Modeling Exotic CO2 Clouds On Mars – MECCOM

Modeling the Martian CO2 clouds is a real challenge. In the mesosphere, the scarcity of the ice nuclei and the low density of the atmosphere force us to carefully revise the microphysical theories used. In the polar troposphere, the clouds are convective and/or generated by atmospheric waves, and the coupling between the dynamics and the latent heat release is strong. The approach we will apply in the MECCOM project is multiscale modeling of these clouds and their interactions with their environment based on unique modeling tools. Recently our group developed a state-of-the-art microphysical column (1D) model for CO2 clouds. The coupling of this model to the Mars Global Climate Model (MGCM) developed jointly by LATMOS and LMD laboratories was achieved by our team and our close collaborators and the MECCOM project has allowed the validation of this coupling. In addition, the MGCM and the Mars Mesoscale Model (MMM) developed also at the LMD share the same physical parameterizations, allowing for immediate transfer of the developed microphysics to the MMM. The MMM model that has a higher resolution than the MGCM in a more limited simulation area, allows for even higher spatial and temporal resolution simulations, important for studying convective processes.
With this method, and with the baseline of the existing observational
datasets, we will be able to construct a unified view of the CO2 cycle, the role of the clouds in that cycle, and of the microphysics that pilots their formation and evolution.
We have also applied machine learning methods to reanalyse an old dataset that provides information on the polar CO2 clouds on Mars.

We have produced the first simulations coupling CO2 cloud microphysics and a global climate model. The microphysics account for all necessary processes required for detailed modelling of the formation and the evolution of the clouds. The formation of these clouds involves three types of ice nuclei: dust, water ice crystals and small nanoparticles formed as a result of the ablation of meteorites in the middle atmosphere. These three sources are accounted for and have profound effects on the formation of the clouds. In particular in the mesosphere water ice crystals and meteoric particles are required for the formation of CO2 clouds.
Snowfall in the polar regions has a significant effect on the emissivity of the surface and CO2 clouds influence that of the atmosphere. Especially the particle size has a major influence on the emissivity. Now the particle size in calculated by the model microphysics in a self-consistent manner, providing a realistic baseline for the radiative transfer calculations. The radiative effect of the CO2 clouds has been added in the model and is significant mainly in the polar regions where thick CO2 clouds form during the polar night.
We have also produced a new cloud catalog through Machine Learning applied to a decades-old dataset. We applied a k-means unsupervised machine learning method to the dataset of the MOLA altimeter, flown on Mars Global Surveyor twenty years ago. MOLA was made for mapping the Martian surface, but it also detected ice and dust clouds. We have reanalysed these data with the modern machine learning methods and have produced a cloud catalog providing a larger cloud dataset than what has been previously published with these data. This catalog will be used for in-depth studies on the nature of the clouds and we aim at distinguishing different cloud categories thanks to the multiple nature of Martian aerosols (dust, water ice, CO2 ice).

Our project provides two state-of-the-art tools: two numerical atmospheric models for Mars including CO2 cloud microphysics and the radiative effect of the clouds. These two models, the global climate model and the mesoscale model, are used to answer remaining questions on these clouds that observations have not been able to elucidate. These models will be used for studying multiple aspects of the Martian atmosphere and will be among the most complete in the community.

Our machine learning project is among the first to apply machine learning techniques on data on planetary atmospheres. It is paving the way for future applications of these methods.

We have produced a cloud catalog based on the results of our machine learning project. It provides a map of cloud detections with the MOLA altimeter during more than a Martian year, covering the whole planet. The catalog will be published soon and provided to the scientific community through a data repository.

The Mars global climate model and the mesoscale model, both including CO2 cloud microphysics and the cloud radiative effect, have been used to produce first simulations of one Martian year on the formation and evolution of CO2 clouds. These clouds form predominantly during the polar night at the winter pole, and strongly influence the radiative properties of the surface and the atmosphere. Their formation in the mesosphere is very sensitive to the model state and inputs. In particular, the ice nuclei for these clouds, provided by three different sources, are a limiting factor. These results will be published soon and the model outputs will be provided to the community through a data repository.

MECCOM is a fundamental research project on the formation of carbon dioxide (CO2) clouds of planet Mars that aims at realizing the first ever modeling at all scales (from microphysical to global) of these exotic clouds. They form out of the main constituent of the Martian atmosphere in the winter poles in the troposphere and in the mesosphere at tropical latitudes. Second only to Earth’s atmosphere, Martian atmosphere is among the most observed atmospheres in our Solar System, yet these clouds were only detected in the turn of last century. Thus, their formation is no more a myth; however, they still hold on to their mysteries at all scales. Their impact in the global CO2 cycle has not been quantified, their complex dynamical nature within the polar night, and also in the mesosphere, remains to be elucidated. In addition, the key to their formation in the mesosphere, the ice nuclei available above 40 km altitude, is still searched for. The formation of clouds in a near-pure vapor has obliged us to revisit the used microphysical theories, in particular concerning condensation and sublimation. The MECCOM team has been involved in studying the CO2 clouds since their discovery on both the observational and modeling fronts, and the team members are international leaders on this topic. Now the team has access to a set of models, developed in-house and in tight collaboration, ranging from detailed microphysical column models through mesoscale up to the global scale, capable of modeling the Martian weather and climate including CO2 cloud microphysics. We propose with the MECCOM project to perform numerical modeling of the Martian CO2 clouds at all scales to answer the burning questions on the role of these clouds in the Martian climate. This is required for the full understanding of the current CO2 cycle on Mars, which is one of the major climatic cycles, and of the role of the clouds in the past, warmer climate, where liquid water may have flown on the surface. The constant flow of observations of the Martian atmosphere and the development of high-resolution models allow us now to start digging into the mesoscale phenomena of the Martian atmosphere that can play an important role in its global dynamics. The polar CO2 clouds are an example of such a phenomenon: their formation probably induces dynamics similar to moist convection on the Earth, influencing the polar vortex and larger scale dynamics of the atmosphere. The mesospheric clouds seem to be a result of interaction of planetary scale and mesoscale atmospheric waves and the availability of ice nuclei that possibly are provided by hydrated mesospheric ions, hypothesized only recently. This study draws also from a comparative planetology approach: latent heat-driven ("moist") convection, orographic waves and mesospheric clouds all are known phenomena also on the Earth. MECCOM project will allow the PI to build and consolidate a team, based at LATMOS, and surrounded by a strong, highly qualified national and international network of collaborators, which it will strengthen and in which it can evolve. MECCOM will also use and promote French supercomputing resources and European space mission data.