”Tropical Clouds and Cloud Feedback”
The importance of radiative constraints
Dennis L. Hartmann
Department of Atmospheric Sciences
University of Washington
Seattle, Washington USA
Workshop on Large-Scale Circulations in Moist
Convecting Atmospheres
October 15-16, 2009
Papers online: Google Dennis L. Hartmann
Outline
• Motivation from AR4 simulations
• Radiation-Convection-Dynamics
Interaction
• Fixed Anvil Temperature
Hypothesis (FAT)
• Application of FAT to AR4 GCM
Simulation Interpretation
LW feedbacks positive and comparable magnitude.
SW feedbacks positive/negative, and dominate total feedback.
SW and LW cloud
feedback
Net cloud feedback
from 1%/ yr CMIP3/AR4
simulations
Courtesy of B. Soden
Clouds, Convection and Radiation
Atmospheric Energy Balance
• Atmospheric Energy Balance is Radiative –Convective
• Radiative Cooling = Latent Heating + Advection of Energy
• Clear-Sky Radiative Cooling is a key parameter.
Clear-sky Radiative Cooling and Relaxation:
for tropical climatological conditions
In the tropical atmosphere, and the in the global atmosphere,
radiative cooling approximately balances
heating by latent heat release in convection.
The global mean precipitation rate is about 1 meter per year,
Adiabatic
which equals an energy
input of about 80 Watts/sq. meter,
Heating
Requiring a compensating atmospheric radiative cooling
of about 0.7 ˚K/day, averaged over atmosphere.
-2.0 -1.0
Atmospheric Radiative Cooling
Altitude vs Frequency
Upper
Troposphere
Cooling
from Water
Rotation Lines
50 m 20 m
106.7
mm
5 m
Lower
Troposphere
Cooling
from Water
Continuum
Harries, QJRMS, 1996
The FAT Hypothesis,
The Fixed Anvil Temperature Hypothesis.
Tropical anvil clouds appear at a fixed temperature
given by fundamental considerations of:
• Clausius-Clapeyron definition of
saturation vapor pressure dependence
on temperature.
• Dependence of emissivity of
rotational lines of water vapor on
vapor pressure.
Testing the FAT Hypothesis with a CRM.
‘Cloud-Resolving’ Model 1km horizontal resolution
Doubly periodic domain
64km x 64km box with uniform SST (28, 30, 32C)
Bulk microphysics
RRTM radiation model
Basically a radiative-convective model in which the
Clouds are explicitly resolved at 1km resolution.
Run to equilibrium and average last 50 days.
Zhiming Kuang’s work: Updated by Bryce Harrop
Recreating Kuang & Hartmann (2007) Results Using SAM with CAM
5˚C
Radiation
• Change the level of clear-sky convergence
• Two possibilities
– Remove water vapor to lower convergence level
– Add more water vapor to raise convergence level
• SAM model: Two different water vapor variables
– Bulk microphysics
– Radiation
Altering Water Vapor in the Radiation Code Part I
Water vapor change only applied to radiation calculation!!
Temperature
Base Case
Removal Case
Reduces emissivity =
Less cooling = ?
qv,
stratospheric
Water Vapor (radiation only)
Removal of Water Vapor Comparison
Base
Removal
Altering Water Vapor in the Radiation Code Part II
Water vapor change only applied to radiation calculation!!
Temperature
Base Case
Removal Case
Addition Case
qv,
Water
Vapor (radiation only)
stratospheric
Addition of Water Vapor Comparison
Addition
Base
Radiative Control
In radiative-convective equilibrium in a CRM
• If you change SST, cloud temperature remains
about the same - FAT
• If you change the emissivity of the upper
troposphere in the Tropics, you can change the
cloud temperature and associated circulation.
LW feedbacks positive and comparable magnitude.
SW feedbacks positive/negative, and dominate total feedback.
SW and LW cloud
feedback
Net cloud feedback
from 1%/ yr CMIP3/AR4
simulations
Courtesy of B. Soden
Motivation: Why is the Longwave Cloud
Feedback Robustly Positive in the AR4
GCMs?
• We hypothesize that it is largely due to the fact
that tropical high clouds remain at approximately
the same temperature as the climate warms
• The clouds become higher as the surface
warms, but do so in such a way as to remain at
approximately the same temperature
• If high cloud emission temperature stays
constant (or warms less than the surface), then
this would lead to a positive cloud feedback,
assuming no change in cloud fraction.
Predicting level of abundant high
cloudiness from clear-sky balance
• Input to Fu-Liou code: tropical-mean profiles of
temperature and humidity averaged over decades
 calculate net (LW+SW) radiative cooling
profiles
• Assume that this radiative cooling is balanced by
diabatic subsidence  take vertical derivative to
get clear-sky UT convergence  assume from
mass continuity that this is balanced by convective
detrainment  should see clouds there
Mark Zelinka’s Work
Dashed: Clouds, Solid: Convergence
SRES A2 Ensemble-Mean
Radiative cooling
2000-2010
2070-2080
2090-2100
Static stability
(T/θ)dθ/dp
2000-2010
2070-2080
2090-2100
Diabatic ω
2000-2010
2070-2080
2090-2100
Diabatic convergence
2000-2010
2070-2080
2090-2100
SRES A2 Ensemble-Mean
CTT
warms
~1 K
Sfc Warms ~3 K
Upper
Troposp
here
warms
~6 K
Attempting to Quantify Contribution
of FAT to Longwave Cloud Feedback
First calculate ΔLWCF, then use radiative kernel
technique to estimate LW Cloud Feedback
Very difficult because cloud properties
are not saved and so cannot calculate radiative
effect of clouds
Compare ΔLWCF for ‘FAT’ and ‘FAP’
• FAT ΔLWCFtropics = Δfhi(OLRclr– OLRhicld) –
fhiΔOLRhicld
– floΔOLRlocld + fΔOLRclr
• FAP ΔLWCFtropics = Δfhi(OLRclr–OLRhicld) –
fhiΔOLRhicld
– floΔOLRlocld + fΔOLRclr
assuming that OLRhi = σCTT4 in which the CTT increases as much as
the temperature at a fixed pressure level (the initial cloud-weighted
pressure)
• Finally, apply the cloud mask as explained in
Soden et al. 2008 to convert ΔLWCF to LW cloud
feedback
Actual
ENSEMBLE MEAN
LW
CLOUD
FEEDBACK
Actual
FAP
FAT
FAP minus Actual
FAT minus Actual
Conclusion.
• Radiative Convective Equilibrium, constrained by
Clausius Clapeyron and basic radiation physics, seems to
be a strong constraint on the depth of the convective
layer in the Tropics.
• One result of this is that the detrainment layer in the
Tropics tends to have a nearly fixed temperature as the
climate changes, or a nearly fixed anvil cloud temperature.
• Another result of this is that climate models tend to give a
relatively strong positive cloud longwave feedback.
• Also, the Hadley Cell will deepen in pressure thickness
with global warming.
High Cloud-weighted P
Red: 1:1 line, with
nonzero yintercept
Each x is a decadal
mean
UT Convergence-weighted P
High Cloud-weighted T
Red: 1:1 line, with
nonzero yintercept
Each x is a decadal
mean
UT Convergence-weighted T
Radiative cooling
2000-2010
2070-2080
2090-2100
Static stability
(T/θ)dθ/dp
2000-2010
2070-2080
2090-2100
Diabatic ω
2000-2010
2070-2080
2090-2100
Diabatic convergence
2000-2010
2070-2080
2090-2100
Attempting to Quantify Contribution
of FAT to Longwave Cloud Feedback
First calculate ΔLWCF, then use radiative kernel
technique to estimate LW Cloud Feedback
Very difficult because cloud properties
are not saved and so cannot calculate radiative
effect of clouds
Decomposing the change in LWCF for
cloud fraction (f)
and cloud properties
• If OLR = f OLRcld + (1-f)OLRclr then
LWCF = OLRclr – OLR = f (OLRclr – OLRcld)
• ΔLWCF = Δf (OLRclr–OLRcld) + f ΔOLRclr
– fΔOLRcld
Actual
HAD CM3
ΔLWCF
Sum
Δf(OLRclr – OLRcld)
– fΔOLRcld
fΔOLRclr
Sum minus actual
Decomposing the change in
LWCF
• LWCF = OLRclr – OLR = f(OLRclr – OLRcld)
• ΔLWCF = Δf(OLRclr–OLRcld) + fΔOLRclr– fΔOLRcld
• This term dominates, but not because of warming
or cooling high clouds, but apparently because of
different abundances of high vs. low clouds (see
next slide)
HAD CM3
ΔLWCF<<0 due to ΔOLRcld>>0
Dashed: 2000-2010
Solid: 2090-2100
HAD CM3
ΔLWCF>>0 due to ΔOLRcld<<0
Another ΔLWCF decomposition
• Let’s assume we can break OLRcld and f into
contributions from high and low clouds.
• We do this separation only in the Tropics
• Rather than trying to pretend like we know the
effective high and low cloud fractions, lets
assume that the high cloud-weighted
temperature is a reasonable estimate of the
high cloud emission temperature and that the
low cloud emission is the same as clear-sky
emission.
• Then we can determine what fhi and flo must
be such that fOLRcld = fhiOLRhicld + floOLRlocld
• [1] LWCF = OLRclr - OLR = f(OLRclr – OLRcld)
• [2] ΔLWCF = Δf(OLRclr – OLRcld) + fΔOLRclr – fΔOLRcld
• If we assume that f and OLRcld can be broken into a component
from high and from low clouds:
• [3] fOLRcld = fhiOLRhicld + floOLRlocld, where flo is the fraction of area
covered by low clouds that are not covered by high clouds
• Using a cloud-weighted temperature for clouds that are between
the freezing level and the tropopause as CTT, we write
[4] OLRhicld = σCTT4
• Using f = fhi + flo, we can solve [3] for fhi:
• [5]
OLR

OLR
cld
locld
f

f
hi
OLR

OLR
hicld
locld
where OLRcld is given by [1], OLRhicld is given by [4], and we
assume OLRlocld = OLRclr
• [6] ΔLWCF = Δfhi(OLRclr– OLRhicld) – fhiΔOLRhicld – floΔOLRlocld
+ f ΔOLRclr
So the formulas are….
• ΔLWCFtropics = Δfhi(OLRclr– OLRhicld) –
fhiΔOLRhicld – floΔOLRlocld + f ΔOLRclr
• ΔLWCFextra-tropics = Δf(OLRclr–OLRcld) –
fΔOLRcld + fΔOLRclr
Predictions from Clear-Sky
Radiative Cooling
• In the Tropics we should see two or three
levels of cloud.
– Boundary layer cloud - from strong radiative
cooling of moist, warm low level air - H2O
continuum
– High cloud from strong cooling under
tropopause by rotation bands of H2O
– Middle cloud from 6.7 micron V/R band
MODIS Temperature-Optical Depth Histogram
Eastern Equatorial Pacific Ocean
Three Levels of Cloud
Tropopause
High
High-Anvil and
Cirrus Clouds
Middle
Low
MiddleCongestus
Low Cumulus+
Stratocumulus
Optical Depth
Kubar et al. 2007
Fundamental energy balance in atmosphere is:
Convective heating = Radiative Cooling
Question is, Which places a more fundamental
Constraint on the climate system in the tropics?
Answer: In the deep tropics radiative cooling,
particularly in clear skies, may provide a more
fundamental prediction of the depth of the
convective layer.
First Law of Thermodynamics
 T
T
T 
J
 t  u x  v y   S p  c
p
In Tropics ~
J

Spcp
Using continuity in pressure coordinates

  J 
gV 

p p  S p c p 
Fact: The radiatively-driven
divergence in the clear regions
is related to the decrease of
water vapor with temperature
following the Clausius-Clapeyron
relation and the consequent
low emissivity of water vapor
at those low temperatures.
Fact: 200 hPa
Convective outflow and
associated large-scale
divergence near 200 hPa
are both associated with
radiatively-driven divergence
in clear skies.
Hypothesis:
The temperature at which the
radiatively-driven divergence
occurs will always remain the same,
and so will the temperature of
the cloud anvil tops.
t>1
9%
22%
10%
Use Cloudsat to detect cloud tops
and AMSR to estimate precipitation rate
Heavy rain is 90th Percentile, 10% of frequency,
but ~50% of total rainfall.
West Pacific
East Pacific
Kubar & Hartmann 2008
Use Cloudsat to detect cloud tops
and AMSR to estimate precipitation rate
Heavy rain is 90th Percentile, 10% of frequency,
but ~50% of total rainfall.
Kubar & Hartmann 2008
Why should convection
stop/detrain at a fixed temperature?
Vapor pressure depends only on temperature,
and decreases exponentially as T decreases with altitude.
Emissivity (radiative relaxation time)
depends most importantly on vapor pressure.
Temperature where water vapor emissivity becomes small
is only weakly dependent on relative humidity and pressure.
Heating of air by condensation also becomes small
at this temperature
Testing the FAT Hypothesis in a model.
Larson and Hartmann (2002a,b) Model Study:
MM5 in doubly periodic domain
a) 16x16 box with uniform SST (297, 299, 301, 303K)
b) 16x160 box with sinusoidal SST
c) 16x16 box with uniform SST and rotation.
Clouds and circulation are predicted
Clouds interact with radiation
Basically a radiative-convective model with
parameterized convection, in which the large-scale
circulation is allowed to play a role by dividing
the domain into cloudy (rising) and clear (sinking) regions.
Radiative Cooling
in non-convective
region for SST’s
ranging from
297K to 303K.
From Larson &
Hartmann (2002a).
The temperature of the
200 hPa surface increases
about 13K, while the
surface temperature rises
6K.
The temperature at which
the radiative cooling reaches
-0.5 K/day remains constant
at about 212K.
The temperature at which the
visible optical depth of upper
cloud reaches 0.1 remains
constant at about 200K.
Cloud Fraction versus Air Temperature
6˚C
CRM in Rad/Conv. Equilibrium
28˚C, 30˚C and 32˚C SST
Kuang &Hartmann, J. Climate 2007
Cloud Fraction
vs Air Temp.
vs Pressure
Stratospheric Transport and Exchange
1. Apply upward motion of Brewer-Dobson Circulation.
28, 30, 32C
30C +BDC
Test Impact of Radiation
Same SAM framework as Kuang & Hartmann
• Alter the water vapor that the radiation
code sees to change the emissivity of the
upper tropical troposphere.
• Expect that increasing upper tropospheric
radiative cooling vapor will cool the
average cloud tops, and vice versa.
Bryce Harrop’s work
Divergence Calculations
Subsidence warming = radiative cooling
Comparing CAM and RRTM Radiation Codes
Comparing CAM and RRTM Radiation Codes Continued
Convergence computed
from clear-sky radiative
cooling, and
Cloud fraction from MODIS
plotted versus
air temperature (solid)
for
West Pacific (WP)
and East Pacific (EP)
Good agreement between
clear-sky divergence and
cloud fraction.
Kubar et al. 2007
217K
221K
et al (2007)
MODIS Anvil Top vs Convergence Kubar
Temperature
Kubar et al. 2007
Radiation Code Adjustments: Comparing Weighted Temperatures
this study
Moist Thermodynamics
• Double the Latent Heat of Fusion
• Two Possibilities:
– Lift the parcel
– Warm the parcel
Doubling Latent Heat of Fusion Comparison
Radiation and Lf Adjustments: Comparing Tconv and Tcld
2x Lf
2x Lf
Conclusions
• When the radiation is changed, the cloud
profile adjusts so that the cloud amount
peaks near the level of clear-sky
convergence.
• A relationship exists between convergence
weighted and cloud weighted
temperatures.
AR4 Climate Simulations
Robust Longwave Cloud Feedback
• All AR4 models produce a similar positive
longwave cloud feedback, compared to
the large variability in shortwave cloud
forcing.
• Can basic constraints like saturation vapor
pressure and radiative cooling explain this
consistency in the models?
Mark Zelinka’s work
Is any of this believable?
• It is likely that some portion of Δfhi is actually including information
about changes in the emission temperature of high clouds as well.
• Because we enforce
OLR

OLR
cld
locld
f

f
hi
OLR

OLR
hicld
locld
any error in our estimate of OLRhicld or OLRlocld will be subsumed into
fhi (and by extension flo)
• This could result in the ΔLWCF term due to Δfhi being overestimated
and the ΔLWCF term due to ΔOLRhicld being underestimated
• Would like a good method of assessing sensitivity to our
assumptions
– fhiΔOLRhicld
Δfhi(OLRclr – OLRhicld)
f ΔOLRclr
ENSEMBLE MEAN – floΔOLRlocld
ΔLWCF
Δfextra-tropical(OLRclr – OLRcld)
– fextra-tropicalΔOLRcld
Tropical-Mean Results
• Varying degrees of agreement between UT convergence and level
of high cloud abundance in models
• In all models, both convergence- and cloud-weighted pressure
(temperature) decrease (VERY slightly increase) in a nearly 1:1
fashion, but with a nonzero y-intercept (see previous point)
• Tropical mean UT convergence and high cloud amount decrease
slightly over the course of the 21st century (enhanced static stability
out-pacing enhanced radiative cooling – see previous slide)
– Can this explain Trenberth and Fasullo’s results about decreases in
cloudiness allowing for more absorbed shortwave (next slide)?
– Also, if high cloud coverage strongly impacts absorbed shortwave, and
static stability vs. radiative cooling determines high cloud coverage, then
this implies some dependency of SW cloud feedback on lapse rate
feedback (at least in the tropics)
• Those models with larger (negative) lapse rate feedback should tend to have
larger positive (or less negative) SW cloud feedback due to this effect
because strong increases in static stability will cause strong decreases in
high cloud cover (I guess it depends on the importance of high clouds
changes for SW cloud feedback)
Issues (1 of 2)
•
•
Clouds plotted in previous figures are total cloud fraction in each pressure bin
reported by the model: There is no information about cloud optical properties,
nor does it provide information about cloud tops, which are emitting to space.
Can look at ISCCP simulator output from models that have participated in
CFMIP, but
– there are no CO2 scenarios, just 2XCO2 runs with slab oceans
– The ISCCP pressure bin resolution is inadequately poor (7 vertical bins)
•
•
•
Have done simulations with the GFDL model in aquaplanet mode at .5x, 1x,
2x, and 4x CO2 using the ISCCP simulator – results look similar to here, but
with more dramatic warming of high clouds / UT convergence and more
dramatic decrease in high cloud coverage  more dramatic because of factor
of 8 variation in CO2?
To what degree should model clouds be collocated with the UT diabatic
convergence? Probably depends on details of each model’s convective
parameterization (detrainment based on neutral buoyancy?). Very thin stuff
near tropopause probably unrelated to detrainment – but we don’t know how
much of that type of cloud is represented in these profiles
Still need to show that the prevailing thought that detrainment occurs once the
parcels reach neutral buoyancy is either incorrect or is consistent with this – if
Issues (2 of 2)
•
•
•
Probably should be running the Fu-Liou code for each lat, lon, & season
rather than just for tropical-mean annual-mean profiles  currently working
with Marc to run Fu Liou code more efficiently than I have been (currently
have a Matlab script that calls the fortran code)
Very difficult to be quantitative: can only say that – in all the models – the
entire cloud profile rises vertically but as a function of temperature the cloud
profile stays nearly constant (warms slightly). (How realistic is the cloudweighted temperature as a proxy for CTT?)
Two issues with using tropical-mean temperature and humidity profiles as
input to the Fu-Liou code
– 1. mean profiles calculated from clear-only regions will likely be different
(certainly drier) than those calculated from both clear and cloudy  this will
affect the shape and magnitude of UT convergence (need to assess sensitivity)
– 2. The presence of clouds alters the radiative cooling rates substantially. This is
much more difficult to take into account in the radiation code, since one needs to
know more about the cloud properties than is provided in the AR4 diagnostics. It
is not clear to me to what extent real-world UT detrainment is affected by clouds
in the surrounding regions altering the radiative cooling rate.
Rainrates from two different algorithms.
TOP: Satellite-derived method, based on cloud top temperature;
BOTTOM: Derived from Microwave Sounding Unit, (Figure from Berg et al. 2002)
Modeling Tropical Convection
in a Box or on a Line.
For this study we will use the SAM model from CSU.
Khairoutdinov and Randall (2003)
The first set of experiments will be from a 3D doublyperiodic model run with fixed forcing in a dx=1km
256km x 256km domain with 64 levels, also use 2D version.
The dynamics are anelastic, the radiation is that of the
NCAR CCM.
The cloud physics scheme has conservation equations for
total water and precipitating water,
apportionment among types is based on temperature.
Note that 3D model has 5m/s shear imposed.
3-D Model
External forcing from Reanalysis for EP and WP,
but use same SST of 302.49K
Top Heavy
Bottom-Heavy
converting cloud in to precip.
Model Movie
Use 2-D to Test Sensitivity
• Use same SAM model in 2-D version
• Apply SST sinusoid to force circulation.
• No external forcing other than SST and Radiation
MODIS Temperature-Optical Depth Histogram
Eastern Equatorial Pacific Ocean
Three Levels of Cloud
Tropopause
Thin
Anvil
Thick
High-Anvil and
Cirrus Clouds
Middle
Low
MiddleCongestus
Low Clumulus+
Stratocumulus
Optical Depth
Kubar et al. 2007
We will see that,
with same microphysics
2-D model has similar strengths and
weaknesses as 3-D Model
• Decent thick and thin cloud distributions, but
• Anvil clouds (intermediate optical depths)
are too few and do not have correct
dependence on precipitation rate.
Validation Methodology
• Average over comparable subdomains - about
100km square sub-domains to define local precip
and cloud properties.
• Use precipitation rate as an independent variable.
• Tests relationship of cloud stuff to precipitation
rate
• Works equally well for column model, regional
model, global model and data.
• Don’t have to adjust anything about methodology
in going from 3D to 2D
Test 3-D & 2-D run against Satellite Data
a la Kubar et al. 2007
2-D Base
MODIS
AMSR
Thick
Cloud
- about
right
Lopez et al. 2007
Test 3-D run against Satellite Data a la
Kubar et al. 2007
Anvil
Cloud
Error
2-D case
Lopez et al. 2007
Test 3-D run against Satellite Data
Albedo & OLR PDFs - Domain Mean
Anvil Cloud
Signature
Anvil Cloud is Missing
Lopez et al. 2007
Use 2-D to Test Sensitivity
Summary:
• We can increase ice cloud by reducing ice
sedimentation, but this also increases thick
cloud unrealistically.
• Increasing the Autoconversion/Accretion
rate reduces the thick cloud preferentially.
• AA rate preferentially controls water cloud,
which is responsible for thick cloud fraction.
• Accretion is more important than
autoconversion.
Adjustments suggested by 2-D
Sensitivity Tests
• We need to increase ice and decrease water
to get the right albedo distribution of cold
cloud.
• This means decreasing ice sedimentation,
while increasing accretion of cloud water.
Use 2-D to Test Sensitivity
Multiple Changes
• NOSED - set ice sedimation velocity to zero,
but lower threshold for autoconversion of ice
by a factor of 100.
• AALIQN - increase liquid water accretion
rate by factor of N.
• NOSEDAALIQ5 - NOSED, plus increase
liquid water accretion by factor of 5.
Multiple Changes to Cloud Physics
2D Results
2-D Base
Thick
Cloud
Lopez et al. 2007
Multiple Changes to Cloud Physics - 2D
Anvil
Cloud
2-D Base
Lopez et al. 2007
Use 2-D to Test Sensitivity
Multiple Changes
• We found a set of cloud physics parameters
that produces better anvil cloud amounts and
maintains the observed amount of thick
cloud as a function of rain rate NOSEDAALIQ5.
• Let’s put these back in the 3-D West Pacific
run and see what happens.
Improvement!
• Cloud Forcing looks more reasonable.
But most
is thin
cloud,
and out
highofcoverage
Cloud
fraction
climbs
sight! of
thin cloud may not be unreasonable
for the conditions of the simulation.
Lopez et al. 2007
Conclusions
• Satellite data can be used to effectively test
CRM cloud simulations, and GCM’s too.
• It is very effective to do the test as a
function of rain rate.
• Something approaching the observed
behavior of convective cores, anvil clouds
and thin clouds can be achieved with
judicious tuning of a simple bulk scheme.
• Work continues. . .
Multiple Changes to Cloud Physics - 2D
Thin
Cloud
2-D Base
Lopez et al. 2007
Test 2-D run against Satellite Data a la
Kubar et al. 2007
Thin
Cloud Not bad
2-D case
3D WP
3D EP
Lopez et al. 2007
Model Clouds too Cylindrical
General Approach
• Focus on Pacific ITCZ regions
• Observe and model same regions. EP &WP
• Average over comparable subdomains about 100km square subdomains to define
local precip and cloud properties.
• Use precipitation rate as an independent
variable.
Testing the Relative Roles of Radiation
and Latent Heating in Determining the
Temperature of Tropical Cloud Tops
Bryce Harrop
Clouds and Radiation in the
Tropics
• Greatest uncertainty in clouds
• Changing cloud forcing can drive changes in
worldwide circulations
LWCF = Fclear - Ftotal
Strong
COLD
LWCF
Weak
LWCF WAR
M
SWCF = Stotal - Sclear
Strong
SWCF
Weak
SWCF
Hartmann et al (2001)
Hartmann et al (2001)
Temperature Dependent
Clear-sky
Convergence
at 200hPa
Anvil
Detrainment
at 200hPa
Fixed Anvil
Temperature
Hartmann & Larson
(2002)
PULL vs PUSH
• PULL Mechanism
– Clear-sky convergence level determines height of
clouds
• PUSH Mechanism
– Buoyancy determines height of clouds
Hartmann & Larson (2002)
Kuang & Hartmann (2007)
Xu et al (2007)
>300km
L G
A E
R
M U 150-300km
E M
D
I
S
M
A
L
100-150km
Future Work
• How do the moist thermodynamics influence
the cloud level?
• Is there a relationship between Tconv and Tcld
when we change the moist thermodynamics?
• Can we modify the moist thermodynamics in
such a way that the cloud will reach a different
level than the clear-sky convergence?
Acknowledgements
• Dennis Hartmann
• Peter Blossey
• Grads 08
Questions?
Greenhouse Effect
Sensitivity of OLR to Water Vapor
50 m 20 m
10 m
Harries, QJ, 1996
5 m
Atmospheric Radiative Cooling
Altitude vs Frequency
Upper
Troposphere
Cooling
from Water
Rotation Lines
50 m 20 m
106.7
mmicron
5 m
Lower
Troposphere
Cooling
from Water
Continuum
Harries, QJRMS, 1996
MODIS Temperature-Optical Depth Histogram
Eastern Equatorial Pacific Ocean
Three Levels of Cloud
Tropopause
High
High-Anvil and
Cirrus Clouds
Middle
MiddleCongestus
Low Cumulus+
Stratocumulus
Low
Optical Depth
Kubar et al. 2007
Rotational Lines
of Water Vapor
and UpperTropospheric
Cooling
Total Beyond 18.5m -->
Cooling efficiency
of atmosphere
declines in upper
troposphere because
of lack of water
vapor to emit and
absorb radiation.
Recreating Kuang & Hartmann (2007) Results Using SAM with CAM
Kuan-Man Xu, Personal Communication
Dr. Kuan-Man Xu
NASA Langley Research Center
Analysis of New Data
March 2003-November 2004: Aqua.
• MODIS optical depth and cloud top temperature
- 5km data
• AMSR rain rates and column vapor.
• Collocated three-day, 1-degree, averages
• MLS upper tropospheric humidity: Aura
• GPS upper tropospheric temperature profiles
Regions of Interest
Fig 1. Ensemble SSTs for March 2003-November 2004
West Pacific
Central Pacific
East Pacific
▪Latitude band of 5ºN-15ºN allows focus to be almost exclusively
on convective areas, even in the East Pacific
Cloud as a function of rainrate
R
A
I
N
R
A
T
E
WEST
CENTRAL
EAST
Temp-Opt Depth histograms as a function of rainrate (MODIS/AMSR).
Rain Rate/Anvil Cloud Area
More high cloud per unit of rain in the warmer West Pacific.
-3C
+20%
Modifying the
Fixed Anvil Temperature Hypothesis
• Cloud Top Temp. 218-216K ( -3C)
as SST 27C-30C ( +3C)
• Cloud Fraction from 20-40%
• Relative humidity ~ high cloud fraction
 es~ -20% for T~ -3C (es =Saturation Vapor Press.)
 RH ~ 20%, so
 e ~ (RH•es) ~ 0 at median anvil level.
• Can conclude anvil cloud occurs at fixed e, or fixed T if
RH constant.
Relative Humidity
Profiles
From MLS and AIRS
and reanalysis for
the Western Pacific (WP)
Central Pacific (CP)
and Eastern Pacific (EP)
MLS humidity above
316mb, Temps. from
GPS.
WP
EP with WP humidity
EP
Fixed Anvil Temperature Hypothesis
• Cloud Top Temp. 218-216K ( -3C)
as SST 27C-30C ( +3C)
• Cloud Fraction from 20-40%
• Relative humidity ~ high cloud fraction
 es~ -20% for T~ -3C (es =Saturation Vapor Press.)
 RH ~ 20%, so
 e ~ (RH•es) ~ 0 at median anvil level.
• Can conclude anvil cloud occurs at fixed e, or fixed T if
RH constant.
Conclusions:
The favored temperature for tropical anvil cloud tops should
remain approximately constant during climate changes of
reasonable magnitude. FAT Hypothesis.
The emission temperature of the rotational lines of water
vapor should also remain approximately constant during
climate change.
These assertions imply relatively strong water vapor and IR
cloud feedback, all else being equal.
Hartmann and Larson, GRL, 2002.
Some Remaining Questions:
How do tropical cloud albedos respond to climate change?
Will the area occupied by tropical convection change with
climate? If so, how?
How do tropical convective clouds interact with other
cloud types like marine boundary layer clouds?
What will happen at the tropical tropopause? Will it get
warmer or colder and what will this mean for climate?
Global models should be able to get FAT right, but do they?
Fin
Greenhouse Effect
BB Curve minus OLR
50 m 20 m
10 m
5 m
Harries, QJ, 1996