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 CO 2 for several GCM results Le Treut and McAveney, 2000 & IPCC TAR, 2001

3. 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

4. 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)

5. 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

6. 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

7. 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-day3-day6-day

8. 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

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), 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

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 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

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 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).