We revisit the concept of the cloud vertical structure (CVS) classes we have
previously employed to classify the planet's cloudiness (Oreopoulos et al.,
2017). The CVS classification reflects simple combinations of simultaneous
cloud occurrence in the three standard layers traditionally used to separate
low, middle, and high clouds and was applied to a dataset derived from
active lidar and cloud radar observations. This classification is now
introduced in an atmospheric global climate model, specifically a version of
NASA's GEOS-5, in order to evaluate the realism of its cloudiness and of the
radiative effects associated with the various CVS classes. Such classes can
be defined in GEOS-5 thanks to a subcolumn cloud generator paired with the
model's radiative transfer algorithm, and their associated radiative effects
can be evaluated against observations. We find that the model produces
50 % more clear skies than observations in relative terms and produces
isolated high clouds that are slightly less frequent than in observations,
but optically thicker, yielding excessive planetary and surface cooling. Low
clouds are also brighter than in observations, but underestimates of the
frequency of occurrence (by ∼20 % in relative terms) help
restore radiative agreement with observations. Overall the model better reproduces
the longwave radiative effects of the various CVS classes because
cloud vertical location is substantially constrained in the CVS framework.
Introduction
The large impact of clouds on the Earth's radiation budget and the growing
wealth of satellite-based cloud observations are strong motivators for their
systematic assessment in climate models. Such evaluation exercises focus on
either cloud properties, the metrics of cloud radiative impact, or ideally on
both (Pincus et al., 2008; Nam et al., 2012; Klein et al., 2013; Wang and
Su, 2013; Dolinar et al., 2015).
Assessments of cloud properties with satellite observations are not always
straightforward for a variety of reasons such as inability to define in the
model a particular satellite-observed property or limitations in the
satellite observations. For example, the vertically integrated cloud optical
depth of the cloudy portion of a model grid cell is an ill-defined quantity
that cannot be obtained trivially from the model's optical depth profile
since it is intimately associated with a cloud fraction profile, thus making
layer optical depths relevant for only the cloudy portions of the grid cell
that vary by model height and can conceptually be vertically aligned in
various ways. In contrast, vertically integrated cloud optical depth is
quite robustly defined in observations since it is measured with passive
imagers at a much higher resolution for which overcast conditions can be
more safely assumed. Issues such as these have led to the development of
“satellite simulators” that transform global climate model (GCM) cloud
fields to forms that are closer analogs to their counterparts observed by
satellites (e.g., the COSP simulator; Bodas-Salcedo et al., 2011).
The quality of simulated clouds in GCMs can also be measured in terms of the
realism of their radiative impact using quantities such as the cloud
radiative effect (CRE), i.e., the difference between all-sky and clear-sky
fluxes at the spatial scales of a model grid cell (Wang and Su, 2013). This
type of comparison can be performed at a variety of spatiotemporal scales
and is often quite illuminating, but the interpretation of findings can
suffer from inconsistencies in how the estimates are obtained for satellites
and models.
This paper is yet another attempt to evaluate clouds in an atmospheric GCM
(AGCM), specifically a version of the Goddard Earth Observing System version
5 (GEOS-5) model (Rienecker et al., 2008; Molod et al., 2012), a
multipurpose global model that is used for a variety of applications. Both
approaches of cloud assessment are used, namely comparison of the cloud
fields themselves but also comparison of cloud radiative impacts. Our cloud
property evaluation focuses on a single aspect of cloudiness: cloud vertical
structure (CVS). The comparison is possible because of recent progress in
two areas: active cloud remote sensing, which makes resolving cloud vertical
profiles possible, and the development of schemes (subcolumn generators)
that create subgrid cloud vertical structures in GCMs. Being able to
categorize clouds in terms of a few CVS categories facilitates the
comparison between observations and models and enables a more rigorous CRE
comparison that evaluates the model's skill with regard to how it simulates
the radiative impact of individual CVS classes.
Data and methodology
The observational reference dataset of CVS class occurrence and associated
radiative fluxes is essentially the same as Oreopoulos et al. (2017),
hereafter O17, and spans 4 years (2007–2010). A schematic illustration of
the original CVS classes of O17 is reproduced here as Fig. 1. The details of
how cloud layer boundaries available in the 2B-CLDCLASS-LIDAR R04 dataset
(Sassen and Wang, 2012; see also http://tinyurl.com/2b-cldclass-lidar, last access: 19 February 2020), a
joint product coming from CloudSat and CALIPSO (hereafter CC) active
cloud radar and lidar observations, were interpreted as cloud layer profiles
belonging to one of these classes are described exhaustively in the appendix
of O17. The definition of the CVS classes hinges on defining broad
categories of high (H), middle (M), and low (L) clouds that are confined to three
standard atmospheric layers: one above 440 hPa, another between 680 and 440 hPa, and yet another below 680 hPa, respectively. The vertical level
boundaries defining these standard layers come from the International
Satellite Cloud Climatology Project (ISCCP), (Rossow and Schiffer, 1991).
The reference radiative fluxes come from the 2B-FLXHR-LIDAR R04 CC product
(L'Ecuyer et al., 2008; Henderson et al., 2013; Matus and L'Ecuyer, 2017)
and are obtained from a radiative transfer algorithm operating on observed
and reanalysis output that has at its core retrieved CC cloud properties.
The original 10 CVS classes of Oreopoulos et al. (2017) used as
a reference for the comparison of this paper. The multilayer CVS classes
other than HL are merged in this paper, thus reducing the total number of
CVS classes to seven. We essentially do no distinguish between contiguous
and noncontiguous clouds in adjacent standard layers. Dotted lines show
which pairs of CVS classes have been combined for this study.
For the purposes of this study, the CVS classes have been reduced to seven
by merging the CVS classes for which clouds occur simultaneously in the same
two or three standard adjacent layers (all multilayer CVS classes other
than HL). In other words, we no longer distinguish between CVS
classes with clouds occurring in the same adjacent standard layers, even if
those were previously discerned based on whether or not a clear layer of
substantial vertical extent was present to separate the cloud layers. This
means in practice that we no longer distinguish (see Fig. 1) between CVS
=H×M×L and HML (now simply HML), CVS =H×M and HM (now simply HM), or CVS =M×L
and ML (now simply ML). The reason for reducing the CVS classes to
7 from the original 10 is the complexity of the model cloud profiles,
which can consist of numerous distinct cloud layers and which therefore
renders the O17 CVS classification scheme inapplicable. The original scheme
was designed for observed cloud profiles from CC that rarely (less than
1 % of the time) consisted of more than four distinct cloud layers in
which case they were either ignored or processed only in the simplest of
cases (such as multiple individual cloud layers residing within a single
standard layer – see the appendix of O17).
A prerequisite for the evaluation of GEOS-5 clouds in terms of their CVS
class frequency and the CRE statistics associated with these CVS classes is
creating comparable datasets. Assigning CVS classes to grid cell GCM cloud
fields is not possible without the manipulation of the GCM cloud profiles. To
this end, we use the cloud subcolumn generator that is paired with the
RRTMG-LW and RRTMG-SW radiative transfer codes (Mlawer et al., 1997; Iacono
et al., 2008) in the model's Monte Carlo independent column approximation
(McICA; Pincus et al., 2003) implementation. This subcolumn generator
follows Räisänen et al. (2004) and can produce subcolumns that are
consistent with specific assumptions about the vertical overlap of both
cloud fraction and the horizontal distributions of cloud condensate. While
the latter type of overlap is irrelevant to CVS class frequency statistics,
it does matter for the radiative transfer calculations producing the
radiative fluxes used to estimate CREs. The 140 subcolumns created by the
model's generator (which match the number of “g points” in RRTMG-LW's
correlated-k scheme) are essentially assumed equivalent to the cloud
profiles viewed by the active instruments (CALIPSO's lidar and CloudSat's
radar) and whose vertical location information is recorded in the
2B-CLDLASS-LIDAR product. Herein, we will show results from two types of
cloud fraction overlap schemes that have been implemented in the cloud
subcolumn generator: generalized (GN) overlap, also known as exponential
overlap (Hogan and Illingworth, 2000; Oreopoulos and Norris, 2011), and
maximum random overlap (MR overlap; Geleyn and Hollingsworth, 1979).
The model, GEOS-5 tag Jason-2_0, was run with fixed sea
surface temperatures (SSTs) for the same period as the reference dataset,
2007–2010. The model integration was driven by radiative fluxes and heating
rates produced by applying generalized overlap in the radiation calculation.
RRTMG-LW and RRTMG-SW were called for an additional set of flux calculations
this time using the MR overlap assumption to produce cloudy subcolumns but
only in diagnostic mode; i.e., the generated fluxes served only diagnostic
purposes and were not passed back to the model to influence the evolution of
its energetics and dynamics. This way, with one interactive and one
diagnostic call to the RRTMG codes, we were able to obtain two sets of CVS
diagnostics and corresponding CREs. In both cases, the subcolumns come from
a common mean cloud fraction and condensate profile. The layer condensates
are assumed to possess horizontal subgrid condensate heterogeneity as
prescribed in Oreopoulos et al. (2012). This subgrid condensate variability
affects the model's CRE distribution, but not the CVS fields and statistics.
In the subcolumn generator, the decorrelation length (e-folding distance)
for the generalized overlap scheme was set to vary zonally as described in
Oreopoulos et al. (2012). The physical meaning of the decorrelation length
is that cloud layers separated by a distance equal to the decorrelation
length overlap as a mixture of maximum and random overlap in e-1 (≈0.368) and 1-e-1 (≈0.632) proportions (weights), respectively. At
distances greater (smaller) than the decorrelation length the contribution
of random (maximum) overlap contribution increases (decreases) compared to
the above values. In the limit of zero separation cloud overlap is purely
maximum, while in the limit of infinite distance overlap is purely random.
The zonal prescription of decorrelation length by Oreopoulos et al. (2012)
is based on CloudSat observations and is meant to capture a more coherent
vertical cloud alignment (i.e., more maximum overlap and greater
decorrelation length) at low latitudes compared to high latitudes, as also seen
by Barker (2008). This formulation of overlap is an alternative to
maximum random overlap, which was the standard popular choice in earlier
years. The Geleyn and Hollingsworth (1979) implementation of MR overlap in
our generator based on Räisänen et al. (2004) allows for random overlap
even within a “block” of contiguous clouds: immediately adjacent clouds
are maximally overlapped, but nonadjacent clouds within the contiguous
block can have portions that are randomly overlapped if there is a local
minimum in cloud fraction between them. Random overlap applies for those
cloudy portions that do not fully overlap the in-between layers. This
type of MR overlap should be contrasted with other implementations (e.g.,
Chou et al., 1998) in which maximum overlap always takes place within the block,
while the various distinct blocks of the atmospheric column (always
separated by clear layers) are themselves randomly overlapped.
GEOS-5 cloud evaluation with CVSClimatological CVS occurrence
Figure 2 compares the observed and simulated (from GN overlap) multiannual
maps of the relative frequency of occurrence (RFO) for all seven CVS classes
of our study. The observed fields are sampled at rather coarse 4∘×4∘ scales to compensate for the substantial sparseness of the
active observations (gridding at higher resolutions would make for
relatively noisy maps). Above each panel, we provide the area-weighted RFO
global mean of the CVS (equivalent to its global cloud fraction). These
fields include nighttime observations and simulations since the former are
possible for active sensors and the latter are passed as input for the
model's nighttime RRTMG-LW calculations.
Geographical RFO distribution (%) for cloudless skies and the
seven CVS classes according to CloudSat/CALIPSO observations (a)
as well as for GEOS-5 (GN overlap assumption, b). Global mean values
are shown above each panel; in the case of GEOS-5 we provide the global
values for both the GN and MR overlap (in parentheses).
Before examining consistency (or lack thereof) for cloud fields, we first
turn our attention to clear skies. We note that the observations suggest a
cloudier world with clear skies occurring only ∼25 % of the
time (or, alternatively, covering 25 % of the global area between
82∘ S and 82∘ N). The GEOS-5 AGCM, on the other hand,
produces clear skies more frequently, ∼38 % of the time
over the entire globe (90∘ S to 90∘ N) for GN and
∼42 % for MR. Despite the model's positive clear-sky
fraction bias (negative cloud fraction bias), many patterns of clear-sky
occurrence are realistic, with peaks occurring in desert areas, western North
America, and the southern parts of Africa and South America. Over the ocean, the
model seems to be producing clear skies in the Maritime Continent and the
far southern oceans more frequently than observations, but these
overestimates are still much smaller compared to those in wide subtropical
swaths of the Atlantic and Pacific oceans. The model also exhibits
pronounced cloudiness underestimates in the descending branch of the central
Pacific Walker circulation. The only notable model underestimate of
clear-sky frequency occurs over western Antarctica. The MR overlap
assumption makes the clear-sky overestimates worse, with the biggest impact
seen in the central and western tropical Pacific (clear subcolumn in
Fig. 3). Note that the observed global clear-sky fraction is lower in
2B-CLDCLASS-LIDAR compared to passive satellite observations such as those
from MODIS (King et al., 2003) because of CALIOP's enhanced ability to
detect clouds that are optically very thin. Model cloud coverage, on the
other hand, has traditionally been tuned to resemble that seen in cloud
climatologies obtained by satellite observations from passive imagers at
solar and thermal infrared wavelengths.
RFO difference (%) maps for clear skies (divided by two to use
a common color scale) and the seven CVS classes as simulated by GEOS-5 using
the GN and MR overlap assumptions in the cloudy subcolumn generator.
Moving on to cloudy skies, a quick survey of the remaining panels in Fig. 2
reveals that the model exhibits considerable skill in simulating cloudiness
when viewed under the prism of CVS classes. Weaknesses, however, become
apparent upon closer examination. In terms of global values, the only CVS
class in which the model produces a substantial RFO overestimate is HM for
both overlap assumptions. For CVS = HML, global RFOs agree, especially
for the GN overlap assumption. The global RFOs of all other CVS classes are
underestimated to varying degrees, with the underestimates being slightly
worse for the MR overlap assumption, except for CVS = L for which MR
RFO slightly exceeds GN RFO. The total RFO of the four CVS classes
containing H clouds is ∼40 % in observations and
∼36 % (GN) or ∼32 % (MR) in the model. The
remaining CVS classes consisting of only L and M clouds add up to a global RFO
of ∼35 % in observations and ∼26 % in the
model (both GN and MR). Therefore, most of the 13 % discrepancy between
GEOS-5 and GN in global cloud fraction comes from the three CVS classes
containing only L and M clouds, while the larger discrepancy of
∼17 % for GEOS-5 and MR is more evenly split between these
three CVS classes and the remaining four containing H clouds.
A closer comparison of geographical features is also informative. Figure 2b shows only the GN overlap results and can be directly
compared with Fig. 2a showing the observed maps. The performance of the
MR overlap implementation can be gleaned in terms of its deviation from GN
in the Fig. 3 difference maps.
Simulating low clouds has been identified as a challenge for large-scale
models, but this version of GEOS-5 seems to be simulating the isolated low
clouds (CVS = L) quite well, with a global underestimate of
∼5 % for GN overlap and ∼4 % for MR
(absolute values), as well as with characteristic cloud patterns associated with
marine stratocumulus being present albeit with less extensive spatial
coverage. While GEOS-5 does not produce isolated M clouds (CVS class M)
as often as in the observations, the impact is expected to be small as this
CVS class is the least frequently observed exclusively
over land, specifically deserts, ice- and/or snow-covered surfaces, and regions
of pronounced orography. Overall, however, there is not such a great paucity
of M clouds in the model when taking into account the other CVS classes
containing this type of cloud. Setting aside deep and multilayer clouds
(the HML CVS class), M clouds appear only about 11 % (for GN – the
figure rises to 22 % for MR) more frequently (in relative terms) in
observations than the model; the combined RFO of M, ML, and HM is
14.5 % in the observations and 13 % (11.8 %) in the model for the GN
(MR) implementation. Finally, H over L clouds (CVS class HL) are one of
the biggest contributors to the overall cloudiness discrepancy between the
real and simulated worlds as they appear twice as often in the observations
as in the GN version of the model (and even more relative to the MR
implementation of the model). The model seems to be lacking much of the
tropical presence of this CVS class.
A closer look at the influence of the overlap assumption on CVS RFOs can be
gauged from the Fig. 3 maps. We have previously seen that the MR overlap
assumption generally produces less cloudiness than GN. This happens
systematically (i.e., virtually all locations) for five out of seven CVS
classes. The interesting exception is CVS = L (CVS = M is absent
in GEOS-5 for all practical purposes). The Fig. 3 difference map for CVS = L reveals that the GN's reduced cloudiness comes mostly from the
extratropics; tropical and subtropical pockets can be found where the GN cloud
amounts exceed those from MR, as in the other CVS classes. The contrast
between CVS = L and the other CVS classes illustrates the fact that
the specific flavors of these overlap assumptions as implemented in GEOS-5
can produce a variety of outcomes that depend on the total geometrical
extent of contiguous or noncontiguous vertical cloud configurations
and the detailed shape of the cloud fraction profile.
Global CRE comparison by CVS class
Figure 4 compares the global mean CRE between the model and observations,
the latter (r‾) coming from the aforementioned 2B-FLXHR-LIDAR CC
product. It shows the mean values only when the CVS occurs; i.e., CRE is weighted by area, but
not by global RFO. We call this type of CRE the “cloudy column” or
“overcast” CRE since it is calculated by taking the mean of the CRE values
of cloudy subcolumns belonging to the CVS class. CRE values for each cloudy
subcolumn also correspond to overcast conditions since there is no partial
cloudiness at the subcolumn scale. We show overcast CRE from three
perspectives: the top of the atmosphere – TOA (Fig. 4a), the surface – SFC
(Fig. 4c), and the atmospheric column – ATM (Fig. 4b), the latter derived
as the difference between the TOA and SFC CREs. Moreover, we distinguish
between shortwave (SW) and longwave (LW) components and also display their
sum, which we call “total” CRE (also called “net” CRE). With CRE being defined as
the difference between cloudy and clear-sky net (down–up) fluxes,
negative values indicate a radiative cooling effect, while positive values
indicate a radiative warming effect. For TOA and SFC, all SW CREs are
negative. Note also the magnitudes at TOA and SFC being rather similar for
SW, with the slightly larger SFC value resulting from the small positive ATM
SW CRE, which indicates that clouds slightly enhance atmospheric column
absorption. While LW CREs at both TOA and SFC are positive and therefore
indicative of warming, the ATM LW CRE can be either positive or negative.
Note that all positive global means involve H clouds. Again, we show model
results for the two overlap assumptions, GN and MR, although their CREs are
quite close in general. The observed SW CREs depend strongly on the incoming
solar flux at the approximate 13:30 local overpass time and are therefore
scaled to diurnal fluxes by normalizing with the ratio of the instantaneous
to diurnally averaged incoming solar flux at TOA (O17); the LW CREs are
simple averages of the daytime and nighttime overpass values. On the other
hand, both SW and LW CREs from the model are daily averages of 3-hourly
mean outputs.
For TOA SW CRE, the best agreement between model and observations occurs for
CVS = L and CVS = HM. For the remaining CVS classes the model
either overestimates (CVS = H, M, HL) or underestimates (CVS = ML, HML) overcast TOA SW CRE. The overestimate for CVS class
H is very large in relative terms given the small absolute magnitude of
the observed CRE. It appears then that H clouds in the model are optically
thicker than in observations. Discrepancies are smaller for TOA LW
CRE, reflecting the lesser dependence of this quantity on cloud properties
other than cloud-top location (which is constrained because of the CVS class
decomposition) once clouds reach a certain value of optical thickness
(∼5). The biggest bias (underestimate) appears for CVS = HML CVS, but since it is still smaller than the SW CRE bias it results
in an underestimate of net planetary cooling as expressed by total TOA CRE
(purple bars). Given the better agreement between LW CREs, total TOA CRE
biases largely follow the sign of the SW CRE biases. These findings are very
insensitive to the type of chosen overlap, although the differences in
magnitudes between the two simulated values are still large enough to be
distinguishable in most cases.
When moving to an examination of surface (SFC) CREs (Fig. 4c)
our conclusions about the SW CRE component are the same as before since
atmospheric (ATM) SW CREs are small positive values (panel b). LW CRE
values are again simulated quite well since most of the variability is
driven by the location of the cloud bottom, which is constrained by CVS
class. The largest biases occur for CVS = L and HL (overestimates
by the model), and since the TOA CREs have small biases for those cases,
errors (excessive cooling) materialize in the ATM LW CRE. Still, the largest
ATM LW CRE error occurs for CVS = HM (excessive warming by the model)
because the TOA and SFC CRE errors are in the opposite direction. Given the
small magnitude of ATM SW CRE, the total ATM CRE errors track those of the
LW component.
Comparison between observations and the model (GN and MR) of global
overcast CREs (Wm-2): top of the atmosphere (TOA) (a), surface (SFC)
(c), and atmospheric column (ATM) (b) derived as the difference
between the TOA and SFC CREs. CREs are distinguished into shortwave (SW) and
longwave (LW) components, and their sum, the “total” CREs for each CVS class,
are also shown. Note that the y-axis range is the same for TOA and SFC CRE,
but it is substantially more compressed for ATM CRE.
Figure 5 compares observed and modeled CRE values that are now weighted by
the global mean RFO (f‾ for observations) of the CVS classes in
addition to areal weighting. We can call this type of CRE “all-sky” CRE
since in the calculation of the mean all subcolumns that do not belong to
the CVS class under consideration contribute zero errors. Summing then these
CVS-specific values yields the true global CRE of observed and modeled CRE
fields. Since the all-sky CRE values and the range of the y axis are much
smaller than in Fig. 4, it makes sense to compare the two figures only with
respect to relative biases, essentially focusing on the position of the
symbols (simulated values) relative to the bar (observed values). While this
will be shown more explicitly in Fig. 6, comparison of Figs. 4 and
5 basically indicates whether RFO errors suppress (i.e., compensate for) or
amplify cloud property only errors. Take CVS = HL, for example: RFO
errors (underestimates) help suppress the TOA and SFC SW (and total)
overestimates. In general, we do not see much of the opposite effect, i.e.,
an amplification of relative error CRE when moving from overcast to all-sky
CRE. Of course, a very low RFO also makes an overcast CRE that previously
seemed substantial disappear, with CVS = M being a characteristic
case in point. The discussion of all-sky CRE error interpretation continues
in the next subsection where a more formal error decomposition framework is
introduced.
As Fig. 4, but for all-sky (RFO-weighted) CREs.
CRE error decomposition
Figure 6 shows the decomposition of GEOS-5 all-sky CRE global errors
ΔCRE in Fig. 5 to overcast CRE and RFO error contributions
for the GN case only (the conclusions remain the same for MR). The decomposition
can be expressed as follows (e.g., Tan et al., 2015):
ΔCRE=f‾×Δr+r‾×Δf+Δr×Δf.
This representation of CRE error arises when the model global all-sky CRE of
a CVS class (Fig. 5) is expressed as the product of a deviation
Δr from the observed mean overcast CRE r‾ (Fig. 4), and the model global RFO is expressed as a deviation Δf
from the observed mean RFO, f‾:
CREGEOS-5=(r‾+Δr)×(f‾+Δf).
Basically, the model's grid-mean CRE error for a CVS class arises from a
combination of overcast CRE bias Δr under the observed RFO
f‾ and the simulated RFO bias Δf under observed
overcast CRE r‾, plus a covariation term of RFO and CRE errors under
observed f‾ and r‾ (Tan et al., 2015). Such a decomposition of
CRE error allows us to infer, for example, whether the model's poor
simulation of all-sky CRE is mostly due to errors in simulating the
occurrence frequency of the CVS class or errors in the optical and physical
properties of the CVS class that drive the overcast CRE. Similarly, it
potentially reveals cases in which good simulations of global all-sky CRE in
Fig. 5 benefit from compensating errors in simulated RFO (Fig. 2) and
overcast CRE (Fig. 4).
Decomposition of all-sky CRE error (Eq. 1) for GEOS-5 CVS classes
when the GN overlap assumption is used. Gray bars represent the overall
all-sky CRE error, and the remaining bars represent contributions to that error as
follows: red bars represent overcast CRE errors, blue bars RFO errors, and
green bars covariation errors. The nine panels represent all combinations
of CRE, namely SW, LW, total at TOA, SFC, and within ATM.
Separate panels are used in Fig. 6 for SW (a, d, g), LW (b, e, h), and total (c, f, i) CRE. The breakdown by TOA, SFC, and ATM is
also preserved, thus yielding a total of nine panels. In the SW, TOA (Fig. 6a), and SFC (Fig. 6g) results look again very similar, while the ATM CRE
errors (Fig. 6d) are too small to merit discussion. For most CVS classes
(five out of seven) all-sky SW CRE errors (gray bars) come from overcast CRE
errors (red bars), namely errors in CVS optical properties. The excessive
planetary cooling of the cloudy columns (negative red bars, four CVS
classes) is always dampened by compensating errors, sometimes virtually
eliminating the error (as in CVS = L, HL), reducing it slightly
(CVS = H), or overcorrecting (CVS = M). SW TOA overcast CREs (red
bars in Fig. 6a) in the opposite direction (cooling underestimates) become
bigger all-sky errors due to RFO errors for CVS = ML and HML, while
the all-sky errors for CVS = HM come almost exclusively from RFO
errors (blue bars in Fig. 6a). Finally, three CVS classes have sizable
covariation errors (green bars) in the same direction as RFO errors. The
above error interpretation is virtually the same for surface (SFC) SW
errors (Fig. 6g).
Contrary to the SW, the LW CRE errors for all three vantage points (TOA in
Fig. 6b, SFC in Fig. 6h, ATM in Fig. 6e) deserve their own discussion as
they have different characteristics. At TOA and SFC, the errors are
substantially smaller than their SW counterparts. Three of the four CVS
classes with H clouds (the exception being CVS = H) exhibit
∼2 Wm-2 (absolute) errors, coming from RFO contributions
in two out of the three classes. These three classes have smaller all-sky
errors at the SFC, in one case (CVS = HL) because of compensating
errors. The largest component errors occur for CVS = L, which has the
largest absolute magnitude of all-sky SFC CRE, but with component errors in
the opposite direction, compensation reduces the all-sky CRE error. Because
the TOA errors for this CVS class are small, the SFC errors carry to the ATM
errors. The other CVS class with large ATM error is HML, whereby TOA and
SFC errors of the opposite sign conspire to magnify the ATM error.
Errors in total all-sky CRE are driven mainly by SW errors at TOA and SFC
(Fig. 6c and i) as well as LW errors for ATM (Fig. 6f). Errors of the
opposite sign reduce the overcast cooling error at the SFC for CVS = L
and HL and the all-sky warming error for CVS = ML. But because the
SFC LW CRE errors are in general small, the total CRE SFC errors largely
retain the characteristics of the SW component. In the atmospheric column,
SW and LW overcast (and all-sky) errors are additive for CVS = HML and
opposing for CVS = L, the only two classes for which ATM SW CRE
registers errors of notable magnitude (see Figs. 4b and 5b).
In summary, this decomposition analysis showed the multiple ways relatively
good agreement with observed all-sky CRE values from various vantage points
can be achieved by GEOS-5 (or any other model evaluated this way). Overcast
CRE and RFO errors can compensate, TOA and SFC all-sky CRE errors can
compensate (for ATM LW CRE, e.g., CVS is M, ML), SW and LW errors
can compensate for total CREs, and finally the errors among various CVS
classes can compensate towards decreasing the global CRE error.
Seasonal CRE comparison
Figures 7–9 compare the multiyear mean annual cycle of TOA, SFC, and ATM
total (SW + LW) all-sky CRE zonal averages between observations and the
model (employing the GN overlap assumption) for the four CVS classes with
the greatest all-sky SW or LW CREs according to Fig. 5.
Comparison of the multiyear annual cycle of TOA total
(SW + LW) all-sky CRE zonal averages (W m-2) between observations
(top row, panels a to e) and the model (bottom row, panels f to j) when
employing the GN overlap assumption for the four CVS classes with the
greatest all-sky CREs according to Fig. 5. The rightmost panels
displays the scaled (half) total CRE of all CVS classes combined.
Inspection of the TOA and SFC CRE plots shows that the model has some skill
in simulating the seasonal competition between SW and LW CRE, but this
should not come as a surprise as it is driven mainly by seasonal changes in
insolation. Basically, with everything else staying the same, the SW CRE
contribution to total CRE scales with the amount of incoming solar energy.
Positive values of total TOA and SFC CRE occur when the solar insolation is
weak during the winter, thus allowing the positive LW CRE to exceed the
negative SW CRE. At TOA, this takes place only for the HML CVS class
since this is the class with competing SW and LW CREs of relatively large
magnitude. Note that the model summer planetary cooling is stronger than in
the observations. At the SFC, besides CVS = HML the seasonal switch
from cooling to warming also takes place for CVS = L because the LW
CRE is of comparable magnitude to its SW counterpart. The model's CVS = H is virtually neutral radiatively at TOA throughout the year, in
contrast to the observations, for which it provides planetary radiative heating
in the tropics and subtropics. It seems then that in the model CVS = H
consists of optically thicker clouds that reflect more solar radiation to
space than in the real world. H clouds in GEOS-5 also appear to be optically
thicker when overlapping with L clouds (CVS = HL), in this case
producing planetary cooling in the tropics throughout the year and in the
extratropics during the summer months of high insolation, in contrast to the
observations for which their cooling effect is very weak and occurs only in the
austral extratropics during summertime. Evidence for optically thicker H
clouds in both CVS = H and HL is also seen at SFC total CREs, which
are more negative in the model than in the observations. Overall (all CVS
classes combined; Figs. 7e, j and 8e, j), the model produces
a rather realistic pattern of seasonal variations in zonal mean total CRE.
As Fig. 7, but for SFC total all-sky CRE.
As Fig. 7, but for ATM total all-sky CRE.
Total ATM CREs are driven, as we have seen earlier, by the LW component, and
their seasonal cycles are fairly well represented by the model for three of
the four most radiatively important CVS classes (Fig. 9). The nature of CVS = HML, however, seems to be different in GEOS-5 compared to
observations. At high latitudes, the atmospheric column is cooled by this
type of cloudiness, especially during the summer months, as the SFC total
CRE (Fig. 8) exceeds the TOA CRE (Fig. 7). Since the SW contribution is
relatively small, it then seems that L clouds within CVS = HML have
lower bases or are optically thicker during the summer months in the model
compared to observations, making their downward emission towards the
surface, and therefore also the contrast between TOA and SFC emission,
stronger in the model than the observations. Figure 9i also shows that the
near-zero total ATM CRE for CVS = HML in GEOS-5 (Fig. 5) is a result of
positive and negative total ATM CRE regional compensations. Overall, the
model captures the basic zonal pattern of atmospheric heating and warming
(Fig. 9e, j), with heating prevailing in the tropics and
cooling in the extratropics. The tropical heating is, however, weaker than in
the observations, while the extratropical atmospheric cooling is stronger.
Conclusions
We have introduced a method of cloud evaluation for large-scale atmospheric
models that focuses on the vertical structure of cloudiness. Cloud vertical
structure (CVS) is resolved in a rather simplified way based on the various
combinations of cloud presence in three standard layers that have been
traditionally used to distinguish between high, middle, and low clouds. A
reference dataset for such CVS classification now exists because of CloudSat
and CALIPSO active sensor observations (Oreopoulos et al., 2017). For the
purposes of model evaluation, the initial dataset of 10 CVS classes was
simplified to consist of 7 classes by merging some of the original
classes that had clouds in adjacent standard layers. Beyond comparison of
the frequency of occurrence of the CVS classes we also compared their
radiative impact in terms of the cloud radiative effect (CRE). While the CVS
classes by design constrain cloud vertical location (albeit not in the
strictest of ways), they constrain extinction to a lesser extent and mostly
qualitatively (e.g., multilayer cloud configurations are expected to have a
greater total column extinction). This is taken into account when examining
the performance of the model in terms of SW and LW CRE. We developed a
framework wherein we can compare CRE for only when a CVS class occurs
(overcast CRE) or perform a comparison that also accounts for how
frequently the CVS class occurs (all-sky CRE). We can then naturally examine
to what extent errors in the latter type of CRE come from errors in the
overcast CRE of the class and/or biases in the frequency of occurrence.
The GEOS-5 model under evaluation produces about 50 % more clear skies
than observations in relative terms. It produces isolated high clouds (cloud
top and base above the 440 hPa level) that are slightly less frequent than
in observations but are optically thicker, yielding excessive planetary and
surface cooling. Low clouds (cloud tops and bases within the lowest layer of
the troposphere up to 680 hPa) are usually a challenge for global models, but
GEOS-5 is doing reasonably well and compensates for a lower frequency of
occurrence (by ∼20 % in relative terms) with overestimates
in extinction, producing in the end an excellent agreement with observations
for SW and LW all-sky CREs at either the TOA, SFC, or the atmospheric column
vantage points. Overall LW CREs are better simulated since they are mainly
driven by vertical cloud location, which is substantially constrained when
clouds are broken by CVS class. But either component of CRE can be off in
terms of the contribution to the global CRE if the frequency of occurrence is
deficient. The other side of the coin is, of course, that incorrect
simulation of the frequency of occurrence can compensate for biased cloud
optical and physical properties that determine the overcast CRE of the CVS
class. Needless to say, CRE biases among different CVS classes can also
cancel out to various degrees when global or regional CREs encompassing all
clouds represented by the CVS classes are calculated. In such a holistic
view, the model appears able, for example, to reproduce the aggregate
planetary feature of atmospheric radiative warming in the tropics and
cooling in the extratropics driven by cloud configurations dominated by high
and low clouds, respectively, albeit with magnitudes that differ from those
observed.
The evaluation we conducted requires that the model has the capability to
produce cloudy subcolumns, which are then considered equivalent to the
atmospheric column profiles seen by the active observations. There is no
unique way to go from mean cloud fraction profiles to subcolumns having
layer cloud fractions that are either one or zero. We tried two ways to
produce subcolumns that assume different cloud fraction overlaps and
obtained rather close results. By adopting our framework of cloud
evaluation, which, incidentally, should be used in conjunction with other
cloud evaluation methodologies (e.g., cloud regimes as in Jin et al., 2017a, b), one can assess whether other large-scale models are more sensitive
(i.e., produce a greater diversity of CVS climatologies) to different
overlap assumptions applied to the same original mean cloud fraction
profiles. What one should always keep in mind, however, is that no matter how
good the cloud subcolumn generator is, observed CVS class global frequencies
and patterns cannot be reproduced if the model's underlying mean cloud
profiles used as input to the generator are deficient.
Code availability
The GEOS-5 source code is available under the NASA Open-Source Agreement at:
http://opensource.gsfc.nasa.gov/projects/GEOS-5/ (last access: 19 February 2020; NASA, 2020).
Author contributions
DL and LO designed the metrics and
experiments. DL adapted the model code for the new metric and performed
the simulations. NC processed the observational dataset. DL and NC created the graphics and figures. DL and LO authored the
text with contributions from NC.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
Dongmin Lee gratefully acknowledges funding support from NASA's
NIP program, while Lazaros Oreopoulos acknowledges support from NASA's CloudSat
and CALIPSO Science Team Program. Resources supporting this work were
provided by the NASA High-End Computing (HEC) Program through the NASA
Center for Climate Simulation (NCCS) at Goddard Space Flight Center. The
reference data used for model evaluation (2B-CLDCLASS-LIDAR and
2B-FLXHR-LIDAR) are available from the CloudSat Data Processing Center at:
http://www.cloudsat.cira.colostate.edu (last access: 19 February 2020). We thank our colleague
Donifan Barahona for helpful discussions about various model tags.
Financial support
This research has been supported by the National Aeronautics and Space Administration (NASA) (grant no. 80NSSC18K0997).
Review statement
This paper was edited by Holger Tost and reviewed by two anonymous referees.
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