Introduction
The predominant trade-off in climate modeling is that of systematic
complexity vs. computational expense. While comprehensive global climate
models (GCMs) attempt to resolve the complex interactions between Earth
systems, their computational expense limits the exploration of parametric
uncertainty. Conversely, more simplified models, such as Earth system models
of intermediate complexity (EMICs), can be employed for large-ensemble
analysis of parametric variability, but their reliance on fixed boundary
conditions or generalized parameterizations of earth processes may not
capture all important feedbacks driving system dynamics.
One of the largest sources of inter-model spread in GCM-based climate
projections is the magnitude and direction of radiative cloud feedbacks
(Soden and Held, 2006; Dufrense and Bony, 2008; Tomassini et al., 2013; Vial
et al., 2013). Clouds affect climate through their impacts on both shortwave
(solar radiation mostly in the visible part of the spectrum) and longwave
(terrestrial, infrared radiation) fluxes and therefore determine the
sensitivity of GCMs to changes in radiative forcing (Andrews et al., 2012;
Sherwood et al., 2014). Because clouds are more reflective than most other
surfaces, an increase in clouds will reduce the amount of shortwave energy
absorbed by the Earth and lead to cooling. Conversely, clouds ability to
absorb upward longwave fluxes and re-radiate them back down causes warming
at the surface (Hartmann and Short, 1980). The relative magnitude and net
effect of these feedbacks depends on cloud altitude. For low clouds, which
radiate longwave fluxes at a similar temperature as the surface, shortwave
effects dominate and their net effect is cooling. High clouds, on the other
hand, radiate at much colder temperatures than the surface, which can make
the longwave effect dominate and lead to net warming (Hartmann et al.,
1992). However, the cloud liquid water content and associated optical depth
of high and low clouds likely also plays a role in the absorption and
reflection of incoming shortwave fluxes (Tselioudis et al., 1992).
Therefore, the net cloud feedback may be positive or negative feedback
depending on whether low vs. high cloud cover, and cloud optical depth
responds more to local and global temperature change. For state-of-the-art
GCMs, the spread in cloud feedbacks is primarily driven by model differences
in low cloud cover changes (Sherwood et al., 2014). In addition, the spread
in GCM cloud feedbacks manifests in both the global mean as well as regional
variability (Tomassini et al., 2013; Vial et al., 2013). This spatial
variability likely has a profound impact on the magnitude of climate
response to perturbations (Marvel et al., 2016).
Since EMICs use simplified atmospheric components, the cloud radiative
forcing is typically fixed (Plattner et al., 2001; Joos et al., 2001;
Driesschaert, 2005; Crucifix et al., 2002; Weaver et al., 2001). Therefore,
the uncertainties in cloud feedbacks demonstrated in GCMs are typically
neglected in the non-interactive cloud schemes of EMICs. Schmittner et al.
(2011), e.g., hypothesized that their estimate of climate sensitivity,
determined using the University of Victoria (UVic) EMIC and paleoclimate
observations, resulted in a too narrow probability distribution due to the
neglect of cloud feedback uncertainties. Here we describe and evaluate a new
method for diagnosing and applying cloud feedbacks of state-of-the-art GCMs
into an EMIC, thereby creating a computationally less-expensive emulator of
more complex models.
Methods
Model description
The UVic Earth System Climate Model (Weaver et al., 2001) is an EMIC with a
three-dimensional ocean general circulation model coupled to a
dynamic–thermodynamic sea ice model, a two-dimensional single-layer
energy–moisture balance atmosphere, and a dynamic land (Meissner et al.,
2003) and vegetation model (Cox, 2001). Surface wind speeds used in the
calculations of air–sea exchange and atmospheric transport of heat and
moisture are prescribed in the model, thereby limiting variability in the
atmospheric model. The model conserves heat and moisture without the need
for a flux correction (Weaver et al., 2001). We employ version 2.9 of UVic
(Eby et al., 2013), in which atmospheric heat diffusion varies with changes
in global-mean surface air temperature; this modification has been shown to
improve the latitudinal temperature gradient for the Last Glacial Maximum
when compared with high-latitude proxy data (Fyke and Eby, 2012). All model
components have a horizontal grid resolution of 1.8∘ latitude by
3.6∘ longitude, with 19 vertical levels in the ocean model
increasing from 50 m thickness in the surface level to 590 m thickness in
the deepest grid cell.
The net radiative balance (NETRAD) at the top of the atmosphere (TOA) is the
difference between the net shortwave radiation (SWTOA) and the outgoing
longwave radiation (OLW):
NETRAD=SWTOA-OLW.
Clouds impact SWTOA through prescribed monthly fields of atmospheric
albedo (αatm):
SWTOA=SWin,TOA-SWin,TOA×αatm-SWin,TOA×1-αatm×αsfc×τ2,
where S is the flux of incoming (incident) solar radiation energy per unit
area (W m-2) at the top of the atmosphere (with seasonal and
latitudinal variation), τ is a constant atmospheric transmission
coefficient (0.77), and αsfc is the surface albedo. The second
term of Eq. (2) represents the proportion of incoming SW radiation that is
immediately reflected by clouds, whereas the third term represents the portion
that is reflected by the surface, which passes through the atmosphere twice.
During its first (downward) pass, SWTOA×1-αatm×1-τ is absorbed by the atmosphere. During
its second (upward) pass, SWTOA×1-αatm×1-τ×αsfc×1-τ is absorbed. All variables except for τ vary over space
and time, but while αsfc is allowed to evolve with changes in
surface model components (sea ice, snow cover, vegetation, etc.), αatm is a fixed boundary condition at monthly resolution to resolve
seasonal changes in regional cloud cover. In the control version of UVic,
αatm is estimated with the following relationship:
αatm=f×αplt-αsfc1-αsfc×τ2,
where
αplt=SWout,TOASWin,TOA,αsfc=SWout,sfcSWin,sfc,
where the planetary (αplt) and surface albedo (αsfc) are calculated using the incoming and outgoing shortwave
satellite observational measurements at the surface and top of the
atmosphere from the Earth Radiation Budget Experiment (ERBE; Barkstrom,
1984; Barkstrom and Smith, 1986; Ramanathan et al., 1989). This αatm relationship is directly derived from Eq. (2) so as to be
internally consistent with the radiative balance relationship from the UVic
model. The variable f in Eq. (3) is a constant planetary albedo adjustment
factor to account for radiative imbalances that arise in the implementation
of the derived αatm.
The OLW is parameterized in UVic using an
empirical relationship (Thompson and Warren, 1982; Weaver et al., 2001) that
determines clear-sky OLW as a function of on surface relative humidity (RH) and
temperature (SAT):
OLW=c00+c01RH+c02RH2+c10+c11RH+c12RH2SAT+c20+c21RH+c22RH2SAT2+c30+c31RH+c32RH2SAT3+ΔF2xCO2ln[CO2]tCO2o,
where the final term adjusts OLW for a change in the atmospheric CO2
concentrations. The value of ΔF2xCO2= 5.35 W m-2
is selected as the radiative forcing associated with 3.71 W m-2
(IPCC, 2001). The constants (cxx) are provided by Thompson
and Warren (1982). Since this was originally estimated as a clear-sky
relationship, the effect of clouds on the OLW radiative balance is not
explicitly included.
CERES update to atmospheric albedo boundary conditions
Because of discontinuities in satellite coverage, missing data, and poor
resolution, the Clouds and the Earth's Radiant Energy System (CERES;
Wielicki et al., 1996) was launched in late 1999 to better observe the
Earth's radiative balance (Fasullo and Trenberth, 2008). The CERES
experiment uses an updated satellite architecture and provides higher
spatial resolution observations over a longer time domain (2000–2013 for
CERES compared with 1985–1989 for ERBE), thereby providing more robust
modern climatology on the impact of clouds on atmospheric albedo (Wielicki
et al., 1996). In addition, the duration of the ERBE experiment between 1985
to 1989 spans a somewhat large El Niño event (1987), which may bias the
equatorial Pacific toward enhanced cloudiness in the calculation of
atmospheric albedo climatology using the ERBE data (Cess et al., 2001).
In this paper, we use the climatology (2000–2013) of CERES surface and top
of the atmosphere shortwave fluxes to better estimate αatm
boundary conditions in UVic (using Eq. 3). Under low-light conditions
(winter, high-latitudes), satellite-derived estimates of incoming SW are
small, which occasionally results in values of αplt and
αsfc that are greater than 1. Therefore, we limit αplt and αsfc to values less than 1, which ensures
that αatm is within appropriate limits.
An ensemble of control simulations was performed using the new CERES-based
estimates of αatm with varying values of the f parameter in Eg. (3).
From the resulting equilibrium simulations, a value of f=0.95 in Eq. (3)
was selected in order to match 20th century global mean temperature
data estimates of ∼ 13.9 ∘C (NOAA, 2016) in a UVic
control simulation. This final estimate of CERES-based αatm was
smoothed and regridded to the UVic grid.
Comparison of annual-averaged atmospheric albedo (αatm) as calculated using Eq. (3) and the climatology of ERBE (left)
and CERES (right) data.
Figure 1 compares the annual-mean values of αatm as derived
from the ERBE and CERES datasets. In the tropics, the ERBE-based estimates
of αatm generally match those of the CERES-based values (Fig. 1).
In the high latitudes, however, the ERBE-based αatm values
are generally higher than the CERES-based values. Such differences are
likely related to improvement in sampling orbit of the CERES satellite and
the associated reduction in zenith angle-dependent biases, which may result
in large errors in the top-of-the-atmosphere flux measurements in the ERBE
data (Loeb et al., 2009). Furthermore, the use of CERES-based estimates of
αatm provides an improvement in UVic, particularly at high
latitudes.
Innovations
With the use of CERES-based αatm estimates, the UVic model now
includes an updated effect of clouds on the Earth's shortwave radiative
balance. However, the control UVic model design does not incorporate any
change in the shortwave or longwave radiative effect of clouds due to
changes in temperature. This lack of cloud feedbacks may significantly limit
the ability of UVic to capture global temperature in perturbed simulations.
Here, we provide a simple method of diagnosing cloud radiative forcings from
GCM results of the Coupled Model Intercomparison Project 5 (CMIP5) and
Paleoclimate Model Intercomparison Project 3 (PMIP3) archives (Braconnot et
al., 2011; Taylor et al., 2012) and incorporate the associated shortwave and
longwave cloud feedbacks into UVic for both 4 times CO2 (4xCO2)
and Last Glacial Maximum (LGM) climate simulations. Reanalysis of satellite
observations suggests that the range of CMIP5 models present widespread
agreement with cloud data, both in spatial extent and vertical distribution,
across the historical record (Norris et al., 2016). We have selected model
output from seven GCMs: Community Climate System Model version 4 (abbreviated as CCSM), Centre National de Recherches Meteorologiques version CM5 (CNRM), Goddard Institute for Space Sciences Model E2-R (GISS), Institute Pierre Simon Laplace CM5A-LR (IPSL), Model for Interdisciplinary Research on Climate-Earth System Model (MIROC), Max Planck Institut model ESM-P (MPI), Meteorological Research Institute model CGCM3 (MRI).
These models were chosen because they have results for both
4×CO2 and LGM simulations and all of the relevant variables for
calculating shortwave and longwave cloud feedbacks (see below). The
following innovations demonstrate how we employ UVic as a cloud feedback
emulator (CFE version 1.0; henceforth CFE) of the full GCMs.
Shortwave cloud feedbacks in UVic
Since UVic incorporates the shortwave impact of clouds through atmospheric
albedo, we assess the shortwave cloud feedback as the change in αatm due to the change in temperature in each of the GCM simulations.
Albedo anomalies are not mathematically additive; therefore, we first
calculate αatm for each perturbed state (4×CO2, LGM) by adding
GCM anomalies of each of the individual fluxes to the CERES observations:
SWin,TOA,GCM=(SWin,TOA,perturbed-SWin,TOA,control)+SWin,TOA,CERES,SWout,TOA,GCM=(SWout,TOA,perturbed-SWout,TOA,control)+SWout,TOA,CERES,SWin,sfc,GCM=(SWin,sfc,perturbed-SWin,sfc,control)+SWin,sfc,CERES,SWout,sfc,GCM=(SWout,sfc,perturbed-SWout,sfc,control)+SWout,sfc,CERES.
For each of the variables, we have calculated a 12-month climatology
(separate averaging for each month) that is assessed over the final 10 years
of the 150 year transient 4×CO2 simulations, the final 100 years of the
LGM equilibrium simulations, and the final 100 years of the equilibrium
control simulations. The anomaly perturbed values of each of the shortwave
fluxes (Eqs. 7–10) are then used to calculate an αatm,perturbed for each of the perturbed GCM simulations using Eqs. (3)–(5).
Again, because albedo values are not additive, we calculate the albedo
anomaly as the ratio of the atmospheric albedo of the GCM perturbed state to
CERES-derived atmospheric albedo. Therefore, the αatm feedback
(αatmFB) is this albedo anomaly divided by the change in
temperature:
αatmFB=αatm,perturbedαatm,perturbedαatm,CERESαatm,CERES- 1SATperturbed-SATcontrol.
The subtraction of 1 in the numerator is necessary such that when there is
no change in αatm (αatm,perturbed=αatm,CERES), then there is no atmospheric albedo feedback. This
αatm feedback is calculated as a 12-month climatology at each
grid cell of the seven GCMs that are sampled in this analysis (Figs. 2, 3).
Positive (negative) values for this atmospheric albedo feedback indicate a
negative (positive) shortwave cloud feedback since increases in temperature
cause an increase (decrease) in atmospheric albedo, which cools (warms) the
surface. The magnitude of these atmospheric albedo feedbacks varies
considerably among the GCMs and between perturbed climate states (4×CO2
vs. LGM), which is consistent with the large spread in cloud shortwave
feedbacks found in previous studies (Tomassini et al., 2013; Vial et al.,
2013). For example, GISS-E2-R shows a strongly positive atmospheric albedo
feedback from the 4×CO2 results, whereas IPSL-CM5A-LR generally shows a
strongly negative atmospheric albedo feedback, particularly in the tropics
(Fig. 2).
Maps of annual-mean atmospheric albedo feedback term (αatmFB), as calculated using Eq. (11) and the 4×CO2 results of the
seven
CMIP5 models discussed in the text. Units are albedo fraction change per
∘C.
The innovation to UVic is the application of these GCM-diagnosed αatm feedbacks to the shortwave radiative balance. First, we calculate
a SAT climatology from a long-term control simulation of UVic that uses
αatm,CERES as the control atmospheric albedo. Then at each
time step (t) of a model simulation, we calculate the difference in surface
air temperature from this control monthly climatology, and perturb
atmosphere albedo at each grid cell using the GCM-derived αatmFB of Eq. (11):
αatmt=αatmFB×SATt-SATctl+ 1×αatm,CERES.
The above calculation is done at every time step and each grid cell, allowing
for spatially and monthly specific atmospheric albedo feedbacks as
diagnosed from the GCMs.
Longwave cloud feedbacks in UVic
Because UVic lacks a longwave cloud feedback in the calculation of OLW, we
provide an additional term to Eq. (6), which now includes the OLW due to
changes in the cloud longwave effect in the GCM simulations. First, we
diagnose the outgoing longwave radiation at the top of the atmosphere from
the GCM output:
OLWcloud=OLWtotal-OLWclear sky.
The outgoing longwave cloud feedback is therefore the cloud longwave forcing
anomaly divided by temperature anomaly:
OLWcloudFB=OLWcloud,perturbed-OLWcloud,controlSATperturbed-SATcontrol,
as diagnosed from results of the GCM perturbed simulations. These outgoing
longwave cloud feedbacks are calculated as monthly climatologies at each
grid cell, and are assessed separately for both the 4×CO2 and LGM
perturbed states (Figs. 4, 5). Again, OLWcloudFB values are assessed
using the 12-month climatologies assessed over the final 10 years of the
150-year transient 4xCO2 simulations, the final 100 years of the LGM
equilibrium simulations, and the final 100 years of the equilibrium control
simulations. We note that by calculating the OLW cloud radiative effect using
the total OLW minus clear-sky OLW (Eq. 13), we are implicitly including the
effects of cloud masking and rapid cloud adjustments (Zelinka et al., 2013).
Including both of these effects has been shown to reduce both LW and SW
cloud feedbacks relative to a more explicit cloud radiative kernel method
(Zelinka et al., 2012, 2013). Both effects may limit the
magnitude of the total cloud feedback.
Maps of annual-mean atmospheric albedo feedback term (αatmFB), as calculated using Eq. (11) and the LGM results of the 7 PMIP3
models discussed in the text. Units are albedo fraction change per
∘C. Note that because the LGM represents a period of global
cooling (Braconnot et al., 2012), the direction of change in αatm is opposite that shown in these figures.
Most models show more areas of positive OLWcloudFB. This indicates a
negative climate feedback since increasing temperatures lead to more OLW, which
cools the surface. Again, the outgoing longwave cloud feedbacks vary
considerable between models and climate state. The largest variability in
OLW cloud feedbacks between models exists in the tropics, which is consistent
with prior results suggesting that model differences in convective mixing
and resulting cloud height greatly impacts the magnitude and direction of
cloud feedbacks (Sherwood et al., 2014). Generally, the OLW cloud feedback is
stronger in magnitude for the LGM state (Fig. 5) than for the 4×CO2
state.
Similar to the inclusion of the atmospheric albedo feedbacks in UVic, we
multiply the outgoing longwave cloud feedback by the temperature difference
from the long-term control UVic simulation:
OLWcloudt=OLWcloudFB×SATt-SATctl.
This OLWcloud term is calculated at each time step and grid cell in the
model and is added to the OLW parameterization (Eq. 6) as an additional cloud
longwave feedback term.
Numerical experiments
To estimate how well our CFE captures the original
cloud radiative effects from the GCMs, we present an ensemble of CFE control
and perturbed experiments (4×CO2 and LGM) that use the αatm and OLWcloud feedbacks diagnosed from each of the seven GCMs
employed in this analysis. Because our diagnosed cloud feedbacks differ
between the 4×CO2 and LGM climate states (Figs. 2–5), we ran two
separate preindustrial control simulations for each ensemble member: one
with 4×CO2 cloud feedbacks (ctl4x) and one with LGM cloud feedbacks
(ctlLGM). Indeed, the inclusion of these cloud feedbacks in the control
climate state leads to slight differences in control global mean
temperature, indicating that separate controls are necessary in the
calculation of resulting radiative feedbacks. Therefore, we present the
results from 28 separate CFE simulations: four simulations (ctl4x, ctlLGM,
4×CO2, LGM) for each of the seven GCM-derived cloud feedbacks.
Preindustrial control and LGM simulations with each of the GCM-derived cloud
feedbacks were run to extended equilibrium (> 2000 years) to be
certain of minimal model drift (global mean SAT trend < 0.04 ∘C per 100 years). Both 4×CO2 and LGM simulations follow the
CMIP5/PMIP3 protocol (Braconnot et al., 2011; Taylor et al., 2012) as
closely as possible as these are the boundary conditions used in the
original GCM simulations. Our 4×CO2 simulations use modern boundary
conditions, an instantaneous increase in atmospheric CO2 concentration
to 1120 ppm, and a simulation length of 150 years, starting from the end of
the preindustrial control simulation (ctl4x). Our LGM simulations have
reduced greenhouse gas concentrations (atmospheric CO2= 185 ppm;
radiative forcing adjusted for appropriate CH4 / N2O concentrations;
Schmittner et al., 2011), altered orbital state, full glacial ice sheet
extent/topography (Peltier, 2004), modified river pathways, and +1 PSU
(practical salinity unit) increase in mean ocean salinity. In addition, we
apply LGM surface wind stress anomalies that are diagnosed from the LGM GCM
results (Muglia and Schmittner, 2015). Wind stress anomalies at the end of
the CMIP5 4×CO2 simulations are small; therefore, we use the prescribed
wind stress fields of the control UVic 2.9 model (from NCEP reanalysis) in
our 4×CO2 simulations.
Results
Assessment of GCM-diagnosed cloud feedbacks
Across the historical record with a warming climate, the cloud trends in
CMIP5 models have been shown to be in agreement with satellite observations,
with robust reductions in cloudiness across the mid-latitude and tropics, as
well as an increase in cloud top height at all latitudes (Norris et al.,
2016). Our calculated 4×CO2 atmospheric albedo feedbacks are consistent
with these observations, generally showing a reduction in αatm
in the mid-latitudes and tropics (Fig. 2). Only one model (GISS) shows an
increase in αatm across the 4×CO2 simulations. Most of the
4×CO2 GCM-diagnosed αatm feedbacks seem to suggest an
increase in αatm in the high-latitudes with warming
(particularly over the Southern Ocean), which is likely related to a
poleward shift in the storm tracks due to warming (Lu et al., 2007; Norris
et al., 2016).
Maps of annual-mean outgoing longwave feedback term
(OLWcloudFB), as calculated using Eq. (14) and the 4×CO2 results of the
7 CMIP5 models discussed in the text. Units are W m-2 ∘C-1.
The 4×CO2 GCM-derived OLWcloud feedbacks are also most prominent in
the tropics with considerable variability in the location, magnitude, and
direction of peak feedback (Fig. 4). However, all models show a negative
OLWcloud feedback across the equatorial Pacific and a positive
OLWcloud feedback over the Indonesia Archipelago, South America, and off
the Equator. Outside of the tropics, most models show positive
OLWcloud feedbacks in the mid-latitudes and slight negative feedbacks in
the polar regions. These data are consistent with observations of increased
cloud top height (Norris et al., 2016), as regions with enhanced cloudiness
(increased αatm, Fig. 2) also typically show decreased OLW
(Fig. 4).
For the LGM, GCM-derived cloud feedbacks are less coherent. Nearly all
models show large changes in the tropical αatm feedback,
particularly across the equatorial Pacific and Indonesian Archipelago
(Fig. 3). Such changes may be suggestive of changes in the position of the
intertropical convergence zone (ITCZ)-associated changes in deep convective
cloud systems that are specific to each model (Braconnot et al., 2007;
Arbuszewski et al., 2013). In addition, nearly all GCM-derived feedbacks
show a reduction in αatm over the North Atlantic (note that LGM
cooling indicates that direction of feedback change is opposite that shown
in Fig. 3), which may be indicative of a shift in the position of the Gulf
Stream seen in some models (Otto-Bliesner et al., 2006). The prominent
feature in the LGM GCM-derived OLWcloud feedback is a large
reduction in the tropics (green–blue–purple colors in Fig. 5), which is
likely related to the reduction in tropical convection due to lower sea
surface temperatures (Yin and Battisti, 2001). However, this spatial extent
and magnitude of reduction in OLWcloud for the LGM vary appreciably among
the GCMs.
Radiative balance in CFE 4×CO2 simulation
Maps of annual-mean outgoing longwave feedback term
(OLWcloudFB), as calculated using Eq. (14) and the LGM results of the 7
PMIP3 models discussed in the text. Units are W m-2 ∘C-1. Note that because the LGM represents a period of global cooling
(Braconnot et al., 2012), the direction of change in OLWcloud is opposite
that shown in these figures.
To compare the global radiative balance of CFE with that of the GCMs, we
calculate the total change in TOA shortwave and longwave fluxes per global
mean surface temperature change from the final 10 years of the 150-year
4×CO2 simulations (relative to the control simulation) and compare the
raw GCM results with our cloud feedback-forced CFE simulations (Fig. 6).
The changes in longwave fluxes include the CO2 forcing, which may
differ by ∼ 15 % between models (Andrews et al., 2012).
Because the forcing is included in the longwave fluxes, the flux / temperature
ratios shown in Fig. 6 are not a true “feedback”, strictly speaking;
therefore, we use the term “radiative-temperature response.” However,
variations in the forcings are presumably relatively small compared to
variations in feedbacks. The shortwave flux / temperature ratios in Fig. 6 are
true feedbacks and consistent with numbers reported previously (Tomassini et
al., 2013).
In general, the spread of TOA shortwave and longwave radiative-temperature
response in the 4×CO2 CFE simulations matches that of the original GCM
results (Fig. 6) and is consistent with previous work (Tomassini et al.,
2013). For instance, the IPSL model exhibits the largest positive shortwave
and largest negative longwave radiative-temperature response in the GCM
results, which is also captured in our CFE simulations (Figs. 2, 4).
Conversely, the GISS model is the only simulation to show a negative
shortwave and positive longwave radiative-temperature response, which is
consistent with the CFE results. All other GCM and CFE simulations have
positive shortwave and negative longwave radiative-temperature response that
are both smaller in magnitude than the IPSL-based simulations.
Comparison of 4×CO2 (top) and LGM (bottom)
top-of-the-atmosphere feedbacks calculated from raw CMIP5/PMIP3 output from
each of the seven GCMs (CMIP5/PMIP3) and from UVic simulations using
GCMs-derived cloud feedbacks (UVic). Shortwave feedbacks are shown on the
left, longwave feedbacks on the right. Positive values designate an
increased forcing TO the climate system with increased temperature (i.e.,
positive feedback). Feedbacks from the UVic control simulation without cloud
feedbacks is shown in gray.
While the relative magnitude of the CFE radiative-temperature response
results captures that of the original GCM results, the absolute magnitude of
the radiative-temperature response is generally slightly reduced in CFE. We
also present the results from a control 4×CO2 UVic simulation, without
the implementation of any cloud feedbacks (gray bar, Fig. 6). Here, the
TOA shortwave radiative-temperature response is ∼ 0.40 W m-2 ∘C-1 and the TOA longwave radiative-temperature
response is ∼ -0.03 W m-2 ∘C-1, whereas the
average radiative-temperature response from the GCMs are ∼ 0.87
and ∼ -0.55 W m-2 ∘C-1, respectively. Therefore, the application of αatm and OLWcloud feedbacks in CFE are prominent drivers in
the spread of total TOA shortwave and longwave radiative-temperature
response. In general, the GCMs show a greater reduction in global surface
albedo with increasing temperature compared to the CFE (not shown).
Therefore, the differences in surface albedo processes between the GCMs and
CFE, likely explains some of the reduction in TOA shortwave
radiative-temperature response magnitude in the CFE simulations.
Radiative balance in CFE LGM simulations
For the CFE LGM simulations, we calculate TOA shortwave and longwave
radiative-temperature response at equilibrium conditions, averaged over the
last 100 years of the LGM and ctlLGM experiments. Note that in this case the
shortwave fluxes include forcing from prescribed ice sheets and therefore
are not strictly speaking feedbacks. CFE generally captures the spread of
the shortwave and longwave radiative-temperature response from the GCMs
although it is slightly reduced (Fig. 6). The total imbalance seems to be
smaller in CFE compared with most GCMs indicating that CFE is closer to
equilibrium, perhaps because it was integrated longer. Thus, a larger
remaining imbalance could contribute to the larger spread in the GCMs
compared with CFE.
The absolute magnitude of the radiative-temperature response is mostly
reduced in the CFE relative to the GCM simulations. Similar to the
4×CO2 results, the IPSL-based simulations present the strongest
shortwave and longwave radiative-temperature response. Conversely, the
CNRM-based CFE simulation shows enhanced shortwave and longwave
radiative-temperature response relative to those of the GCM, suggesting that
non-cloud processes or differences in the forcings are likely important for
this model.
Effect of CFE on modeled temperature evolution and spatial
distribution
As expected, the incorporation of cloud feedbacks into CFE has a direct
impact on modeled surface temperature anomalies in perturbed experiments.
For the 4×CO2 experiments, global mean surface air temperature
anomalies at the end of the 150-year simulation range from +3.9 ∘C
(GISS) to +8.8 ∘C (IPSL), where the control UVic
simulation without cloud feedbacks results in a final anomaly of
+5.1 ∘C (Fig. 7). Only two CFE simulations (GISS and MRI)
result in a year 150 temperature anomaly that is less than the UVic control,
confirming that the 4×CO2 net cloud feedbacks are generally positive
(see above) and consistent with the analysis of the individual models
themselves (Vial et al., 2013; Tomassini et al., 2013).
Global mean surface air temperature anomalies for the 4×CO2
(upper left) and LGM (upper right) CFE simulations. Zonal mean surface air
temperature anomalies from the CFE simulations, averaged over the last 10 years of the 4×CO2 simulations (lower left) and the last 100 years of
the LGM simulation (lower right).
The spatial variability in GCM cloud feedbacks (Figs. 2, 4) is also
expressed in the 4×CO2 zonal mean temperature anomalies (Fig. 7). All
models show the effects of strong polar amplification by the end of the
4×CO2 simulations, but the addition of cloud feedbacks to CFE appears
to enhance this polar amplification in most cases. In addition, the change
in temperature due to cloud feedbacks is not uniform for all models. For
example, the CCSM-driven simulation presents some of the largest temperature
anomalies in the southern high-latitudes but relatively reduced anomalies
at the low-latitudes, resulting in an overall global anomaly that is similar
to the that of the control UVic simulation (Fig. 7).
For the LGM simulations, the global mean temperature change at the end of
the simulation ranges from -4.1 ∘C (CCSM) to -8.2 ∘C
(CNRM), whereas the control UVic simulation has a cooling of 5.7 ∘C (Fig. 7).
Nearly half of the UVic simulations show enhanced global mean
cooling (CNRM, IPSL, and MRI) relative to the UVic control (Fig. 7), whereas
the other four simulations show reduced cooling (CCSM, GISS, MIROC, and
MPI). Again, zonal mean temperature anomalies at the LGM show that enhanced
cloud feedbacks lead to enhanced polar amplification, but spatial
differences in the magnitude of feedbacks may impact regional temperature
change. For example, the CNRM-based simulation shows the strongest cooling
in the southern high latitude, whereas the IPSL-based simulation has the
largest cooling in the northern high latitudes (Fig. 7).
Using CFE to estimate climate sensitivity
Inter-model spread in GCM cloud feedbacks has been shown to have a large
impact on the modeled sensitivity to perturbation in greenhouse gas
radiative forcing (Fasullo and Trenberth, 2012; Andrews et al., 2012;
Sherwood et al., 2014). To estimate the effect of the cloud feedbacks in CFE
on global climate, we calculate effective equilibrium climate sensitivity
(ΔT2xC,eff) from the 150-year 4×CO2 simulations by
regressing the global net downward heat flux at the TOA onto the change in
temperature. The slope of this regression is the climate response parameter
(α) and the intercept is the 4×CO2 forcing (F4xCO2)
specific to each model (Gregory et al., 2004). These values can be used to
estimate the effective equilibrium climate sensitivity to a doubling of
CO2 by dividing the implied global 2xCO2 forcing (F2xCO2=F4xCO2/2) by α (Gregory et al., 2004). We calculate ΔT2xC,eff for both the raw GCM model output as well as the associated
CFE simulations.
Comparison of effective equilibrium climate sensitivity (ΔT2xC,eff) calculated from raw CMIP5 output from each of the seven GCMs
(CMIP5) and from UVic simulations using GCMs-derived cloud feedbacks (UVic).
ΔT2xC,eff from the UVic control simulation without cloud
feedbacks is shown in gray.
With the introduction of cloud feedbacks, CFE is able to capture much of the
inter-model variability in climate sensitivity (Fig. 8). The seven GCMs
sampled in this analysis show values of ΔT2xC,eff ranging from
2.15 ∘C (GISS) to 4.10 ∘C (IPSL), which agrees well
with Andrews et al. (2012) for those models that were used in both studies.
In the CFE simulations, ΔT2xC,eff values range from 2.34 ∘C
(GISS) to 7.00 ∘C (IPSL). Again, the IPSL-based CFE
simulation is a noticeable outlier, whereas all of the values of ΔT2xC,eff in CFE are more comparable to the values from the raw GCM
output and the magnitude relative to each of the models is generally the
same (Fig. 8). However, most of the CFE simulations show elevated ΔT2xC,eff relative to their GCM counterpart (Fig. 8). The ΔT2xC,eff in the 4×CO2 control UVic simulation (gray bar, Fig. 8)
is 3.63 ∘C, a value that is higher than most of the GCM
results, suggesting that the control UVic climate sensitivity without
explicit cloud feedbacks may already be higher than that of most of the
sampled GCMs. This suggests that the control UVic model's clear-sky (without
explicit clouds) feedbacks are larger than those of most GCMs. Adding the
mostly positive cloud feedbacks thus makes the UVic model's climate
sensitivities considerably larger than those of the GCMs. Clear-sky
feedbacks in the UVic model could be tuned by, e.g., varying the coefficients
of Eq. (6) if a better match with individual GCM's climate sensitivity was
desired.
Discussion and Conclusions
The cloud feedbacks (αatm and OLWcloudfeedbacks) derived from
the GCMs and employed in CFE are generally consistent between climate states
(4×CO2 vs. LGM) for each GCM, with some notable exceptions. For example,
the 4xCO2αatm feedbacks (Fig. 2) are generally
consistent between models in showing a prominent negative feedback across
the Southern Ocean, with CCSM being the only model with a positive αatm feedback. However, for the LGM, the CCSM-derived αatm feedback is negative along with all other models in general
(Fig. 3). In addition, the αatm feedbacks across the
equatorial Pacific are not always consistent between climate states, with
the CNRM-, GISS-, MIROC-, and MPI-based fields showing a pronounced
difference in the direction of the αatm feedback (Figs. 2, 3).
Similarly, the OLWcloud feedbacks across the equatorial Pacific and
North Pacific differ in magnitude and direction between the climate states
in nearly all models (Figs. 4, 5). These differences likely arise due to
shifts in the ITCZ and Gulf Stream between climate states (Otto-Bliesner et
al., 2006; Braconnot et al., 2007; Arbuszewski et al., 2013), and they
suggest that such cloud feedbacks are not universal to all climate states.
Furthermore, the cloud feedbacks derived from the GCMs should only be applied to
a consistent climate state experiment when using CFE.
In general, the application of GCM-derived cloud feedbacks to CFE captures
the changes in TOA radiative balance of the original GCMs, for both the
4×CO2 and LGM experiments. Differences in total radiative feedbacks
between each GCM and the associated CFE may exist for several reasons.
First, the derivation of the cloud feedbacks are parameterized from the
original GCM results and therefore may not be a perfect representation of
the full complexity of cloud radiative forcing in each GCM. This is
particularly the case for the shortwave cloud feedback, which is applied
using a calculation of the αatm feedback, which uses an
assumption of a global mean atmospheric transmissivity (Eq. 3). The
OLWcloud feedbacks, on the other hand, are a direct calculation of
the longwave cloud feedbacks from each GCM.
Second, total TOA feedbacks in CFE may not perfectly match those of the
source GCMs because the resulting feedbacks are still partly controlled by
the control radiative balance code of the UVic model. Other components of
the Earth system, apart from clouds, impact the shortwave and longwave
radiative balance in UVic, which may feedback on the simulated climate in a
different manner than in the GCMs. For instance, the total TOA shortwave
feedbacks include the effect of surface albedo change. Therefore,
differences in vegetation and sea ice dynamics and their effect on surface
albedo in the GCMs relative to UVic may help explain some of the differences
in the shortwave feedbacks. Similarly, the longwave feedback in UVic is in
part controlled by the SAT-based parameterization of OLW in Eq. (6), which may be
different from the clear-sky feedbacks in the GCMs
Third, the ratios of TOA flux and temperature changes shown in Fig. 6
include forcings (greenhouse gas for both 4xCO2 and LGM and surface albedo
for LGM). Therefore, differences in the forcings would also impact the total
TOA “feedbacks”. The forcings differ between the GCMs but are constant
among the CFE experiments. In addition, our method of estimating cloud
feedbacks neglects the effects of cloud masking and cloud rapid adjustment
(Zelinka et al., 2013), which may explain some of the loss of spread in CFE
compared with the GCMs.
However, despite the potential for differences in total radiative feedbacks,
our results suggest that a simple parameterization of cloud shortwave and
longwave feedbacks may be applied to UVic to generally capture dominant
inter-model spread in total radiative feedbacks. This result confirms that
cloud feedbacks dominate the multi-model uncertainty in GCM radiative
balance (Soden and Held, 2006; Dufrense and Bony, 2008; Tomassini et al.,
2013; Vial et al., 2013). The addition of GCM-derived cloud feedbacks to the
UVic leads to only small increases in computational expense, while capturing
an important component of the Earth's radiative balance that is otherwise
lacking in the default UVic model. Indeed, the inclusion of cloud feedbacks
leads to a large spread in surface air temperature anomalies for both the
4×CO2 and LGM experiments (Fig. 7). In addition, spatial variability
in the cloud feedbacks (Figs. 2–5) leads to some differences in the
latitudinal distribution of this temperature change (Fig. 7), suggesting
that certain regional cloud changes may be important on the global scale.
Differences in Equator–pole temperature contrast do to cloud feedbacks in
CFE could impact ocean heat transport in the model.
The application of cloud feedbacks in CFE provides an important source of
inter-model uncertainty that is present in CMIP5/PMIP3. Recent model–data
comparisons suggest that the state-of-the-art CMIP5 simulations capture
important cloud feedbacks across the observational record (Norris et al.,
2016), providing assurance that the feedbacks in CFE are also within the
range of observations. However, as model physics of cloud dynamics and
spatial distribution continue to improve in future GCM simulations, the GCM
cloud radiative effects can again be applied in CFE ensemble analyses to
emulate the multi-model uncertainty in cloud feedbacks.
Finally, we confirm that the cloud feedbacks in each of the GCMs plays a
prominent role in determining the resulting climate sensitivity of each
simulation (Fasullo and Trenberth, 2012; Andrews et al., 2012; Sherwood et
al., 2014). By incorporating cloud feedbacks into CFE, we generally capture
the relative spread ΔT2xC,eff of the GCMs (Fig. 8). The
absolute magnitude of ΔT2xC,eff is typically larger in our CFE
simulations relative to each of the GCMs. Since net cloud feedbacks are
generally positive in CMIP5 (Vial et al., 2013; Tomassini et al., 2013), the
addition of these radiative feedbacks may require a revision of the overall
radiative balance in CFE. Specifically, future versions of CFE may consider
the effects of cloud masking and rapid adjustment in the cloud feedback
parameterization (Zelinka et al., 2013). Conversely, the full radiative
balance may be adjusted through an enhanced OLW parameterization by slight
modification to the constants in Eq. (6). This method of has been applied to
UVic to effectively adjust ΔT2xC,eff (Schmittner et al., 2011).
The CFE is currently being applied to a study of climate sensitivity using
paleoclimate reconstructions (Ullman et al., 2017).