CAPT: The Cloud-Associated Parameterizations Testbed
The Cloud-Associated Parameterizations Testbed (CAPT) aims to diagnose and improve the representation in climate models of cloud-associated physical processes. In the CAPT, weather forecast techniques are applied to climate models , with an emphasis on the simulations of the Community Atmosphere Model. We will be extending the concept of weather forecasts from the atmosphere to the fully coupled ocean-atmosphere model. Three foci of the project include:
- Comparing of model simulations to detailed process observations available from the ARM data
- Diagnosing the origin of errors in model simulations of climate
- Testing new model parameterizations in order to identify their strengths/weaknesses in simulating cloud-associated processes.
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 Environmental 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.
Stephen Klein (co-Principal Investigator)
Shaocheng Xie (co-Principal Investigator)
Jim Boyle (retired, 2013)
John Tannahill (retired, 2013)
Jerry Potter (retired 2005)
Cecile Hannay (collaborator at the National Center for Atmospheric Research)
Brian Medeiros (collaborator at the National Center for Atmospheric Research)
Dave Williamson (collaborator at the National Center for Atmospheric Research)
Barton, N. P., S. A. Klein, J. S. Boyle, and Y. Zhang, 2012: Evaluating Arctic Clouds in Climate Models
Ma H, S Xie, JS Boyle, SA Klein, and Y Zhang. 2012: Metrics and Diagnostics for Climate Model Short-Range Hindcasts
Xie S, H Ma, JS Boyle, SA Klein, and Y Zhang, 2012: Many Forecast Errors Are Climate Errors.
Zhao C, SA Klein, S Xie, X Liu, JS Boyle, and Y Zhang, 2012: CAM5 Shows Reasonable Aerosol First Indirect Effects on Non-Precipitating Low Liquid Clouds
Boyle JS and SA Klein, 2010: Impact of Horizontal Resolution on Climate Model Simulations of Tropical Moist Processes
J. Boyle,S. Klein,G. Zhang,S. Xie,X. Wei, 2008: Weather Forecasting in the Tropics with Climate Models Is Feasible
Xie, S, J Boyle, SA Klein, X Liu, and S Ghan, 2008: ARM M-PACE Data Used to Evaluate and Improve Arctic Mixed-Phase Clouds Simulated in Climate Models
Klein, Stephen A., X. Jiang, J. Boyle, S. Malyshev, and S. Xie, 2006: Weather Forecasts Help to Understand Climate Model Biases
Phillips, T. J. G.L. Potter, D.L. Williamson, R.T. Cederwall, J.S. Boyle, M. Fiorino, J.J. Hnilo, J.G. Olson, S. Xie, J.J. Yio, 2004: Weather Prediction and Climate Simulation: a Meeting of the Models
de Boer, G., O. Persson, M. D. Shupe, P. M. Caldwell, S. E. Bauer, J. S. Boyle, S. A. Klein and M. Tjernstrom, 2013: Near-surface meteorology during ASCOS: Evaluataion of representation in Re-analyses and GCMs. Atmos. Chem. Phys. Discuss., submitted.
- Phillips, T.J., and S.A. Klein, 2013: Land-atmosphere coupling manifested in warm-season observations on the U.S. Southern Great Plains. J. Geophys. Res., submitted.
- Lin, Y., M. Zhao, Y. Ming, J-C. Golaz, L. J. Donner, S. A. Klein, V. Ramaswamy, and S. Xie, 2013: Precipitation partitioning, tropical clouds and intraseasonal variability in GFDL AM2. J. Clim., 26, 5453-5466, 10.1175/JCLI-D-12-00442.1
- Williams, K. D., A. Bodas-Salcedo, M. Deque, S. Fermepin, B. Medeiros, M. Watanabe, C. Jakob, S. A. Klein, C. A. Senior, and D. L. Williamson, 2013: The Transpose-AMIP II experiment and its application to the understanding of Southern Ocean cloud biases in climate models. J. Clim., 26, 3258 - 3274, doi: 10.1175/JCLI-D-12-00429.1.
- Barton, N. P., S. A. Klein, J. S. Boyle, and Y. Zhang, 2012: Arctic synoptic regimes: Comparing domain-wide Arctic cloud observations with CAM4 and CAM4 during similar dynamics. J. Geophys. Res., 117, 1 - 9, doi:10.1029/2012JD017589.
- Chuang, C. C., J. Kelly, J. Boyle, and S. Xie, 2012: Sensitivity of aerosol indirect effects to cloud nucleation and autoconversion parameterizations in short-range weather forecasts over the Southern Great Plains during May 2003 IOP. J. Adv. Model. Earth Syst., 4, M09001, doi:10.1029/2012MS000161.
Lin, Y., L. J. Donner, J. Petch, P. Bechtold, J. Boyle, S. A. Klein, T. Komori, K. Wapler, M. Willett, X. Xie, M. Zhao, S. Xie, S. A. MaFarlane, C. Schumacher, 2012: TWP-ICE global atmospheric model intercomparison: Convection responsiveness and resolution impact. J. Geophys. Res., 117, D09111, doi: 10.1029/2011JD017018.
- Ma, H-M., Shaocheng Xie, James S. Boyle, Stephen A. Klein, and Yuying Zhang, 2012: Metrics and diagnostics for precipitation-related processes in climate model short-range hindcasts Journal of Climate, 26, 1516-1534 .
- Xie, S., Hsi-Yen Ma, James S. Boyle, Stephen A. Klein, and Yuying Zhang, 2012: On the correspondence between short- and long-time-scale systematic errors in CAM4/CAM5 for the Year of Tropical Convection Journal of Climate, 25, 7937-7955 .
Zhang, Y., S. Xie, C. Covey, D. D. Lucas, P. Gleckler, S. Klein, J. Tannahill, C. Doutriaux,and R. Klein, 2012: Regional assessment of the parameter-dependent performance of CAM4 in simulating tropical clouds. Geophys. Res. Lett., 39, L14708, doi:10.1029/2012GL052184.
- Zhao, C., Stephen A. Klein, Shaocheng Xie, Xiaohong Liu, James S. Boyle, and Yuying Zhang, 2012: Aerosol first indirect effects on non-precipitating low-level liquid cloud properties as simulated by CAM5 at ARM sites Geophysical Research Letters, 39, L08806, doi: 10.1029/2012GL051213.
Liu, X., S. Xie, J. Boyle, S. A. Klein, X. Shi, Z. Wang, W. Lin, S. J. Ghan, M. Earle, P. S. K. Liu, and A. Zelenyuk1, 2011: Testing Cloud Microphysics Parameterizations in NCAR CAM5 with ISD AC and M-PACE Observations. J. Geophys. Res., 116, D00T11, doi:10.1029/2011JD015889.
- Boyle, J., and Stephen A. Klein, 2010: Impact of horizontal resolution on climate model forecasts of tropical convection and diabatic heating for the TWP-ICE period Journal of Geophysical Research, 115, D23113, doi: 10.1029/2010JD014262 .
- Gettelman, A., X. Liu, S. J. Ghan, H. Morrison, S. Park, A. J. Conley, S. A. Klein, J. Boyle, D. L. Mitchell, and J.-L. F. Li, 2010: Global simulations of ice nucleation and ice supersaturation with an improved cloud scheme in the Community Atmosphere Model. J. Geophys. Res., 115, D18216, doi:10.1029/2009JD013797.
- Wyant, M. C., R. Wood, C. S. Bretherton, C. R. Mechoso, J. Bacmeister, M. A. Balmaseda, B. Barrett, F. Codron, P. Earnshaw, J. Fast, C. Hannay, J. W. Kaiser, H. Kitagawa, S. A. Klein, M. Kohler, J. Manganello, H.-L. Pan, F. Sun, S. Wang, and Y. Wang, 2010: The PreVOCA experiment: modeling the lower troposphere in the Southeast Pacific Wyant. Atmos. Chem. Phys., 10, 4757-4774, doi:10.5194/acp-10-4757-2010.
- Zhang, Y., et al, 2010: Evaluation of tropical cloud and precipitation simulations of CAM3 using CloudSat and Calipso data Journal of Geophysical Research
- Hannay, C., et al, 2009: Evaluation of forecasted Southeast Pacific stratocumulus in the NCAR, GFDL, and ECMWF modelsJournal of Climate, 22, 2871-2889.
Wang, W., X. Liu, S. Xie, J. Boyle, and S. McFarlane, 2009: Testing ice microphysics parameterizations in NCAR CAM3 using TWP-ICE data, J. Geophys. Res., 114, D14107, doi:10.1029/2008JD011220.
- Boyle, J., et al., 2008: Climate model forecast experiments for TOGA COARE Monthly Weather Review , 136, 808-832.
- Xie, S., et al, 2008: Simulations of Arctic mixed-phase clouds in forecasts with CAM3 and AM2 for M-PACEJournal of Geophysical Research , 113 D04211, doi:10.1029/2007JD009225 .
- Williamson, D., and J. Olson, 2007: A comparison of forecast errors in CAM2 and CAM3 at the ARM Southern Great Plains Site Journal of Climate, 20, 4572-4585.
- Klein, S., et al, 2006: Diagnosis of the summertime warm and dry bias over the U.S. Gouthern Great Plains in the GFDL climate model using a weather forecasting approach Geophysical Research Letters, 33, L18805, doi: 10.1029/2006GL027567 .
- Boyle, J., et al, 2005: Diagnosis of Community Atmospheric Model 2 (CAM2) in numerical weather forecast configuration at Atmospheric Radiation Measurement sites Journal of Geophysical Research, 110, D15516, doi: 10.1029/2004JD005109.
- Williamson, D., et al, 2005: Moisture and temperature balances at the Atmospheric Radiation Measurement Southern Great Plains Site in forecasts with the Community Atmosphere Model (CAM2) Journal of Geophysical Research, 110, D15516, doi: 10.1029/2004JD005109.
- Phillips, T.J., et al., 2004: Evaluating parameterizations in general circulation models: Climate simulation meets weather prediction Bulletin of the American Meteorological Society, 85, 1903-1915.
- Xie, S., et al, 2004: Impact of a revised convective triggering mechanism on Community Atmosphere Model, Version 2, simulations: Results from short-range weather forecasts Journal of Geophysical Research, 109, D14102, doi:10.1029/2004JD004692 .
From the 1999 Report of the
Working Group on Numerical Experimentation (WGNE) on
"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) (2):
"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
- Tony Hollingsworth
- WGNE, 1999: Discussion of the
'Transpose AMIP' Project. In Report of the Fourteenth Session of the
CAS/JSC Working Group on Numerical Experimentation, CAS/JSC WGNE Report No. 14, pp. 7-8, WMO/TD-No. 964.
- ECMWF, 1999: ECMWF Ten-Year Plan, 1999-2009, A. Hollingsworth (ed.)
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).
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