Evaluating GCM clouds using instrument simulators
Benjamin R. Hillman
University of Washington
September 24, 2009
Benjamin R. Hillman Evaluating GCM clouds using instrument simulators
Why do we care about evaluation of clouds in GCMs?
General Circulation Models (GCMs) project future climate change
Cloud feedbacks primary sources of inter-model differences in climate sensitivity
Quantitative evaluation
How do we determine which models to place our confidence in over others?
So, what’s the problem with clouds?
Differing scales
Cloud processes: 100 to 250 meters GCM resolution: hundreds of kilometers
Use parameterizations for sub-grid scale processes No consensus on the “right” parameterization
Compensating errors
Constrain integral to get top of atmosphere radiation What about the integrand?
Can get the right top of atmosphere radiation with differing cloud profiles
If we have confidence in the clouds, we can be confident we get the right top of atmosphere radiation for the right reasons
Benjamin R. Hillman Evaluating GCM clouds using instrument simulators
How are climate models evaluated?
Component testing
Ensure pieces behave as expected
Inter-model comparison
Consistency across different models
Comparison to observational data
Test ability of models to reproduce general observed features of past and current climates
Look closer at results: are the distributions consistent with observations?
Quantitative measures
But it’s not as easy as it sounds...
The model world Geophysical cloud properties (from parameterizations) Familiar mathematical quantities
Gridbox mean fields on gridbox scale
The real world
Measure some sort of signal
Retrieval algorithm employed
Instrument sensitivity?
Cloud attenuation?
Multi-layer profiles?
Spatial resolution?
Benjamin R. Hillman Evaluating GCM clouds using instrument simulators
Instrument simulators connect the two worlds
cloud properties
remote sensing signals
retrieval algorithms
retrieved cloud properties
model cloud properties
simulator
synthetic signals
retrieval algorithms
retrieved model cloud properties
What would the instrument see in the model world?
Simulator takes model output and produces simulated instrument signal
Allows comparison between model and observation
Satellite simulator on model output easier than inverse retrieval on observational data
About the instrument simulators
Cloud Feedback Model Inter-comparison Project (CFMIP) Observational Simulator Package (COSP)
Benjamin R. Hillman Evaluating GCM clouds using instrument simulators
About the MISR instrument
Multi-angle Imaging SpectroRadiometer One of five instruments on-board NASA Terra platform
Sun-synchronous polar orbit
Nine different camera views
Four different wavelengths 275 meter along track, 250 meter crosstrack resolution
MISR stereo cloud top height
Parallax used to get cloud top height
Geometric retrieval Minimal sensitivity to sensor calibration
Benjamin R. Hillman Evaluating GCM clouds using instrument simulators
About CloudSat
Part of NASA “A-Train”
constellation of satellites Launched 1 June 2006 millimeter wavelength cloud radar
measures power
backscattered by clouds 500 meter vertical resolution
1.4 km cross-track, 1.7 km along-track resolution
Example of CloudSat data
Benjamin R. Hillman Evaluating GCM clouds using instrument simulators
Other data of interest
ISCCP
International Satellite Cloud Climatology Project Established 1982
Large dataset
Collect radiance measurements from various satellites Lower resolution
Fewer channels
CALIPSO
Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation
Active lidar plus passive infrared/visible imagers
Using joint histograms
Get optical depth and cloud top height from MISR and ISCCP (both from observation and model/simulator) Compute relative
occurance for each optical depth and cloud top height combination More complete picture of cloud distribution
Benjamin R. Hillman Evaluating GCM clouds using instrument simulators
Now, we need a model...
NCAR Community Atmosphere Model (CAM) GFDL Atmosphere only Model (AM2)
Different components Different results
What do we need from the models?
We want model cloud radiative and optical properties Longwave emissivity
Visible wavelength optical depth Precipitation fluxes
Benjamin R. Hillman Evaluating GCM clouds using instrument simulators
So, we’ll just go to the archive...
Model output from IPCC Fourth Assessment Report (AR4) is all archived (PCMDI)
Output available from all climate models used in AR4 But, no cloud radiative and optical properties saved And it is all time averaged output
Now what?
Where we are now
Identified the need for model output not immediately available from archives
Setup CAM and AM2 to save the outputs we need
Wrote wrapper to run simulators on output from CAM and AM2
Where we go next
Long model runs forced with observed SST
Model output concurrent with available observational data Run simulators and compare simulated retrievals to observations
Do the models produce profiles we observe?
Quantitative analysis
Benjamin R. Hillman Evaluating GCM clouds using instrument simulators
CloudSat example: September, October, November
MISR example: Hawaiian Trade Cumulus
Benjamin R. Hillman Evaluating GCM clouds using instrument simulators
Where this is headed
New versions of models will become available shortly Need to evaluate those models
How do we decide which models to put our confidence in?
Acknowledgements
Tom Ackerman Roger Marchand Cecilia Bitz Dargan Frierson Mark Zelinka Grads 08
Benjamin R. Hillman Evaluating GCM clouds using instrument simulators
CloudSat example: trail run with CAM
Thank you!
CloudSat example: trail run with CAM
Benjamin R. Hillman Evaluating GCM clouds using instrument simulators
