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lmd_Dufresne2009_abstracts.html

2009 .

(2 publications)

V. Eymet, R. Fournier, J.-L. Dufresne, S. Lebonnois, F. Hourdin, and M. A. Bullock. Net exchange parameterization of thermal infrared radiative transfer in Venus' atmosphere. Journal of Geophysical Research (Planets), 114:11008, November 2009. [ bib | DOI | ADS link ]

Thermal radiation within Venus atmosphere is analyzed in close details. Prominent features are identified, which are then used to design a parameterization (a highly simplified and yet accurate enough model) to be used in General Circulation Models. The analysis is based on a net exchange formulation, using a set of gaseous and cloud optical data chosen among available referenced data. The accuracy of the proposed parameterization methodology is controlled against Monte Carlo simulations, assuming that the optical data are exact. Then, the accuracy level corresponding to our present optical data choice is discussed by comparison with available observations, concentrating on the most unknown aspects of Venus thermal radiation, namely the deep atmosphere opacity and the cloud composition and structure.

A. Hannart, J.-L. Dufresne, and P. Naveau. Why climate sensitivity may not be so unpredictable. Geophysical Research Letters, 36:16707, August 2009. [ bib | DOI | ADS link ]

Different explanations have been proposed as to why the range of climate sensitivity predicted by GCMs has not lessened substantially in the last decades, and subsequently if it can be reduced. One such study (Why is climate sensitivity so unpredictable?) addressed these questions using rather simple theoretical considerations and reached the conclusion that reducing uncertainties on climate feedbacks and underlying climate processes will not yield a large reduction in the envelope of climate sensitivity. In this letter, we revisit the premises of this conclusion. We show that it results from a mathematical artifact caused by a peculiar definition of uncertainty used by these authors. Applying standard concepts and definitions of descriptive statistics to the exact same framework of analysis as Roe and Baker, we show that within this simple framework, reducing inter-model spread on feedbacks does in fact induce a reduction of uncertainty on climate sensitivity, almost proportionally. Therefore, following Roe and Baker assumptions, climate sensitivity is actually not so unpredictable.

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