Steve Klein and Jim Boyle Program for Climate Model Diagnosis and Intercomparison Lawrence Livermore National Laboratory Mark Webb United Kingdom Meteorological

Steve Klein and Jim Boyle

Program for Climate Model Diagnosis and Intercomparison

Lawrence Livermore National Laboratory

Mark Webb

United Kingdom Meteorological Office

Jay Mace

University of Utah

CFMIP/GCSS Boundary Layer Cloud Working Group Workshop On Evaluation and Understanding of Cloud Processes in GCMs

Vancouver, Canada

June 9, 2009

Updates and Recent Science Related to the ISCCP Simulator

Stephen A. Klein, 9 June 2009, p. 2

Outline

• ISCCP simulator update• Commonality of model differences with respect

to ISCCP observations• Evaluation of ISCCP data and simulator with

ground-based radar and lidar observations– How good are ISCCP observations?


ISCCP Simulator Update


ISCCP Simulator update: Mundane

• ISCCP simulator has been promoted from version 3.5 to version 4.0. What happened?– Cloud-top pressure is calculated through

interpolation of the model’s sounding to the radiance-retrieved cloud-top temperature. Interpolation replaces assignment.

– Replaced cumbersome table that converts cloud-optical depth to cloud albedo with an analytic formula

– Added output diagnostics for the clear-sky and all-sky 11 m brightness temperature averaged over sub-columns



– Added a consistent output missing value for undefined quantities such as mean cloud-top pressure in grid-boxes that are either clear or nighttime

– Added consistent “light-weight” diagnostics of total cloud cover, average cloud albedo and cloud-top pressure. “Consistent” means all of these diagnostics are calculated from only daylight grid boxes counting cloudy subgrid-scale columns with column cloud optical thicknesses > 0.3. (Note: Special time averaging is required for mean cloud albedo and cloud-top pressure)



– Separated Subgrid Cloud Overlap Profile Sampler (SCOPS) from ISCCP Clouds and Radiances Using SCOPS (ICARUS) parts of the ISCCP simulator. This permits the passing of sub-columns generated for Monte Carlo Independent Column Approximation for radiation to ICARUS. This was reported on by Swati Gehlot in her talk this morning. This has also been done by other modeling centers.


ISCCP Simulator update: Significant

– Cloud-top pressure for clouds under a temperature inversion is assigned to the lowest pressure within the troposphere with the radiance-derived cloud-top temperature

– Previously the ISCCP simulator assigned the cloud-top pressure to the highest pressure with matching cloud-top temperature

– The purpose of this change is to replicate the error ISCCP makes in assigning cloud-top pressure to clouds under strong temperature inversions



• Marine Stratocumulus Example: EPIC (2001) Southeast Pacific Stratocumulus (Garay et al. 2008)

• In the example below, MODIS retrieves the correct cloud-top temperature yet derives a cloud-top height of 3890 m when the actual cloud-top height is 1350 m. ISCCP makes the same error.

Garay et al. (2008)



• This error is partly due to the poor temperature sounding in the NCEP analysis used by MODIS. But even if you had a perfect sounding, it is more likely that you would retrieve the higher cloud-top height. Just consider adding a little bit of thin cirrus above this cloud.

• While the lapse-rate method is available for marine stratocumulus clouds, ISCCP does not use this for the cloud-top pressure – optical thickness histograms



• This error explains the erroneous nature of the cloud-top optical depth ISCCP histograms in marine stratocumulus regions

• As cloud optical thickness increases above 3.6, ISCCP more often reports clouds between 680 and 800 hPa than between 800 and 1000 hPa. This cannot be correct, as the trade inversion is around 850 – 900 hPa and there should be no cloud tops between 680 and 800 hPa



• What is happening is that low clouds with greater optical thicknesses tend to occur in situations with stronger temperature inversions and thus ISCCP more often mislocates the low-cloud top pressure in this situation

• The easiest solution to this problem is to have the ISCCP simulator make the same mistake. This is easily done by selecting the cloud-top pressure as the lower pressure (highest altitude) whose temperature matches the radiance retrieved cloud-top temperature



• What is the impact of this change? Results are shown for ISCCP simulator applied to CAM3.

Californian stratus region JJA 2006 pc- histogram

Old New

Change in mean JJA cloud-top pressure


Common Model – ISCCP Differences


Common model – ISCCP differences

• The problem of model clouds with too great an optical depth and too few middle-level topped thin model clouds is very common

Zhang et al. (2005)



• These biases are also apparent in the Multiscale Modeling Framework model, which has a cloud-resolving model in each grid-box

• Too many optically thick clouds

• Better middle level clouds

Wyant et al. (2006): Tropical clouds sorted by 500 hPa vertical velocity



• These biases are also apparent in the NICAM Global Cloud Resolving Model (x ~ 14 km)

• Too many optically thick clouds

• A lot of very thin cirrus ( < 0.3)

Figure courtesy of Yoko Tsushima

(JJA)

NICAM (July)

From CS4L100 run of Iga et al. (2007)



• Primary model – ISCCP differences are:

1. Greater model cloud optical thickness

2. Fewer model middle level-topped clouds

3. Lower model total cloudiness • Differences #1 and #3 compensate and permit

models to simulate a balanced radiation budget• Are these differences model errors?• Could these differences reflect ISCCP retrieval

errors (which haven’t yet been built into the simulator)?


ISCCP Data and Simulator Evaluation


ISCCP data & simulator evaluation

• Mace et al. (2006) compare the cloud-top pressure and optical thickness of radar-retrieved clouds over the ARM Oklahoma site to ISCCP observations



• Mace et al. (2006) compare the cloud-top pressure and optical thickness of radar-retrieved clouds over the ARM Oklahoma site to ISCCP observations

ISCCP ARM

• ARM clouds have greater optical thickness• ARM has fewer middle and low level thin clouds

Mace et al. (2006)



• ARM cloud optical thickness, calculated from retrievals of ice & liquid water path and effective radii, agree well with cloud optical thickness derived from another ground-based instrument. Furthermore, unbiased radiative closure is obtained with ARM cloud radiative properties

• The amount of optically thin clouds in ISCCP retrievals agree well with other satellite retrievals such as Minnis’s MODIS cloud retrievals

• Could there be a significant ‘beam-filling’ problem in satellite retrievals for thin clouds with sizes less than 1 km (e.g. small cumulus)?



• To understand differences in cloud-top pressure, Mace applied the ICARUS code in from the ISCCP simulator to ARM clouds

ISCCP ARM ARM + ICARUS

cloud-top pressure for high clouds, but not thin middle level clouds

Mace et al. (2006)• ICARUS improves agreement of



• At the time this comparison was made ICARUS did not account for the boundary layer cloud that is placed too high because of the difficulty of locating clouds under an inversion

ARM CTP (hPa)

ISC

CP

CT

P (

hP

a)

Houser and Mace (2008) unpublished

ARM + ICARUS CTP (hPa)

ISC

CP

CT

P (

hP

a)

Boundary-layer clouds misidentified as middle-level clouds



• This comparison partially demonstrates that ICARUS is doing well in reproducing ISCCP cloud-top pressures

• Furthermore, while ISCCP is overall doing very well, it is possible that ISCCP cloud optical thicknesses are biased somewhat low and that ISCCP overestimates the amount of middle level topped clouds



• However, the comparison of models to CloudSat and Calipso also suggests that models underestimate the number of clouds with low optical thickness and which occur at middle-levels (Bodas-Salcedo et al. 2008, Chepfer et al. 2008, Zhang et al. 2009)

• Because this comparison of models to data independent of ISCCP also suggests that models underestimate the amount of low optical thickness and middle-level clouds, it is more likely that these model-observation differences are true model errors


The End


Extra Slides


Why too few middle-level clouds?

• Potential reasons include:– Convection parameterizations do not detrain

enough at middle-levels– Many middle-level clouds are very thin (100 –

300 m) yet model resolution in the middle troposphere is much coarser (500 – 1000 m)

– Many middle-level clouds consist of supercooled water which is difficult for models to simulate because their conversion of liquid to ice (the Bergeron process) is too efficient

– Unresolved gravity waves may be responsible for the formation of some middle level clouds


Why too large optical thicknesses?

• Potential reasons include:– Too coarse vertical resolution– Inability to simulate cloud formation and

dissipation stages properly– Inability to simulate weak forcing which

might accompany optically thin clouds– Inability of parameterizations to recognize

that frontal or mesoscale ascent is concentrated in only a portion of a grid box

Documents

Steve Klein and Jim Boyle Program for Climate Model Diagnosis and Intercomparison Lawrence Livermore National Laboratory Mark Webb United Kingdom Meteorological