Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2503.09136

Earth as an Exoplanet: Investigating the effects of cloud variability on the direct-imaging of atmospheres

Soumil Kelkar·Prabal Saxena·Ravi Kopparapu·Joy Monteiro
View original source ↗Read source document ↗Export metadata ↓

Abstract

A planet's spectrum is dynamic and only represents a time-dependent snapshot of its properties. Changing atmospheric conditions due to climate and weather patterns, particularly variation in cloud cover, can significantly affect the spectrum in ways that complicate the understanding of a planet's baseline atmospheric properties. Variable cloud cover and cloud properties affect the detectability of atmospheric constituents, and also greatly influence the radiative transfer that determines a planet's spectrum. This has considerable implications for direct imaging observations of potentially habitable exoplanets and thus it is critical to study and characterize the effects of clouds on their spectra. Clouds have been extensively modeled before and their effects have been incorporated across climate frameworks spanning a spectrum of complexity. Given the challenges associated with modeling clouds, we adopt a novel approach in this work to study the effects of clouds by using real-time cloud data from Earth observations. Treating Earth as an exoplanet and using detailed observations from the MERRA 2 data collection, we quantify the effects of cloud variability on the spectrum as well as on the detectability of atmospheric constituents, specifically biomarkers like O2, O3 and H2O. The coverage and vertical position of clouds significantly affects the SNRs of these gases and subsequently their detectability in exo-Earth atmospheres. Moreover, we show that variations in the amount of cloud cover will potentially confound efforts to retrieve a stable baseline atmosphere for a planet. This work has important applications to future direct-imaging missions like the Habitable Worlds Observatory (HWO).

Explore related subjects

astro-ph.EP

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Soumil Kelkar, Prabal Saxena, Ravi Kopparapu, Joy Monteiro. 2025-03-12. Earth as an Exoplanet: Investigating the effects of cloud variability on the direct-imaging of atmospheres. https://arxiv.org/abs/2503.09136

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Strict Limits on Helium Absorption from LHS 1140 b from Four JWST NIRISS Transits

Orbiting in the habitable zone of its host star, the 1.7 $R_\oplus$, 5.6 $M_\oplus$ planet LHS 1140 b is a target of great interest. Recently Cherubim et al. (2026) published a detection of metastable He escaping from the atmosphere of LHS 1140 b, simultaneously providing the first concrete inference of an atmosphere on this planet and indicating that the atmosphere is He-rich and H-poor as would be expected due to Gyrs of fractionated mass loss. In this work, we analyze four archival transits of LHS 1140 b, spanning Dec 2023 to Jul 2026, taken with the NIRISS instrument on JWST, for evidence of He absorption. Each of the four visits disfavours the presence of He absorption compared to a flat continuum with odds ratios ranging from 3.9--11.6:1. He absorption with an amplitude and width equivalent to that observed by Cherubim et al. (2026) is strongly ruled out by the data with odds ratios from 300--8.6$\times$10$^4$:1 compared to a flat continuum --- though it should be noted that none of the JWST transits are contemporaneous with the Cherubim et al. (2026) detection. We also fit the absolute out-of-transit stellar spectra from these four visits, as well as an additional JWST NIRISS transit of planet c, to search for evidence of stellar variability, but find consistent photosphere and herterogeneity parameters in all five datasets. In all, our work provides a set of strict limits on He escape from LHS 1140 b that will be valuable to future studies into the nature and evolution of this intriguing world.

astro-ph.EP↗
arXiv2026

Bridging magnetothermal winds and photoevaporation to model discs dispersal

Protoplanetary disc dispersal is driven by two processes usually modelled separately: photoevaporative and magnetohydrodynamic (MHD) disc winds. Global simulations indicate that in the inner disc these are not distinct outflows but a single magnetothermal wind. We assemble a closed-form, two-phase model that respects it. A single-field-line wind, whose base is fixed by the irradiated temperature and penetration column, supplies the launch and feeds a secular evolution, with photoevaporation convolved on as a sink. The flux closure $B_z\proptoΣ^q$ is self-limiting: for $q\le1/2$ depletion alone cannot demagnetise the disc, so dispersal requires independent flux loss, parameterised by the magnetic Reynolds number $\mathcal{R}_m$. Integrating the coupled system yields two regimes. Efficient flux loss ($\mathcal{R}_m\lesssim1$) lets the magnetisation front recede by over an order of magnitude and opens a photoevaporative gap. Flux retention ($\mathcal{R}_m\gg1$) drives the front outward, sustains accretion, and defers dispersal by $\approx2.7$~Myr. Deriving the base from stellar irradiation instead of prescribing it, we find that the cold-launch approximation is valid during the early stages of disc evolution: anchoring the base at plasma equipartition ($β_{\rm base} \sim 1$) confines irradiation's influence on the magnetic lever arm to the magnetothermal annulus, decoupling the peak accretion rate from the incident flux. Both regimes clear the disc inside-out, through either a photoevaporatively amplified cavity wall or an expanding magnetothermal front.

astro-ph.EP↗
arXiv2026

Faithful Neural Embeddings for 3D Exoplanet Climate Modeling

With the rapid advancement of telescopes like JWST and Ariel, there is an urgent need for efficient 3D climate models to interpret observations of exoplanet atmospheres. Traditional 3D general circulation models (GCMs) are computationally intensive, prompting the development of machine learning (ML) emulators to accelerate simulations. Recent work, such as that by Plaschzug et al. 2026 \cite{plaschzug2026accelerating}, uses a dense neural network (DNN) to predict local gas temperatures and winds from input parameters, including local gas pressure, spatial coordinates (longitude and latitude), and global temperature. However, this model relies on predicting individual temperature values (points) at specific grid points, which can be limited by the resolution and constraints of the training grid. In this work, we investigate a couple of alternative frameworks based on latent-space representations of local gas temperature ($\text{T}_{\text{gas}}$) to obtain a faithful, low-dimensional representation of these profiles. This represents the first step toward developing a latent space regression model, offering a structurally cohesive alternative to the existing point-wise prediction method \cite{plaschzug2026accelerating}. By capturing the optimal embedding space of atmospheric data, our proposed framework can produce simulated profiles while maintaining computational efficiency, making it suitable for large-scale exoplanet ensemble studies.

astro-ph.EP↗