  Cloud computing offers an opportunity to run compute-resource intensive
climate models at scale by parallelising model runs such that datasets useful
to the exoplanet community can be produced efficiently. To better understand
the statistical distributions and properties of potentially habitable planetary
atmospheres we implemented a parallelised climate modelling tool to scan a
range of hypothetical atmospheres.Starting with a modern day Earth atmosphere,
we iteratively and incrementally simulated a range of atmospheres to infer the
landscape of the multi-parameter space, such as the abundances of biological
mediated gases (\ce{O2}, \ce{CO2}, \ce{H2O}, \ce{CH4}, \ce{H2}, and \ce{N2})
that would yield `steady state' planetary atmospheres on Earth-like planets
around solar-type stars. Our current datasets comprises of \numatmospheres
simulated models of exoplanet atmospheres and is available publicly on the NASA
Exoplanet Archive. Our scalable approach of analysing atmospheres could also
help interpret future observations of planetary atmospheres by providing
estimates of atmospheric gas fluxes and temperatures as a function of altitude.
Such data could enable high-throughput first-order assessment of the potential
habitability of exoplanetary surfaces and sepcan be a learning dataset for
machine learning applications in the atmospheric and exoplanet science domain.
