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MLS Cloud Forcing: IWC validation & Cloud Feedback Determination

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

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MLS Cloud Forcing: IWC validation & Cloud Feedback Determination

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  1. MLS Cloud Forcing:IWC validation & Cloud Feedback Determination MLS Science Team Teleconference: June 15, 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 • Introduction

  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 • Introduction

  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 calculations • Cloud Water Path • Particle Diameter • Cloud Fraction • T(z), H2O(z), O3(z) • Appropriate Spatio-Temporal Averaging • RT Calculations

  5. 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 From http://lposun.larc.nasa.gov • CERES Data

  6. MLS Standard (IWC, T, H2O,O3) + AIRS L3: 01/2005 vs. CERES • CERES Intercomparison

  7. MLS Standard (IWC, T, H2O,O3) + AIRS L3: 07/2005 vs. CERES • CERES Intercomparison

  8. LW Comparison with ECMWF calculations • ECMWF Intercomparison

  9. Validation Data: ARM Sites BBSS SKYRAD • 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 MMCR MPL Images from www.arm.gov • ARM Site Data

  10. CERES-ARM intercomparison: 09/2004 – 12/2004 from http://www-cave.larc.nasa.gov/ • ARM Data Intercomparison

  11. MLS-ARM intercomparison: 09/2004 – 12/2004 • ARM Data Intercomparison

  12. 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 • Conclusions

  13. 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 • 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) • Future Work

  14. Acknowledgements • The following individuals/groups have been invaluable for this work: • Frank Li • Duane Waliser • Baijun Tian • Yuk Yung’s IR Group • Acknowledgements

  15. References • Radiative Transfer 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. • 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). • 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. • Cloud Forcing • 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 Letters33(5). • AIRS L3 Data: • AIRS V4 Data Release Description: (http://disc.gsfc.nasa.gov/AIRS/documentation/v4_docs/V4_Data_Release_UG.pdf) • CERES Data: • 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 Meteorology42(12): 1748-1769. • ARM Data: • CERES/ARM Validation Experiment: (http://www-cave.larc.nasa.gov/cave/cave2.0/Pubs.html) • CloudSat • 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 Society83(12): 1771-1790. • L’Ecuyer, T.S. CLOUDSAT L2 ATBD (2004) • BSRN • 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

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