MLS Cloud Forcing: IWC validation & Cloud Feedback Determination

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MLS Cloud Forcing:

IWC validation & Cloud
Feedback Determination

MLS Science Team Teleconference:

June 8, 2006

Dan Feldman

Jonathan Jiang

Hui Su

Yuk Yung

Cloud Forcing Intro


Clouds are a prominent
radiative feedback mechanism
with substantial impact on SW
and LW radiative budget


SW, LW impact nearly
balanced currently


Surface, TOA forcing depends
on vertical cloud structure


Motivation to understand
relative roles of liquid and ice
clouds under:


Current conditions


Climate change scenarios

Change in TOA CRF from 2 x CO
2

for several GCM results

Le Treut and McAveney, 2000 &

IPCC TAR, 2001

Cloud feedback & surface temperature


Cloud forcing and cloud
feedbacks operate on many
scales


On regional scales, feedback
mechanisms may regulate
SSTs


Thermostat hypothesis testing


“The correct simulation of the
mean distribution of cloud
cover and radiative fluxes is
therefore a necessary but by
no means sufficient test of a
model’s ability to handle
realistically the cloud feedback
processes relevant for climate
change.”

IPCC TAR

Su et al, 2005

After Stephens et al, 2002

Cloud

Properties

Atmospheric

Circulation

Radiative &

Latent heating

Calculation of Cloud Forcing


Correlated
-
K RT commonly used in
GCMs, reanalyses


RRTM_LW :


Fluxes:
±
0.1 W/m
2

relative to LBLRTM


Cooling Rates:
±
0.1 K/day in
troposphere,
±
0.3 K/day in stratosphere


Liquid, ice water clouds


RRTM_SW :


Fluxes:
±
1.0 W/m
2

direct,
±
2
.0 W/m
2

diffuse


DISORT: (4
-
stream w/
δ
-
M scaling)


Liquid, ice clouds + aerosols


Fu
-
Liou:


Longwave flux + correlated
-
k flux


Shortwave flux


Parameters relevant to Cloud Forcing


Cloud Water Path


Particle Diameter


Cloud Fraction


T(z), H
2
O(z), O
3
(z)

Cloud Optical Property Modeling


CWP, D
e

are relevant input parameters for
β
(
λ
), g
(
λ
)

Hu & Stamnes, 1993

Fu, 1996

Liquid

Cloud

Parameters

at several

wavelengths

Ice

Cloud

Parameters

at several

wavelengths

Shortwave Radiative Forcing

for Non
-
Unity Cloud Fraction


Accurate RTM calculations with overlapping clouds non
-
trivial &
requires sub
-
grid
-
scale modeling





For large scale analyses of fluxes, 1
-
D RT at correlated
-
k intervals
(16 LW, 14 SW) are radiometrically sufficient


Monte
-
Carlo Independent Cloud Approximation (Pincus et al, 2003)


Computationally
-
efficient


Statistically unbiased


Cloud Fraction

PDF of Cloud

Fraction States

Clear
-
Sky Flux

Mapping from

Band to Total Flux

Temporal & Spatial Averaging


MLS IWC CF comparison
CERES data (and ground
truthing) requires appropriate
temporal, spatial scales


Many RT calculations OR


Cloud forcing bias estimates


Hughes et al, 1983

Spatial Averaging


This analysis can be extended
using MODIS data sets


How to address multi
-
level
cloud fraction problem?


Temporal Averaging

2x10
3

km
2

3x10
4

km
2

1x10
6

km
2

5x10
6

km
2

9x10
6

km
2

2x10
6

km
2

1
-
day

3
-
day

6
-
day

Validation Data: AQUA CERES


CERES measures OSR, OLR,
and cloud forcing aboard
TRMM, TERRA, and AQUA


Shortwave (0.3
-
5.0
µm)


Total (0.3
-
50.0 µm)


Window (8
-
12 µm)


ES4, ES9 products: monthly
gridded data at 2.5x2.5
resolution with ERBE heritage


FM3 + advanced angular
distribution models provide
fluxes


ERBE
-
like accuracy:
±
5 W/m
2


SSF accuracy:
±
1 W/m
2

From http://aqua.nasa.gov

From http://eobglossary.gsfc.nasa.gov

MLS Standard (IWC, T, H2O,O3) + AIRS L3:
01/2005

CERES 01/2005

MLS Standard (IWC, T, H2O,O3) + AIRS L3:
07/2005

CERES 07/2005

Comparison with ECMWF
calculations


Validation Data: ARM Sites


Heavily
-
instrumented sites at NSA
& TWP include


ARSCL data: active cloud
sounding


Micropulse Lidar


Millimeter
-
Wave Cloud Radar


SKYRAD:



Diffuse, Direct SW Irradiance


Downwelling LW Irradiance


Balloon
-
borne Sounding
System


Sonde profiles for

clear
-
sky TOA, surface flux


T(z), H
2
O(z)


State
-
of
-
the
-
art instrument
calibration so cloud forcing
calculations can be validated

Images from www.arm.gov

SKYRAD

MPL

MMCR

BBSS

ARM data intercomparison


Measured LW, SW flux,
expected clear
-
sky flux …
cloud forcing


CERES surface forcing
products (scatterplot)


MLS measurements

Conclusions


Cloud forcing is important to understand


Unbiased monthly estimates required


MLS scanning pattern can provide most inputs for
suffic


MLS IWC product tends to overestimate cloud
forcing as derived from CERES


ECMWF product TBD


ARM sites provide surface cloud forcing which
can be readily compared with CERES, MLS
surface forcing estimates


Future Work


Ground
-
based validation:

Baseline Surface Radiation Network


Direct/diffuse SW downward


LW downward


Radiosonde data


Cloud base height determination


CLOUDSAT


Operational product specs: resolve
TOA, SRF flux to 10 W/m
2

instantaneously

Cloudsat’s first radar profile:

5/20/06 N. Atlantic squall line

(from http://cloudsat.atmos.colostate.edu)

GEBA network stations

Acknowledgements


Frank Li


Duane Waliser


Baijun Tian


Yuk Yung’s IR Group

References


Fu, Q. and K. N. Liou (1992). "On the Correlated K
-
Distribution Method for Radiative
-
Transfer in
Nonhomogeneous Atmospheres."
Journal of the Atmospheric Sciences

49
(22): 2139
-
2156.


Fu, Q. A. (1996). "An accurate parameterization of the solar radiative properties of cirrus clouds for
climate models."
Journal of Climate

9
(9): 2058
-
2082.


Hu, Y. X. and K. Stamnes (1993). "An Accurate Parameterization of the Radiative Properties of
Water Clouds Suitable for Use in Climate Models."
Journal of Climate

6
(4): 728
-
742.


Hughes, N. A. and A. Henderson
-
sellers (1983). "The Effect of Spatial and Temporal Averaging on
Sampling Strategies for Cloud Amount Data."
Bulletin of the American Meteorological Society

64
(3):
250
-
257.


Le Treut, H. and B. McAvaney, 2000: Equilibrium climate change in response to a CO2 doubling: an
intercomparison of AGCM simulations coupled to slab oceans. Technical Report, Institut Pierre
Simon Laplace,
18
, 20 pp.


Loeb, N. G., K. Loukachine, et al. (2003). "Angular distribution models for top
-
of
-
atmosphere
radiative flux estimation from the Clouds and the Earth's Radiant Energy System instrument on the
Tropical Rainfall Measuring Mission satellite. Part II: Validation."
Journal of Applied Meteorology

42
(12): 1748
-
1769.


Mlawer, E. J., S. J. Taubman, et al. (1997). "Radiative transfer for inhomogeneous atmospheres:
RRTM, a validated correlated
-
k model for the longwave."
Journal of Geophysical Research
-
Atmospheres

102
(D14): 16663
-
16682.


Pincus, R., H. W. Barker, et al. (2003). "A fast, flexible, approximate technique for computing
radiative transfer in inhomogeneous cloud fields."
Journal of Geophysical Research
-
Atmospheres

108
(D13).