MLS Cloud Forcing:IWC validation & Cloud Feedback

DeterminationMLS Science Team Teleconference:

June 8, 2006Dan Feldman

Jonathan JiangHui 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 resultsLe 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

AtmosphericCirculation

Radiative &Latent heating

Calculation of Cloud Forcing

• Correlated-K RT commonly used in GCMs, reanalyses

• RRTM_LW :– 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• RRTM_SW :

– Fluxes: ±1.0 W/m2 direct, ±2.0 W/m2 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), H2O(z), O3(z) TOA

SFCTOASFC

TOASFC CLRFTOTFCF __

Cloud Optical Property Modeling• CWP, De are relevant input parameters for β(λ), g(λ)

Hu & Stamnes, 1993

Fu, 1996

LiquidCloudParametersat severalwavelengths

IceCloudParametersat severalwavelengths

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

ddxdyyxFSFFR

DICA ,,1

ddssFspSAdFSAF DcCLRDc

ICA ,1 11

Cloud FractionPDF of Cloud Fraction States

Clear-Sky FluxMapping 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

2x103 km2

3x104 km2 1x106 km2

5x106 km2

9x106 km2

2x106 km2

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/m2

– SSF accuracy: ±1 W/m2

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), H2O(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/m2 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).