A Large Repository of 3D Climate Model Outputs for Community Analysis and Postprocessing

Abstract

As the number of known exoplanets has climbed into the thousands, efforts by theorists to understand the diversity of climates that may exist on terrestrial planets in the habitable zone have also accelerated. These efforts have ranged from analytical, to simple 0-D, 1-D, and 2-D models, to highly-sophisticated 3D global climate models (GCMs) adapted from Earth climate and weather models. The advantage of the latter is that fewer physical processes are reduced to simple parameterizations and empirical fits, and may instead be represented by physically-motivated algorithms. However, many such models are difficult to use, and take a long time to reach a converged state relative to simpler models, thereby limiting the amount of parameter space that can be explored. We use PlaSim, a 3D climate model of intermediate complexity, to bridge this gap, allowing us to produce hundreds to thousands of model outputs that have reached energy balance equilibrium at the surface and top of the atmosphere. We are making our model outputs available to the community in a permanent Dataverse repository (https://dataverse.scholarsportal.info/dataverse/kmenou). A subset of our model outputs can be used directly with external spectral postprocessing tools, and we have used them with petitRADTRANS and SBDART in order to create synthetic observables representative of fully-3D climates. Another natural use of this repository will be to use more-sophisticated GCMs to cross-check and verify PlaSim's results, and to explore in more detail those regions of the exoplanet parameter space identified in our PlaSim results as being of particular interest. We will continue to add models to this repository in the future, including more than

The Sparse Atmospheric Model Sampling Analysis (SAMOSA) Intercomparison: Motivations and Protocol Version 1.0: A CUISINES Model Intercomparison Project

“…Directly comparing the complete set of 460 ExoPlaSim cases to a full GCM would be computationally expensive, but such comparisons can instead be accomplished by using sparse sampling methods to select a small number of cases from the complete set to be simulated with a full GCM. Paradise et al (2020) emphasized the need for such comparisons by calling for "studies which sparsely re-sample the PlaSim-surveyed parameter space with higher-complexity GCMs," which would "be useful in verifying PlaSim's results, as well as in helping to indicate where PlaSim is inaccurate or likely has missing physics (such as a dynamic ocean or sea-ice drift)." This model protocol paper represents an attempt to conduct such a sparse sampling comparison between ExoPlaSim and other models.…”

ExoPlaSim models for "Climate uncertainties caused by unknown land distribution on habitable M-Earths"

“…Our work highlights the need for detailed surface conditions in M-Earth climate models. All of our simulation outputs, as well as the files needed to reproduce them, are available in a permanent Dataverse repository 1 (Paradise et al 2020;Macdonald et al 2021).…”