HR: 1340h
AN: A23A-0938    [Abstracts]
TI: Using Space-based Observations of Infrared Hyperspectral Radiance and Radio Occultation to Test Global Climate Models
AU: * Dykema, J A
EM: dykema@fas.harvard.edu
AF: Harvard University, Division of Engineering and Applied Science, 12 Oxford Street, Cambridge, MA 02138
AU: Leroy, S S
A23A-0938 AF: Harvard University, Division of Engineering and Applied Science, 12 Oxford Street, Cambridge, MA 02138
AU: Anderson, J G
A23A-0938 AF: Harvard University, Division of Engineering and Applied Science, 12 Oxford Street, Cambridge, MA 02138
AB: Accurate forecasting of future climate is a goal demanded by public and private decision makers worldwide and detailed in governmental planning documents [Goody et al., 2002; CCSP, 2003; NRC, 2005]. Examination of forecasts of key indicators of climate on regional spatial scales among state-of-the-art Global Climate Models (GCMs) indicates that levels of uncertainty acceptable to policy makers have not yet been achieved. Thus further improvements of GCMs, to be rigorously tested against observations which constrain uncertain climate feedbacks, are required. Methodologies have been described to attain this goal through regimes of climate model testing and improvement using space-based observations, particularly GPS radio occultations and hyperspectral infrared measurements [Goody et al., 1998]. These observation types are selected for their high information content, absolute calibration, and complementary sensitivity to uncertain climate feedbacks. The GCMs are tested by comparing the trends in atmospheric refractivity and top of the atmosphere outgoing spectral infrared radiance obtained from the first moments of the space-based observational ensembles. Obtaining these trends, which evolve over periods of years to decades, from independent satellite instruments requires absolute calibrations which are demonstrably tied on-orbit to international measurement standards. This level of calibration assurance is most credibly achieved through specialized instrument designs which provide the ability to validate the measurement quality independently of other instruments, climatologies, or climate model results. Testing GCMs according to this trend analysis is an offshoot of the problem of climate signal detection and attribution, derived by putting that problem in its Bayesian context [Leroy, 1998]. We show examples of this methodology applied to the multi-model ensemble of the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (4AR). The variation in trends in atmospheric refractivity and infrared hyperspectral radiance among models indicates uncertainty in model representation of cloud-radiative and water vapor-longwave feedbacks. The results from this model analysis inform the correct design of climate monitoring systems to meet the goals for long-term climate forecasting required by societal decision makers.
DE: 1610 Atmosphere (0315, 0325)
DE: 1616 Climate variability (1635, 3305, 3309, 4215, 4513)
DE: 1620 Climate dynamics (0429, 3309)
DE: 1626 Global climate models (3337, 4928)
DE: 1640 Remote sensing (1855)
SC: Atmospheric Sciences [A]
MN: Fall Meeting 2005