This study evaluates the impact of atmospheric horizontal resolution on the
representation of cloud radiative effects (CREs) in an ensemble of global
climate model simulations following the protocols of the High Resolution
Model Intercomparison Project (HighResMIP). We compare results from four
European modelling centres, each of which provides data from “standard”-
and “high”-resolution model configurations. Simulated radiative fluxes are
compared with observation-based estimates derived from the Clouds and Earth's
Radiant Energy System (CERES) dataset. Model CRE biases are evaluated using
both conventional statistics (e.g. time and spatial averages) and after
conditioning on the phase of two modes of internal climate variability,
namely the El Niño–Southern Oscillation (ENSO) and the North Atlantic
Oscillation (NAO). Simulated top-of-atmosphere (TOA) and surface CREs show
large biases over the polar regions, particularly over regions where seasonal
sea-ice variability is strongest. Increasing atmospheric resolution does not
significantly improve these biases. The spatial structure of the cloud
radiative response to ENSO and NAO variability is simulated reasonably well
by all model configurations considered in this study. However, it is
difficult to identify a systematic impact of atmospheric resolution on the
associated CRE errors. Mean absolute CRE errors conditioned on the ENSO phase
are relatively large (5–10 W m-2) and show differences between
models. We suggest this is a consequence of differences in the
parameterization of SW radiative transfer and the treatment of cloud optical
properties rather than a result of differences in resolution. In contrast,
mean absolute CRE errors conditioned on the NAO phase are generally smaller
(0–2 W m-2) and more similar across models. Although the regional
details of CRE biases show some sensitivity to atmospheric resolution within
a particular model, it is difficult to identify patterns that hold across all
models. This apparent insensitivity to increased atmospheric horizontal
resolution indicates that physical parameterizations play a dominant role in
determining the behaviour of cloud–radiation feedbacks. However, we note
that these results are obtained from atmosphere-only simulations and the
impact of changes in atmospheric resolution may be different in the presence
of coupled climate feedbacks.
Introduction
Clouds cover about 70 % of the Earth's area and have multiple effects on
climate (). They regulate the Earth's radiation budget
by modulating the incoming solar radiation as well as the outgoing longwave
radiation (). Cloud processes occur from micrometre (e.g.
condensation or freezing) to kilometre scales (e.g. convective systems).
Clouds also have a strong dynamic character and vary substantially in space
and time in the atmosphere (). Given the complexity of
cloud–climate interactions, cloud processes are heavily parameterized in
climate models. Considering their tight coupling to the radiation budget,
they are one of the key components of the Earth system that need to be
evaluated in the global climate models. Evaluating clouds requires a
two-pronged approach, wherein both statistical and process-oriented
comparisons with observations are needed. In the former, the absolute biases
in cloud properties and cloud radiative effects by statistical comparisons of
mean fields are carried out, whereas the degree with which a certain cloud
process is simulated by climate models is assessed in the latter.
Atmospheric processes, especially those related to cloud–climate
interactions, are sensitive to the spatial resolution of climate models. For
example, increasing the spatial resolution in models is shown to be crucial
to accurately reproduce the large-scale features such as the El
Niño–Southern Oscillation , Intertropical Convergence
Zone (ITCZ) , jet streams and storm tracks
. Improvements are also seen in the simulation of synoptic-scale
phenomena such as tropical cyclones and polar lows
. A detailed overview of the improvements in the key climate
processes is addressed in . In light of these studies, the
EU-funded PRIMAVERA (PRocess-based climate sIMulation: AdVances in high
resolution modelling and European climate Risk Assessment) project
(https://www.primavera-h2020.eu/, last access: 9 February 2019) aims at
improving our understanding of the role that an increased spatial resolution
plays in simulating climate processes and their feedbacks.
Here, in the context of this PRIMAVERA project, the surface and top-of-the-atmosphere
cloud radiative effects (CREs) are analyzed in global climate
models from four European modelling centres, each with varying spatial
resolutions. The observed flux estimates from NASA's CERES-EBAF (Clouds and
the Earth's Radiant Energy System-Energy Balanced And Filled) instrument is
used for the evaluation. CERES provides the longest, continuous space-based
global observations of cloud forcings. Evaluating climate models provides a
positive feedback loop, wherein as the climate models improve, in part due to
better observations; the requirements on observations have also increased
. Particularly, the last decade has seen an
exponential increase and maturity in observations and, as a result, has
provided greater insights into model deficiencies and limitations
.
In the present study, we carry out evaluations using both approaches, i.e.
the statistical and process-oriented comparisons. For the latter, we focus on
two major modes of natural variability, namely ENSO and North Atlantic Oscillation (NAO), that govern the atmospheric
variability in the tropical Pacific and North Atlantic oceans and the
surrounding continents. First, the typical cloud radiative response to ENSO
and NAO is investigated, and then we test how well this response is simulated
by climate models. Cloud radiative response is defined as the change in cloud
radiative effects observed during the positive and negative phases of ENSO
and NAO compared to climatology. We further investigated if high spatial
resolution adds value while capturing the cloud radiative response during
these two major modes of natural variability.
Models, observations and methods used in the studyModels participated in the PRIMAVERA project
The shortwave (SW), longwave (LW) and combined cloud radiative effects (CREs)
are evaluated in the High Resolution Model Intercomparison Project
(HighResMIP; ) models with varying resolutions that
participated in the PRIMAVERA project. A brief description of these models
used in this study is provided in the table below. The atmosphere-only
simulations are forced by sea surface temperature (SST) and sea ice
concentrations from the Hadley Centre Sea Ice and Sea Surface Temperature
data set version 2.2 (HadISST2.2; ). The HadGEM3 model is the
only model that is run at three different spatial resolutions at
approximately 40, 90 and 200 km (at the Equator). All the other models are
run at two different horizontal resolutions, as shown in Table .
Longer simulations from 1950 to 2014 were carried out with these models as
part of HighResMIP; however, the period from 1982 to 2014 is used for this
study. Each model uses its own background aerosol climatology. However, the
aerosol forcing from the anthropogenic sources is generated by the MACv2-SP
method proposed by . By this method, the aerosol forcing is
calculated based on the aerosol optical properties and fractional change in
cloud droplet number concentrations. More details of these high-resolution
simulations (HighResMIPv1.0) are given in . Monthly means of SW
and LW, clear-sky and all-sky fluxes are used to derive the CREs. The CREs at
the top of the atmosphere (TOA) and surface (SFC) are defined as the
difference between all-sky and clear-sky fluxes. For the analysis, the models
are separated into high-resolution (Hi-res) and standard-resolution (Std-res)
model configurations. The models that are included in the Hi-res
configurations are HadGEM3-GC31-HM, EC-Earth3-HR, MPI-ESM-XR and ECMWF-HR.
Their respective low-/standard-resolution counterparts constitute the Std-res
configurations.
The model-simulated TOA and SFC CREs for the December–January–February
(DJF) and June–July–August (JJA) averaged months are evaluated against the
CERES-EBAF satellite observational data
(https://ceres-tool.larc.nasa.gov, last access: 3 April 2019). The
CERES instrument aboard NASA's satellites aims at understanding the clouds
and Earth's energy budget. The first CERES instrument was launched aboard
NASA's Tropical Rainfall Measurement Mission (TRMM) in 1997 and thereafter
similar instruments were flown aboard three satellite missions, namely Terra
and Aqua satellites and Suomi National Polar-orbiting Partnership (S-NPP)
satellite. The clear- and all-sky TOA and SFC fluxes are available at a
1×1∘ resolution for the period 2000–2016. For the fluxes at
the TOA and SFC, the CERES_EBAF_TOA_Ed4.0 version and
CERES_EBAF_Surface_Ed2.8 are used, respectively. CERES cloud
forcing and flux datasets have been used in a number of studies for model
evaluations . For the analysis, the model data
are also regridded to a 1×1∘ grid. However, in order to
increase the number of cases with enhanced positive and negative phases of
ENSO and NAO, we consider the whole time period in our simulations from 1982
to 2014, even though the observational reference period is shorter. In the
era of both Terra and Aqua satellites (i.e. from 2002 onwards), both the
global and regional uncertainties in the CERES-EBAF TOA and SFC fluxes are
reduced dramatically. The typical overall uncertainty, after considering the
uncertainties in the calibration, diurnal corrections and radiance-to-flux
conversions in the TOA SW and LW, remain in the range of 2–5 W m-2.
The uncertainties in the surface fluxes are higher, typically in the range
5–18 W m-2. The detailed data quality summaries are found at the
following links:
https://ceres.larc.nasa.gov/documents/DQ_summaries/CERES_EBAF_Ed4.0_DQS.pdf
(last access: 3 February 2019)
https://ceres.larc.nasa.gov/documents/DQ_summaries/CERES_EBAF-Surface_Ed4.0_DQS.pdf
(last access: 26 May 2017)
ENSO analysis
ENSO is the leading mode of interannual climate variability in the tropics,
where it has an impact on the Walker circulation and the local Hadley
circulation, and thereby has a big response in the CREs
. To compute the CRE response to ENSO, first, the
Niño3.4 index is computed to extract the positive and negative phases of
El Niño. This index is based on the sea surface temperature (SST)
anomalies over the Niño3.4 region (5∘ N–5∘ S,
170–120∘ W). When the SST anomalies over this region are positive
(negative) and more (less) than 1 standard deviation, ENSO is considered to
be in a stronger positive (negative) phase (denoted hereafter as ENP and ENN,
respectively). This method is applied to all the models used in this study to
extract the months when these phases are encountered. For our reference
dataset, CERES, the positive and negative phases are chosen from observations
(https://www.esrl.noaa.gov/psd/enso/, last access: 5 April 2019). The
TOA and SFC cloud radiative fluxes associated with these phases are then
computed. To extract the cloud response associated solely with ENP and ENN
phases, the differences from the monthly climatological CREs are taken. This
would give the change in the CREs during El Niño/La Niña years with
respect to normal years. The ensemble mean of the CRE response from the
Hi-res and Std-res models is evaluated.
NAO analysis
NAO is the most prominent mode of winter variability in the North Atlantic
region. To evaluate the CREs associated with the positive and negative phases
of NAO, the standard NAO index is calculated by taking the difference between
normalized sea level pressure (SLP) anomalies between Ponta Delgada, Azores
(southernmost point), Portugal, and Stykkishólmur, Iceland (northernmost point)
. To extract the stronger positive and negative phases of NAO in
the observational reference dataset, the NAO indices are calculated using
the SLP from ERA-Interim data. This study focuses on stronger- and weaker-than-normal
NAO phases. If the NAO index is positive (negative) and is more (less)
than 1 standard deviation, the NAO is considered to be in the stronger
positive (negative) phase (NAOP/NAON). For this analysis, we consider the
extended winter period from November to April. This method is followed
to compute the NAO indices in all the models. As in the case of the ENSO
analysis, to quantify the response of the NAO to the TOA and SFC CREs, the
difference of the CREs associated with the phases from the climatological
mean is taken. Here, since the focus is on the winter half of the year, the
seasonal climatological mean is considered. Here, too, the CRE responses based
on Hi-res and Std-res model setups are analyzed separately.
The model-simulated SW, LW and combined TOA cloud radiative effects
in W m-2 shown as differences from the CERES-EBAF observations
for (a, b, c) DJF mean and (d, e, f) JJA mean. The green
lines correspond to the simulations with the HadGEM3 model, red lines to the
EC-Earth3 model, blue lines to the MPI-ESM model and yellow lines to the
ECMWF model. The grey-coloured envelope indicates 1 standard deviation of
CREs based on CERES, shown here as a measure of natural variability in the
observations.
A statistical evaluation of the cloud radiative effects
In this section, the statistical comparison and evaluation of TOA and SFC
cloud radiative effects and their sensitivity to model resolution are
presented in Figs. to . The SW and LW components are
evaluated separately for DJF mean (left) and JJA mean (right) seasons and are
presented as zonally averaged differences from the observations. Also shown
are the net CREs (i.e. SW + LW). The grey envelopes in Figs.
and show 1 standard deviation of CREs in the CERES observations
over the 16-year period, as a measure of natural interannual variability in
the zonal means.
CREs at the TOA
In DJF, all models, irrespective of their resolution, overestimate the SW TOA
CRE by 20–40 W m-2 over the bright and persistent decks of Southern
Ocean clouds (Fig. , left). This overestimation is well above the
expected variability seen in the observations (grey envelope). A clear
distinction can be seen in the MPI-ESM model, where the lower-resolution
simulation has the lowest positive bias compared to the other models. All the
models underestimate the SW TOA CRE by 10 W m-2 over the convective
regimes in the Southern Hemisphere (SH), while HadGEM3 setups better
simulate this response, irrespective of their resolution. Over the tropical
belt, the two models (HadGEM3 and MPI-ESM) show a positive bias by up to
15 W m-2, and the other models seem to have a slight negative bias. On
the contrary, the LW CREs are underestimated by all the models over this
region. Here, too, the biases are significantly higher than the observational
variability. The standard-resolution versions of the respective models better
simulate the LW effects in DJF mean over the tropical belt. The high biases
in the SW CREs in the south are clearly seen in the combined response.
In JJA months, a large discrepancy is seen north of 30∘ N in the SW
CREs (Fig. , right). The model resolution of the respective models
does not play an important role in this case. While the HadGEM3 simulations
have a strong positive bias, all the other models tend to have a more
negative bias. This is also reflected in the combined CREs, as the biases in
the LW tend to be relatively smaller. The model biases vary widely over the
warm pool area in the western Pacific. While the HadGEM3 and MPI-ESM model
simulations overestimate the TOA SW cloud radiative fluxes in the tropical
monsoon belt, they underestimate the TOA LW fluxes by up to 15 W m-2.
It is evident that, in both DJF and JJA averages, the opposite sign in the TOA
SW and LW effects nearly compensates for the biases in the fluxes over the
tropics in the net effects at the TOA.
Differences in the CREs at the TOA during DJF (W m-2) between
the Hi-res and Std-res configurations of the respective models in the
SW (left) and LW (right).
Same as Fig. but during JJA.
As can be seen in Fig. , when the zonal averaging of the CREs is
performed, the differences between the high- and standard-resolution models
remain low, mainly due to averaging out of over- and underestimations.
Therefore, it looks as if the choice of the resolution does not seem to have
a major impact on the simulation of the CREs. The regional differences emerge
as we look in detail into the spatial patterns of the CRE differences between
the Hi-res and Std-res setups of the respective models. This is presented in
Figs. and at the TOA for the DJF and JJA months,
respectively. It can be seen that the EC-Earth3 and the ECMWF models have
lower differences, indicating insensitivity to the resolution. The Hi-res
setup of MPI-ESM model is, however, strongly overestimating the SW response by
around 15 W m-2 over the Southern Ocean compared to its corresponding
standard-resolution setup during DJF mean months. Though a slight
underestimation by the Hi-res setup is observed in the HadGEM3 model over
this region, the major difference is observed over the tropics, where the
Hi-res setup is overestimating the SW CRE over the tropical Pacific and
Indian oceans compared to the respective Std-res setup. However, the Hi-res
model configurations of the respective models seem to underestimate the CREs
globally in the LW compared to their standard-resolution counterparts. The
most notable underestimation is over the equatorial west Pacific. While the
Hi-res EC-Earth3 and ECMWF model setups tend to slightly underestimate the
LW CRE, the Hi-res HadGEM3 model setup tends to overestimate this over the
southeast Asian region. A completely different picture can be seen in the
JJA mean CREs at the TOA (Fig. ). Strong differences in the SW CREs
are simulated in the MPI-ESM and HadGEM3 models, with significant
overestimation in Hi-res setups over the North Pacific in the HadGEM3 models
and north of 40∘ N in the MPI-ESM model compared to their Std-res
model counterparts. The impact of the resolution seems to be fairly
negligible in the ECMWF model. The Hi-res setups of the respective models
underestimate the LW CRE in general. This underestimation is prominent over
southeast Asia and equatorial Pacific in the EC-Earth and HadGEM3 models.
Stronger response to increased resolution is simulated over southern India
and northern Africa in the Hi-res HadGEM3 model.
It is noteworthy that the cloud regimes that seem to be affected by
increasing resolution are different in different models. For example, in DJF,
the HadGEM3 models show largest differences in the convective ITCZ regions,
while MPI-ESM over the Southern Ocean stratocumulus regions. The most
drastic change in resolution occurs in the HadGEM3 models (from 200 to
50 km). This may have an impact on SST resampling and thus convection. In the
case of Southern Ocean clouds, the increasing resolution in MPI-ESM may
change the humidity PDFs (probability density functions) in a way that would
change cloud fraction (since the relative humidity is already persistently
high in this region). In addition, the lack of tuning in higher-resolution versions
can further explain the observed differences.
Same as Fig. but at the surface.
CREs at the surface
The differences in the model-simulated CREs at the SFC from the observations
are shown in Fig. in the SW, LW and SW + LW averaged over DJF
(left) and JJA (right) months. A similar picture, as observed at the TOA, can
be seen at the SFC in SW CREs over the Southern Ocean clouds in the DJF
season. All the models show a positive bias over this region, similar in
magnitude to the TOA CREs. The MPI-ESM models tend to simulate a lower
positive bias with the standard-resolution setup reducing this bias even
more. Over the tropical belt, the models exhibit a similar variability, but
a marginally stronger bias is simulated in SW CREs at the SFC compared to
that what is seen at the TOA. A similar tendency compared to that at the TOA is observed in
JJA mean SFC CREs in tropics and beyond 30∘ N. The differences are
enhanced during DJF and JJA months in LW CREs at the SFC, when compared to
that seen at the TOA. While all the models, irrespective of their
resolutions, tend to simulate the LW CREs reasonably well over the tropics in
both seasons, large discrepancies can be seen at higher latitudes. A strong
overestimation is simulated by all the models in LW CREs south of
60∘ S and a strong underestimation north of 30∘ N. The
EC-Earth and ECMWF models simulate a lower positive bias in LW CREs compared
to the other model setups over the Southern Ocean in the mean DJF months.
The JJA mean LW CREs are poorly simulated by all the models southward of
45∘ S and northward of 60∘ N. The biases in the SW CREs at
the TOA and the surface are correlated, while they are less so in the LW. This is
mainly due to the fact that the LW CRE at the surface is heavily dependent on
the cloud base heights and the surface conditions. Both of these factors do
not change significantly in the models for optically thicker clouds. In
comparison, the different description of convection can heavily impact cloud
top pressure and thus the LW TOA CREs.
During polar summers in both hemispheres, strong biases are observed in the
surface SW CREs. These biases are most pronounced over the regions where
seasonal sea-ice melt drives the intraseasonal variability in sea ice. The
magnitude of these biases can reach up to 40 W m-2 over sea-ice
regions near Antarctica and up to 30 W m-2 over the Arctic Ocean. The
signs of the biases are however different in the both hemispheres during
their respective summers. While the models mostly tend to underestimate the
SW CRE over the Arctic in NH summer, they tend to overestimate it over
Antarctica in the SH summer. Having a correct description of surface albedo
in models is crucial to minimize these biases. However, it is evident that
the models differ considerably from observations and from one another, as each
model has its own formulation of sea-ice albedo ; for example, a
climatological annual cycle is used in EC-Earth3 models. This in turn has an
impact on the formation of clouds through air–sea interaction processes. The
biases in the LW CREs are also high in the polar regions at the surface, most
likely originating from the biases in describing dominant atmospheric
processes such as the strength of temperature inversions and heat and
moisture transport . The higher positive bias north of
60∘ N in the HadGEM3 model simulations both in the SW CREs during
JJA months at both the SFC and TOA results in a much higher positive bias
compared to the other models in the net CREs.
Similar to the TOA, the differences in spatial distribution in the SW CREs
between the Hi-res and the Std-res model configurations are analyzed at the
surface and are shown in Figs. and in Appendix A for
mean DJF and JJA, respectively. It can be seen that the differences at the
surface in the SW CREs are similar, both spatially and in magnitude to what
is seen at the TOA in winter. However, large differences are seen in the
surface LW CREs. As in the case of the TOA, the ECMWF model is insensitive to
a change in resolution. The Hi-res setup of the MPI-ESM model significantly
underestimates the LW CREs north of 40∘ N compared to its Std-res
configuration. The DJF mean surface LW CRE biases are much smaller in
EC-Earth3 model, but the Hi-res setup overestimates the LW forcing over the
oceans and underestimates it over the continents. A strong overestimation is
also seen in the Hi-res setup of the HadGEM3 model over the Southern Ocean and
Eurasia. In summer, the SW CREs at the surface follow the same pattern as is
seen at the TOA. However, the summer LW CRE biases at the surface are
considerably weaker as compared to those in winter.
Response of cloud radiative effects to ENSO
The TOA and SFC CREs associated with the ENP and ENN cases from model
simulations are analyzed. The top row in Fig. first shows the CREs
associated with ENP from CERES-EBAF observations at the TOA in the SW (left
panels) and LW (right panels). To investigate the simulated responses, the
ensemble mean of the Hi-res and Std-res model configurations is analyzed.
This would give us an understanding if increasing the spatial resolution
results in an improvement of the response in the models. Hence, the second
and third rows in Fig. show the differences of the model ensemble
mean of Hi-res and Std-res from the observations, respectively, and the
intermodel differences are plotted in the bottom row. The intermodel
differences are calculated as follows. At each grid point, if all nine model
setups agree on the sign of bias with respect to the CERES observations, the
absolute difference between the model setups showing the highest and lowest
bias is reported as the intermodel difference. The regions, where all nine
model setups do not agree in the sign of the bias, are marked in grey colour.
Figure shows the same but at the surface. Furthermore,
Figs. and show similar responses but during the ENN
case at the TOA and SFC, respectively.
The SW (left) and LW (right) cloud radiative fluxes at the TOA as a
response to positive phase of ENSO (ENP) from the top row: CERES-EBAF
observations; second row: the ensemble mean Hi-res; and third row: Std-res
model-simulated differences of this response from observations. Bottom row: the
ensemble intermodel differences in W m-2. The grey shaded areas are
the regions where all the nine model setups do not agree with the sign of the
bias.
Same as above but for the cloud radiative response at the
surface.
The SW (left) and LW (right) cloud radiative fluxes at the TOA as a
response to negative phase of ENSO from top row: CERES-EBAF observations; second
row: the ensemble mean Hi-res; and third row: Std-res model-simulated
differences of this response from observations. Bottom row: the ensemble
intermodel differences in W m-2. The grey shaded areas are the regions
where all the nine model setups do not agree with the sign of the bias.
Same as above but for the cloud radiative response at the
surface.
The ENP case
In the ENP case, negative CRE anomalies (cooling) of up to 35 W m-2
over the western and central Pacific in the SW and positive anomalies (warming)
of magnitude 20 W m-2 over the same region in the LW at the top of the
atmosphere are observed. This is expected, because, during the positive phase
of El Niño, the Walker circulation weakens, resulting in warmer ocean
surface temperatures over the eastern and central Pacific, which favours
increased deep convective and stratiform clouds in this region and reduced
cloud cover over the southeast Asian regions (Fig. ), and the
opposite is observed during the La Niña phase . This
induces enhanced cooling/warming in the SW/LW, respectively, not only at the TOA
but also at the surface in the SW. The LW signal at the surface during ENP is
considerably weaker, as for similar convective systems; the cloud base
heights over the oceans do not change significantly in the models.
Ensemble model mean of simulated total cloud fraction anomalies
(%) as a response to the positive phase of ENSO (a) and negative
phase of ENSO (b).
It is observed that the pattern correlations (i.e. the Pearson product–moment
coefficient of linear correlation) with CERES observations in the tropical
belt (30∘ N–30∘ S) are approximately above 0.75 for all
the models irrespective of their resolution (not shown here). This suggests
that the models realistically reproduce the spatial variability of the CRE
response. However, the magnitude of this response and the location of the
peak cooling/warming vary substantially regionally among the models, as can be
seen from the differences of the ensemble model means from the observations.
Both model setups, i.e. Hi-res and Std-res, simulate the peak cooling
region in SW cloud radiative fluxes at the TOA and surface over the western
and central Pacific during ENP reasonably well. The multi-model ensemble mean
strongly overestimates the TOA and surface SW CREs north and south of the
peak cooling region over the western Pacific by around 10 W m-2 and
underestimates the cooling by around 5 W m-2 over the central Pacific. The
Hi-res model setups simulate a stronger bias than the Std-res models over
this region. Both the Hi-res and the Std-res models slightly
overestimate the cooling over the tropical Indian Ocean and underestimate the
warming over SE Asia at the surface and at the TOA. Over the SE Asian region,
the underestimation at the TOA is around 5–8 W m-2, more so, in the
Hi-res ensemble model mean.
The models, irrespective of their resolution, tend to simulate the peak ENSO
response over the central Pacific to the TOA LW cloud radiative fluxes reasonably
well. The LW biases over the southwest Pacific are marginally stronger in the
Hi-res compared to the Std-res ensemble mean model configurations. An
opposite sign in the biases is observed in the LW CREs compared to the SW
CREs at the TOA. Although the model biases in the LW at the TOA during the
positive phase of ENSO are small, clear hemispherical differences can be seen
over the central and eastern Pacific at the TOA in the ENP case characterized by
negative biases north of 5∘ N and positive biases south of
5∘ N. Considering that the models do capture the broad spatial
pattern in the CRE response but at the same time exhibit wave-like
structures in the SW biases and hemispheric nature of LW biases, it can be
due to the fact that the shift of Walker circulation in the models is not
followed with corresponding changes in cloud optical and physical
characteristics. The signal in the LW CREs at the surface during ENP is muted
and hence are the biases.
Large variability of up to 20 W m-2 can be seen in the intermodel
differences on the simulation of TOA and surface SW CREs associated with ENP
and are mainly over the Pacific. The model bias is higher in the SW CREs
compared to the LW CREs. The TOA biases are consistent with those observed in
the set of CMIP5 models carried out with varying resolutions forced by AMIP
(Atmospheric Model Intercomparison Project) SSTs . These
strong model over-/underestimations over the tropical convective regions could
be because of the discrepancies in the simulation of convective clouds
, as models have a tendency to produce optically thicker and
deeper clouds compared to observations, whereas thin cirrus is prevalent in
observations in those regions.
The ENN case
In the ENN case, a signal of opposite sign to that of ENP is observed with
positive CREs in the SW at the TOA and the surface and negative anomalies in
the LW at the TOA over the western and central Pacific. Over southeast Asia,
a weaker signal is observed. Though the models marginally underestimate the
warming in the SW associated with ENN at the TOA and the surface, they
simulate the response in the LW at the TOA reasonably well. The Hi-res model
setups tend to slightly intensify this underestimation in the SW compared to
the Std-res model setups. The LW CRE associated with ENN at the surface is
weaker. No notable improvements can be seen in simulating the cloud response
in the LW using Hi-res model setups. The intermodel differences are smaller
in the simulation of the TOA and surface LW CREs compared to the SW.
The regional absolute biases
In order to better understand the role of varying spatial resolution locally,
we further examine individual models under their high- and standard-resolution
setups. Figure shows the average absolute biases in TOA and SFC
CREs in the SW and LW during the positive and negative phases of ENSO, with
reference to CERES, over the Niño3.4 region (170–120∘ W,
5∘ N–5∘ S). It can be seen that the absolute biases across
the models are high in the SW at the TOA and SFC and in the LW at the TOA during the
positive phase of ENSO, particularly in the HadGEM3 and EC-Earth3 models. The
uncertainty bars show 1 standard deviation in the CERES anomalies for the
respective cases as a measure of the variability in the observation data. It
is to be noted that, in all cases, the observed biases over the selected
region remain below the variability in the CERES data. The Hi-res setups of
HadGEM3 and EC-Earth models have a lower bias compared to their Std-res
setups. The opposite is seen in MPI-ESM models. ECMWF models, irrespective of
their resolution, show a similar bias.
Average absolute errors in the Hi-res and Std-res of the model
versions with reference to CERES averaged over the Niño3.4 region
(170–120∘ W, 5∘ N–5∘ S) in the SW (left) and LW
(right) during the enhanced positive and negative phases of El Niño at
the TOA (rows 1–2) and at the SFC (rows 3–4). The uncertainty bars show 1 standard deviation in the CERES anomalies for the respective cases as a
measure of the variability in the observation data.
Response of cloud radiative effects to NAO
The TOA and SFC CREs associated with NAOP from observations are shown in top
row of Figs. and , respectively. The second and third
rows show the ensemble mean difference of the response simulated in Hi-res
and Std-res models with the observations, respectively, and the intermodel
differences are shown in the bottom row. Figures and
show the same but for the NAON case. The SW (left) and LW (right) components
of the total CREs are shown separately in each of the NAOP and NAON cases.
The NAOP case
During the positive phase, as the polar vortex strengthens trapping the cold
air in the central Arctic, the winter storms in the North Atlantic penetrate
further to the north, with their remnants reaching deep over the northern
Norwegian and Greenland seas. The northeast Atlantic is usually
persistently cloud covered. However, the additional transport of heat and
moisture brought about by winter storms leads to increased opacity of these
cloud systems. This is evident in the slight decrease in the TOA and surface
SW CRE. The increased opacity of clouds leads to additional reflection of
solar radiation back to the space, while the clouds also emit at warmer
temperatures than normal. LW CRE is especially stronger over Scandinavia
and the Norwegian Sea at the TOA. The LW CRE anomalies over the North Atlantic
are quite muted at the surface, whereas the Greenland and Canadian sectors of
the Arctic show increased LW CREs. This is mainly due to the fact that, in
contrast to open oceanic waters in the North Atlantic, clouds can exert
strong LW forcing over the ice- and snow-covered areas in the Arctic. Over the
Mediterranean region and Iberian Peninsula, colder and drier conditions
prevail during the NAOP case due to the northward shift of the storm tracks.
This results in a significant reduction in cloud cover over this region, as
can be seen in the model-simulated NAO-related total cloud fraction anomalies
during this phase (Fig. ), which are also consistent with the
previous studies . Clearer conditions result in an
increase in SW CRE and a decrease in LW CRE at the TOA and at the surface.
The Hi-res and Std-res model ensemble mean differences against CERES
observations are generally quite low (below ±5 W m-2) and do not
exceed 1 standard deviation of the CRE anomalies observed in the CERES data
over the majority of the regions (not shown). The models capture the spatial
cloud radiative response to the positive phases of the NAO quite well. For
example, the models, irrespective of their resolution, simulate the response
reasonably well over the North Atlantic, over Scandinavia and over the
Mediterranean at the TOA in both SW and LW and at the SFC in the SW. The models
overestimate the cooling by 3–4 W m-2 over continental Europe in the
SW at the TOA and SFC. The LW TOA CRE is, on the other hand, underestimated
over this region. However, strong discrepancies can be noted in the SFC LW
CREs with models overestimating the response by more than 5 W m-2 over
northern Europe. Strong underestimation of similar magnitude in the LW CRE at
the surface can be noted in the Canadian sector of the Arctic Ocean and also
over Greenland.
The CRE biases in the Hi-res and Std-res model setups do not seem to be
strikingly different from one another at a first glance. However, the Hi-res
models seem to amplify the positive SW bias over eastern Europe at the SFC
during NAOP compared to the Std-res model ensemble mean. On the other hand,
the Hi-res models better simulate the TOA LW CREs over continental Europe. No
notable improvement is seen in the SFC LW CREs with resolution. The
intermodel differences are of the same magnitude as those of the under- and
overestimations in the CRE response at the TOA. The SW and LW biases are,
respectively, much higher at the surface over continental Europe and over
Scandinavia.
The SW (left) and LW (right) cloud radiative fluxes at the TOA as a
response to positive phase of NAO from top row: CERES observations; second row:
the ensemble mean Hi-res; and third row: Std-res model-simulated differences of
this response from observations. Bottom row: the ensemble intermodel
differences in W m-2. The grey shaded areas are the regions where all
the nine model setups do not agree with the sign of the bias.
Same as above but for the cloud radiative response at the
surface.
The SW (left) and LW (right) cloud radiative fluxes at the TOA as a
response to negative phase of NAO from top row: CERES observations; second row:
the ensemble mean Hi-res; and third row: Std-res model-simulated differences of
this response from observations. Bottom row: the ensemble intermodel
differences in W m-2. The grey shaded areas are the regions where all
the nine model setups do not agree with the sign of the bias.
The NAON case
In the NAON case (Fig. ), the winter storms are not as intense and
do not penetrate deeper into the northern North Atlantic as the cold air
outbreaks from the Arctic over the northern high latitudes and midlatitudes prevail,
shifting the zonal temperature gradient southwards. As a result, the TOA SW
CRE is higher than usual over northern midlatitudes, and the TOA LW CRE is
lower than usual as the clouds emit at the colder temperatures, especially
over Scandinavia. The LW CRE at the surface is decreased over Greenland and
the Canadian Arctic and increased over the Eurasian Arctic
(Fig. ). This response is opposite to that observed in the NAOP
case. The CRE response in the Mediterranean region is also, as expected,
opposite to that of the NAOP case.
Same as above but for cloud radiative response at the surface.
Ensemble mean of model-simulated total cloud fraction anomalies (%)
as a response to the (a) positive phase of NAO and
(b) negative phase of NAO.
Both at the TOA and the SFC, though the biases are small in the SW CREs over
the Atlantic and Scandinavia, the models underestimate the SW CREs over
continental Europe by -4 W m-2. The models simulate the LW CREs at
the TOA reasonably well; however, marginal underestimation in the cooling in
the North Atlantic in LW CREs at the TOA can be noted. At the SFC, the biases
in the LW CREs are highest over northern continental Europe, Greenland and
along the west coast of Norway but are of opposite sign, in that the models
underestimate CREs over northern Europe and the west coast of Europe and
overestimate it over Greenland (locally exceeding 5 W m-2). Over the
Eurasian and Canadian Arctic regions, the biases in the surface LW CREs are
of opposite sign to that of the NAOP case.
An improvement in the SW CREs at the TOA can be noted in the Hi-res model
ensemble mean over continental Europe at the TOA and SFC. Though there is a
marginal improvement in the LW CREs at the TOA over North Atlantic, no
notable differences are seen at the SFC. The intermodel differences, like in
the case of NAOP, are much higher in the SW than the LW at the TOA and SFC,
particularly over continental Europe. The differences are the same or even
lower in magnitude compared to that of the under- and overestimations of the CRE
response.
The absolute regional biases
Figure shows the absolute biases in the high- and standard-resolution
model setups for the different phases of NAO over Europe
(30–75∘ N, 40∘ W–40∘ E). This region is active
with winter storms during the positive phases of the NAO, which eventually
transport heat and moisture to the northernmost latitudes. Here, it has to be
noted that the biases are comparatively smaller than the biases observed in
the ENSO cases. Further, there is no noticeable improvement with increased
resolution. This indicates that improving the surface description and
treatment (e.g. surface snow and ice variability) in models might be more
important than increasing only the horizontal resolution for cloud processes.
The uncertainty bars show that the biases over the selected region remain
below the variability in the CERES data. This means that the model biases are
not significant.
Average absolute errors in the Hi-res and Std-res of the model
versions with reference to CERES averaged over Europe
(30–75∘ N, 40∘ W–40∘ E) in the SW (left) and LW
(right) during the enhanced positive (NAOP) and negative (NAON) phases of NAO
at the TOA (rows 1–2) and at the SFC (rows 3–4). The uncertainty bars show
1 standard deviation in the CERES anomalies for the respective cases as a
measure of the variability in the observation data.
Conclusions
In the present study, we evaluated four global climate models at different
spatial resolutions to assess how well they simulate CREs, both at the top of
the atmosphere and at the surface, as well as their shortwave and longwave
components. The focus is placed on evaluating cloud radiative response to two
leading modes of natural variabilities, namely ENSO and NAO, allowing
process-oriented evaluations. The simulations from the high- and
standard-resolution model setups were contrasted to investigate if any value
can be added by increasing the spatial resolution of the different models.
The retrievals of CREs from CERES instruments aboard a series of satellites
were used as the observational reference. The following conclusions can be
drawn from the evaluations.
The largest disagreement between models and observations occurs over
the polar regions, both at the TOA and the SFC, and especially over the
locations where seasonal sea-ice variability is strongest. The surface SW CRE
plays an important role during the melt season. The models, however,
overestimate this forcing by up to 35 W m-2 over the coastal Antarctic and
underestimate it by 20–30 W m-2 over the Arctic. This will have an
implication for quantifying the cloud feedbacks on the sea ice and estimating
future changes in sea ice during the melt season.
The zonally averaged CREs do not seem to be resolution dependent.
This means that all the models follow a similar response irrespective of the
resolution in most regions. However, regional differences emerge when looking
at the spatial patterns of the forcings. Here, it is seen that different
cloud regimes are affected by increasing resolution in different models.
The spatial patterns of cloud radiative response to ENSO in the tropical
belt is simulated reasonably well by the models, with spatial correlations up
to 0.75. However, strong biases in the magnitude of this response are noted.
The model biases are generally half as large as those of the actual cloud radiative
response seen in the CERES data for the ENSO cases (5–10 W m-2) at
both the TOA and the surface, with Hi-res model setups simulating a stronger
bias than the respective Std-res models. The biases in the LW CRE tend to be
smaller than in the SW CRE. The intermodel differences in the SW CRE at the
TOA and surface over the convectively active regions are stronger, nearly of
the same order as the actual response. The intermodel differences in the LW
CRE are lower at the surface during both ENP and ENN, typically within a few
W m-2. This suggests that the parameterization of SW radiative transfer
and the treatment of cloud optical properties vary strongly among the models.
The large-scale organization of convection and associated cloud types can
also be different.
In the case of NAO, the model biases are less than observational
uncertainties and also well within the observational variability (less than
1σ) in the CREs. The spatial patterns of the response are also
simulated quite well by the models during the positive and negative phases of
the NAO. The biases in the surface LW CREs have a strong meridional
character, in that they are of opposite sign over the eastern and western
parts of the Arctic across 20∘ W and also have the opposite sign of
that of the cloud radiative response observed in the CERES data.
The average absolute biases over the Niño3.4 region for the ENP and
ENN cases and over Europe (30–75∘ N, 40∘ W–40∘ E)
for the NAOP and NAON cases are investigated in the high-
and standard-resolution setups of each model. The absolute biases in both cases
are well below the variability in the observational data. The average biases
in the case of NAO are smaller than the biases seen over the Niño3.4
region. The Hi-res setup of HadGEM3 and EC-Earth3 models has a lower bias
compared to their Std-res counterparts over the Niño3.4 region, whereas
an opposite signal is seen in MPI-ESM models. ECMWF model setups exhibit the
same biases irrespective of the resolution.
From this study, it is clear that the well-known issue of the large biases in
SW CREs over the polar regions during the melt season does not improve by
increasing the resolution of the models chosen here. This would require
improvements not only in the parameterization schemes involving the
microphysical properties of clouds but also in the surface description.
Analysis of the spatial pattern of the TOA SW CREs during winter reveals that
different cloud regimes are affected drastically with a change in resolution
in MPI-ESM and HadGEM3 models. For example, the Hi-res HadGEM3 model shows an
overestimation over the convective ITCZ regions compared to its Std-res
counterpart, and this may have an impact on SST resampling and thus
convection. On the other hand, the Hi-res MPI-ESM overestimates the CREs over
the Southern Ocean stratocumulus region and this may have an impact on the
cloud fraction. The observed differences can be attributed to the lack of
tuning in higher-resolution versions. Though the models tend to simulate the
spatial variability in cloud radiative response to ENSO and NAO variability,
they vary widely in the magnitude of the response. The CRE biases associated
with the NAO phase are smaller compared to those with the ENSO phase.
Although some improvements can be seen regionally, it is difficult to
identify patters that hold across all models. Hence, it can be concluded that
improving the physical parameterization schemes rather than increasing the
resolution is perhaps important in better simulating the CREs. However, it
has to be noted that these are atmospheric-only simulations and the impact
may be different in the presence of coupled climate models.
Data availability
Access to the model output data used in this study will be
available through the European Research Council Horizon 2020 PRIMAVERA
project
(https://www.primavera-h2020.eu/modelling/,
last access: 24 April 2019). More information regarding model configurations
and data availability are available from the authors upon request.
Differences in the CREs at the SFC during DJF (W m-2) between
the Hi-res and Std-res of the respective models in the SW (left) and LW (right).
Same as Fig. but during JJA.
Author contributions
MAT performed the analysis and drafted the paper.
AD and TK helped in the interpretation of the results. The output for the
respective models used in this paper was provided by KW, MR, CR and KL. All
the authors contributed to the revision of the paper text.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
This study was financially supported by PRIMAVERA (PRocess-based climate
sIMulation: AdVances in high resolution modelling and European climate Risk
Assessment), a Horizon 2020 project funded by the European Commission.
Review statement
This paper was edited by Klaus Gierens and reviewed by two
anonymous referees.
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