DOI: 10.5061/dryad.2fqz612x7
title: Eccentric orbits may enhance the habitability of Earth-like exoplanets

description: The detection and characterization of Earth-like planets around Sun-like
 stars is a crucial goal of exoplanetary research, given their promise for
 hosting potentially habitable conditions. Key orbital parameters, such as
 eccentricity, can influence a planet's climate response and, as a
 consequence, affect its potential habitability. Utilizing the Earth-system
 model - the Whole Atmosphere Community Climate Model (WACCM6), we
 simulated Earth-like exoplanets with two different orbital parameters: one
 circular (e = 0) and another highly eccentric (e = 0.4), both with zero
 obliquity but fixing the annual mean insolation. The highly eccentric case
 exhibits a 1.9 K warmer surface temperature due to lower surface and cloud
 albedo and a weaker longwave cloud forcing. Exploring the annual global
 mean climate difference, we analyzed latitudinal and seasonal variations
 in hydrological cycle variables, such as sea ice, land snow, and clouds.
 Land habitability metrics based on temperature and precipitation reveal
 that the e=0.4 case has over 25% more habitable land area for more than
 80% of its orbit compared with the e=0 case. Additionally, the global
 circulation pattern shifts from a three-cell to a two-cell system in the
 e=0.4 case, expanding the Hadley cell to higher latitudes, enhancing
 meridional latent heat transport, and improving land habitability at
 higher latitudes. Our study suggests that Earth-like exoplanets with high
 eccentricity orbiting Sun-like stars may have greater land habitability
 than their circular counterparts.
description: The netCDF (.nc files) data were produced from simulations using
 the WACCM6 (CESM2.1.3) model. See https://www2.acom.ucar.edu/gcm/waccm and https://doi.org/10.1029/2019JD030943 for more information. These simulations were performed on the ARC4 HPC at the University of Leeds. The <strong><em>output variables</em></strong> from the simulations are monthly mean files (h0.files) grouped in two folders for the circular (e=0) and eccentric cases (e=0.4). To produce the figures in the manuscript, Python programming language was used along with the xarray packages.
description: # Eccentric orbits may enhance the habitability of Earth-like exoplanets
 [https://doi.org/10.5061/dryad.2fqz612x7](https://doi.org/10.5061/dryad.2fqz612x7) The datasets include multiple variables which are generated using WACCM6 in pre-industrial configuration with a 2-degree horizontal resolution and a simplified middle atmosphere chemistry (See [https://www2.acom.ucar.edu/gcm/waccm](https://www2.acom.ucar.edu/gcm/waccm) and [https://doi.org/10.1029/2019JD030943](https://doi.org/10.1029/2019JD030943) for more information.) ## Description of the data and file structure The datasets are divided into two folders, one for the high-eccentric orbit (`e=0.4`) and another for the circular orbit case (`e=0`). Each folder has the same list of variable outputs in netCDF format (.nc files). The netCDF (.nc files) data were produced from simulations using the WACCM6 (CESM2.1.3) model. The file names in each folder indicate the variable names, followed by the simulation case's name abbreviation. i.e., `TS_e0.nc` represents the surface temperature data over the simulation years for the `e=0` case and `TS_e4.nc` represents the surface temperature data over the simulation years for the `e=0.4` case. The following variables are used for the data analysis and are included in the folder `e0` and `e4`. Their definitions, units and data types are included in the data array of the corresponding netCDF files. * ASDIF:          Surface Albedo (shortwave, diffuse) (unit: number from 0 to 1) * CLDHGH:     Vertically-integrated high-level cloud fraction (unit: number from 0 to 1) * CLDLOW:     Vertically-integrated low-level cloud fraction (unit: number from 0 to 1) * CLDTOT:      Vertically-integrated total cloud fraction (unit: number from 0 to 1) * CLOUD:       Cloud fraction (unit: number from 0 to 1) * FLNT:           Net longwave flux at top of model (unit: W/m2) * FSDS:           Downwelling solar flux at surface (unit: W/m2) * FSDSC:         Clearsky downwelling solar flux at surface (unit: W/m2) * FSNS:           Net solar flux at surface (unit: W/m2) * FSNT:           Net solar flux at top of model (unit: W/m2) * FSNTOA:      Net solar flux at top of atmosphere (unit: W/m2) * FSUTOA:      Upwelling solar flux at top of atmosphere  (unit: W/m2) * ICEFRAC:     Fraction of surface area covered by sea-ice (unit: number from 0 to 1) * LWCF:          Longwave cloud forcing  (unit: W/m2) * PRECC:        Convective precipitation rate (liq + ice) (unit: m/s) * PRECL:         Large-scale (stable) precipitation rate (liq + ice) (unit: m/s) * RELHUM:     Relative humidity (unit: percent) * SOLIN:         Solar insolation (unit: W/m2) * SWCF:          Shortwave cloud forcing (unit: W/m2) * TGCLDLWP: Total grid-box cloud liquid water path (unit: kg/m2) * TS:                 Surface temperature (unit: K) * V:                   Meridional wind (unit: m/s) * Z3:                 Geopotential Height (above sea level) (unit: m) ## Code/Software Data associated with the article are analyzed/visualized using python 3 in Jupyter notebook kernel with, i.e., Xarray, Numpy and Matplotlib packages. Alternative methods are possible using other programming languages.