lazaros oreopoulos (nasa-gsfc)
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Lazaros Oreopoulos (NASA-GSFC)
What new have we learned about cloud radiative effect and feedback
in the last decade? Lazaros Oreopoulos (NASA-GSFC) Effect vs
feedback Cloud Radiative Effect (CRE) can be observed (TOA) by
satellites CRE = F Fclr To obtain CRE for SFC and ATM some modeling
is often necessary Cloud Radiative Feedback is associated with CRE
CRE = F Fclr and is much harder to observe and relate to Ts. We
mostly use CGCMs to quantify it F is the net flux = down up. Cloud
Radiative Effect (CRE) Global CRE from various sources (no
uncertainty bars) SW LW net
TOA SFC Sources: ERBE: Harrison et al. (1990) ISCCP FD: Zhang et
al. (2004) CERES EBAF: CloudSat/CALIPSO: Henderson et al. (2013).
Scales have been expanded to highlight the differences. Note that
according to CloudSat/CALIPSO clouds significantly warm the
atmosphere! ATM Vertical variability of CRE (expressed as cloud
effect on HRs)
DJF JJA LW Radiative heating rates were calculated from the R04
2B-FLXHR-LIDAR product [Henderson et al ., 2012; LEcuyer et al .,
2008], which uses the CloudSat microphysical retrievals and
combined CloudSat/CALIPSO cloud mask as inputs to a broadband,
two-stream, plane-parallel, adding and doubling radiative transfer
model. The model produces upward and downward fluxes at 240m
vertical increments. There is a crude correction to convert
instanenous SW fluxes to diurnal means. The SW CRH is, in the
annual mean, 2.1 times smaller than the LW CRH (note the differing
scales). High clouds tend to reduce SW absorption by the underlying
atmosphere, while low clouds tend to enhance it by increasing the
geometric path length of photons, thus increasing the probability
of absorption. The Cloudsat/CALIPSO-derived SWCRH is consistent
with both these ideas: it is maximum in the tropical upper
troposphere, where cirrus are ubiquitous, and also large in the
subtropics and midlatitudes of the summer hemisphere, where low
cloud occurs in the presence of a low summertime solar zenith
angle. This zone of stronger SW CRH also extends into the lower
troposphere, especially in the higher latitudes where low clouds
are particularly prevalent. A maximum in LW CRH can be seen in the
tropical lower and middle troposphere. As demonstrated in L08, this
is a result of enhanced infrared cloud radiative warming by
high-topped clouds, particularly cirrus that prevent LW radiation
from escaping to space, while also radiating toward the surface.
The resulting atmospheric heating is maximized in the lower and
upper troposphere, but the tropical region near 550 hPa actually
experiences cloud-related cooling. The more convectively active
portions of the tropics, such as the West Pacific and Indian Ocean
basins, have been observed to contain a peak in midlevel clouds,
probably associated with a climatological mean stable layer near
the height of the freezing level. The area of strong infrared
cooling (and negative LW CRH, i.e., cloud radiative cooling) near
775 hPa is dominated by emission from cloud tops, especially over
the SHEM where the maximum mean JJA cooling is 3.4K/d . Haynes et
al . [2011] reported a 3 year, area-averaged cloud fraction from
Cloudsat/CALIPSO of 0.81 for the region between 30 S and 65 S and
showed that clouds with average tops near 2.5 km (or approximately
750 to 800 hPa) dominate this region. Low clouds are also common in
the middle to high latitudes of the NHEM [e.g., Mace , 2010], but
they cover a smaller fraction of the total area and therefore have
a reduced radiative impact. It is not surprising, therefore, that
there is a hemispheric asymmetry in radiative cooling at 775 hPa,
with the SHEM midlatitudes to high latitudes undergoing radiative
cooling during the entire year, while the corresponding latitudes
in the NHEM cool during the boreal winter and warm during the
boreal summer. Another feature is an area of reduced cooling in the
low levels (enhanced LW CRH) apparent in both hemispheres in the
vicinity of 925 hPa. This is a result of infrared warming from
cloud bases and is largest when a cold boundary layer overlies a
relatively warm surface. During the NHEM summer, the region of
enhanced low-level LW CRH persists even where the overlying cloud
radiative cooling is largely absent. This occurs because the
clear-sky component of the cooling is also large, which we
speculate is due to relatively high water vapor concentration in
the upper-boundary layer during the NHEM summer. SW From Haynes et
al. (2013) Atmospheric LW CRE from CERES
(Wm-2) (Courtesy of Norman Loeb) Horizontal variability of (SW) CRE
(bias when ignoring tau and re horizontal variations at 1 deg
)
Biases for liquid are larger at midlats during their respective
summers because of increased illumination. Ice cloud bias peaks re
in the tropics and reflect the movement of the ITCZ. These are
diurnal averages obtained by using a few assumptions. Horizontal
variations are known from the joint histograms of tau and re. From
Oreopoulos et al. (2009) Overlap effects on CRE (from a GCM)
SW LW Cloud are assumed inhomogeneous (beta distribution in each
layer) in all experiments. Generalized overlap with fixed
decorrelation lengths produces the largest cloud fraction (in
general) and therefore the largest CREs. Barker has also studied
CRE bias using CloudSat observations. top row: max-ran generalized
(fixed parameters)~4 Wm-2 bottom row: generalized (CloudSat)
generalized (fixed parameters) ~1 Wm-2 Oreopoulos et al. (2012)
Decomposing CRE Net TOA CRE from CERES Wm-2 (Courtesy of Norman
Loeb) Breakdown of CRE by cloud type
Many ways to define cloud type Chen et al. 2000 Hartmann et al.
1992 Chen et al. (2000) used four days only (one for each season)
to represent the entire year. Based on RT calculations. Hartmann et
al. (1992) uses multi-variable regressions between ERBE CRE and
ISCCP cloud fractions for each cloud type. Spectral/cloud type
breakdown of (LW) CRE (tropical oceans)
H2O, & > 1400 cm-1 CO2, cm-1 O3, cm-1 H2O continuum, cm-1
Cloud type (high, low middle) presence and impact on CRE can be
inferred by normalized band CRE (fCRE). In this ratio he common
factor of cloud fraction cancels and fCRE is only sensitive to
cloud-top temperature, making it a useful quantity in diagnosing
and evaluating modeled CRE. The largest values of fCRE
[;(0.250.35)] for Band 1 (H2O) are found over the regions with
frequent occurrence of high clouds, such as ITCZ, SPCZ, and Indian
monsoon regions. The smallest values occur in regions frequently
covered by marine stratus (i.e., low cloud), such as the Pacific
coast off South America, the Namibia coast, and the ocean region
west of Australia. For band 2 (CO2 band;), the contrast in fCRE
between high-cloud and low-cloud regions (0.16 vs 0.11) is much
smaller than that of band 1 (0.3 vs 0.05). The observed band 3 (H2O
continuum) fCRE peaks over the low-cloud regions and drops over the
high-cloud regions. The observed spatial map of band 4 fCRE (ozone
band) is similar to that of band 3, owing to the fact that the
ozone band is sensitive to the cloud and surface thermal contrast
in a similar way as for neighboring bands. These maps have been
compared with their counterparts from 3 GCMs. Huang et al. (2013)
Breakdown of CRE bycirculation regime
(Tropics, ERBE + ERA-40) LW and SW (x -1) CRE from ERBE ( )
composited by ERA-40 vertical velocity at 500 mb. Vertical bars
indicate the seasonal deviation within each regime. This is for the
tropics only (30 S to 30 N) A more continuous idealization of the
tropical circulation was proposed by Bony et al. (2004). This uses
the 500-hPa large-scale vertical velocityas a proxy for large-scale
vertical motions of the atmosphere and decomposes the HadleyWalker
circulation as a series of dynamical regimes defined using . In the
Tropics, nearly all of the upward motion associated with ensemble-
average ascent occurs within cumulus clouds, and gentle subsidence
occurs in between clouds. Since the rate of subsidence in between
clouds is strongly constrained by the clear-sky radiative cooling
and thus nearly invariant, an increase of the large-scale mean
ascent corresponds, to first order, to an increase of the mass flux
in cumulus clouds (Emanuel et al. 1994). Therefore, considering
dynamical regimes defined from allows us to classify the tropical
regions according to their convective activity, and to segregate in
particular regimes of deep convection from regimes of shallow
convection. The statistical weight of the different circulation
regimes (Fig. 4) emphasizes the large portion of the Tropics
associated with moderate sinking motions in the midtroposphere
(such as found over the trade wind regions), and the comparatively
smaller weight of extreme circulation regimes associated with the
warm pool or with the regions of strongest sinking motion and
static stability such as found at the eastern side of the ocean
basins. These extreme regimes correspond to the tails of
theprobability distribution function. The atmospheric vertical
structure (observed or modeled) can then be composited within each
dynamical regime. Illustrations of the dependence of cloud
radiative properties and of precipitation on the large-scale
circulation are displayed in Figs. 4b,c, showing the
satellite-derived precipitation and cloud radiative forcing (CRF)
as a function ofomega (omega being derived from meteorological
reanalyses). These increase as the vigor of the convective mass
flux increases. Bony et al. (2004, 2006) Breakdown of (daytime) CRE
by Weather State
ISCCP extended topics weather states TOA CRE, extended tropics (8
WS), Oreopoulos and Rossow (2011)
highest RFO lowest RFO The left figure shows that annually averaged
TOA LW CREs below 20 Wm2 characterize WS5, WS6, WS7, WS8 (the least
convectively active states), but the SW CREs assume a wide range
(as do the net CREs) of values between 35 Wm2 and 195 Wm2 that
correlate well with the dominant taus WS3 and WS4 are almost
indistinguishable in terms of their average LW CRE, but separate
very clearly in terms of SW (and net) CRE, both being larger for
WS3, the more convectively active state of the two. WS1 and WS2
appear quite apart in LWSW CRE space not just from the other
weather states, but also from each other (their net CRE differs by
150 Wm2). In addition to having the strongest LW CRE, WS1 also has
the strongest SW CRE and net CRE. In contrast, WS8 has the weakest
SW, LW and net CREs. WS4 and WS7 representing completely different
cloud mixtures have almost indistinguishable SW CREs, but differ in
net CRE because of their LW CRE differences. The greatest
similarity in net CRE is between WS4 and WS8, even though they
correspond to entirely different cloud mixtures with distinct LW
and SW CRE components. Seasonal variations of LW CRE are very weak
for all weather states (the maximum sdev of 9% of the annual mean
occurs for WS6). For the tropics, even the SW CRE cycle is rather
weak (8%, also for WS6). A notable feature of Figure 4 (top) is
that only two states approach a nearly zero daytime TOA net CRE,
one being the boundary layer cumulusdominated WS8 and the other
being the cirrusdominated WS4, contradicting some claims that
tropical deep convection produces this condition. [18] In terms of
contribution to the tropical TOA CRE (Figure 4, bottom) we observe
the following: The states with the greatest mean LW CRE when
present, WS1 and WS2, are two of the strongest contributors to the
total LW CRE, but they are surpassed in contribution by WS3 which
has a far larger RFO (see Figure 3). The top SW CRE contributor is
again WS3, followed by WS1. WS3 achieves this because of its large
RFO, while WS1 because of its large CF (Figure 3) and larger CRE
when present (due to optically thicker clouds; see Figure 1).
Despite the attention given to marine stratus as causing
differences of climate model CRE, the weather states where they are
prevalent are not the largest contributors to tropical and
subtropical CRE; as Figure 4 shows, it is the convectively active
states that contribute most of the SW CRE. WS1 and WS3 also stand
out in terms of net daytime TOA CRE contributions. A second group
of states (WS2, WS5 and WS8) has significant net CRE contributions
as well, ranging from 10 to 14%. WS6 is one of the weakest
contributors in all components of CRE, probably because of its low
RFO, but WS4 (with many thin cirrus) is the weakest contributor to
the overall net TOA CRE. The most seasonally varying contribution
to CRE comes from WS5 which exhibits a sdev close to 20% of the
mean for all three CRE components. We have previously seen that
this weather state also stood out for its seasonal variability of
CF contribution because of significant RFO seasonal variations.
WS1CF-1st, RFO-7th WS3CF-4th, RFO-2nd WS4CF-6th, RFO-4th WS8CF-8th,
RFO-1st CERES CRE vs. MODIS cloud regimes
ISCCP MODIS-CERES MODIS CRE is diurnal, not daytime SFC vs TOA LW
CRE, extended tropics
ATM cool ATM heat This figure compares the LW CRE at TOA and SFC
for the tropical region, the sum of which is indicative of the
radiative heating of the atmosphere by clouds: weather states
falling above the 1to1 line indicate cloud regimes cooling the
atmosphere whereas states below the line indicate regimes warming
the atmosphere. Although WS3 plays a major role in the TOA CRE, its
net effect on LW heating of the atmosphere is nearly zero as is the
contribution from the cloud regime with lots of scattered cumulus
(WS8). The two stronger convective states, WS1 and WS2, produce a
large heating of the tropical atmosphere, whereas all the boundary
layer weather states (except WS8) produce a weaker cooling. These
results reflect the general situation in low latitudes where the
fair weather atmosphere is cooled by radiation and the surface is
heated, even with some clouds present, but the CRE reinforces storm
system latent heating of the atmosphere while cooling the surface.
WS2 WS5 Breakdown of CRE by cloud regime in GCMs
Note that MODIS and ERBE are NOT combined. MODIS is treated as
another model for top panel and ERBE also as another model for
bottom three panels. Clusters are not defined from p-tau histograms
directly, but using a more simplified method (tot_CF, mean CTP and
albedo of original regimes) Williams and Webb (2009) Cloud
Radiative Feedback
From leaked IPCC AR5 WGI Second Order Draft: likely positive, with
a likely range of 0.2 to 1.4 Wm-2K-1. The cloud feedback remains
the most uncertain radiative feedback in climate models. Some
consensus among CGCMs: High clouds rise, high and middle cloud
amounts decrease, storm tracks shift poleward, low cloud amount
decreases (especially in subtropics) Based on the above synthesis
of cloud behaviour, the net radiative feedback due to all cloud
types is judged likely (>66% chance) to be positive. This is
reasoned probabilistically as follows. First, since evidence from
observations and process models is mixed as to whether GCM cloud
feedback is too strong or too weak overall, and since the positive
feedback found in GCMs comes mostly from mechanisms now supported
by other lines of evidence, the central (most likely) estimate of
the total cloud feedback is taken as the mean from GCMs (+0.8 W m2
K1). Second, since there is no accepted basis to discredit
individual GCMs, the probability distribution of the true feedback
must be at least as broad as the distribution of GCM results.
Third, since feedback mechanisms are probably missing from GCMs
(particularly involving 1 thin high clouds or low clouds) and some
CRMs suggest feedbacks outside the range in GCMs, the probable
range of the feedback must be broader than its spread in GCMs. We
estimate the likely range of this feedback by doubling the spread
(quadrupling the variance) about the mean of the data in Figure
7.9, that is assuming an uncertainty 170% as large as that
encapsulated in the GCM range added to it in quadrature, and
assuming Gaussian errors. This yields a 90% (very likely) range of
0.2 to 1.4 W m2 K1, with a 16% probability of a negative feedback.
Why so uncertain? Difficult to constrain from observations: time
series too short; signal is weak (many cancellations, SW/LW),
observations are imperfect, contamination Clouds imperfectly
represented in GCMs Multiple ways used to extract even from GCMs
surface temperature-mediated change of CRE in response to forcing
(e.g. 2xCO2) According to Zelinka et al. (2013) some locations the
cloud adjustments act in opposition to and in other locations act
in the same direction as the cloud feedbacks, according to CGCMs.
CRE also changes in response to rapid changes in land Ts,
troposphere (WV, T), and As (rapid adjustments); circulation; these
CRE changes should be removed changes in clear sky fluxes (due to
changed CO2, WV, T, Ts, As), in both terms, also alter CRE;
corrections should be applied Correcting CRE using Soden radiative
kernels
SW LW Corrections to CRE Shell et al. (2008) Corrected CRE vs.
uncorrected CRE Corrections to SWCRE seem to come mainly from sfc
albedo masking of DCRE (or cloud masking of sfc albedo changes!)
and occur of course at high latitudes. Corrections to LWCRE are
more substantial and mainly reflect temperature and humidity
effects on the clear sky flux components folded into DCRE. The
noncloud variables also make CRFLW more negative, except at high
latitudes (Fig. 7b). The effect is largest in the tropics. The
global-average CRFLW from CAM is quite close to zero (0.06 W m2);
CRFkLW 1.2 W m2. The atmospheric temperature contribution is 0.9 W
m2; in a warmer climate, clouds are warmer and thus more effective
at radiating to space, a cooling effect. The water vapor
contribution is 1.3 W m2, and CO2 contributes 0.7 W m2. When no
clouds are present, water vapor and CO2 absorb more longwave
radiation in the doubled-CO2 case than in the present-day
simulation. Thus, the difference between all- and clear-sky fluxes
(CRF) is reduced in the doubled-CO2 case, resulting in a negative
contribution to CRFLW. The only positive contribution is from the
surface temperature (1.7Wm2). By trapping the upwelling longwave
radiation from the surface, clouds decrease the outgoing longwave
radiation. In a warmer climate, more longwave radiation is emitted
by the surface, so clouds trap more radiation, leading to a larger
positive CRF in the doubled-CO2 climate. (Application of ISCCP
simulator in CGCMs instrumental)
Breakdown of cloud feedback parameter (CMIP5) (Application of ISCCP
simulator in CGCMs instrumental) LW, SW, NET Red is LW, black is
net, blue is SW. Zelinka kernels give feedback without corrections
necessary (as in the DCRE method), but rapid adjustments should be
accounted for. Our ability to understand where cloud feedbacks are
coming from and how the models differ is enhanced by the breakdowns
by cloud type or type of cloud change. Zelinka et al. 2013; Please
refer to Zelinka talk on Thursday Breakdown of cloud feedback
parameter by circulation regime
Vial et al. (2013) CMIP5 CGCMs Tropics only Tropically-averaged
cloud feedback parameter (estimated using the NCAR kernels) plotted
as a function of the change in cloud radiative effect (i.e.,
including cloud adjustments, and without correction of the
cloud-masking effect) normalized by the global mean surface
temperature change over the tropics. Models that predicts a greater
tropically-averaged NET cloud sensitivity (i.e., cloud feedback or
change in CRE) than the tropically-averaged multimodel mean NET
cloud sensitivity are shown in red (5 models), and those predicting
a lower cloud sensitivity than the multi-model mean are in black (6
models) Note that the proper cloud feedback in LW and NET is larger
than the uncorrected (for cloud masking effects) feedback (although
the latter includes rapid adjustments correction). SW (top), LW
(middle) and NET (bottom) cloud feedback composited in each
dynamical regime. Results are presented for two groups of models:
models that predicts a greater tropically-averaged NET cloud
feedback than the tropically-averaged multi-model mean NET cloud
feedback (in red, 5 models), and those with a lower cloud
sensitivity than the multi-model mean (in blue, 6 models). Vertical
bars show the inter-model standard deviation in each group. Cloud
feedbacks are estimated using the NCAR models radiative kernels
Breakdown of CRE by cloud regime in GCMs
Williams and Webb (2009) Change in the regime mean RFO, SWCRE,
LWCRE and NCRE in response to doubling CO2 in CFMIP models. As in
the Zelinka example, we can trace back where DCRE (not cloud
radiative feedback!) is coming from. Observed cloud radiative
feedback
SW Zhou et et al. (2013) That this work is an analysis of the cloud
response to short-term climate variations is an important caveat.
Previous work has shown little correlation between the cloud
feedback in response to these short-term (mainly ENSO) climate
variations and the response to long-term global warming. LW Note
that the deviations between the two methods are more pronounces in
the LW and result in net feedback deviations mainly in the NH. net
MODIS uses Zelinka kernels CERES uses Soden kernels Short- vs.
long-term cloud radiative feedbacks
Can Dessler use short term variations for cloud feedbacks? The
CGCMs give different results for short vs. long-term feedbacks
although there aresomesimilarities in the patterns. This is a
multi-model ensemble of 13 CMIP3 models. Soden kernels have been
used to correct DCRE. The response is computed for each model as
follows: The long-term climate change response is determined by the
difference in temperature, water vapour, surface albedo and global
mean surface temperature between global model simulations of
future-climate (average over the period from the A1B experiments)
versus present-day conditions (average over from the 20C3M
experiments). The response due to short-term interannual climate
variations is computed from the slope of the linear least squares
fit between the monthly anomalies in temperature, water vapour and
surface albedo and the monthly anomalies in global mean surface
temperature. The model responses are computed from 30-yr period ( )
anomalies of the coupled model 20C3 experiments. The climate
feedbacks for each variable derive from the product of the above
climate responses with the three-dimensional kernels from the GFDL
model. long-term short-term CMIP3 models, Koumoutsaris (2013) So,
where do we stand? We have expanded our observational sources of
CRE, and have moved beyond TOA CRE (incl. better attribution) But
climate change-relevant CRE from obs remains elusive (time series
too short) When we turn to CGCMs, CRE is not, strictly speaking,
enough to derive cloud radiative feedback But most importantly, we
dont know how realistic model CRE is (e.g., CMIP5 better than
CMIP3?) Still, we have made progress in tracing model CRE to the
nature of cloud changes and in understanding better what
contributes to differences among CGCMs Additional slides Zhou et et
al. (2013) Average cloud fraction in each Ptop-tau bin (%). (b)
Slope of the regression of cloud-fraction anomaly in each bin vs.
Ts (%/K). (c-e) The contribution to the net cloud feedback, SW
cloud feedback, and LW cloud feedback, respectively, in W/m2 /K.
Note that the multiplication of cloud radiative kernels with cloud
fraction anomalies occurs at every location and is then spatially
averaged for display in this figure. Bins where the regression
slope is statistically significant (>95%) are marked with black
crosses.
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