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On atmospheric radiative feedbacks associated with climate variability and change
Authors:R A Colman  L I Hanson
Institution:1. Centre for Australian Weather and Climate Research, Bureau of Meteorology, GPO Box 1289, Melbourne, VIC, 3001, Australia
Abstract:Using the method of radiative ‘kernels’ an analysis is made of water vapour, lapse rate and ‘Planck’ (uniform vertical temperature) long wave feedbacks in models participating in the World Climate Research Program (WCRP) Coupled Model Intercomparison Project phase 3 (CMIP3). Feedbacks are calculated at climate change timescales from the A1B scenario, and at three ‘variability’ timescales from the corresponding preindustrial experiments: seasonal, interannual and decadal. Surface temperature responses show different meridional patterns for the different timescales, which are then manifest in the structures of the individual feedbacks. Despite these differences, mean water vapour feedback strength in models is positive for all models and timescales, and of comparable global magnitude across all timescales except for seasonal, where it is much weaker. Taking into consideration the strong positive lapse rate feedback at seasonal timescales, combined water vapour/lapse rate feedback is indeed similar across all timescales. To a good approximation, global water vapour feedback is found to be well represented by the temperature response along with an assumption of unchanged relative humidity under both variability and climate change. A comparison is also made of model feedbacks with reanalysis derived feedbacks for seasonal and interannual timescales. No strong relationships between individual modelled feedbacks at different timescales are evident: i.e., strong feedbacks in models at variability timescales do not in general predict strong climate change feedback, with the possible exception of seasonal timescales. There are caveats on this (and other) findings however, from uncertainties associated with the kernel technique and from, at times, very large uncertainties in estimating variability related feedbacks from temperature regressions.
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