We present results of simulations with the Max Planck Institute Martian general circulation model implementing a hydrological cycle scheme. The simulations reveal a seasonal water “pump” mechanism responsible for the upward transport of water vapor. This mechanism occurs in high latitudes above 60° of the southern hemisphere at perihelion, when the upward branch of the meridional circulation is particularly strong. A combination of the mean vertical flux with variations induced by solar tides facilitates penetration of water across the “bottleneck” at approximately 60 km. The meridional circulation then transports water across the globe to the northern hemisphere. Since the intensity of the meridional cell is tightly controlled by airborne dust, the water abundance in the thermosphere strongly increases during dust storms.

Simulations with the Max Planck Institute Martian general circulation model for Martian years 28 and 34 reveal details of the water “pump” mechanism and the role of gravity wave (GW) forcing. Water is advected to the upper atmosphere mainly by upward branches of the meridional circulation: in low latitudes during equinoxes and over the south pole during solstices. Molecular diffusion plays little role in water transport in the middle atmosphere and across the mesopause. GWs modulate the circulation and temperature during global dust storms, thus changing the timing and intensity of the transport. At equinoxes, they facilitate water accumulation in the polar warming regions in the middle atmosphere followed by stronger upwelling over the equator. As equinoctial storms decay, GWs tend to accelerate the reduction of water in the thermosphere. GWs delay the onset of the transport during solstitial storms and change the globally averaged amount of water in the upper atmosphere by 10%–25%.

Within the numerical general-circulation model of the Martian atmosphere MAOAM (Martian Atmosphere: Observation and Modeling), we have developed the water cycle block, which is an essential component of modern general circulation models of the Martian atmosphere. The MAOAM model has a spectral dynamic core and successfully predicts the temperature regime on Mars through the use of physical parameterizations typical of both terrestrial and Martian models. We have achieved stable computation for three Martian years, while maintaining a conservative advection scheme taking into account the water–ice phase transitions, water exchange between the atmosphere and surface, and corrections for the vertical velocities of ice particles due to sedimentation. The studies show a strong dependence of the amount of water that is actively involved in the water cycle on the initial data, model temperatures, and the mechanism of water exchange between the atmosphere and the surface. The general pattern and seasonal asymmetry of the water cycle depends on the size of ice particles, the albedo, and the thermal inertia of the planet’s surface. One of the modeling tasks, which results from a comparison of the model data with those of the TES experiment on board , is the increase in the total mass of water vapor in the model in the aphelion season and decrease in the mass of water ice clouds at the poles. The surface evaporation scheme, which takes into account the turbulent rise of water vapor, on the one hand, leads to the most complete evaporation of ice from the surface in the summer season in the northern hemisphere and, on the other hand, supersaturates the atmosphere with ice due to the vigorous evaporation, which leads to worse consistency between the amount of the precipitated atmospheric ice and the experimental data. The full evaporation of ice from the surface increases the model sensitivity to the size of the polar cap; therefore, the increase in the latter leads to better results. The use of a more accurate dust scenario changes the model temperatures, which also strongly affects the water cycle.

Observations of excited hydroxyl (OH*) emissions are broadly used for inferring information about atmospheric dynamics and composition. We present several analytical approximations for characterizing the excited hydroxyl layer in the Martian atmosphere. They include the OH* number density at the maximum and the height of the peak, along with the relations for assessing different impacts on the OH* layer under night-time conditions. These characteristics are determined by the ambient temperature, atomic oxygen concentration, and their vertical gradients. The derived relations can be used for the analysis of airglow measurements and the interpretation of their variations.

Observations of vibrationally excited hydroxyl (OH*) emissions are widely used to obtain information on atmospheric dynamics and composition. In this paper, several analytical approximations are presented for characteristics of the hydroxyl layer in the Martian atmosphere, such as OH* concentrations at the maximum and the height of the maximum. Relationships are also given for estimating the influence of various factors on the OH* layer in nighttime conditions. These characteristics are determined by the ambient temperature and the concentration of atomic oxygen, including their vertical gradients. The obtained relationships are applied to the results of numerical modeling using the global atmospheric circulation model to predict the seasonal behavior of the hydroxyl layer on Mars. Based on the modeling data, the annual and intra-annual variations in the concentration of excited hydroxyl and the height of the OH* layer on Mars show both similarities to and considerable differences from those on Earth. The concentration and the height of the maximum in the equatorial, northern, and southern middle latitudes vary with the season, with the maximum concentrations and the lowest height being recorded in the first half of the year. Model calculations confirmed the presence of a peak in the OH* concentration in polar latitudes in winter at a height of approximately 50 km with volume emission densities of 2.1, 1.4, and 0.6 × 10⁴ photons cm^–3 s^–1 for the transitions of vibrational levels 1–0, 2–1, and 2– 0, respectively. The resulting relationships can be used to analyze measurements and interpret their variations.