Transcript MLS Cloud Forcing

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 CO2
for several GCM results
Le Treut and McAveney, 2000 &
IPCC TAR, 2001
Cloud feedback & surface temperature
Su et al, 2005
• 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
After Stephens et al, 2002
Cloud
Properties
Atmospheric
Circulation
Radiative &
Latent heating
Calculation of Cloud Forcing
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•
Correlated-K RT commonly used in
GCMs, reanalyses
RRTM_LW :
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RRTM_SW :
–
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Fluxes: ±1.0 W/m2 direct, ±2.0 W/m2
diffuse
DISORT: (4-stream w/δ-M scaling)
Liquid, ice clouds + aerosols
Fu-Liou:
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Fluxes: ±0.1 W/m2 relative to LBLRTM
Cooling Rates: ±0.1 K/day in
troposphere, ±0.3 K/day in stratosphere
Liquid, ice water clouds
Longwave flux + correlated-k flux
Shortwave flux
Parameters relevant to Cloud Forcing
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Cloud Water Path
Particle Diameter
Cloud Fraction
T(z), H2O(z), O3(z)
TOA
TOA
TOA
CFSFC
 F _ TOTSFC
 F _ CLRSFC
Cloud Optical Property Modeling
• CWP, De are relevant input parameters for β(λ), g(λ)
Hu & Stamnes, 1993
Liquid
Cloud
Parameters
at several
wavelengths
Fu, 1996
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
F  F
ICA


  S   F1D x, y,  dxdyd
R

• 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
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
F ICA  1  Ac  S  F1CLR
D  d  Ac  S    p s F1D s,  ds d
Cloud Fraction
Mapping from
Band to Total Flux
Clear-Sky Flux
PDF of Cloud
Fraction States
Temporal & Spatial Averaging
Temporal Averaging
• MLS IWC CF comparison
CERES data (and ground
truthing) requires appropriate
temporal, spatial scales
1-day
3-day
6-day
– Many RT calculations OR
– Cloud forcing bias estimates
Spatial Averaging
3x104 km2
1x106 km2
2x103 km2
5x106 km2
9x106 km2
Hughes et al, 1983
2x106 km2
• This analysis can be extended
using MODIS data sets
• How to address multi-level
cloud fraction problem?
Validation Data: AQUA CERES
From http://aqua.nasa.gov
• 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/m2
– SSF accuracy: ±1 W/m2
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
SKYRAD
BBSS
– Balloon-borne Sounding
System
•
• Sonde profiles for
clear-sky TOA, surface flux
• T(z), H2O(z)
State-of-the-art instrument
calibration so cloud forcing
calculations can be validated
MPL
MMCR
Images from www.arm.gov
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
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Ground-based validation:
Baseline Surface Radiation Network
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Direct/diffuse SW downward
LW downward
Radiosonde data
Cloud base height determination
CLOUDSAT
– Operational product specs: resolve
TOA, SRF flux to 10 W/m2
instantaneously
Cloudsat’s first radar profile:
5/20/06 N. Atlantic squall line
(from http://cloudsat.atmos.colostate.edu)
GEBA network stations
Acknowledgements
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Frank Li
Duane Waliser
Baijun Tian
Yuk Yung’s IR Group
References
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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 ResearchAtmospheres 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).