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The change in the amount of water on Mars has seriously affected the greenhouse effect of the planet

Previously, many rivers were present on the Red Giant, but the reasons for the planet's drying up are still unknown.

Early Mars had rivers, but the cause of Mars’s wet-to-dry transition remains unknown. Past climate on Mars can be probed using the spatial distribution of climate-sensitive landforms. We analyzed global databases of water-worked landforms and identified changes in the spatial distribution of rivers over time. In other words, river-forming climates on early Mars were warm and wet first, and cold and wet later. Unexpectedly, analysis of the greenhouse effect within our ensemble of global climate model simulations suggests that this shift was primarily driven by waning non-CO2 radiative forcing, and not changes in CO2 radiative forcing.


Our approach uses Mars’s geologic record of precipitation-fed water runoff (meltwater and/or rain) spanning multiple eras and references therein. Early on, spatially pervasive and regionally integrated valley networks formed. Later, 3.6 to 3.0 Ga ago, spatially patchy alluvial fans formed. The fans mostly did not result from localized impact-induced precipitation and record a time span of >20 million years of river flow. Data suggest that during this period, conditions were only intermittently wet enough for surface runoff. The early period of valley networks and the later formation of alluvial fans show distinct spatial distributions of rivers. (We exclude rivers not formed by precipitation, e.g., associated with groundwater outbursts, as well as other features that might record nonprecipitation processes; see the Supplementary Materials.) We use the shifts in the spatial distribution of precipitation-fed rivers to assess past changes in the strength of the atmospheric greenhouse effect. In broad outline, this approach has been attempted previously, using Viking data to analyze pre–valley network era changes. Here, we study post–valley network era change using new data and new models, and draw a different conclusion.





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