NSF Org:	AGS Division of Atmospheric and Geospace Sciences
Recipient:	UNIVERSITY OF CALIFORNIA SANTA CRUZ
Initial Amendment Date:	January 29, 2004
Latest Amendment Date:	January 29, 2004
Award Number:	0405451
Award Instrument:	Standard Grant
Program Manager:	Jay S. Fein AGS  Division of Atmospheric and Geospace Sciences GEO  Directorate for Geosciences
Start Date:	January 15, 2004
End Date:	December 31, 2004 (Estimated)
Total Intended Award Amount:	$24,998.00
Total Awarded Amount to Date:	$24,998.00
Funds Obligated to Date:	FY 2004 = $24,998.00
History of Investigator:	Bruno Sanso (Principal Investigator) bruno@soe.ucsc.edu
Recipient Sponsored Research Office:	University of California-Santa Cruz 1156 HIGH ST SANTA CRUZ CA  US  95064-1077 (831)459-5278
Sponsor Congressional District:	19
Primary Place of Performance:	University of California-Santa Cruz 1156 HIGH ST SANTA CRUZ CA  US  95064-1077
Primary Place of Performance Congressional District:	19
Unique Entity Identifier (UEI):	VXUFPE4MCZH5
Parent UEI:
NSF Program(s):	Climate & Large-Scale Dynamics
Primary Program Source:	app-0104
Program Reference Code(s):	OTHR, 0000, 9237
Program Element Code(s):	574000
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.050

ABSTRACT

This research uses statistical tools to evaluate the simulation of chemical constituent transport in the Community Climate System Model (CCSM). Traditional methods for evaluating the ability of climate models to represent the transport of constituent gases rests on simple correlation and mean quantity analyses of simulated transports and observed fields. The new approach of the PIs is to use an off-line chemical model that includes the same representations (physics) for advection, convective transfer and boundary layer mixing as used in the CCSM atmospheric model (CAM). The off-line chemical transport model is very efficient, enabling long simulations in relatively short computer wall-clock time. The diagnostics tools to be developed will enable improved analyses of the spatial and temporal variability of the simulated constituent transports, as measured against decades of reanalysis data, including better evaluations of the statistical significance of the model results.

Broader Impacts:
Improvements in the ability of climate models to simulate the atmospheric transport of radiatively active trace gases is important for future climate projections; a benefit for environmental managers and policy makers in their efforts to understand and manage climate change. The CCSM is used by many university climate scientists who should benefit through the results of this research.

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