The CCPP-ARM parameterization testbed (CAPT): Evaluating climate models in a weather forecasting framework

Thomas J. Phillips; Gerald L. Potter; David L. Williamson; Richard T. Cederwall; James S. Boyle; Michael Fiorino; Justin J. Hnilo; Jerry G. Olson; Shaocheng Xie; J. John Yio

The CCPP-ARM parameterization testbed (CAPT): Evaluating climate models in a weather forecasting framework

Thomas J. Phillips, Gerald L. Potter, David L. Williamson, Richard T. Cederwall, James S. Boyle, Michael Fiorino, Justin J. Hnilo, Jerry G. Olson, Shaocheng Xie, J. John Yio

Research output: Contribution to journal › Conference article › peer-review

1 Scopus citations

Abstract

The climate change prediction program (CCPP)-atmospheric radiation measurement (ARM) parameterization testbed (CAPT), used to improve the performance of model parameterizations as manifested by reduced general circulation models (GCM) systematic errors, is discussed. It improves the performance of model parameterizations first in short-range weather forecasts, but ultimately in climate simulations. The reduction of GCM systematic errors entails both increases in the resolution at which the model state variables are predicted and improvements in the parameterizations of unresolved sub-grid processes. In GCM designed for climate simulations, parameterization development is important for correct representation of relevant processes.

Original language	English
Pages (from-to)	4139-4145
Number of pages	7
Journal	Bulletin of the American Meteorological Society
State	Published - 2004
Externally published	Yes
Event	Combined Preprints: 84th American Meteorological Society (AMS) Annual Meeting - Seattle, WA., United States Duration: Jan 11 2004 → Jan 15 2004

Cite this

@article{6180865322f44cabb7afd4e835383c4f,

title = "The CCPP-ARM parameterization testbed (CAPT): Evaluating climate models in a weather forecasting framework",

abstract = "The climate change prediction program (CCPP)-atmospheric radiation measurement (ARM) parameterization testbed (CAPT), used to improve the performance of model parameterizations as manifested by reduced general circulation models (GCM) systematic errors, is discussed. It improves the performance of model parameterizations first in short-range weather forecasts, but ultimately in climate simulations. The reduction of GCM systematic errors entails both increases in the resolution at which the model state variables are predicted and improvements in the parameterizations of unresolved sub-grid processes. In GCM designed for climate simulations, parameterization development is important for correct representation of relevant processes.",

author = "Phillips, \{Thomas J.\} and Potter, \{Gerald L.\} and Williamson, \{David L.\} and Cederwall, \{Richard T.\} and Boyle, \{James S.\} and Michael Fiorino and Hnilo, \{Justin J.\} and Olson, \{Jerry G.\} and Shaocheng Xie and Yio, \{J. John\}",

year = "2004",

language = "English",

pages = "4139--4145",

journal = "Bulletin of the American Meteorological Society",

issn = "0003-0007",

publisher = "American Meteorological Society",

note = "Combined Preprints: 84th American Meteorological Society (AMS) Annual Meeting ; Conference date: 11-01-2004 Through 15-01-2004",

}

TY - JOUR

T1 - The CCPP-ARM parameterization testbed (CAPT)

T2 - Combined Preprints: 84th American Meteorological Society (AMS) Annual Meeting

AU - Phillips, Thomas J.

AU - Potter, Gerald L.

AU - Williamson, David L.

AU - Cederwall, Richard T.

AU - Boyle, James S.

AU - Fiorino, Michael

AU - Hnilo, Justin J.

AU - Olson, Jerry G.

AU - Xie, Shaocheng

AU - Yio, J. John

PY - 2004

Y1 - 2004

N2 - The climate change prediction program (CCPP)-atmospheric radiation measurement (ARM) parameterization testbed (CAPT), used to improve the performance of model parameterizations as manifested by reduced general circulation models (GCM) systematic errors, is discussed. It improves the performance of model parameterizations first in short-range weather forecasts, but ultimately in climate simulations. The reduction of GCM systematic errors entails both increases in the resolution at which the model state variables are predicted and improvements in the parameterizations of unresolved sub-grid processes. In GCM designed for climate simulations, parameterization development is important for correct representation of relevant processes.

AB - The climate change prediction program (CCPP)-atmospheric radiation measurement (ARM) parameterization testbed (CAPT), used to improve the performance of model parameterizations as manifested by reduced general circulation models (GCM) systematic errors, is discussed. It improves the performance of model parameterizations first in short-range weather forecasts, but ultimately in climate simulations. The reduction of GCM systematic errors entails both increases in the resolution at which the model state variables are predicted and improvements in the parameterizations of unresolved sub-grid processes. In GCM designed for climate simulations, parameterization development is important for correct representation of relevant processes.

UR - https://www.scopus.com/pages/publications/2442516183

M3 - Conference article

AN - SCOPUS:2442516183

SN - 0003-0007

SP - 4139

EP - 4145

JO - Bulletin of the American Meteorological Society

JF - Bulletin of the American Meteorological Society

Y2 - 11 January 2004 through 15 January 2004

ER -

The CCPP-ARM parameterization testbed (CAPT): Evaluating climate models in a weather forecasting framework

Abstract

Other files and links

Fingerprint

Cite this