MLS Cloud Forcing: IWC validation & Cloud Feedback Determination

1. MLS Cloud Forcing:IWC validation & Cloud Feedback Determination MLS Science Team Teleconference: June 8, 2006 Dan Feldman Jonathan Jiang Hui Su Yuk Yung

2. 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

3. Cloud Properties AtmosphericCirculation Radiative & Latent heating 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

4. 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)

5. Cloud Optical Property Modeling Hu & Stamnes, 1993 • CWP, De are relevant input parameters for β(λ), g(λ) Liquid Cloud Parameters at several wavelengths Fu, 1996 Ice Cloud Parameters at several wavelengths

6. PDF of Cloud Fraction States Clear-Sky Flux Mapping from Band to Total Flux Cloud Fraction 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

7. Temporal & Spatial Averaging Temporal Averaging 1-day 3-day 6-day • MLS IWC CF comparison CERES data (and ground truthing) requires appropriate temporal, spatial scales • Many RT calculations OR • Cloud forcing bias estimates Spatial Averaging 3x104 km2 1x106 km2 2x103 km2 • This analysis can be extended using MODIS data sets • How to address multi-level cloud fraction problem? 5x106 km2 2x106 km2 9x106 km2 Hughes et al, 1983

8. 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

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

10. CERES 01/2005

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

12. CERES 07/2005

13. Comparison with ECMWF calculations

14. 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 BBSS SKYRAD MMCR MPL Images from www.arm.gov

15. ARM data intercomparison • Measured LW, SW flux, expected clear-sky flux … cloud forcing • CERES surface forcing products (scatterplot) • MLS measurements

16. 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

17. Future Work GEBA network stations • 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)

18. Acknowledgements • Frank Li • Duane Waliser • Baijun Tian • Yuk Yung’s IR Group

19. References • Fu, Q. and K. N. Liou (1992). "On the Correlated K-Distribution Method for Radiative-Transfer in Nonhomogeneous Atmospheres." Journal of the Atmospheric Sciences49(22): 2139-2156. • Fu, Q. A. (1996). "An accurate parameterization of the solar radiative properties of cirrus clouds for climate models." Journal of Climate9(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 Climate6(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 Society64(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 Meteorology42(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-Atmospheres102(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-Atmospheres108(D13).