HR: 08:45h
AN: A51F-04    [Abstracts]
TI: Attributing Climate Change Using GPS Occultation Data
AU: * Leroy, S
EM: leroy@huarp.harvard.edu
AF: Harvard University, Anderson Group 12 Oxford St., Link Building, Cambridge, MA 02138 United States
AU: Anderson, J
EM: anderson@huarp.harvard.edu
AF: Harvard University, Anderson Group 12 Oxford St., Link Building, Cambridge, MA 02138 United States
AU: Dykema, J
EM: dykema@huarp.harvard.edu
AF: Harvard University, Anderson Group 12 Oxford St., Link Building, Cambridge, MA 02138 United States
AB: Climate signal detection using optimal techniques has been shown to be a natural result of a Bayesian formulation for testing climate models. We use the ensemble of runs of climate models generated for the IPCC Fourth Assessment Report to investigate what signals are common to all models, how models differ, and whether a robust indicator of anthropogenic climate change exists. Considering that GPS radio occultation provides the most absolutely accurate record of the climate, especially in the vicinity of the tropopause, we apply optimal methods to GPS occultation data. We find that the most reliable indicator of anthropogenic change and most robust forecast for climate change is poleward migration of the mid-latitude jet streams, which should be detectable with 99% confidence in 10-20 years. Also, thermal expansion of the troposphere is a more reliable diagnostic of global change than global surface air temperature because the former is far less sensitive to surface effects than the latter. We use the 10-year baseline of GPS occultation data as collected by GPS/MET (1995-1997), CHAMP (2001-present), and SAC-C (2001-present) to detect poleward migration of the jet streams using optimal methods. Confirmed detection would amount to the most basic test of climate models' forecasting capability. Results of this analysis will be presented.
DE: 1610 Atmosphere (0315, 0325)
DE: 1616 Climate variability (1635, 3305, 3309, 4215, 4513)
DE: 1626 Global climate models (3337, 4928)
DE: 1640 Remote sensing (1855)
SC: Atmospheric Sciences [A]
MN: Fall Meeting 2005