Björn Grieger

MAIC-2, a latitudinal model for the Martian surface temperature, atmospheric water transport and surface glaciation

Abstract The Mars Atmosphere-Ice Coupler MAIC-2 is a simple, latitudinal model, which consists of a set of parameterisations for the surface temperature, the atmospheric water transport and the surface mass balance (condensation minus evaporation) of water ice. It is driven directly by the orbital parameters obliquity, eccentricity and solar longitude ( L s ) of perihelion. Surface temperature is described by the Local Insolation Temperature (LIT) scheme, which uses a daily and latitude-dependent radiation balance. The evaporation rate of water is calculated by an expression for free convection, driven by density differences between water vapor and ambient air, the condensation rate follows from the assumption that any water vapour which exceeds the local saturation pressure condenses instantly, and atmospheric transport of water vapour is approximated by instantaneous mixing. Glacial flow of ice deposits is neglected. Simulations with constant orbital parameters show that low obliquities favour deposition of ice in high latitudes and vice versa. A transient scenario driven by a computed history of orbital parameters over the last 10 million years produces essentially monotonically growing polar ice deposits during the most recent 4 million years, and a very good agreement with the observed present-day polar layered deposits. The thick polar deposits sometimes continue in thin ice deposits which extend far into the mid latitudes, which confirms the idea of “ice ages” at high obliquity.

Inverse radiation modeling of Titan's} atmoshere to assimilate {Solar Aureole Imager} data of the {Huygens probe

Abstract During the descent of the Huygens probe through Titans atmosphere in January 2005, the Descent Imager/Spectral Radiometer (DISR) will perform upward and downward looking measurements at various spectral ranges and spatial resolutions. This internal radiation density could be estimated by radiative transfer calculations for Titans atmosphere. However, to do this, the optical properties—i.e. volume extinction coefficient, single scattering albedo and scattering phase function—have to be prescribed at every altitude, and these are apriori not known. Herein, an inverse approach is investigated, which retrieves the single scattering albedo and the phase function of the aerosols from DISR observations. The method uses data from a DISR subinstrument, the Solar Aureole imager (SA), to estimate the optical properties of the atmospheric layer between two successive observation altitudes. A unique solution for one layer can in principle be calculated directly from a linear system of equations, but due to the sparseness of the data and the unavoidable noise in the measurements, the inverse problem is ill-posed. The problem is stabilized by the regularization method requiring smoothness of the resultant solution. A consistent set of solutions for all layers is obtained by iterating several times downward and upward through the layers. The method is tested in a simulated radiation density scenario for Titan, which is based on a microphysical aerosol model for the haze layer. Within this scenario, the expected coverage of SA data allows a reconstruction of the angular dependence of the scattering phase function with an explained variance better than 90%.

Interpolating paleovegetation data with an artificial neural network approach

Abstract To drive an atmospheric general circulation model (AGCM), land surface boundary conditions like albedo and morphological roughness, which depend on the vegetation type present, have to be prescribed. For the late Quaternary there are some data available, but they are still sparse. Here an artificial neural network approach to assimilate these paleovegetation data is investigated. In contrast to a biome model the relation between climatological parameters and vegetation type is not based on biological knowledge but estimated from the available vegetation data and the AGCM climatology at the corresponding locations. For a test application, a data set for the modern vegetation reduced to the amount of data available for the Holocene climate optimum (about 6000 years B.P.) is used. From this, the neural network is able to reconstruct the complete global vegetation with a kappa value of 0.56. The most pronounced errors occur in Australia and South America in areas corresponding to large data gaps.