ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-621-2018Response to marine cloud brightening in a multi-model ensembleStjernCamilla W.camilla.stjern@cicero.oslo.nohttps://orcid.org/0000-0003-3608-9468MuriHelenehttps://orcid.org/0000-0003-4738-493XAhlmLarsBoucherOlivierhttps://orcid.org/0000-0003-2328-5769ColeJason N. S.https://orcid.org/0000-0003-0450-2748JiDuoyingJonesAndyhttps://orcid.org/0000-0003-1814-7601HaywoodJimKravitzBenhttps://orcid.org/0000-0001-6318-1150LentonAndrewMooreJohn C.NiemeierUlrikehttps://orcid.org/0000-0003-0088-8364PhippsSteven J.https://orcid.org/0000-0001-5657-8782SchmidtHaukehttps://orcid.org/0000-0001-7977-5041WatanabeShingoKristjánssonJón EgillCICERO Center for International Climate and Environmental Research
Oslo, Oslo, NorwayDepartment of Geosciences, University of Oslo, Oslo, NorwayDepartment of Meteorology, Stockholm University, Stockholm, SwedenBolin Centre for Climate Research, Stockholm University, Stockholm, SwedenLaboratoire de météorologie dynamique, Université Pierre
et Marie Curie, Paris, FranceCanadian Centre for Climate Modelling and Analysis, Environment and
Climate Change Canada, Victoria, CanadaCollege of Global Change and Earth System Science, Beijing Normal
University, Beijing, ChinaMet Office Hadley Centre, Exeter, UKAtmospheric Sciences and Global Change Division, Pacific Northwest
National Laboratory, Richland, USACSIRO Oceans and Atmosphere, Hobart, AustraliaJoint Center for Global Change Studies, Beijing, 100875, ChinaArctic Centre, University of Lapland, P.O. Box 122, 96101 Rovaniemi, FinlandMax Planck Institute for Meteorology, Hamburg, GermanyInstitute for Marine and Antarctic Studies, University of
Tasmania, Hobart, AustraliaJapan Agency for Marine-Earth Science and Technology, Yokohama,
JapandeceasedCamilla W. Stjern (camilla.stjern@cicero.oslo.no)19January20181826216345July201719July20176December20178December2017This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/18/621/2018/acp-18-621-2018.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/18/621/2018/acp-18-621-2018.pdf
Here we show results from Earth system model simulations from the
marine cloud brightening experiment G4cdnc of the Geoengineering Model
Intercomparison Project (GeoMIP). The nine contributing models prescribe a
50 % increase in the cloud droplet number concentration (CDNC) of low
clouds over the global oceans in an experiment dubbed G4cdnc, with the
purpose of counteracting the radiative forcing due to anthropogenic
greenhouse gases under the RCP4.5 scenario. The model ensemble median
effective radiative forcing (ERF) amounts to -1.9 W m-2, with a
substantial inter-model spread of -0.6 to -2.5 W m-2. The large spread
is partly related to the considerable differences in clouds and their
representation between the models, with an underestimation of low clouds in
several of the models. All models predict a statistically significant
temperature decrease with a median of (for years 2020–2069) -0.96 [-0.17 to
-1.21] K relative to the RCP4.5 scenario, with particularly strong cooling
over low-latitude continents. Globally averaged there is a weak but
significant precipitation decrease of -2.35 [-0.57 to -2.96] % due to a
colder climate, but at low latitudes there is a 1.19 % increase over
land. This increase is part of a circulation change where a strong negative
top-of-atmosphere (TOA) shortwave forcing over subtropical oceans, caused
by increased albedo associated with the increasing CDNC, is compensated for by
rising motion and positive TOA longwave signals over adjacent land regions.
Introduction
The Paris Agreement of the United Nations Framework Convention on Climate
Change (UNFCCC) 2015, 21st Conference of Parties (UNFCCC, 2015), with its
ambitious aims of limiting global warming to 2 ∘C, if not
1.5 ∘C, to avoid dangerous climate change, has raised concerns over
how to actually reach those targets. Climate engineering, also referred to as
geoengineering, could be considered as part of a response portfolio to
contribute to reach such targets. Climate engineering can be defined as the
deliberate modification of the climate in order to alleviate negative effects
of anthropogenic climate change (Sheperd, 2009). Marine cloud brightening
(MCB) is one such technique (Latham, 1990), which falls into the category of
solar radiation management or albedo modification, and aims to cool the
climate by increasing the amount of solar radiation reflected by the Earth.
The MCB method involves adding suitable cloud condensation nuclei (CCN), for
instance sea salt, into the marine boundary layer. As existing cloud droplets
tend to distribute themselves on the available nuclei, a larger number of CCN
has the potential to enhance the cloud droplet number concentration (CDNC) in
a cloud, which (given constant liquid water paths) can reduce the droplet
sizes and therefore increase the cloud albedo (Twomey, 1974). In reality,
however, the end result of adding CCN to the marine boundary layer is highly
uncertain, as there are many processes involved – each of which has a number
of dependencies and uncertainties. For instance, Alterskjær and
Kristjánsson (2013) simulated sea-salt seeding of marine clouds using the
Norwegian Earth System Model (NorESM) and found that for seeding particles
above or below given size thresholds, a strong competition effect ultimately
led to a warming of the climate, contrary to the intention. Chen et
al. (2012) studied observations of ship tracks and found that the magnitude
and even the sign of the albedo response is dependent on the mesoscale cloud
structure, the free tropospheric humidity, and cloud top height. Similarly,
Wang et al. (2011), using the WRF model, found that the effectiveness of
cloud albedo enhancement is strongly dependent on meteorological conditions
and background aerosol concentrations. There is therefore a great need for
more studies of the processes behind and possible effects of MCB.
Existing model studies of MCB (e.g., Alterskjær et al., 2013; Bala et al.,
2011; Bower et al., 2006; Jones et al., 2009; Latham et al., 2014; Philip et
al., 2009; Stuart et al., 2013; Wang et al., 2011) have shed some light on
potential benefits (e.g., in terms of climate cooling) as well as drawbacks
(e.g., hydrological cycle changes). For instance, while all of the above studies
show that MCB “works” in terms of cooling climate, a spin-down of the
hydrological cycle in the cooler climate leads to reduced precipitation in
the global average, with potential detrimental effects to humans and
vegetation (Muri et al., 2015). Jones et al. (2009) found a significant
drying for the Amazon basin from increasing CDNC in marine stratocumulus
decks. Bala et al. (2011) noted that differential cooling of oceans (where
clouds were modified) versus land might result in circulation changes (seen
also in Alterskjær et al., 2013) involving sinking motion over oceans and
rising motion over land, which therefore might experience an increase in
precipitation. Thus, MCB may have very different regional effects. For
instance,
Latham et al. (2014) investigated possible beneficial regional effects of
MCB, finding that MCB might help stabilize the West Antarctic Ice Sheet.
However, comparison between studies has been difficult partly due to
different experimental design. Also, in studies based on one or only a few
models, results will be very dependent on the particular models'
parametrizations. For instance, Connolly et al. (2014) suggested that the
unintended warming found in Alterskjær and Kristjánsson (2013) for
seeding of some particle sizes (as mentioned above) was likely to be an
artifact of the cloud parametrization scheme in the model used. To alleviate
some of these issues, the Geoengineering Model Intercomparison Project
(GeoMIP) initiated a series of experiments where a number of models were to
simulate MCB in a particular manner, with different degrees of complexity in
the simulation design (Kravitz et al., 2013). The G1ocean-albedo experiment
prescribes an increase in ocean albedo in the models at a rate intended to
offset increasing global temperatures in response to a quadrupling of
atmospheric CO2 concentrations. The G4cdnc experiment investigated in
this work simulates a 50 % increase in the CDNC of low marine clouds as
described in more detail in Sect. 2. Finally, the G4sea-salt experiment
involves an increase in sea-salt emissions over tropical oceans at a rate
intended to produce a radiative forcing of -2.0 W m-2 under the
RCP4.5 scenario. The three experiments have an increasing degree of
complexity or realistic representation.
Information on the contributing models, including resolution, number
of realizations and their representation of the aerosol indirect effect.
ModelNo. ofNo. of vert.RCP4.5/Representation of aerosol indirect effectgrid cellslayersG4cdnc(lat × long)(type)realizationsBNU-ESM64 × 12826 (hybrid sigma)1/1Single-moment microphysics scheme; Rasch and Kristjánsson (1998) with modification by Zhang et al. (2003). NOTE: to achieve effects of 50 % increase of CDNC over ocean regions below 680 hPa, a direct alteration of liquid droplet size by dividing (1.5(1/3)) is done.CanESM264 × 12835 (hybrid sigma)5/3Prognostic microphysics scheme accounting for the first indirect effect but not the second indirect effect (von Salzen et al., 2013)CSIRO-Mk3L-1-256 × 6418 (hybrid sigma)3/3Prescribed CDNCGISS-E2-R90 × 14421 (hybrid sigma)3/3Prognostic calculations of CDNC (Menon et al., 2010), based on Morrison and Gettelman (2008)HadGEM2-ES145 × 19238 (hybrid height)4/1Diagnostic CDNC scheme based on Jones et al. (2001)IPSL-CM5A-LR96 × 9639 (hybrid sigma)4/1CDNC is computed from the total mass of soluble aerosol through the prognostic equation from Boucher and Lohmann (1995).MIROC-ESM64 × 12880 (hybrid sigma)1/1Prognostic calculation of CDNC (Abdul-Razzak and Ghan, 2000)MPI-ESM1-LR96 × 19247 (hybrid sigma)1/1Prescribed CDNCNorESM1-M96 × 14426 (hybrid sigma)1/1Double-moment microphysics scheme with prognostic calculation of CDNC (Abdul-Razzak and Ghan, 2000; Hoose et al., 2009; Morrison and Gettelman, 2008)
Less idealized experiments, such as sea-salt injection simulations, will
include more of the processes in play between geoengineering and climate
impacts. Such experiments have taught us, for instance, that a substantial
part of the cooling will originate from direct aerosol effects, and that the
indirect cloud effects are just part of a number of responses of the climate
system (Ahlm et al., 2017; Alterskjær et al., 2012; Partanen et al.,
2012). In order to get a clearer understanding of the causes of the
inter-model spread in cloud response, we therefore look more closely into the
simpler experiment G4cdnc, where only perturbations to cloud simulations are
made. The justification for performing more simplified simulations such as
G4cdnc is that many models may participate, giving a more complete
multi-model ensemble, but also that the spatial climate response in near-surface air temperature and precipitation where CDNC are perturbed directly
in a single fully coupled GCM is similar to that when sea-salt aerosol
microphysics are included explicitly (Jones et al., 2009; Jones and Haywood,
2012). In this paper we provide an initial overview of the climate response
in the G4cdnc experiment with a particular focus on the atmosphere. Our main
goal is to determine the range of responses associated with such a forcing
and furthermore to assess the extent to which the climate effects are
dependent on the quantity, type, and location of clouds.
In the next section, we describe the G4cdnc experimental design, and the data
analysis approach. Section 3 reviews the model climatologies, with a
particular focus on differences in clouds. Results from the climate
engineering experiment are presented in Sect. 4, Sect. 5 provides the
discussion, whilst conclusions are drawn in Sect. 6.
Data and methodsThe G4cdnc experiment design
The G4cdnc experiment uses the CMIP5 (Coupled Model Intercomparison
Project 5) RCP4.5 (Taylor et al., 2012) scenario as its baseline. RCP4.5 is
the middle-of-the-road scenario and assumes continued greenhouse gas emission
increases from today's levels (albeit at reduced rate from a no-policy
scenario like RCP8.5), followed by a decrease from year 2040 and
stabilization by year 2100, at which time the anthropogenic radiative forcing
amounts to 4.5 W m-2 above preindustrial levels (Meinshausen et al.,
2011). The G4cdnc experiment starts the climate engineering in year 2020, and
prescribes a 50 % increase in the CDNC of marine low clouds. Marine low
clouds are defined as clouds below 680 hPa over ocean grid boxes at all
latitudes, except where sea ice is present. The experiment is run for
50 years, from 2020 to 2069, after which the cloud brightening is terminated,
and the simulations are continued for a further 20 years (until 2089) to
assess the termination effect. Nine CMIP5 models participated in the
experiment. However, the termination period was only investigated in six of
the models. See Table 1 for a list of participating models.
Global averages for the 20 first years (2006–2025) of the RCP4.5
experiment. Model spread is given as one standard deviation. “Low cloud
cover” is calculated using a random overlap assumption for all vertical
levels below 680 hPa.
Total cloud coverPrecipitationLiquid water pathLow cloud cover(%)(mm day-1)(g m-2)(%)BNU-ESM52.742.89151.1577.54CanESM260.802.78119.0757.69CSIRO-Mk3L-1-266.982.76118.3859.97GISS-E2-R61.013.22192.7131.19HadGEM2-ES53.323.0992.14–IPSL-CM5A-LR56.672.7668.1146.06MIROC-ESM50.372.82139.2855.88MPI-ESM-LR61.932.8862.8549.59NorESM1-M53.942.83142.4773.29Model average and spread57.5 (±5.4)2.9 (±0.2)120.7 (±41.6)55.9 (±23.4)Observations68 (±3)a2.68b30–50 [10 to 100]c(not comparable to observations)
a As averaged from daytime measurements over several
remote sensing datasets; see Stubenrauch et al. (2013). b
Climatology for 1951–2000 from the Global Precipitation Climatology Centre
(Schneider et al., 2014). c Observations from NASA's “A-Train”
satellite observations (Jiang et al., 2012).
In the aforementioned G4sea-salt experiment, sea-salt particles are injected
close to the ocean surface to simulate the entire life-cycle from aerosol
injections to climate effects (Ahlm et al., 2017). The G4cdnc experiment,
however, simplifies the process and addresses the adjustment of CDNC without
an actual increase in any cloud condensation nuclei (CCN) from sea spraying.
Consequently, the resulting climate effects will only include aerosol–cloud
interactions and will not include aerosol–radiation interactions (Boucher et
al., 2013) or the climate system's adjustments to them. Note also that for
BNU-ESM, the model design precluded a direct change in the CDNC. Therefore,
instead of multiplying CDNC by 1.5 to obtain the 50 % increase, they had
to approximate the cloud seeding through a direct alteration of liquid
droplet sizes (droplet radii are multiplied by the factor 1.51/3), which
means that its experiment setup is slightly different from the other models.
Postprocessing and analyses
Some details of the nine contributing models can be seen in Table 1. For each
model, annual averages are first calculated from monthly mean model output
and the ensemble means over all available realizations are then calculated,
before the data is regridded to a horizontal grid corresponding to the
average grid size of models: 2.1 × 2.7
latitude × longitude. When calculating differences between the
G4cdnc experiment and the RCP4.5 scenario, we base our analyses on years
2020–2069. To assess whether the differences between G4cdnc and RCP4.5 are
statistically significant, a two-sample non-parametric Kolmogorov–Smirnov
(K-S) test (Conover, 1971) is used.
In our assessment, “low clouds” are quantified using a random overlap
assumption, in which clouds in contiguous layers overlap in a random way
(Tian and Curry, 1989), based on the cloud cover for all grid cells from
1000 hPa and up to 680 hPa. Please note that this estimate is based on
monthly means, and these low cloud amounts will therefore not be equal to the
low cloud fractions calculating during model runs (for models that does
this); these numbers tend to be higher. However, they should give a fair
representation of geographical distribution, and numbers are comparable
between models. To estimate the effective radiative forcing of increasing
CDNC by 50 % in the different models, we use the method of Gregory et
al. (2004), whereby the top-of-atmosphere (TOA) radiative flux imbalance is regressed against the
globally averaged surface air temperature change compared to the RCP4.5
simulations. To estimate the effective radiative forcing of increasing CDNC
by 50 % in the different models, we use the method of Gregory et
al. (2004), whereby the global mean top of atmosphere radiative flux
imbalance is regressed against the globally averaged surface air temperature
change compared to the RCP4.5 simulations. Because of the timescales of the
adjustments to the applied forcing, we follow Williams et al. (2008) and
Gregory et al. (2004), using annual means for the first decade and decadal
means thereafter: the period of 2030–2069. This allows more weight to the
years when the rapid adjustments dominate, before the slow feedbacks have
more impact in the later part of the simulation.
Panel (a) shows cloud fraction (%) from
CloudSat/CALIPSO, adapted from Fig. 7.5c of Boucher et al. (2013) with
permission. Panel (b) shows the GeoMIP model median for the first
20 years of the RCP4.5 scenario, and panels in (c) show individual
RCP4.5 GeoMIP model results for the same years. Note different vertical axes
for the AR5 and GeoMIP plots.
Modeled cloud climatologies
Earth system models have large differences in their treatment of clouds, as
well as in their aerosol concentrations and climatologies. The 5th assessment
report of the Intergovernmental Panel on Climate Change (IPCC), similarly to
the previous ones, stressed the important role of clouds and their
parameterizations in contributing towards the inter-model spread in estimates
of climate change (Stocker et al., 2013). As the G4cdnc experiment is
designed so that changes are induced only to low clouds over ocean, we expect
differences in cloud fields to be a particularly large source of uncertainty
in our results. This section therefore provides a brief comparison of
different climatological values between the models.
In Table 2, a selection of cloud-related variables is given, based on global
annual means from the first 20 years of the RCP4.5 simulation, to see how
these values compare to present-day observations. Zonal mean cloud cover over
the same period is shown in Fig. 1. The lowermost row in Table 2 shows values
from observations. Looking at the observed cloud fraction of Fig. 1 (upper
left panel), we see that low cloud amounts are particularly high near the
poles, with also relatively high amounts extending towards the subtropics and
mid-latitudes, especially in the Southern Hemisphere. Observations show a
high fraction of low stratocumulus clouds around 30∘ S (Wood, 2012),
an area that, given its distance from major pollution sources, might be
particularly susceptible to cloud seeding (Alterskjær et al., 2013; Jones
et al., 2009; Partanen et al., 2012). It is therefore interesting to note
that several models are not able to realistically reproduce these clouds.
Low cloud fraction (%) for the first 20 years of the RCP4.5
scenario, for the model median as well as the individual models. Low clouds
are estimated using a random overlap assumption (Tian and Curry, 1989).
Comparing individual model cloud fractions in Fig. 1 (lower three rows) to
the observed cloud fraction, we see that few models compare well to the
observed low-level cloud amounts. It is a well-known problem, commonly
referred to as the “too few, too bright” problem, that climate models tend
to underestimate the amount of low clouds, while concurrently overestimating
their optical thickness, and Nam et al. (2012) confirmed that this is true
for most of the CMIP5 models. Among the contributing G4cdnc models, GISS-E2-R
and IPSL-CM5A-LR have particularly few low-level clouds in the region around
30∘ S; see Fig. 1. This is stressed in Schmidt et al. (2014), who
compared cloud data from GISS-E2-R to satellite measurements and found
underestimated cloud covers over mid-latitude ocean regions and a particular
deficiency in subtropical low clouds. Likewise, Konsta et al. (2016) compared
tropical clouds in IPSL-CM5A-LR to satellite observations and found an
underestimation of total cloud cover (underestimated low- and mid-level
tropical clouds and overestimated high clouds) associated with a high bias in
cloud optical depth. Other models have similar issues. For instance, Stevens
et al. (2013) showed that MPI-ESM-LR has prominent negative biases in the
major tropical stratocumulus regions. Although the low clouds in this study
are an approximation as explained in Sect. 2.2, a comparison between our
Fig. 2 and the satellite-based observations of low clouds in Fig. 1 of Cesana
and Waliser (2016) clearly demonstrates this. Although MPI-ESM-LR has a
globally averaged cloud fraction close to the multi-model average (Table 2),
there is a concentration of clouds around the poles rather than at lower
latitudes (see Fig. 2). Brightening polar clouds may be less efficient than
if the majority of clouds were located at lower latitudes, due to the low
solar angle at high latitudes, although influences of cloud changes on the
long wave spectrum may still be large (Kravitz et al., 2014). Yet, as we will
show in the next section, the climate response of MPI-ESM-LR is still the
highest of all the models.
G4cdnc minus RCP4.5 difference (based on years 2020–2069) in the
key variables, including the effective radiative forcing as estimated in
Fig. 3a). An asterisk denotes that the change is not significant at the
95 % level by the Kolmogorov-Smirnov test.
In an evaluation of 19 CMIP5 models against NASA's “A-Train” satellites,
Jiang et al. (2012) found a best estimate global mean observed liquid water
path (LWP) of 30–50 g m-2, with an uncertainty range from 10 to
100 g m-2. Table 2 shows a vast inter-model spread in annual average
LWP. Values range from 61.7 g m-2 (MPI-ESM-R) to 194.7 g m-2
(GISS-E2-R). These variations in cloud thickness can be decisive to a model's
response to cloud seeding since (given similar levels of cloud condensation
nuclei) clouds with LWP above a certain level will be less susceptible to
changes in the number of cloud droplets (Sorooshian et al., 2009).
Climate response to G4cdnc
The G4cdnc experiment results in a model median ERF (calculated using the
Gregory regression method, as explained in Sect. 2.2) of -1.91 [-0.58 to
-2.48] W m-2, where the numbers in brackets indicate model minimum
and maximum values; see Fig. 3a and Table 3. There is a factor of 4.3 difference
between the highest and lowest model ERFs; while CSIRO-Mk3L-1-2 has the
largest radiative forcing of -2.48 W m-2, GISS-E2-R has the weakest
(-0.58 W m-2). The models with a weak ERF typically also have a weak
correlation between temperature change and change in TOA radiative flux
imbalance (Table S1 in the Supplement). The model median geographical pattern
of this radiative perturbation is shown in Fig. 4a.
(a) Regression of global annual means of the net TOA
radiative flux imbalance and near-surface temperatures for each model (see
Gregory et al., 2004). Each square represents global annual mean for each of
the first 10 years and decadal means for the remaining part of the
simulations (i.e. last four decades, 2030–2069). Numbers to the right gives
the intercept – i.e., the effective radiative forcing. (b) Time
series of the difference in global annual mean near-surface temperature
between G4cdnc and RCP4.5 for each model. Dotted vertical line
indicates the onset of the termination period.
Ensemble median (taken in each grid cell) G4cdnc-RCP4.5 difference
based on years 2020–2069 of (a) TOA net radiative flux imbalance
(W m-2), (b) near-surface air temperature change (K),
(c) total cloud cover (%), and (d) precipitation (%).
Hatched areas are grid cells where fewer than 75 % of the models agreed on
the sign of the change. Zonal averages are given to the right of each panel,
where brown and blue lines indicate land-only and ocean-only averages. See
Figs. S2 and S3 in the Supplement for individual model changes in cloud cover
and precipitation, respectively.
The negative forcing of increasing CDNC cools the near-surface air
temperatures with a model median of 0.96 [-0.17 to -1.21] K, compared to
the RCP4.5 scenario. Figure 3b shows the time series from year 2020 to 2090
of the G4cdnc–RCP4.5 difference in global mean near-surface air temperature.
The figure shows that NorESM1-M, GISS-E2-R and IPSL-CM5A-LR are the models
that yield the weakest temperature response and these are the models with the
weakest effective radiative forcing. Conversely, MPI-ESM-LR, with the next
largest forcing, shows the strongest cooling.
Shown in Table 3 is also the “MCB sensitivity”, defined here as the global
temperature change normalized by the ERF. We find a model median value of
0.47, with a much smaller inter-model spread (a factor of 1.8 difference
between highest and lowest model value). MPI-ESM-LR and BNU-ESM give the
strongest cooling per degree forcing.
Figure 4b shows the geographical pattern of the ensemble median temperature
difference between the G4cdnc and RCP4.5 experiments, based on years
2020–2069 (for individual models, see Fig. S1 in the Supplement). There is a
strong polar amplification of the cooling signal, with largest cooling over
the Arctic from positive sea-ice feedback, and a somewhat weaker cooling
around Antarctica. Individual model numbers of the Arctic amplification is
given in Table S2, but the median value is 1.9. Some of the models
(NorESM1-M, BNU-ESM and MIROC-ESM) show a particularly large spatial
correlation (around -0.5 and significant at the 99 % level) between the
magnitude of the cooling (averaged over 2020–2069) and the baseline
(averaged over the 20 first years of the RCP4.5 simulation) low cloud
fractions; see Table S1. Such a tendency can also be seen in the ensemble
median temperature change of Fig. 4b; typical stratocumulus regions such as
parts of the tropical Atlantic Ocean and the Pacific Ocean off the coasts of
Peru and the USA (Wood, 2012) show stronger cooling.
Over oceans, the cooling also has a slight tendency to be stronger in regions
which have a low baseline LWP. Correlations between the average change in
temperature and the baseline LWP for each model gives the individual model
correlation coefficients in Table S1, and correlations between grid cell
model medians of these quantities gives a spatial correlation coefficient of
0.42 (significant at the 99 % level). Such a tendency might indicate that
cloud susceptibility, or the potential of a cloud to produce cooling by
increased albedo in response to the increase in droplet numbers, is larger in
clouds that are not too dense to begin with. However, two of the models
(NorESM1-M and BNU-ESM) have correlations of -0.30 and -0.47,
respectively, indicating larger cooling in areas of larger water paths. Note
also that changes in LWP are highly variable between models (Table 3 and
Fig. S2), with particularly strong positive changes for the two models with
prescribed CDNC (MPI-ESM-LR and CSIRO-Mk3L-1-2). Although the CDNC is
increased solely over the oceans, the land masses, particularly at low
latitudes, cool more than the ocean regions. We find that land areas between
35∘ S and 35∘ N cool by 1.08 K, while low-latitude ocean
areas cool by 0.83 K.
Ensemble median (taken in each grid cell) G4cdnc–RCP4.5 difference
of years 2020–2069 of (a) outgoing shortwave radiation at TOA
(W m-2), (b) outgoing longwave radiation at TOA
(W m-2), (c) change in low cloud fraction (%), and
(d) change in high cloud fraction (%). Hatched areas are grid
cells where fewer than 75 % of the models agreed on the sign of the
change. Zonal averages are given to the right of each panel, where brown and
blue lines indicate land-only and ocean-only averages, respectively.
As a consequence of the cooling climate, there is a weakening of the
hydrological cycle resulting in a decreasing global mean precipitation of
-2.35 [-0.57 to -2.96] %. As expected, the model with the strongest
cooling has the largest reduction in global precipitation. The total cloud
cover increases in all models but BNU-ESM, and there is a strong and
statistically significant correlation (coefficient of -0.71) between how
much the global mean cloud cover changes for a model and its baseline
fraction of low clouds. The model median cloud covers (Fig. 4c) are reduced
in high northern and southern latitudes due to the particularly strong
cooling and increase in sea ice in these regions (see Fig. S3 for individual
total cloud change patterns). Clouds are also reduced over large regions of
mid-latitude land masses, such as over Russia, northern Europe and North
America, and in the Intertropical Convergence Zone (ITCZ). Changes in
precipitation (Fig. 4d) is mostly correlated to the cloud changes, with
notable exceptions being the northern Pacific and Atlantic, where
precipitation is reduced in spite of an increase in clouds. In contrast to
the marine stratocumulus geoengineering experiment in Jones et al. (2009)
only two models (MPI-ESM-LR and IPSL-CM5A-LR) show drying over the Amazon
(see individual precipitation change patterns in Fig. S4). Both total cloud
cover and precipitation show distinct differences in land/sea responses.
Specifically, the cloud cover between 35∘ S and 35∘ N tends
to increase more over land (2.15 %) than over the ocean (0.32 %).
While precipitation increases by 1.2 % over land, there is for the same
latitudes a drying of 3.8 % for the oceans, over which the applied
geoengineering causes suppression of evaporation (not shown).
Figure 5a shows changes in outgoing shortwave radiation at TOA, while Fig. 5b
shows outgoing longwave radiation (OLR). The increase in outgoing shortwave
radiation is strongest in ocean regions typically associated with low clouds,
and it changes little over land. In contrast, the OLR has a band of increases
around the ITCZ, but otherwise decreases. The decrease is most pronounced
near the poles, but also over tropical land masses. Figure 5c shows that the
low cloud cover (see Fig. S5 for individual model changes) generally has a
pattern similar to the total cloud cover change, and seems to be a dominant
cause of the changes we see in outgoing shortwave radiation (a significant
spatial correlation of 0.35 is found between Fig. 5a and c). Figure 5d,
on the other hand, shows that high clouds increase primarily over tropical
and subtropical land masses, producing the reduction in OLR. This is from
increasing convection over land, producing more anvils and ice clouds, as
also reflected in the enhancement of precipitation in these areas. The
model-median spatial correlation between OLR and change in high clouds is
-0.55 and highly significant.
The ability of ecosystems to adapt, and the risk for extinction, dramatically
increases under rapid climate change (e.g., Jump and Peñuelas, 2005;
Menéndez et al., 2006). One of the concerns regarding climate engineering
is the possibility that, due to unforeseen events or government issues, there
may be a sudden suspension of the climate engineering efforts, casting
Earth's climate into a phase of rapid re-warming. This termination effect has
been investigated in several studies (Aswathy et al., 2015; Jones et al.,
2013; Matthews and Caldeira, 2007). As mentioned in Sect. 2.1, six models in
the present study simulated the termination effect by turning off the CDNC
perturbation and continuing the simulations for 20 years. The effect on
global temperatures (relative to the RCP4.5 scenario) in this termination
period can be seen in the last 20 years in Fig. 3b. At the end of these
20 years, temperatures are almost back at the RCP4.5 levels. Previous GeoMIP
publications investigating stratospheric aerosol injections (see, e.g.,
Berdahl et al., 2014) have noted a much faster warming in the rebound or
termination period than in the RCP4.5 scenario, and this we find also here:
for years 2070 to 2090 there is a warming of 0.007 K yr-1 for RCP4.5
and a warming of 0.040 K yr-1 for G4cdnc. In a GeoMIP study of
stratospheric aerosol injections using three global climate models, Aswathy
et al. (2015) find that comparing the mean temperatures of the years
2050–2069 to 2020–2079 (where the latter is the termination period), there
was a strong Arctic amplification when looking at the RCP4.5 scenario, but a
weaker amplification in the climate engineering scenario. Consistently with
this, we find that the model median Arctic amplification is 3.6 for the
RCP4.5 scenario and 1.7 for G4cdnc, for the same years (see Table S2). The
pattern of change in the termination period (Fig. S5) is broadly just a
reversal of the geographical patterns seen in Fig. 4. However, the spread
between the models, as indicated by the hatching in the maps, is much larger
for the termination effect precipitation response than for the temperature
response, as found also in Jones et al. (2013).
Discussion
Increasing CDNC in low marine clouds results in global cooling. This is
accompanied by a global mean reduction in precipitation. These signals are
robust across all nine models, but the magnitude of the change varies by
50 % between models for the temperature change, and by 40 % for the
precipitation change.
Although the CDNC is only increased over oceans, we find stronger cooling
over land masses, particularly in the lower latitudes. High clouds increase
the most over low-latitude land, and the OLR is reduced over low-latitude
land and increases over oceans. This is consistent with a shift in convection
from ocean to land, which also explains the increase in precipitation of
+1.2 % over low-latitude land as opposed to a drying of -3.8 %
over oceans. This kind of pattern has been seen in previous modeling studies
of marine cloud brightening. For instance, Bala et al. (2011) used an Earth
system model to perform simulations where the effective droplet size in
liquid clouds was reduced over oceans globally. They found that the
differential enhancement of albedo over oceans and land triggered a monsoonal
circulation with rising motion over land and sinking motion over oceans. This
happened as the vertically integrated air mass cooled more over the oceans
where the cloud albedo was increased than over land. The resulting density
change caused an increase in net land to ocean transport above 400 hPa and
an increase in the net ocean to land transport below 400 hPa. Alterskjær
et al. (2013) analyzed an ensemble of three Earth system models that are all
included in the present study and noted the same land–sea difference in
warming and associated hydrological cycle changes. Similarly, Niemeier et
al. (2013) compared three types of solar radiation management (stratospheric
sulfur injections, mirrors in space and MCB) and found that only the MCB
experiment induced changes to the Walker circulation.
Marine cloud brightening through emissions of some agent, for instance sea
salt, into the marine boundary layer influences the low-lying clouds. But we
find that the amount and location of clouds vary greatly between the models,
which will cause variation in their response to MCB (see, e.g., Chen and
Penner, 2005, who found cloud fraction to be one of the most important
sources of uncertainty in model-differences in estimates of the first
indirect effect). We also find a substantial inter-model spread in the LWP,
which is one of the factors determining how susceptible a cloud is to albedo
changes through the Twomey effect (Twomey, 1974). For instance, MPI-ESM-LR
has a fraction of low clouds that is close to the model average, but a
geographical distribution of those low clouds that could imply a reduced
efficiency of the CDNC enhancement. Low clouds at higher latitudes are less
effective at cooling, as the solar angle, and hence the climate effect, is
lower. MPI-ESM-LR also has the lowest globally averaged LWP of all models.
The potential that an increase in CDNC has to enhance the cloud albedo (the
cloud albedo susceptibility) has been shown to be smaller in clouds with low
LWP. For instance, Painemal and Minnis (2012) investigated satellite data for
typical stratocumulus regions and found an increase in cloud albedo
susceptibility with LWP up to about 60 g m-2, after which the
susceptibility leveled off or even decreased slightly. Even so, this model
has the second strongest ERF of all the models, possibly due to a very strong
increase in cloud amounts between around 40∘ S to 40∘ N.
GISS-E2-R, on the other hand, is the model with the weakest ERF. We find that
this model has the smallest low cloud fractions among all the models, and
there is also a tendency for these clouds to be concentrated at higher
latitudes. In addition, the LWP is extremely high, which could conceivably
mean that the clouds are already relatively saturated with respect to further
changes in droplet numbers, in line with the findings of Painemal and
Minnis (2012) mentioned above.
Geoffroy et al. (2017) investigated the role of different stratiform cloud
schemes to the inter-model spread of cloud feedbacks, looking at 14 models –
four of which were used in the present study. They found that NorESM1-M,
which diagnoses stratiform clouds based on relative humidity and atmospheric
stability, had an opposite cloud feedback from models (including HadGEM2-ES,
CSIRO-Mk3L-1-2 and IPSL-CM5A-LR) that diagnoses such clouds based solely on
relative humidity. BNU-ESM, whose change in TOA radiative flux imbalance is
that of the model median, uses the same stratiform cloud scheme as NorESM1-M
(Ji et al., 2014), and like NorESM1-M the change in low cloud cover is near
zero. It does not, however, have the negative change in LWP seen in
NorESM1-M. This may be due to the specific setup of the simulations in
BNU-ESM, where to obtain the 50 % decrease in CDNC a direct alteration of
liquid droplet size was done. In terms of TOA radiative flux imbalance,
CSIRO-Mk3L-1-2 has the strongest response of all models. LWP, amount of low
clouds, and the geographical distribution of low clouds, however, are all
similar to the model average. The model has the strongest cooling per forcing
(ERF) and shows the strongest increase in low cloud cover.
As seen in Table 1, there are large differences between models as to how the
aerosol–cloud processes are parametrized, and this presumably has an impact
on the MCB climate response. For instance, Morrison et al. (2009) found that
the complexity of the cloud schemes had large impacts on stratiform
precipitation; Penner et al. (2006) compared several models and concluded
that the method of parameterization of CDNC can have a large impact on the
calculation of the first indirect effect. CSIRO-Mk3L-1-2 and MPI-ESM-LR are
the only models that have prescribed CDNC levels in their simulations, and
these models stand out as having ERFs well above any of the other models.
While an evaluation of which liquid cloud parametrizations are more
appropriate is beyond the scope of this paper, this might be an indication
that prescribing CDNC levels may lead to exaggerated responses to marine
cloud brightening.
A caveat of the present study is that the spatial resolution of global
climate models is too coarse to resolve important processes such as
convection or precipitation formation. These processes are instead
parametrized, which may lead to unknown artifacts in the responses, dependent
on the specific formulations used (see, e.g., Clark et al., 2009, who compared
precipitation forecast skills for convection-allowing and
convective-parametrized ensembles). It is also important to point out that
the G4cdnc experiment was not designed to give a realistic representation of
the magnitude of cooling and other climate responses to MCB, but rather to
test the robustness of models in simulating geographically heterogeneous
radiative flux changes and to see their effects on climate. Increasing CDNC
by 50 % over all oceans is clearly an exaggeration of what could credibly
be done. A more realistic GeoMIP marine cloud brightening experiment,
G4sea-salt, is analyzed by Ahlm et al. (2017). Their results show what areas
in the participating models where the sea-salt seeding is the most effective
at brightening the clouds. This includes the decks of persistent low-level
clouds off the west coasts of the major continents in the subtropics.
Alterskjær et al. (2012) investigated the susceptibility of marine clouds
to MCB based on satellite as well as model data and reached a similar
conclusion; large regions between 30∘ S and 30∘ N, and
especially clean regions in the Pacific and Indian oceans, along with regions
in the western Atlantic, were susceptible.
Summary and conclusion
Nine models have conducted a coordinated idealized marine cloud brightening
experiment G4cdnc of the GeoMIP project, producing a median ERF of -1.91
[-0.58 to -2.48] W m-2. While climate in general cools as
intended, there are large geographical variations in the climate response.
For instance, although the global cooling leads to a general drying, robust
model responses also include a slight precipitation increase over
low-latitude land regions, such as subtropical Africa, Australia and large
parts of South America. This is a result of the land–sea contrast in
temperature initiated by brightening clouds only over ocean regions, which
results in a circulation pattern with rising motion over land and sinking air
over ocean, particularly over lower latitudes.
Responses in precipitation and clouds show particularly large inter-model
spreads. For some models we find a strong dependency of the climate response
on the location of low clouds in the baseline simulation, RCP4.5, and also on
the thickness in terms of LWP of the clouds. For other models no such clear
dependency is found. Variations in the complexity of microphysical schemes
contribute to the large model spread in responses, with an apparent tendency
for stronger responses for the more simple representations. Conceivably, the
more realistically the processes between CDNC perturbation and cloud albedo
change are simulated, the more buffering processes are included, dampening
the total climate response (Stevens and Feingold, 2009). Indeed, climate
changes from G4cdnc do not include all processes in play between seeding of
the clouds and the actual climate response even in the models with the most
advanced microphysics schemes. While the increase in CDNC is assumed to
originate from an increase in for instance sea-salt particles in the marine
boundary layer, one caveat with the G4cdnc experiment is that it does not
simulate these particles specifically. This is, however, done in the follow-up
experiment G4sea-salt, which was performed by three of the models
investigated here. Consistent with previous findings (Jones and Haywood,
2012; Partanen et al., 2012), Ahlm et al. (2017) found for G4sea-salt that
the direct effect (aerosol–radiation effect) of the sea-salt aerosols
themselves contributes significantly to the total radiative effect of sea-salt
climate engineering. It should be noted that even experiments that capture
this direct effect may be subject to biases caused by processes not included
in the experiments. For instance, Stuart et al. (2013) noted that sea-spray
climate engineering studies assume a uniform distribution of the emitted
sea salt in ocean grid boxes, which does not account for sub-grid aerosol
coagulation within sea-spray plumes. They find that accounting for this
effect reduces the CDNC (and the resulting radiative effect) by about
50 % over emission regions, with variations from 10 to 90 % depending
on meteorological conditions.
Cloud feedbacks remain the largest source of inter-model spread in
predictions of future climate change (Vial et al., 2013). While detailed,
high-resolution simulations of marine sky brightening give crucial insights
into processes that contribute to the total climate response, multi-model
idealized experiments such as the G4cdnc are still important to untangle the
cloud responses. We hypothesize that liquid cloud parameterizations ought to
be of appropriate complexity in order to attempt to model marine cloud
brightening and the climate response.
All model data are available through the Earth System Grid
or upon request to the contact author.
The Supplement related to this article is available online at https://doi.org/10.5194/acp-18-621-2018-supplement.
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
The work of Jón Egill Kristjánsson, Camilla W. Stjern, Helene Muri
and Lars Ahlm was supported by the EXPECT project, funded by the Norwegian
Research Council, grant no. 229760/E10. Camilla W. Stjern wishes to thank
CICERO for letting her continue and complete the present project. Helene Muri
was also funded by RCN grant 261862/E10. Lars Ahlm was also supported by the
Swedish Research Council FORMAS (grant 2015-748). The Pacific Northwest
National Laboratory is operated for the US Department of Energy by Battelle
Memorial Institute under contract DE-AC05-76RL01830. Steven J. Phipps was
supported by the Australian Research Council's Special Research Initiative
for the Antarctic Gateway Partnership (project ID SR140300001). The work of
Duoying Ji and John C. Moore was supported by the National Basic Research
Program of China (grant number 2015CB953600).
Edited by: Lynn M. Russell Reviewed by: two anonymous referees
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