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Evaluating the MMF Using High Resolution Data

Evaluating the MMF Using High Resolution Data. Thomas Ackerman Roger Marchand University of Washington. The Question. As we move towards higher resolution models with more realistic simulations of cloud processes and cloud properties, how do we evaluate model cloud properties?

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Evaluating the MMF Using High Resolution Data

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  1. Evaluating the MMF Using High Resolution Data Thomas Ackerman Roger Marchand University of Washington

  2. The Question • As we move towards higher resolution models with more realistic simulations of cloud processes and cloud properties, how do we evaluate model cloud properties? • What are the metrics? • How do know we are improving those metrics?

  3. The Metrics • Occurrence in space and time – cloud fraction • Cloud top height • Optical depth – an optical (radiation) measure of the total condensed water and ice in a cloud • Statistical distributions of these quantities

  4. The tools CloudSat – 3 mm radar NASA A-Train, launched 2006 Profiles of cloud reflectivity MISR – multi-angle radiometer NASA Terra, launched 1999 Cloud height and optical depth ARM sites – multiple instruments Established 1996 – 1998 Cloud profiles and integrated properties

  5. CloudSat data – an example Cyclone Nargis in the Bay of Bengal before landfall in Myanmar MODIS image CloudSat curtain along red line

  6. CloudSat Instrument Simulator MMF August Composite

  7. MAM (Diff = MMF – CS)

  8. JJA (Diff = MMF – CS)

  9. SON (Diff = MMF – CS)

  10. DJF (Diff = MMF – CS)

  11. What have we learned • MMF captures general cloud structure and seasonal movement • MMF generally overproduces convective cloud • Too much high cloud and too optically thick • MMF underpredicts BL cloud (Stratus, Trade Cu) • Produces too much precipitation • Simulated Radar reflectivity values are too high • Too much drizzle

  12. CloudSat and ARM • CloudSat has limited temporal coverage • ARM radar has limited spatial coverage • Combine them to provide detailed regional data • Work in progress • Detailed comparison ofradar signals • Apply to simulations inTWP

  13. Using MISR Joint Histograms • MISR measures stereo cloud-top height and cloud optical depth • Plot as 2D joint histogram • MISR simulatorincorporated into MMF

  14. Multi-scale Modeling Framework (MMF)Testing the effect of increasing resolution • 3-month MMF runs Increasing resolution • Control run • 4 km horizontal • 64 columns • 26 vertical layers • Test A • 1 km horizontal • 64 & 128 columns • 26 vertical layers • Test B • 1 km horizontal • 64 columns • 52 vertical layers 2.5° 2° 64 or 128 Columns Run on SDSC Datastar with support from CMMAP

  15. Sensitivity of low cloud amount to CRM resolution

  16. Hawaiian Trade Cumulus

  17. Summary of Low Cloud Response • Going from 4 km to 1 km reduced low cloud amount. • Much (but not all) due to dissipation of “stratofogulus” • Generally, little change in amount of low cloud with optical depths less than 10. • Going from 4 km to 1 km and vertical resolution to 52 levels (50 in CRM) resulted in … • Small increase in the amount of low-level cloud relative to the simulations with 4 km horizontal resolution. • Increase in cloud with optical depths less than 10 (better agreement with MISR observational data) • Stratocumulus zones show a significant improvement in cloud top height. • BUT • Total amount of model low cloud remains too low • Too much low cloud with optical depths larger than 23 (the largest two optical-depth bins).

  18. Concluding thoughts • New instruments well suited to evaluating MMF • Model spatial resolution matches sensors • Simulator approach easy to implement in MMF • Provide new metrics • Profiles of cloud occurrence • Optical depth – cloud top height joint histograms • Test model improvements against these same metrics

  19. Thank you for your attention!

  20. References • Mace, G. G., Q. Zhang, M. Vaughan, R. Marchand, G. Stephens, C. Trepte, and D. Winker (2009), A description of hydrometeor layer occurrence statistics derived from the first year of merged Cloudsat and CALIPSO data, J. Geophys. Res., 114, D00A26, doi:10.1029/2007JD009755. • McFarlane, S. A., J. H. Mather, and T. P. Ackerman, 2007: Analysis of tropical radiative heating profiles: A comparison of models and observations, J.Geophys. Res., 112, D14218, doi:10.1029/2006JD008290 • Marchand, R. T., J. Haynes, G. G. Mace, T. P. Ackerman, and G. Stephens, 2009: A comparison of CloudSat cloud radar observations with simulated cloud radar output from the Multiscale Modeling Framework global climate model, J. Geophys. Res., 114, D00A20, doi:10.1029/2008JD009790 • Marchand, R. T., and T. P. Ackerman, 2009: Analysis of the MMF global climate model using ISCCP and MISR histograms of cloud top height and optical depth, manuscript in preparation • Marchand, R. T., T. P. Ackerman, M. Smyth, P. Hubanks, S. Platnick, and W. Rossow, 2009: A comparison of cloud top height and optical depth histograms from MISR, ISCCP, and MODIS, manuscript in preparation Research supported by DOE ARM, NASA, and CMMAP

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