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A Guide to Information on Greenhouse Gases and Ozone Depletion
Published July 1988 through June 1999



Item #d92sep104

Computer Hardware, Advanced Mathematics and Model Physics [CHAMMP]: Pilot Project Final Report (DOE/ER-0541T), U.S. Dept. Energy Off. Energy Res., 122 pp., May 1992. Available to DOE and its contractors from Off. Sci. Tech. Info., POB 62, Oak Ridge TN 37831 (615-576-8401; inquire for price), to others from NTIS.

CHAMMP was launched in 1990 to utilize the emerging capabilities of massively parallel scientific computers for regional-scale predictions of decade-to-century climate change. This report is a compilation of 15 papers describing pilot CHAMMP projects that were carried out to identify the principal challenges and to involve new scientific computing expertise.

Item #d92sep105

Implementation and Validation of Improved Landsurface Hydrology in an Atmospheric General Circulation Model (NASA-CR-189488), K.D. Johnson (Mass. Inst. Technol., Cambridge, Mass.), D. Entekhabi, P.S. Eagleson, 193 pp., Oct. 1991. NTIS: N92-13476/6; $26.

Two types of improvements in landsurface hydrological parameterization were implemented in the NASA-GISS GCM, and tested. Runoff rate, especially in the tropics, was improved with the new schemes, and the remaining components of the heat and moisture balance showed comparable improvements when compared to observations. The model performance was validated at a range of scales from global down to smaller river basins.

Item #d92sep106

Regional Climate Change Predictions from the Goddard Institute for Space Studies High Resolution GCM (NASA-CR-190037), R.G. Crane (Penn. State Univ., Univ. Park, Penn.), B.C. Hewitson, 54 pp., 1991. NTIS: N92-20022/9; $19.

Describes a new diagnostic tool for examining relationships between the synoptic-scale circulation and regional temperature distributions in GCMs. Transfer function relationships are derived between observed synoptic circulation and surface temperatures using principal components analysis and multivariate regression models. Application of these functions to GCM simulations indicates that there is considerable spatial bias present in the GCM temperature distributions. The functions are also used to estimate the change in regional temperatures expected in a doubled CO2 scenario.

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