This study assesses the change in anthropogenic aerosol forcing from the
mid-1970s to the mid-2000s. Both decades had similar global-mean
anthropogenic aerosol optical depths but substantially different global
distributions. For both years, we quantify (i) the forcing spread due to
model-internal variability and (ii) the forcing spread among models. Our
assessment is based on new ensembles of atmosphere-only simulations with five
state-of-the-art Earth system models. Four of these models will be used in
the sixth Coupled Model Intercomparison Project
(CMIP6; ). Here, the complexity of the anthropogenic
aerosol has been reduced in the participating models. In all our simulations,
we prescribe the same patterns of the anthropogenic aerosol optical
properties and associated effects on the cloud droplet number concentration.
We calculate the instantaneous radiative forcing (RF) and the effective
radiative forcing (ERF). Their difference defines the net contribution from
rapid adjustments. Our simulations show a model spread in ERF from -0.4 to
-0.9 W m-2. The standard deviation in annual ERF is 0.3 W m-2, based
on 180 individual estimates from each participating model. This result
implies that identifying the model spread in ERF due to systematic
differences requires averaging over a sufficiently large number of years.
Moreover, we find almost identical ERFs for the mid-1970s and mid-2000s for
individual models, although there are major model differences in natural
aerosols and clouds. The model-ensemble mean ERF is -0.54 W m-2 for the
pre-industrial era to the mid-1970s and -0.59 W m-2 for the pre-industrial
era to the mid-2000s. Our result suggests that comparing ERF changes between two
observable periods rather than absolute magnitudes relative to a poorly
constrained pre-industrial state might provide a better test for a model's
ability to represent transient climate changes.
Introduction
Despite decades of research on the radiative forcing of
anthropogenic aerosol, quantifying the present-day magnitude and
reconstructing the historical change of the forcing remains challenging.
Figure shows the anthropogenic aerosol optical depth for the
mid-1970s and mid-2000s that we use in this study
. The anthropogenic aerosol pollution in the
mid-1970s was larger in Europe and North America than in eastern Asia, whereas
the opposite is the case in the mid-2000s. In addition to these regional
changes in aerosol pollution, differences in the surface albedo, insolation and cloud regimes between the aerosol transport regions of the Pacific and
continental Europe may result in temporal changes in the global effective
radiative forcing (ERF). Based on a single state-of-the-art climate model,
the long-term and global ERF does not change despite the substantial spatial
changes in anthropogenic aerosol optical depth (τa) between the
mid-1970s and mid-2000s . Internal model variability,
however, strongly affects annual estimates of the global-mean effective
radiative forcing.
Mean anthropogenic aerosol optical depth (τa; shaded) and
fractional increase in cloud droplet number (ηN; contours) associated
with anthropogenic aerosol. Shown are annual means of τa at 550 nm and
ηN for the (a) mid-1970s and (b) mid-2000s from
MACv2-SP, which
prescribes annually repeating monthly maps of τa in the participating
models. Note the non-linear scale.
In light of model uncertainties
(e.g. ), the
use of a single model does not necessarily represent the full spectrum of
possible anthropogenic aerosol forcings. In the present study, we therefore
revisit the question of : “Does the substantial spatial
change of the anthropogenic aerosol between the mid-1970s and mid-2000s
affect the global magnitude of ERF?”. This is based on ensembles of simulations from
five global aerosol-climate models, all using identical anthropogenic aerosol
perturbations of reduced complexity. In this context, we additionally ask the following:
“What is the relative contribution of internal model variability to the ERF
spread?”. We document the model diversity for the pre-industrial aerosol as
well as cloud characteristics and the surface albedo that are relevant to
the ERF of anthropogenic aerosol. Such model differences have previously been
identified for other climate models
(e.g. ).
Previously a reduction in the model complexity has been accomplished by
prescribing idealized aerosol radiative properties, e.g. within the
framework of Aerosol Comparisons between Observations and Models
(AeroCom; e.g. ). Here, we prescribe
observationally constrained optical properties of anthropogenic aerosol and
an associated effect on the cloud droplet number concentration with the
simple plume parameterization (MACv2-SP; )
but keep the full model diversity in all other aspects. The approach
eliminates uncertainties in process modelling of anthropogenic aerosol such
that our study represents uncertainties associated with other processes
influencing the radiative forcing. In other words, by using MACv2-SP in the
participating models, the model inter-comparison allows us to investigate
those sources of uncertainty that remain if we pretend to know the spatial
distribution of anthropogenic aerosol. This work can be seen as a pilot study
for the Radiative Forcing Model Inter-comparison Project
(RFMIP; ), endorsed by the sixth Coupled Model Intercomparison Project (CMIP6; ), using the
same experiment set-up with MACv2-SP.
Throughout our model inter-comparison, we consider the effect of
model-internal variability on estimates of ERF. We do so by producing
equally sized ensembles of simulations for all participating models.
Model-internal variability in this context is defined as the year-to-year
changes in model parameters associated with inter-annual variations of the
meteorological state. The results of the climate models are compared with
satellite data and a stand-alone radiative transfer model. The following
section introduces the models and the experiment strategy in more detail,
followed by our discussion of the results in Sect. and
conclusions in Sect. .
MethodParticipating models
This work uses five Earth system models and one stand-alone
radiative transfer code. The participating climate models, which are run here
in an atmosphere-only mode, are the atmosphere component ECHAM6.3 of the
Earth system model MPI-ESM1.2 of the Max Planck
Institute for Meteorology (MPI-M), ECHAM6.3-HAM2.3 from ETH Zürich
, EC-Earth
(e.g. ) run at the Royal Netherlands
Meteorological Institute, NorESM
run at the Finnish
Meteorological Institute and HadGEM3 developed at
the UK Met Office. All models except ECHAM6.3 can treat aerosols and their
interaction with meteorological processes with complex process-based
parameterization schemes linking aerosols to radiation and clouds. In this
study, all physics packages except the parameterization of anthropogenic
aerosols are model-dependent, e.g. the treatment of the pre-industrial
aerosols and clouds differ. Appendix summarizes differences
in radiation, cloud and aerosol physics packages of the participating
models.
In the present study, we prescribe the distributions of anthropogenic
aerosols in all models, following the MACv2-SP approach
. MACv2-SP mimics the spatio-temporal
distribution and wavelength dependence of the optical properties of
anthropogenic aerosols as well as a change in the cloud droplet number
concentration (N) to induce radiative effects associated with the physical
processes of aerosol–radiation interactions (Fari) and aerosol–cloud
interactions (Faci) in a consistent manner. To do so, MACv2-SP uses
analytical functions for approximating the monthly distribution of the
present-day anthropogenic aerosol optical depth and the vertical profile of
the aerosol extinction from the updated MPI-M aerosol climatology
(MACv2; , 2019). Figure
shows the annual mean patterns of the anthropogenic aerosol optical depth
(τa) and the fractional increase in the cloud droplet number
concentration (ηN) relative to the pre-industrial level of 1850 from
MACv2-SP. By design, MACv2-SP does not simulate sub-monthly variability in
anthropogenic aerosol. Absorption of anthropogenic aerosol is prescribed with
a mid-visible single scattering albedo of 0.93 for industrial plumes and 0.87
for plumes with seasonally active biomass burning. The anthropogenic aerosols
are assumed to be small in size, with an Ångström parameter of 2 and an
asymmetry parameter of 0.63. Here, we use MACv2-SP with the CMIP6
reconstructed changes of anthropogenic aerosol emissions, identical to the
settings used by . describe the technical
details of MACv2-SP.
The use of the optical properties from MACv2-SP yields a consistent
description of Fari, including both direct radiative and semi-direct
effects, across the models. All models account for the first indirect or
Twomey effect by multiplying their cloud droplet number concentrations,
calculated for pre-industrial aerosol conditions, by ηN prior to the
radiative transfer calculation. Since ηN is larger than 1 in the
presence of anthropogenic aerosols, the effective radius of cloud droplets is
reduced, which enhances the cloud reflectivity of short-wave radiation. Note
that ηN is only available for regions with τa>0 (see Fig. ).
In addition, the EC-Earth model also includes a second
indirect or cloud-lifetime effect by using the modified cloud droplet number
concentrations in the cloud microphysics scheme .
We neither prescribe the same natural aerosol nor interfere with any other
model components than prescribing the optical properties of anthropogenic
aerosols and ηN. For instance, the pre-industrial aerosol optical depth
(τp) depends on the model (Figs. and ),
which only affects Fari and not Faci, as the prescription of
ηN is identical in the participating models. Regional differences in
τp occur primarily over oceans and deserts, where observations are
typically sparse. It is noteworthy that ECHAM-HAM runs with interactive
parameterizations for dust and sea-salt aerosol, resulting in different
spatio-temporal variability in τp (Fig. ), while in
ECHAM the monthly climatology from MACv1 is prescribed. In the interactive
parameterizations, the natural aerosol emissions, transport and deposition
rely on meteorological processes that are difficult to represent in
coarse-resolution climate models; e.g. desert-dust emissions strongly depend
on the model representation of near-surface winds
(e.g. ) such that constraining the desert-dust burden
remains challenging in aerosol modelling
(e.g. ). The aerosol-climate
models also contain some anthropogenic aerosol in τp, but the majority
of the pre-industrial aerosol optical depth is of natural origin. For
instance, the 1850s global-mean τp in NorESM is 0.096, to which
anthropogenic fossil-fuel aerosols contribute 0.002. For comparison, the global-mean τa prescribed here is 0.029 for 2005.
Mean pre-industrial aerosol optical depth (τp). Shown are
annual means of τp of the radiation band at around 550 nm for each
model.
Annual cycle of the global-mean aerosol optical depth at 550 nm.
Shown are monthly means of τp (colours) from the models and
τa (black) for the mid-1970s (dashed) and mid-2000s (solid) from MACv2-SP.
In addition to the complex climate models listed above, we use the offline
radiative-transfer model of for an assessment of the
instantaneous radiative forcing. This model has 8 solar and 12 infrared bands and reads monthly maps of the atmospheric and surface
properties. These are, for instance, monthly means for the cloud properties
from the International Satellite Cloud Climatology Project (ISCCP) and the surface albedo from the satellite product
MODIS-SSM/I described in
. The radiative-transfer calculation considers nine
different sun elevations and eight randomly chosen combinations of cloud
heights and overlap. The aerosol column properties at 550 nm are defined by
the MPI-M Aerosol Climatology (MAC). The aerosol vertical distribution and
the fine-mode anthropogenic fraction of aerosol optical depth for the
mid-2000s are derived from global models participating in AeroCom
(e.g. ). We calculate the radiation transfer with both MAC
version one (MACv1; ) and two (MACv2; ).
The latter considers more recent observational data, e.g. from the Maritime
Aerosol Network (MAN; ), and a smaller anthropogenic
aerosol fraction. MACv2 is also based on more recent emission data relative
to 1850 , while MACv1 used emission data relative to 1750
. The two climatologies therefore make different
assumptions on the pre-industrial background, shown in Fig. .
The temporal scaling of anthropogenic aerosol optical depth in MACv1 and
MACv2 is from the same transient ECHAM simulation . The
parameterization form of the Twomey effect for MACv1 and MACv2 is
identical to MACv2-SP here, but the assumptions for τp and τa differ.
Experiment strategy
All climate model simulations are carried out with
the atmosphere-only configurations using prescribed monthly mean sea-surface
temperatures and sea ice. Table summarizes the major
characteristics of the model simulations. The modelling groups were free to
set up all model components other than MACv2-SP and choose their own
boundary and initialization data. Specifically, the modelling groups use
their own representation of pre-industrial aerosol for 1850 such that the
present work includes both models with prescribed monthly climatologies and
interactive parameterization schemes for natural aerosol species (Appendix ). Moreover, the physical parameterizations of radiation and
clouds are different across the models (Appendix ).
Motivated by the effect of natural variability in ERF estimates in ECHAM
, each model was run to produce a number of simulation
ensembles: a reference ensemble consisting of six simulations with only
pre-industrial aerosols representative of 1850 and two additional ensembles
consisting of three simulations each with aerosols representative of 1975
and 2005. For each model, we perform a total of 12 experiments for the years 2000–2010. These are six experiments
with τp for the year 1850, three experiments with τp and
anthropogenic aerosol from MACv2-SP for the year 1975, and three experiments
with τp and anthropogenic aerosol from MACv2-SP for the year 2005. The
six pre-industrial simulations serve as the reference for the experiments
with anthropogenic aerosol and therefore efficiently increase the number of
forcing estimates for anthropogenic aerosol. The first year of each run is
considered to be a spin-up period and is excluded from the analysis. A 10-year
period was chosen to account for variability in the boundary conditions.
The instantaneous radiative forcing (RF) of anthropogenic aerosols in clear-
and all-sky conditions is estimated from double radiation calls in the models having
this functionality, namely ECHAM, ECHAM-HAM and NorESM. Aerosol radiative
effects predominantly occur for short-wave radiation. We therefore calculate
the atmospheric transfer of short-wave radiation once with and once without
the contribution from anthropogenic aerosols to the aerosol optical
properties and their effect on the cloud droplet number concentration. For
each model, this gives us in total 30 annual estimates of RF for each of the
two τa patterns shown in Fig. , which is sufficient
to estimate the mean RF and can be directly compared to the offline
radiation-transfer calculations. We calculate RF at the top of the atmosphere (TOA) and at the surface (SFC) and list the global means in Table .
Ensemble averages of the short-wave instantaneous radiative forcing (RF) and effective radiative forcing (ERF),
and net contribution from rapid adjustments (ADJs) at the surface (SFC) and the top of
the atmosphere (TOA) for all sky (clear sky) in W m-2 for the period 1850 to 2005.
The first block shows aerosol-climate models with MACv2-SP, and the second block shows estimates of the offline radiative-transfer model.
The ERF is calculated as the difference in the short-wave radiative flux at
the top of the atmosphere between the simulations with and without
anthropogenic aerosols. For illustrating the effect of year-to-year
variability, we calculate annual ERF estimates for each of the 10 simulation
years. Combining the six pre-industrial experiments with each of the three
experiments with additional anthropogenic aerosol thus yields 6×3 annual ERF
estimates for each year of the simulation, i.e. 180 annual estimates per
model and τa pattern in total. We calculate the standard deviation from
these 180 annual ERF values and use it as a measure of the natural
variability in ERF internal to the models. The means of these 180 values are
used for identifying systematic model differences in ERF. It was shown in an
earlier study using ECHAM that the combination of
ensemble size and simulation length adopted here is sufficient for precisely
estimating the ERF of a model. For comparison, the RFMIP protocol recommends
a 30-year average for diagnosing the ERF of a model .
Finally, we calculate the net contribution of rapid adjustments (ADJs) to ERF
by subtracting RF from ERF for each model. Our rapid adjustments are
associated with atmospheric temperature changes, i.e. semi-direct effects,
except for EC-Earth, accounting also for adjustments in cloud microphysics. A
discussion of the rapid adjustments and the choice for the Twomey effect in
ECHAM is given by .
ResultsSpread in present-day ERF
We characterize the spread in the short-wave effective
radiative forcing (ERF) at the top of the atmosphere in our model ensemble
for the present-day (mid-2000s). For doing so, we first calculate the
multi-model mean as a reference value. The all-sky top-of-atmosphere ERF for
the entire multi-model, multi-member ensemble is -0.59 W m-2, with an
inter-annual standard deviation of 0.3 W m-2, corresponding to a relative
variability of roughly 50 %. The inter-annual variability in ERF is
illustrated by Gaussian distributions fitted to the frequency histogram in
Fig. a. The entire range in annual ERFs from the models
including inter-annual variability is -1.5 to +0.5 W m-2.
Variability in annual ERF estimates for the mid-2000s.
(a) shows Gaussian distributions of annual ERF estimates for present-day from
individual model ensembles (colours) and the entire multi-model,
multi-member ensemble (black). The bars are the frequency histogram of 1-year ERF
estimates from all models, and the legend indicates the means and standard
deviations of the ERF estimates. (b) shows the regional standard
deviation of annual contributions to ERF from the entire multi-model,
multi-member ensemble as measure for the inter-annual variability inherent
in the model ensemble. (c) shows the range in the long-term averaged ERFs
of the models as measure for the spread in ERF associated with model
differences. ERF is for the short-wave (SW) spectrum at the top of atmosphere
(TOA) for all-sky conditions.
The all-sky ERFs from the models are 10 %–50 % less negative than the
clear-sky ERF in all models, except in EC-Earth, because clouds mask the ERF
of low-level anthropogenic aerosol (Table ). That masking by
clouds is most pronounced in HadGEM3. In EC-Earth, the all-sky ERF is more
negative than in clear-sky conditions because EC-Earth includes cloud-lifetime effects
of anthropogenic aerosols, thus simulating a stronger Faci than
all other participating models. The long-term averaged ERFs of ECHAM and
ECHAM-HAM are similar, despite ECHAM using a prescribed climatology of
τp and ECHAM-HAM simulating τp interactively (Sect. ). This similarity suggests that the sub-monthly variability
in
natural aerosol does not substantially affect the mean ERF of anthropogenic
aerosol as long as Faci is treated consistently in the two
models. Using different parameterizations for Faci can change
this result because of non-linear processes. The magnitude of
Faci, however, remains uncertain . One
contributing uncertainty is the poor quantitative understanding of the
pre-industrial aerosols (e.g. ).
The multi-model spread in the ensemble mean all-sky ERF of individual models
is rather small, with a range of -0.40 to -0.9 W m-2,
compared to the internal variability in the entire multi-model ensemble
(Fig. a). This multi-model spread corresponds to a range of
deviations from the multi-model mean of just -0.31 to
+0.19 W m-2 and is even smaller when the ERF of EC-Earth, which includes
cloud-lifetime effects, is excluded. One could expect less model diversity in
all-sky ERF from our study than from previous inter-comparison projects
(e.g. ) because we prescribe the same
aerosol optical properties and the associated change in cloud droplet
numbers. However, our model diversity in clear-sky ERF is smaller than for
our all-sky ERF (Table ). This points to the influence of model
differences in representing clouds (Appendix ) on the all-sky
ERF. Our results therefore indicate that model differences in meteorological
parameters contribute to the model diversity in all-sky ERF. This is also the
case for the ERF uncertainty in a complex aerosol-climate model
.
The large inter-annual variability implies that it is essential to estimate
ERF of individual models from a sufficiently large number of simulated years
to quantify model differences in ERF. Otherwise the modelled ERF estimates
may not be representative of the long-term average. This could be done either
from sufficiently long simulations with annually repeating aerosol or a
sufficiently large ensemble of simulations with transient changes. Given the
similar year-to-year variability in ERF in the models, the confidence
estimates from ECHAM are a reasonable approximation for
the whole ensemble of models in the present study.
Multi-model, multi-member ensemble mean of the anthropogenic aerosol
radiative effects for the mid-2000s. Shown are the (a) instantaneous and
(b) effective radiative forcing as well as (c) the net contribution from rapid
adjustments for the SW spectrum at the TOA in all-sky conditions. Hatching in
(b, c) indicates non-significant values at a 10 % significance level. The numbers in
the lower left corner are the spatial averages. The ensemble-mean RF is
averaged over three climate models, the ensemble-mean ERF is averaged over five climate
models and the ensemble-mean adjustment is their difference.
Regional contributions to ERF
The distributions of ERF for 2005 are shown as
ensemble averages in Fig. and are shown for each model in
Fig. . Eastern Asia is the largest contributor to
globally averaged ERF, as expected from the regional maximum in τa
prescribed there (Fig. b). The mean pattern of regional
contributions to ERF is in general similar in the models, but differences in
its magnitude and detectability appear in some regions. For example, the
contributions to the global ERF modelled over central Africa range from
positive to negative, averaging to a small value in this region
(Fig. ).
Another interesting example for where regional contributions to
globally averaged ERF differ is the North Atlantic. In this region, the
variability in the multi-model ensemble is relatively large, 3–6 W m-2
(Fig. b), but the small multi-model mean radiative effects
are nevertheless detectable (Fig. ), although ECHAM and
the Hadley Centre Global Environment Model (HadGEM) by themselves have regional signals over the North Atlantic that are
not statistically significant.
Multi-member ensemble mean of effective radiative effects of
anthropogenic aerosol for the mid-2000s. Shown is the effective radiative
forcing for the SW spectrum at the TOA in all-sky conditions for each model. Hatching
indicates non-significant values at a 10 % significance level.
Taken together, the size of year-to-year variability and regional model
differences in contributions to the global ERF imply that an ensemble of
simulations with more than one model, as done here, is needed for
constraining the radiative effect of anthropogenic aerosol regionally. The
spread in modelled regional contributions to ERF is typically smaller than
the differences associated with natural variability in the model ensemble
(Fig. b–c). Irrespective of whether we compute the
regional standard deviations for the aerosol pattern of the mid-1970s or the
mid-2000s, the pattern and strength of the regional natural variability in
contributions to ERF are robust (not shown). In regions where the
anthropogenic aerosol burden was relatively large in 2005, like eastern Asia,
the models disagree on the magnitude of the regional contributions to ERF
(Fig. c), which means that even for a relatively large
anthropogenic aerosol optical depth, natural variability in the atmosphere
remains a hurdle against constraining the regional radiative effect.
Contributions from RF and adjustments
The modelled ERF is decomposed into the contributions of
rapid adjustments and RF by diagnosing the latter from double calls to the
radiation scheme in the models with this functionality (Fig. ). The RF is considerably less variable from year to year
than ERF. Moreover, RF clearly dominates the ERF magnitude in all models that
use ηN in the radiation transfer calculation (Table ).
Remember that these models consider Faci from the Twomey effect
only. The net contribution of rapid adjustments to the global-mean ERF ranges
from 0.03 W m-2 in NorESM to 0.2 W m-2 in ECHAM-HAM at the TOA and acts
to weaken the forcing magnitude. The positive net contribution from
adjustments is consistent with buffering of perturbations by atmospheric
processes.
We compare the climate model estimates of RF with the results of the offline
radiation-transfer calculations described in Sect. . The
offline estimates of the all-sky RF with MACv2-SP (Offline-v1-SP and
Offline-v2-SP) are in close agreement with the RF of the climate models that
represent Faci in the form of the Twomey effect. This agreement is
remarkable, since the aerosol-climate models and the offline model differ in
many aspects, including again the representation of clouds (see Appendix ).
Uncertainties in RF
The offline radiation-transfer model is used to assess the role of
uncertainty in τp and τa in total RF uncertainty. The aerosol
classification of MACv2 (Offline-v2) is used as an alternative representation
to MACv1 (Offline-v1). MACv2 classifies more ambiguous cases of fine-mode
aerosol as anthropogenic than MACv2-SP. These cases primarily occur in remote
uninhabited regions such as the Southern Ocean and the Sahara. These
regions are poorly captured by the ground-based observation network, so there
the MACv2 product primarily uses global model results for separating
anthropogenic from natural aerosols. Classifying additional fine-mode aerosol
as anthropogenic increases the all-sky RF to -1.1 W m-2, which
primarily arises due to stronger Faci in MACv2. Ambiguous aerosol
classifications, which occur especially in regions with a generally low
aerosol burden, and poor observational coverage are therefore causes of
uncertainty in present-day RF, with the RF becoming more negative with
increasing τa.
An even more negative RF is obtained from the offline model, namely an
all-sky RF of -1.4 W m-2, when both a larger anthropogenic fraction and
the lower background burden of 1750 from MACv1 (Offline-v1) are used. Note
that the clear-sky RFs from the offline estimates and the climate models are
in good agreement such that most of the uncertainty stems from the uncertain
magnitude of Faci. This underlines again the importance of the
aerosol background for quantifying the cloudy-sky contribution to all-sky RF
in agreement with previous studies .
Quantitative changes in the natural aerosol burden between the pre-industrial and
present-day eras remain poorly constrained. Since the aerosol of 1750 or 1850 has
not been observed, using the present-day natural aerosol as a background
could yield a better comparability of observational and model estimates in
future inter-comparison studies. By prescribing both the same natural and
anthropogenic aerosol across different models, differences in the radiative
effects of the aerosol can be attributed to model errors in representing
meteorological processes and radiative transfer.
Impact of spatial change of pollution
Although the global-mean τa is similar for 1975 and
2005, the anthropogenic pollution covers very different regions, with the
largest maxima in Europe and the US during the mid-1970s and in eastern Asia
during the mid-2000s. The regional differences in clouds, insolation and
surface albedo can contribute to changes in radiative effects that can result
in a different global ERF. For instance, Figs. – show the spatial patterns of cloud properties
and the surface albedo, illustrating both the regional differences and the
model diversity for their representation (see Appendix ). The
different spatial distributions of τa clearly change the pattern of the
radiative forcing (Fig. ). As expected, the maxima in
regional contributions to RF and ERF occur over Europe and the US in the
mid-1970s and over eastern Asia for the mid-2000s.
Multi-model, multi-member ensemble mean of the anthropogenic aerosol
radiative effects for the mid-1970s, as in Fig. , but with
the anthropogenic aerosol pattern of the mid-1970s.
Despite those regional differences in radiative effects and the inter-model
spread in ensemble-averaged global-mean RF and ERF, the spatial pattern of
τa has little impact on the global-mean RF and ERF in each of the
participating models. The model ensemble mean changes from -0.54 W m-2
for the mid-1970s to -0.59 W m-2 for the mid-2000s. The mean monthly
contributions to RF are also similar for both τa patterns,
irrespective of which model we choose (not shown).
Anthropogenic aerosol forcing of the mid-1970s against the
mid-2000s. Shown are the (a, b) instantaneous and (c, d) effective radiative
forcing for the SW spectrum at the TOA from the pollution of the mid-1970s against the
mid-2000s for (a, c) clear- and (b, d) all-sky conditions. Thick asterisks are the
ensemble means. Blue dots in (c, d) are the model averages of individual
years, representing the year-to-year variability internal to the model
ensemble.
The ensemble-averaged change in ERF is small relative to the natural
inter-annual variability in modelled ERFs (Fig. ). Indeed,
contrasting 1-year estimates from the two τa patterns results in a
large spread in ERF changes ranging from decreases to increases in ERF with
τa patterns (Fig. c, d). This result is in
agreement with previous findings based on ECHAM only . The
result underlines again the importance of using a large number of simulated
years for determining changes in ERF from free-running climate models.
Moreover, it provides evidence that the global-mean ERF does not strongly
depend on the regional distribution of anthropogenic aerosol in the Northern
Hemisphere.
Anthropogenic aerosol effective radiative forcing efficiencies (in
W m-2 per unit optical depth) for (a, d) all-sky, (b, e) clear-sky and (c, f) cloudy-sky conditions. (a–c) show efficiencies for mid-2000s
anthropogenic aerosols. (d–f) show differences made by using the
pattern for the mid-1970s.
The cloudy- and clear-sky contributions to the all-sky efficiency of the ERF,
in other words the ratio of ERF to τa, helps with better understanding why
the two τa patterns yield similar ERFs. All-sky efficiency is the sum
of contributions from cloudy and clear-sky conditions:
ERFallτa=fERFcloudyτa+(1-f)ERFclearτa,
where f is the total cloud fraction, and ERFcloudy and
ERFclear are the ERF in cloudy and clear-sky conditions,
respectively.
Figure shows the regional distribution from the multi-model
ensemble average of the terms of Eq. (). The all-sky
efficiency often increases with increasing distance to major pollution
sources because of the decreasing background aerosol, up to -100 W m-2
per unit of τa. These all-sky efficiencies are primarily explained by the
cloudy-sky contributions. Large efficiencies occur typically in remote areas,
including some regions at the edges of τa plumes (Fig. ). No clear saturation of Faci is evident at all edges of
the τa plumes. Also the spatial distribution of both the all- and
cloudy-sky efficiency is rather inhomogeneous. The inhomogeneity contrasts
with the clear-sky efficiency, which has much smaller spatial variability.
Averaged globally, all-sky forcing efficiencies for the two aerosol patterns
are similar at -26 W m-2 per unit of τa. The regional all-sky ERF
efficiencies, however, change between the mid-1970s and mid-2000s (Fig. ). This change is almost exclusively explained by the
cloudy-sky contribution to the ERF efficiency, reflecting the regional change
in ηN from the mid-1970s to mid-2000s. The strong change in the
cloudy-sky contribution is in strong contrast to the relatively minor changes
in the clear-sky contributions. Differences in regional efficiencies of
anthropogenic aerosol effects on clouds thus become balanced in the global mean and
result in similar global ERFs for the mid-1970s and mid-2000s.
Of all models, NorESM and EC-Earth have the strongest ERF efficiencies around
-30 and -40 W m-2 per unit of τa, respectively; i.e. the same
aerosol perturbation in these two models is much more efficient in inducing
effective radiative effects than in the other models, consistent with the
more negative ERFs (Fig. ). In EC-Earth, the more negative
ERF also arises from perturbing the cloud microphysics with ηN. In
NorESM, the more negative ERF arises from a strong negative RF and a small
net contribution from adjustments.
Conclusions
We assess the radiative effects of anthropogenic aerosol in
ensembles of simulations from five state-of-the-art aerosol-climate models,
prescribing identical anthropogenic aerosol properties of reduced complexity.
Each of the participating models uses annually repeating patterns of
anthropogenic aerosol for obtaining 180 years of radiative forcing estimates.
The multi-model multi-ensemble present-day all-sky short-wave effective
radiative forcing (ERF) at the top of atmosphere is -0.59 W m-2. The
year-to-year standard deviations of around 0.3 W m-2 in the models imply
a typical year-to-year variability of 50 %, reflecting a strong contribution
of model-internal variability to ERF. We therefore recommend caution for the
use of ERF estimates based on single years, as in the standard AeroCom
protocol with varying reference years. These are likely affected by
model-internal variability such that an apparent ERF spread is not associated
with systematic model differences alone. Indeed such studies have shown a
substantial spread in ERF estimates (e.g. ), comparable
to the magnitude of the model-internal variability quantified in the present
work.
We further recommend that model-based assessments of ERF in the future ensure
the elimination of the effects of internal variability, either by averaging over
longer time periods from single transient climate simulations or from
averaging across several ensemble members for shorter time periods. For
instance, the protocol of RFMIP requests 30-year averages for estimating
the present-day ERF and three-member ensembles with 10-year averages for
diagnosing decadal changes in ERF . The precision of the
estimate can be tested by using confidence estimates
(e.g. ). Note that natural variability is equally an
issue in observations. Ensembles of simulations should therefore be used for
constraining ERF with the historical record of observations. The inter-annual
variability in ERF, and hence the number of years needed to estimate ERF,
could be different in nudged model simulations . However,
nudging a model simulation with reanalysis data can change the climatology
and interfere with the rapid adjustments. The resulting ERFs from a nudged
simulation are therefore likely different when compared with free-running model
simulations. The interference of nudging with adjustments deserves closer
attention in future research.
In our study, we obtain an ERF spread of -0.9 to -0.4 W m-2, associated
with systematic model differences (Fig. ). This estimate is
not affected by model-internal variability, is based on identical
anthropogenic aerosol optical properties and makes use of a consistent
perturbation of the cloud droplet number concentrations associated with
anthropogenic aerosol. The model with the most negative ERF accounts also for
changes in cloud microphysics associated with anthropogenic aerosol, whereas
the other participating models account for the Twomey effect only. Based on
our model spread, we conclude that models with a strongly negative ERF have
particularly strong contributions from anthropogenic aerosol effects on
clouds.
Summary of model spread in anthropogenic aerosol forcing for the
mid-2000s. Shown are the instantaneous (RF) and effective radiative forcing
(ERF) of aerosol–radiation and aerosol–cloud interactions for the short-wave
spectrum at the top of the atmosphere for clear- and all-sky conditions from
Table . The RF from the offline radiation-transfer calculations
considers additional uncertainty sources and is shown as separate bars. Refer
to Sect. for details.
Our results highlight that the participating models consistently show little
change in the global ERF of anthropogenic aerosol between the mid-1970s and
mid-2000s, despite the substantially different location of anthropogenic
pollution maxima and the model diversity in their ERF magnitude relative to
the pre-industrial. Model-internal variability, however, produces ERF changes
of different signs and magnitude between the two periods. This result gives
further evidence that model-internal variability has not been sufficiently
considered in past model studies estimating the ERF difference associated
with the mid-1970s to mid-2000s change in anthropogenic aerosol, as
previously suggested based on ECHAM alone . The small
change in global ERF stems from similar global forcing efficiencies of
anthropogenic aerosol in the two periods. These are primarily explained by
globally compensating differences in regional cloudy-sky contributions to the
ERF efficiency. Assuming stronger aerosol–cloud interactions can cause a
larger change in ERF from the mid-1970s to mid-2000s, based on simulations
with ECHAM . The forcing from aerosol–cloud interaction is
a subject of ongoing discussion and research . Given our
multi-model spread in absolute ERF relative to the pre-industrial period,
inter-comparing the relative ERF changes between observable periods might
provide a better test for a model to represent transient climate changes. Our
future work will focus on inter-comparing modelled ERF changes associated
with other aerosol patterns. One such endeavour is the usage of MACv2-SP in
model simulations in the framework of CMIP6
(e.g. ).
Data availability
The model data of this study will be available on the AeroCom community's
data server. Additionally, the model data are archived by the Max Planck Institute for Meteorology
and can be made accessible by contacting publications@mpimet.mpg.de.
Model physics packages
ECHAM6.3 is the latest version of the atmosphere component
of the Earth system model MPI-ESM1.2 of MPI-M, which participates in CMIP6
. ECHAM6.3 is a global hydrostatic model and includes
parameterizations of sub-grid-scale physical processes. The atmospheric
radiative transfer is parameterized with the PSrad scheme using the rapid
radiative transfer model for general circulation models
(RRTMG; ). Surface properties, trace gas concentrations,
and natural aerosols are prescribed by climatological datasets. A major
change in MPI-ESM1.2 compared to previous model
versions is the implementation of MACv2-SP .
The global aerosol-climate model ECHAM6.3-HAM2.3 is an updated version of the
model described by and . Revisions made
in ECHAM6.3-HAM2.3 relate to the atmospheric model and the description of
sea-salt emissions, which have been made dependent on the sea-surface
temperature. The model uses ECHAM6.3 but is coupled to the aerosol module
HAM . An important difference in the
atmospheric components is that ECHAM6.3 uses a single-moment cloud
microphysics parameterization, while ECHAM6.3-HAM2.3 has a two-moment
stratiform cloud scheme for representing the activation
of aerosols as cloud condensation nuclei and ice nuclei in mixed phase
clouds. Emission schemes for sea salt , desert
dust and oceanic dimethyl sulfide
(DMS; ) are run online. Emissions of all other aerosol
species are prescribed from external input files
. In the configuration used in this study, we
prescribe the pre-industrial background of aerosol components from HAM that
are not simulated online. These, in combination with the online-computed
natural aerosol emissions, are the only aerosols seen by the two-moment cloud
microphysics parameterization in this study.
EC-Earth uses the Integrated
Forecasting System (IFS) of the European Centre for Medium-Range Weather
Forecasts (ECMWF) as its atmosphere component. The latest generation of the
model, EC-Earth3, is based on the ECMWF seasonal prediction system 4 with the IFS
cycle 36r4. The radiation scheme is based on the rapid radiative transfer
model with 14 bands in the short-wave spectrum and 16
bands in the long-wave spectrum and uses the Monte Carlo independent column
approximation (McICA) approach . Many new features have
been added to IFS by the EC-Earth consortium. The pre-industrial tropospheric
aerosol climatology that is used in combination with MACv2-SP has been
constructed from a simulation with the TM5 aerosol-chemistry model
, driven by meteorological data from ERA-Interim
for the early 1980s. This simulation used CMIP6 emissions of aerosol and
precursor gases for 1850 and provides the monthly mean aerosol mass and
number concentrations as well as the aerosol optical properties.
Stratospheric aerosols are prescribed using the CMIP6 dataset of radiative
properties. Aerosol–cloud interactions are implemented only for liquid-phase,
stratiform clouds. The cloud droplet number concentration, N, is diagnosed
using the activation scheme of and is modified here by
ηN from MACv2-SP. Cloud microphysics depends on N through
autoconversion of cloud droplets to rain. The model used in this study is
EC-Earth version 3.2.3. It is close to the CMIP6 version described by
but does not include the latest revisions that were
introduced after the simulations for this study were started. Most relevant
to this study is that in the CMIP6 version, the pre-industrial aerosol
climatology has been updated by changing the parameterization of the
production of sea spray in the underlying TM5 model. Specifically, the
whitecap coverage has been made dependent on sea-surface temperature, while
its power-law dependence on the 10 m wind speed has been changed from the W10
expression proposed by to the expression proposed by
. The main effect of this revision is an increase in
aerosol and cloud droplet number concentrations over the Southern Ocean.
Simulations with the Hadley Centre Global Environment Model (HadGEM) use a
modified version of the HadGEM3 Global Atmosphere 7.0 climate model
configuration . HadGEM3 normally uses the Global Model of
Aerosol Processes (GLOMAP; ) to simulate aerosol mass and
number and interactions of aerosols with radiation, clouds and atmospheric
chemistry. That scheme is replaced here with prescriptions of the
three-dimensional distributions of aerosol extinction and absorption
coefficients averaged over HadGEM's six short-wave and nine long-wave wavebands,
waveband-averaged aerosol asymmetry, and N. Those prescriptions are made of
three components. First, pre-industrial aerosol and N distributions are
taken from a HadGEM3–GLOMAP simulation using CMIP6 emission datasets for the
year 1850. Second, stratospheric aerosols are taken from the CMIP6
climatologies for the year 1850. Prescribed N values are used in the calculation
of cloud albedo and autoconversion rates
, although the latter do not see the MACv2-SP N
scalings, ensuring that anthropogenic aerosols do not exert a secondary
indirect effect in the present study. HadGEM3 uses the prognostic cloud
fraction and prognostic condensate scheme (PC2; ) that
simulates the mass-mixing ratios of water vapour, cloud liquid and ice, as
well as the fractional cover of liquid, ice and mixed-phase clouds.
The Norwegian Earth System Model
(NorESM; uses the atmospheric
component of the Oslo version of the Community Atmosphere Model (CAM4-Oslo),
which differs from the original CAM4 through the modified
treatment of aerosols and their interaction with clouds .
The model has a finite-volume dynamical core and the original version 4 of
the Community Land Model (CLM4) of CCSM4 . NorESM uses
the CAM-RT radiation scheme by . Like ECHAM-HAM and ECHAM,
NorESM sets all background aerosol emissions to pre-industrial levels
representative of 1850. These background conditions include sulfate from
tropospheric volcanoes and from DMS as well as organic matter from land and
ocean biogenic processes, mineral dust and sea salt. Sea-salt emissions are
parameterized as a function of wind speed and temperature
, while other pre-industrial aerosol emissions are
prescribed following . These are, in the case of NorESM,
sulfate, organic matter and BC aerosols originating from fossil fuel
emissions and biomass burning .
Model diversity in cloud properties and surface albedo
The model diversity in RF and ERF is larger when cloudy
skies are considered. We therefore assess the model diversity in cloud
properties and compare the model climatologies calculated from the
simulations for the mid-2000s against observational climatologies from
satellite products, listed in Table . The observational products
provide an orientation for realistic values, although satellite retrievals
also have caveats (e.g. ). Moreover, we document the
surface albedos used here for illustrating both the regional differences and
the model diversity.
Gridded climatologies of satellite retrievals used for model evaluation.
NameDescriptionVariableTimeCERESEnergy balanced and filled data of theCloud short-wave radiative effects2001–2014Clouds and the Earth's Radiant Energyat the top of the atmosphere,System, Ed. 4 Fcld (W m-2)ISCCPInternational Satellite Cloud ClimatologyTotal cloud cover,1983–2009Project f (%)MAC-LWPMulti-sensor Advanced ClimatologyLiquid water path,2000–2016lcld (g m-2)MODISClimatology based on Moderate ResolutionCloud droplet number2003–2015Imaging Spectroradiometer aboard Aquaconcentration in warm clouds,N (cm-3)MODIS-SSM/IClimatology based on Moderate ResolutionSurface albedo for1987–2007Imaging Spectroradiometer and microwave datashort-wave radiation,αs (%)Macroscopic cloud properties
We first assess the cloud short-wave radiative effect at the top of the
atmosphere (Fcld), thus the cloud effect on the planetary albedo.
The multi-annual global-mean Fcld for 2001–2010 from the Clouds and the Earth's Radiant Energy System (CERES) Ed.
4 is -45.8 W m-2, i.e. less negative than in most models
(Table ). This behaviour indicates a tendency of the models
to have clouds that are too reflective, consistent with other model evaluations
(, Lohmann and Neubauer, 2019). The spatial
patterns of modelled Fcld are generally similar, but regionally
the differences can be more distinct (Fig. ).
Global-mean statistics for clouds, aerosols and surface albedo.
The numbers given for lcld and N are averages over ocean regions,
consistent with the satellite data availability (Figs. and ).
Details on the satellite products are listed in Table .
Fcld (W m-2)f (%)lcld (g m-2)N (cm-3)τpαs (%)ECHAM-47.56365840.09316ECHAM-HAM-49.16869650.09715EC-Earth-46.26542910.09115HadGEM3-44.36957560.09815NorESM-55.555133340.09614Satellite retrieval-45.8668277–15
Multi-member ensemble means of cloud characteristics for the
mid-2000s compared to climatologies derived from satellite observations
(Table ). Shown are the mean SW cloud radiative
effect at the TOA, Fcld (left column); total cloud cover, f (middle
column); and liquid water path, lcld (right column) from the
satellite products (top row) and the models (rows beneath). Areas without available
data are shaded white.
To better characterize the model diversity in clouds, we compare the
simulated total cloud cover (f) and liquid water path (lcld) to
satellite climatologies from the ISCCP and MAC-LWP, respectively
(Table ). Most models underestimate both f and
lcld over the oceans compared to the satellite retrievals, but
having too few clouds does not necessarily imply too small an amount of liquid
or vice versa (Table ). The spatial patterns (Fig. ) show a tendency of the models for underestimating f in
the stratocumulus decks in the southeastern regions of the Pacific and
Atlantic Ocean, where aerosol–cloud interactions are thought to be important.
The models, however, disagree on the values for f and lcld in
those regions. Moreover, the models show a large diversity in
lcld in the extratropical storm tracks. NorESM shows the largest
maximum lcld exceeding 200 g m-2. Our findings for
lcld are consistent with a similar regional comparison between
HadGEM and CAM , the latter of which having a similar
atmospheric component to NorESM (see Appendix ).
Cloud microphysical properties
The reported differences in macroscopic cloud properties among the models
raise the question of how different the cloud droplet number concentrations
(N) are. We find that the models show large diversity in the pattern of N
for present-day conditions, as shown in Fig. . Note that we
show the mean in-cloud droplet number concentration, which means that regions
without clouds are not included when averaging N. It is noteworthy that in
the models, N is calculated for stratiform cloud types but can additionally
include detrained droplets from anvils of deep convection. The spatial
pattern of N in ECHAM is not shown due to the simplistic treatment in the
model. ECHAM employs statically prescribed values for N, which are constant
with height below 800 hPa and exponentially decrease aloft. The near-surface
values in ECHAM are N=80 cm-3 over ocean and N=180 cm-3
elsewhere (not shown) and are multiplied with ηN from MACv2-SP like in
the other models.
In-cloud droplet number concentration for the mid-2000s. Shown are
the annually and vertically averaged in-cloud droplet number concentrations
(N) from the aerosol-climate models and from the MODIS satellite product by
. Areas without available data are shaded white.
Compared to the satellite product, the models typically underestimate N,
for example, in the stratocumulus decks, where f is also underestimated. How much the quantitative differences between the
models and the satellite product are due to differences in the methods for
diagnosing N in the satellite retrievals and the models remains an open question, but it is unlikely
that the methods solely explain the diversity in the patterns of N. It is
interesting that, despite these quantitative differences in N, the spatial
pattern of Fcld compares reasonably well to observations (Fig. ), which might be a consequence of compensating differences
from tuning the radiation balance at the top of the atmosphere. For instance,
the behaviour of NorESM points to too much short-wave reflectivity by clouds that are too thick that overcompensate the missing reflection due to underestimated
cloud cover.
Surface albedo
An additional influence on the radiative forcing of anthropogenic aerosol is
the surface reflectivity for short-wave radiation. We therefore document the
surface albedo for short-wave radiation from the participating models and the
satellite product used in the offline radiative-transfer calculations of this
study. In the global mean, the models and the satellite product are very
similar, with a surface albedo of 14 %–16 %. However, the spatial
distributions in Fig. indicate differences. The typical
difference between less reflective ocean surfaces compared to land regions is
apparent. Moreover, the analysis reveals diversity in the regional surface
albedos of the participating models, typically related to areas affected by
snow cover. Since such diversity in the surface albedo was already previously
reported for aerosol-climate models with implications for the aerosol
radiative forcing (e.g. ), future efforts are still needed
for constraining the surface albedo in climate models.
Surface albedo for short-wave radiation for the mid-2000s. Shown are
the mean surface albedo for short-wave radiation (αs) from the models
and the satellite product from .
Author contributions
SF designed the study, performed the experiments with ECHAM,
analysed the data of all models and led the writing of the paper. SK performed
the offline radiation-transfer calculations and compiled the surface albedo product MODIS-SSM/I.
PR performed the experiments with NorESM, KH performed experiments for ECHAM-HAM, NB performed experiments for HadGEM, and TvN and DO'D performed experiments for EC-Earth.
All authors contributed to the discussion of the results and the writing of the paper.
Competing interests
The authors confirm that they have no competing interests.
Acknowledgements
We thank the editor Hinrich Grothe for handling our paper and the three
anonymous reviewers for their comments that helped in improving the discussion
article. This work is largely funded by the FP7 project BACCHUS (no.
603445). Stephanie Fiedler further thanks the Max Planck Society for funding. Philip Stier was
additionally supported by the European Research Council (ERC) project
“constRaining the EffeCts of Aerosols on Precipitation” (RECAP) under the
European Union's Horizon 2020 research and innovation programme with grant
agreement no. 724602 as well as by the Alexander von Humboldt Foundation. Joonas Merikanto
acknowledges the Academy of Finland for funding (no. 287440). We acknowledge
the usage of the DKRZ supercomputer for running the simulations with
ECHAM6.3. ECHAM6.3-HAM2.3 simulations were performed through a grant from the
Swiss National Supercomputing Centre (CSCS) under project ID 652. We also
acknowledge the usage of satellite data from the following providers. CERES
data were obtained from the NASA Langley Research Center ordering tool
(http://ceres.larc.nasa.gov/, last access: 5 May 2019), ISCCP data were obtained from the International Satellite
Cloud Climatology Project website (https://isccp.giss.nasa.gov, last access: 5 May 2019) maintained
by the ISCCP research group at the NASA Goddard Institute for Space Studies,
MAC-LWP data were acquired as part of the activities of
NASA's Science Mission Directorate and were archived and distributed by the
Goddard Earth Sciences (GES) Data and Information Services Center (DISC,
https://disc.gsfc.nasa.gov, last access: 5 May 2019), and the cloud droplet number concentration
climatology was provided by the Vanderbilt University Institutional Repository
(https://ir.vanderbilt.edu/handle/1803/8374, last access: 5 May 2019). We thank Akos Horvath for
providing information on MAC-LWP.
Financial support
This research has been supported by the Seventh Framework
Programme (BACCHUS grant no. 603445).The
article processing charges for this open-access publication
were covered by the Max Planck Society.
Review statement
This paper was edited by Hinrich Grothe and reviewed by
three anonymous referees.
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