NSF Org:	AGS Division of Atmospheric and Geospace Sciences
Recipient:	UNIVERSITY OF OKLAHOMA
Initial Amendment Date:	February 24, 2006
Latest Amendment Date:	February 24, 2006
Award Number:	0550178
Award Instrument:	Standard Grant
Program Manager:	Jay S. Fein AGS  Division of Atmospheric and Geospace Sciences GEO  Directorate for Geosciences
Start Date:	May 15, 2006
End Date:	April 30, 2007 (Estimated)
Total Intended Award Amount:	$52,550.00
Total Awarded Amount to Date:	$52,550.00
Funds Obligated to Date:	FY 2006 = $52,550.00
History of Investigator:	David Karoly (Principal Investigator) dkaroly@unimelb.edu.au Harold E Brooks (Co-Principal Investigator)
Recipient Sponsored Research Office:	University of Oklahoma Norman Campus 660 PARRINGTON OVAL RM 301 NORMAN OK  US  73019-3003 (405)325-4757
Sponsor Congressional District:	04
Primary Place of Performance:	University of Oklahoma Norman Campus 660 PARRINGTON OVAL RM 301 NORMAN OK  US  73019-3003
Primary Place of Performance Congressional District:	04
Unique Entity Identifier (UEI):	EVTSTTLCEWS5
Parent UEI:
NSF Program(s):	Climate & Large-Scale Dynamics
Primary Program Source:	app-0106
Program Reference Code(s):	0000, 9150, 9237, OTHR
Program Element Code(s):	574000
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.050

ABSTRACT

Severe thunderstorms and tornados are very important mesoscale weather events in the central United States because of their high frequency and intensity in this region, and the damage and loss of life that they cause every year. Recently, it has been shown that the frequency of favorable conditions for significant severe thunderstorms and tornados can be estimated for the United States and other regions using global atmospheric re-analyses with spatial resolution on the order of 200 km and temporal resolution of 6 hours. Global climate models are unable to simulate severe thunderstorms and tornados because their spatial resolution is too coarse to be able to simulate such mesoscale events. However, they should be able to simulate the environmental conditions under which such severe weather develops, including abundant lower tropospheric moisture, steep mid-tropospheric lapse rates, and strong tropospheric wind shear. High space and time resolution data from control simulations with global climate models archived at NCAR will be used to estimate the frequency of favorable conditions for severe weather, as simulated by the models. The climatological distribution of the severe weather environment in the model simulations will be compared with that from the reanalyses, including the seasonal and geographical variations and its interannual variability. The frequency distributions of the important factors determining the severe weather environment also will be assessed in the models.

The research is exploratory. Only recently have climate model data sets with sufficient vertical, horizontal and time resolution become available to undertake this study. Outcomes from this research will include a detailed assessment of global climate model simulations of the environmental conditions determining severe weather. This will provide a better understanding of some of the causes of model problems with simulation of warm season intense continental convective.

Broader Impacts: If the approach is successful, subsequent research will provide estimates for the first time of changes in the frequency of severe weather in the United States under future climate conditions. The method can then be applied to other climate model simulations to provide greater confidence in the estimates of future changes in the severe weather environment. In addition to the scientific outcomes, there will be important training outcomes from this project. The graduate student employed on this project will gain valuable training and experience in climate diagnostics, climate modeling, and severe weather, which will allow him/her to contribute better to future research and development in climate change and its impacts in the United States. In addition, the PIs anticipate that the datasets generated will be used in the OU National Weather Center's NSF-sponsored Research Experiences for Undergraduates as the basis for projects.

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