Physics
Scientific paper
Nov 2011
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011jgre..11611010m&link_type=abstract
Journal of Geophysical Research, Volume 116, Issue E11, CiteID E11010
Physics
2
Hydrology: Model Calibration (3333), Atmospheric Processes: Clouds And Aerosols, Atmospheric Processes: Global Climate Models (1626, 4928), Atmospheric Processes: Radiative Processes, Planetary Sciences: Solar System Objects: Mars
Scientific paper
Airborne dust is the main driver of Martian atmospheric temperature, and accurately accounting for its radiative effect in Global Climate Models (GCMs) is essential. This requires the modeling of the dust distribution and radiative properties, and when trying to simulate the true climate variability, the use of the observed dust column opacity to guide the model. A recurrent problem has been the inability of Mars GCMs to predict realistic temperatures while using both the observed dust radiative properties and column opacity. One would have to drive the model with a tuned opacity to reach an agreement with the observations, thereby losing its self-consistency. In this paper, we show that using the most recently derived dust radiative properties in the LMD (Laboratoire de Météorologie Dynamique) GCM solves this problem, which was mainly due to the underestimation of the dust single scattering albedo in the solar domain. However, an overall warm temperature bias remains above the 1 hPa pressure level. We therefore refine the model by implementing a “semi-interactive” dust transport scheme which is coupled to the radiative transfer calculations. This scheme allows a better representation of the dust layer depth in the model and thereby removes the remaining warm bias. The LMD/GCM is now able to predict accurate temperatures without any tuning of the dust opacity used to guide the model. Remaining discrepancies are discussed, and seem to be primarily due to the neglect of the radiative effect of water-ice clouds, and secondarily to persisting uncertainties in the dust spatial distribution.
Forget Francois
Madeleine Jean-Baptiste
Millour Ehouarn
Montabone Luca
Wolff Mike J.
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