Multiple hypotheses/models have been put forward regarding Earth’s cooling history. Searching for life beyond Earth has brought these models into a new light as they connect to an energy source that life can tap. Discriminating between different cooling models and adapting them to aid in the assessment of planetary habitability has been hampered by a lack of uncertainty quantification. Here, we provide an uncertainty quantification that accounts for a range of interconnected model uncertainties. This involved calculating over a million individual model evolutions to determine uncertainty metrics. Accounting for uncertainties means that model results must be evaluated in a probabilistic sense, even though the underlying models are deterministic. The uncertainty analysis was used to quantify the degree to which different models can satisfy observational constraints on the Earth’s cooling. For the Earth’s cooling history, uncertainty leads to ambiguitymultiple models, based on different hypotheses, can match observations. This has implications for using such models to forecast conditions for exoplanets that share Earth characteristics but are older than the Earth, i.e., ambiguity has implications for modeling the long-term life potential of terrestrial planets. Even for the most earthlike planet we know of, the Earth itself, model uncertainty and ambiguity leads to large forecast spreads. Given that Earth has the best data constraints, we should expect larger spreads for models of terrestrial planets, in general. The uncertainty analysis provided here can be expanded by coupling planetary cooling models to climate models and propagating uncertainty between them to assess habitability from a probabilistic view.
Thermal history models, historically used to understand Earth’s geologic history, are being coupled to climate models to map conditions that allow planets to maintain life. However, the lack of structural uncertainty assessment has blurred guidelines for how thermal history models can be used toward this end. Structural uncertainty is intrinsic to the modeling process. Model structure refers to the cause and effect relations that define a model and are assumed to adequately represent a particular real world system. Intrinsic/structural uncertainty is different from input and parameter uncertainties (which are often evaluated for thermal history models). A full uncertainty assessment requires that input/parametric and intrinsic/structural uncertainty be evaluated (one is not a substitute for the other). We quantify the intrinsic uncertainty for several parameterized thermal history models (a subclass of planetary models). We use single perturbation analysis to determine the reactance time of different models. This provides a metric for how long it takes low?amplitude, unmodeled effects to decay or grow. Reactance time is shown to scale inversely with the strength of the dominant model feedback (negative or positive). A perturbed physics analysis is then used to determine uncertainty shadows for model outputs. This provides probability distributions for model predictions. It also tests the structural stability of a model (do model predictions remain qualitatively similar, and within assumed model limits, in the face of intrinsic uncertainty?). Once intrinsic uncertainty is accounted for, model outputs/predictions and comparisons to observational data should be treated in a probabilistic way.
The search for an inhabited planet, beyond our own, is a driver of planetary exploration in our solar system and beyond. Using information from our own planet to inform search strategies allows for a targeted search. It is, however, worth considering some span in the strategy and in a priori expectation. An inhabited, Earth-like planet is one that would be similar to Earth in ways that extend beyond having biota. To facilitate a comparative cost/risk/benefit analysis of different potential search strategies, we use a metric akin to the Earth-similarity index. The metric extends from zero, for an inhabited planet that is like Earth in all other regards (i.e., zero differences), toward end-member values for planets that differ from Earth but maintain life potential. The analysis shows how finding inhabited planets that do not share other Earth characteristics could improve our ability to assess galactic life potential without a large increase in time-commitment costs. Search strategies that acknowledge the possibility of such planets can minimize the potential of exploration losses (e.g., searching for long durations to reach conclusions that are biased). Discovering such planets could additionally provide a test of the Gaia hypothesisa test that has proven difficult when using only Earth as a laboratory. Finally, we discuss how an Earth2.0 narrative that has been presented to the public as a search strategy comes with nostalgia-laden baggage that does not best serve exploration.