. Over the ocean, both precipitation
and evaporation decrease, with a net negative P-E of -0.06 (-0.19 to
-0.01) mm day-1.
Annual mean land–ocean contrasts in precipitation and evaporation changes
tend to be more uniform in sign in experiment G1
(Fig. ), resulting in few large regions of change in P-E
with the exception of the tropics mostly driven by a southward shift
in the intertropical convergence zone;. In
G1ocean-albedo, precipitation and evaporation over the oceans are reduced in
most regions, consistent with the applied forcing. Over land, the signs of
precipitation and evaporation changes are regionally heterogeneous, yet the
precipitation and evaporation changes are concordant; e.g., land regions with
increased precipitation also generally show increased evaporation. The net
P-E map is highly heterogeneous, but in general, tropical land areas are
projected to have more available moisture (as measured by P-E) under
G1ocean-albedo, and midlatitude land areas are projected to have less. There
is a general drying (reduced P-E) in the midlatitudes, as well as some
reductions in the intertropical convergence zone, with important implications
for tropospheric circulation (to be evaluated in future work). The
implications of these changes for people and ecosystems are also important to
investigate further.
Precipitation (a), evaporation (b), and
precipitation minus evaporation (c) changes (all panels have units
mm day-1) for experiments G1 and G1ocean-albedo. Values are averages
over years 11–50 of simulation. Stippling indicates where fewer than 8 out
of 11 models agree on the sign of the response.
Discussion and conclusions
In Sect. ,
three hypotheses were posed as to why G1ocean-albedo experienced warming over
both land and ocean. Energy balance arguments point toward global average
warming in G1ocean-albedo. However, energy balance arguments alone cannot
explain the magnitude of oceanic warming. Explaining that warming requires a
model that can represent horizontal transport of heat from the land to the
ocean. Because these processes reach steady state within a decade or less, it
is unlikely that long-term oceanic processes, including changes in global
mean ocean heat content, are responsible for the majority of the changes seen
in G1ocean-albedo.
The results presented here indicate that even though experiments G1 and
G1ocean-albedo both achieve approximate net top-of-atmosphere radiative flux
balance, the climate system responses differ dramatically between the two
experiments. The idea that global energy balance can still result in local
changes is perhaps not surprising, as feedbacks operate locally
. These different climate responses for the same magnitude
in global forcing are effectively an illustration of different efficacies
. Even in the absence of slow responses, forcings with
different efficacies can cause different climate system changes
. G1ocean-albedo serves as an excellent reminder not to
conflate small net top-of-atmosphere radiative flux imbalance with small
temperature change; a clear relationship between those two quantities is not
guaranteed.
Relatedly, the results obtained for G1ocean-albedo were to some extent by
design. The objective of G1ocean-albedo was to achieve net top-of-atmosphere
radiative flux balance, which resulted in warming. Conceivably, one could
define an objective of no global temperature change, implying a net negative
radiative flux at the top of the atmosphere, or no global land temperature
change, requiring adjustments over the oceans to make up the imbalance. It is
unclear whether, unlike G1ocean-albedo, such alternate approaches would
result in transient behavior that lasts longer than a few years. Such an
experiment could be accomplished using feedback methods that have been
introduced to geoengineering research in recent years
e.g.,.
Related to this discussion, Figs. S1–S3 in the Supplement show monthly differences
(from piControl) of net top-of-atmosphere radiative flux change and
temperature change for the abrupt4xCO2, G1, and G1ocean-albedo simulations.
These were calculated by naively subtracting each monthly value of the three
perturbed experiments from the monthly values of the corresponding piControl
simulation, so all differences are subject to noise introduced by chaos. G1
shows an indication of slight transient behavior, starting out with positive
temperature anomaly that relaxes to near zero within a few years.
G1ocean-albedo does not show any discernible anomaly, in that it starts out
slightly warmer (globally) than piControl and stays slightly warm. The
Gregory plot for G1ocean-albedo similarly shows no discernible trend, unlike
the abrupt4xCO2 simulation. There are several possibilities of explanations
for this behavior. One is that the adjustments are happening on a short
enough timescale in G1ocean-albedo that any transient response is difficult
to detect with only monthly averages . Another possibility is
that the noise introduced by chaos on the timescales of interest (months to a
few years) obscures our ability to detect any transient behavior. An ensemble
of shorter simulations e.g., might be well equipped to
reveal transience in the response on these timescales. A third option is
model artifact related to how the climate models treat energy conservation,
indicating that experiments like G1ocean-albedo could be useful in testing
models beyond their originally conceived application space. While it is
beyond the scope of the present paper to fully assess all of these
possibilities, it becomes clear that G1ocean-albedo and simulations of
geoengineering in general are useful for improving understanding about
climate modeling and climate science.
The results presented here have several features that were not necessarily
expected from the outset. found that determining
whether the climate system was in balance took up to 30 years of simulation.
However, once that balance is achieved, the climate does not change
appreciably after the initial rapid adjustment. Potential future work could
investigate these results, shedding light on timescales of climate response
and potential thresholds; e.g., how large does the energy imbalance need to
be to trigger slower adjustments?
Related to this issue of different timescales of adjustment is the
traditional separation of climate response into rapid adjustment and slow
response components e.g.,. The
rapid adjustment is often defined as the climate response unassociated with
global mean temperature change, and the slow response describes a transient
response due to temperature change, largely as a result of climate system
feedbacks. The results from G1ocean-albedo, like those of G1
, show an initial rapid change and no appreciable
slower change. However, in G1ocean-albedo, there is a sustained temperature
increase without appreciable transient behavior. Thus, G1ocean-albedo
represents an experiment that does not cleanly delineate into the traditional
definitions of rapid adjustment and slow response. Additionally, this
sustained temperature increase is to some extent decoupled from net energy
imbalances in the climate system, as ΔRTOA and ΔS
(Eq. ) are both approximately zero. Reconciling all of
these features suggests a potentially rich research topic focused on
understanding the relationships between radiative flux changes, temperature
changes, and the circumstances under which climate feedbacks are excited,
particularly for forcings with strong land–ocean contrast (e.g.,
anthropogenic aerosols).
The results presented here are broadly relevant to more sophisticated
representations of MCB, such as increasing cloud droplet number concentration
or directly injecting sea salt aerosols into the marine boundary layer
. analyzed a multi-model ensemble
of simulations of G4cdnc , involving a 50 %
increase in cloud droplet number concentration in all marine low clouds,
wherever the model forms those clouds. Although smaller in magnitude, they
found similar patterns of top-of-atmosphere radiative flux change as in
G1ocean-albedo. Also similar between the two experiments was an increase in
land precipitation and a decrease in ocean precipitation. Perhaps an even
more realistic representation is G4sea-salt , involving direct
injection of sea salt into the marine boundary layer between 30∘ S
and 30∘ N to achieve an effective radiative forcing of
-2.0 W m-2. In the injection area (the tropics), this experiment
also showed similar patterns of net top-of-atmosphere radiative flux
perturbation and hydrologic cycle response. As such, while G1ocean-albedo is
highly idealized and exerts a perhaps unrealistically large forcing, it has
relevance for other global representations of MCB or sea spray
geoengineering. However, there are important differences in boundary layer
stability changes from surface albedo increases versus marine cloud
brightening. Also, it appears impossible for marine cloud brightening to be
conducted over all ocean regions and with a sufficient magnitude to offset
the radiative forcing from a quadrupling of the CO2 concentration.
The purpose of this paper is to describe the broad features of change
under a uniform ocean albedo increase, and some of these changes are likely
to be present with more realistic scenarios of marine cloud brightening. We
anticipate that future research can more deeply explore the applicability of
this simulation to marine cloud brightening.
G1ocean-albedo may be more apposite to the impact of geoengineering via
“ocean microbubbles,” whereby surfactants are added to the ocean surface,
promoting the formation of microscopic, highly reflective bubbles
. An area of investigation we did not undertake, yet one
that repeatedly emerges in discussions of microbubbles, is the resulting
effects of surface albedo increase on the ocean mixed layer. By reflecting
more solar radiation, microbubbles have the potential to inhibit vertical
mixing and available light in the euphotic zone, which could have profound
effects on marine biota. This implies that another useful future area of
investigation for the G1ocean-albedo simulation is an analysis of the marine
carbon cycle.
There are numerous potential areas of research prompted by this study. The
stark land–ocean contrast in warming has potential implications for ocean
dynamics, including the meridional overturning circulation, western boundary
ocean currents, and mixed layer depths, with consequent implications for
marine ecosystems and the ocean carbon cycle. This contrast also has
implications for the terrestrial biosphere, including ecosystem services and
the land and ocean carbon cycles. Although we did not evaluate seasonal
changes in this paper, such investigations could prove fruitful for more
detailed assessments of variability, such as monsoon precipitation, extreme
events, and sea ice extent. In addition, the changes in precipitation
described earlier indicate important potential changes in large-scale
circulation, atmospheric dynamics, and the hydrological cycle, all of which
warrant further study.