Transcript mls_telecon_final_06_15_06

MLS Cloud Forcing:
IWC validation & Cloud
Feedback Determination
MLS Science Team Teleconference:
June 15, 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
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Introduction
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
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Introduction
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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Longwave flux + correlated-k flux
Shortwave flux
Parameters relevant to Cloud Forcing
calculations
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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
Cloud Water Path
Particle Diameter
Cloud Fraction
T(z), H2O(z), O3(z)
Appropriate Spatio-Temporal Averaging
RT Calculations
TOA
TOA
TOA
CFSFC
 F _ TOTSFC
 F _ CLRSFC
Validation Data: CERES
From http://eosweb.larc.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
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CERES Data
From http://lposun.larc.nasa.gov
MLS Standard (IWC, T, H2O,O3) + AIRS L3: 01/2005 vs. CERES
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CERES Intercomparison
MLS Standard (IWC, T, H2O,O3) + AIRS L3: 07/2005 vs. CERES
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CERES Intercomparison
LW Comparison with ECMWF calculations
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ECMWF Intercomparison
Validation Data: ARM Sites
•
Heavily-instrumented sites at NSA
& TWP include
SKYRAD
BBSS
– ARSCL data: active cloud
sounding
• Micropulse Lidar
• Millimeter-Wave Cloud Radar
– SKYRAD:
• Diffuse, Direct SW Irradiance
• Downwelling LW Irradiance
– Balloon-borne Sounding
System
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MPL
MMCR
• 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
ARM Site Data
Images from www.arm.gov
CERES-ARM intercomparison: 09/2004 – 12/2004
from http://www-cave.larc.nasa.gov/
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ARM Data Intercomparison
MLS-ARM intercomparison: 09/2004 – 12/2004
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ARM Data Intercomparison
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 agrees in LW, SW agreement
with mc-ICA (Pincus et al., 2003) TBD
• ARM sites provide surface cloud forcing which
can be readily compared with CERES, MLS
surface forcing estimates
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Conclusions
Future Work
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GEBA network stations
Ground-based validation:
Baseline Surface Radiation Network
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Direct/diffuse SW downward
LW downward
Radiosonde data
Cloud base height determination
CLOUDSAT
– Radar activated 06/02/06
– Operational product specs: TOA,
SRF flux ±10 W/m2 instantaneously
from http://bsrn.ethz.ch
Cloudsat’s first radar profile:
5/20/06 N. Atlantic squall line
(from http://cloudsat.atmos.colostate.edu)
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Future Work
Acknowledgements
• The following individuals/groups have
been invaluable for this work:
– Frank Li
– Duane Waliser
– Baijun Tian
– Yuk Yung’s IR Group

Acknowledgements
References
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Radiative Transfer References
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Cloud Forcing
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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.
IPCC TAR, Chapter 7 (http://www.grida.no/climate/ipcc_tar/wg1/260.htm)
Su, H., W. G. Read, et al. (2006). "Enhanced positive water vapor feedback associated with tropical deep convection: New evidence from Aura MLS." Geophysical
Research Letters 33(5).
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AIRS L3 Data:
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CERES Data:
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AIRS V4 Data Release Description: (http://disc.gsfc.nasa.gov/AIRS/documentation/v4_docs/V4_Data_Release_UG.pdf)
Wielicki, B.A.; Barkstrom, B.R.; Harrison, E.F.; Lee, R.B.; Smith, G.L.; Cooper, J.E. 1996: Clouds and the earth’s radiant energy system (CERES): An earth
observing system experiment, Bulletin of the American Meteorological Society 77 (5): 853.
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.
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ARM Data:
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CloudSat
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CERES/ARM Validation Experiment: (http://www-cave.larc.nasa.gov/cave/cave2.0/Pubs.html)
Stephens, G. L., D. G. Vane, et al. (2002). "The cloudsat mission and the a-train - A new dimension of space-based observations of clouds and precipitation."
Bulletin of the American Meteorological Society 83(12): 1771-1790.
L’Ecuyer, T.S. CLOUDSAT L2 ATBD (2004)
BSRN
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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.
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).
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.
Heimo A., Vernez A. and Wasserfallen P. (1993) Baseline Surface Radiation Network (BSRN). Concept and Implementation of a BSRN Station. WMO/TD-No. 579,
WCRP/WMO.
http://bsrn.ethz.ch/
References