Evaluating the MMF Using High Resolution Data

Thomas AckermanRoger Marchand

University of Washington

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?

The Question

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

The Metrics

The tools

CloudSat – 3 mm radarNASA A-Train, launched

2006Profiles of cloud reflectivity

MISR – multi-angle radiometer

NASA Terra, launched 1999

Cloud height and optical depthARM sites – multiple instruments

Established 1996 – 1998Cloud profiles and integrated properties

Cyclone Nargis in the Bay of Bengal before landfall in Myanmar

MODIS image

CloudSat curtain along red line

CloudSat data – an example

B

Simulator Simulator

Synthetic Signals

In situ Observations Model Cloud

Properties

Remote Sensing Signals

Retrieved Model Cloud Properties Retrieved

Cloud Properties

Cloud M odel

D

A

C

Retrieval Algorithms

CloudSat Instrument Simulator

MMF August Composite

MAM (Diff = MMF – CS)

JJA (Diff = MMF – CS)

SON (Diff = MMF – CS)

DJF (Diff = MMF – CS)

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

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

CloudSat and ARM

MISR measures stereo cloud-top height and cloud optical depth

Plot as 2D joint histogram

MISR simulatorincorporated into MMF

Using MISR Joint Histograms

64 or 128 Columns

2°

2.5°

Multi-scale Modeling Framework (MMF)Testing the effect of increasing resolution3-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

Run on SDSC Datastar with support from CMMAP

Sensitivity of low cloud amount to CRM resolution

Hawaiian Trade Cumulus

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).

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

Concluding thoughts

Thank you for your attention!

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

References

Research supported by DOE ARM, NASA, and CMMAP