HR: 1340h
AN: A23B-0799    [Abstracts]
TI: Predicting Climate Sensitivity Using Observations of Fluctuations in a Model with Adjustable Feedbacks
AU: * Kirk-Davidoff, D B
EM: dankd@atmos.umd.edu
AF: University of Maryland, 3423 Computer and Space Sciences Bldg., College Park, MD 20742 United States
AB: The availability of high spatial and spectral resolution infrared radiances from instruments such as AIRS places renewed emphasis on the development of analytic tools for the comparison of model output and climate date. Recent work by Cionni et al. (2004) and by Haskins et al. (1999) indicates that analysis of short term fluctuations may provide a measure of equilibrium climate sensitivity. Cionni et al. (2004) analyzed only a single model. We extend their work by making use of a model with variable climate sensitivity, to test the ability of this method to predict the equilibrium climate sensivity on the basis of short-term fluctuations. We analyze the behavior of a one-dimensional radiative-convective model subject to various stochastic forcings, and for various climate sensitivities, modulated by either albedo or water vapor feedbacks. Results will be shown for the model's response to solar and CO$_2$ forcing. We compare the equilibrium model sensitivity to these forcings with the sensitivity derived from the Fluctuation Dissipation Theorem, as discussed in Cionni et al. (2004). We use the radiative-convective model of Emmanuel (1991), modified to incorporate parameters that adjust the strength of various feedbacks. Equilibrium climate sensitivity are compared for control, fixed water vapor, and enhanced surface-albedo feedback runs with fixed carbon dioxide and solar constant. The ratio of the climate sensitivities for the control and modified sensitiviy cases was calculated. The same parameter settings are then used in cases where the CO$_2$ or the solar forcing are varied stochastically. The method of Cionni et al. (2004) is used to estimate the climate sensitivity by calculating the lag covariance of the forcing and response for the fluctuating forcing cases.
UR: http://www.atmos.umd.edu/$\sim$dankd/FDT.html
DE: 3300 METEOROLOGY AND ATMOSPHERIC DYNAMICS
DE: 1620 Climate dynamics (3309)
SC: Atmospheric Sciences [A]
MN: 2004 AGU Fall Meeting