23 May 2023
 | 23 May 2023
Status: this preprint is open for discussion.

Sensitivity analysis of a Martian atmospheric column model with data from Mars Science Laboratory

Joonas Leino, Ari-Matti Harri, Mark Paton, Jouni Polkko, Maria Hieta, and Hannu Savijärvi

Abstract. An extensive sensitivity analysis was performed for a horizontally homogeneous and hydrostatic 1-D column model at the Mars Science Laboratory (MSL) location. Model experiments were compared with observations from the Curiosity Rover Environmental Monitoring Station humidity (REMS-H) device. Based on our earlier column model investigations, model surface temperature and pressure, dust optical depth (τ) and column precipitable water content (PWC) were the initial parameters that we investigated by our sensitivity analysis. Our analysis suggests that the most sensitive initial parameters for the column model temperature profile are τ and the surface temperature. The initial value of PWC does not affect the temperature profile of the model, but it is the most important parameter for the humidity profile. The initial value of τ also seems to have some effect on the humidity profile of the model. Based on our analysis, variations in surface pressure initialization are negligible for the model’s humidity and temperature predictions. The model simulations are generally in good agreement with the observations. Our analysis suggest that a slightly different shape of the model’s initial humidity profile could yield better results in the predicted water vapor volume mixing ratios at 1.6 m.

Joonas Leino et al.

Status: open (extended)

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Joonas Leino et al.

Joonas Leino et al.


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Short summary
The 1-D column model has been used extensively in studying the Martian atmosphere at various landing sites. In this study, we investigated the sensitivity of the column model to its initial parameters. The results of the model were compared with Curiosity rover measurements. Initial value of airborne dust and surface temperature had the greatest influence on the temperature prediction, while the initial value of atmospheric moisture modified the humidity prediction the most.