CCPP Meeting (September 17-19)

Orientation for New Users to PCMDI

Site Map


About CMIP3 Model Output

Data Portal

Diagnostic Subprojects

Subproject Publications

Data Access Survey
    MS Word | Plain Text


CCPP Overview

CCPP (April 2006)
CCPP (March 2005)
CCPP (October 2004)

UCRL-WEB-152471

Privacy & Legal Notice

Thanks to Our Sponsors:

PCMDI > Projects > CAPT Printer Friendly Version
 

CAPT: The Cloud-Associated Parameterizations Testbed

 
CAPT is a joint project of the Atmospheric System Research (ASR) and Regional and Global Climate Modeling (RGCM) Programs of the U.S. Department of Energy's Office of Science/Biological and Energy Research (BER).  We are using analyses of global weather from numerical weather prediction (NWP) centers,  in conjunction with field observations such as those provided by the Atmospheric Radiation Measurement Climate Research Facility, to evaluate parameterizations of sub-gridscale processes in global climate models.  Simply stated, we run realistically initialized climate models in forecast mode to determine their initial drift from the NWP analyses and/or from the available field data, thereby gaining insights on model parameterization deficiencies.

Prior to February 2010, CAPT was known as the CCPP-ARM Parameterization Testbed.

See our publications for further details on the work of the CAPT project.


Origins
From the 1999 Report of the Working Group on Numerical Experimentation (WGNE) on "Transpose AMIP":

"WGNE is continuing to develop the concept of what is termed a "Transpose AMIP", in which climate models would be run in NWP mode, and the evolution of the forecast and of various variables examined, as well as the behaviour of parameterizations before the forecast state diverges too far from the truth. More specifically, predicted variables will be compared with values from reanalyses over regions where these variables are known to be correct from comparison with observations (i.e. data rich areas over the US and/or Europe) in forecasts of only a few days during which the state may be considered 'correct'. The intention is to try and learn why there are model errors, rather than just what the errors are. WGNE recognized that the initialization and spin up of the forecasts were likely to be critical aspects of whether useful results could be obtained, especially in trying to assess model treatments of cloud and radiation. Nevertheless, a pilot project is being undertaken at NCAR with the CCM model using initial data provided by ECMWF (which then have to be interpolated to the CCM grid)." 
 

and from the 1999-2009 Plan of the European Centre for Medium-Range Weather Forecasts (ECMWF)

"One can have confidence in simulated climate scenarios only if one has confidence in the physical formulations and feed-back loops of the GCMs. A strong case could be made that every GCM should be equipped with a data assimilation system, so that one can diagnose its performance with field experiment data and in medium- and extended-range forecasts." 

- Tony Hollingsworth



Methodology

The CAPT protocol  (see schematic) is analagous to a common NWP approach for development of forecast models.  It is also potentially  useful for diagnosing parameterization problems that may produce systematic model errors on climate time scales . Our goal is to adapt this NWP-inspired technique for its practical application in the development cycles of climate models (Phillips et al. 2004).  


CAPT Flow Diagram


Activities

In our initial work as a project, we developed a prototype testbed for  the Community Atmosphere Model-Version 2 (CAM2) , and then closely diagnosed its forecasts near the the ARM Southern Great Plains (SGP) observational site, where multi-year, high-frequency field data are available.  More recently, we have developed testbeds for both the current-generation CAM3 and the Geophysical Fluid Dynamics Laboratory (GFDL) AM2 atmospheric climate models. 

While continuing to deepen our analysis of these model forecasts near the SGP site (Klein et al. 2006, Williamson and Olson 2007), we also are extending the range of our studies to phenomena characteristic of other climatic regimes.   Examples of  the latter work include diagnosis of convective parameterizations using observations from the 1992-93 Tropical Ocean-Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA-COARE) by Boyle et al. 2007, and analysis of  cloud microphysics schemes with the  application of field data from the 2004 Mixed-Phase Arctic Cloud Experiment (M-PACE) by Xie et al. 2007.  

Closely related cloud process studies are also in progress.  These involve identification of tropical-cloud modes of variability using satellite data from the recent CloudSat and Calipso missions (Zhang et al.  2007), as well as comparisons of diurnal-cycle phenomena as predicted by cloud-resolving models and as inferred from Tropical Rainfall Measuring Mission (TRMM) and other satellite data.


Last update: 2/22/2010


UCRL-WEB-151501