PICASO 4.0: Clouds and Photochemistry in Climate Models of Brown Dwarfs and Exoplanets

Abstract

We present a major update to the open-source atmospheric modeling package \texttt{PICASO}, designed for simulating the thermal structure and spectra of hydrogen-rich atmospheres of brown dwarfs and exoplanets. This release, \texttt{PICASO 4.0}, expands upon the existing radiative-convective equilibrium model framework by incorporating several new capabilities. Key additions include the integration of \texttt{Virga} for self-consistent cloud modeling, new flexible treatments for rainout and cold trapping of volatile species, and support for photochemistry. We also introduce a parameterized energy injection scheme to simulate additional external or internal heating processes. These features are motivated by lessons from recent JWST observations that reveal the prevalence of non-equilibrium chemistry and clouds. We benchmark the new functionalities against previously published results in the literature, including the Sonora Diamondback grid, energy injected atmospheres, patchy cloud models, and other photochemical models of WASP-39b. \texttt{PICASO} continues to be actively developed as an open-source package aimed at enabling reproducible, community-driven atmospheric modeling of all substellar objects.

Figures (11)

• Figure 1: Diagram showing the climate modeling workflow of PICASO 4.0. Dashed boxes represent optional steps or features. Dotted arrows show the continuation of the workflow even through optional processes that are not required. Red boxes show the different inputs, with new required inputs for photochem, Virga, and energy injection. Additional input options are also shown in the GitHub tutorials.https://natashabatalha.github.io/picaso/ The blue boxes represent the core of the climate model computational modules. The new features are highlighted in the orange boxes and finally, the green boxes outline the outputs from a climate model. *Note when calculating $K_{\rm zz}$, this also calls for a radiative transfer calculation to retrieve the fluxes across the atmospheric profile.
• Figure 2: A diagram showing how PICASO solves for the radiative-convective boundary in a 90-layer climate model. Starting from the bottom of the atmosphere, the lapse rate for the climate profile is compared to the adiabatic lapse rate, and if it is super-adiabatic, that layer is fixed to follow the adiabat and the convective zone grows up. This process occurs iteratively (left to right) as the climate model marches towards radiative convective equilibrium.
• Figure 3: Left: Volume mixing ratio profiles of NH$_3$ in a 250 K brown dwarf with log(g) = 4.0 showing the different chemistry options in PICASO: chemical equilibrium (blue), chemical disequilibrium (turquoise), disequilibrium with volatile rainout (vol_rainout, green), and disequilibrium with volatile rainout and "cold-trapping" (cold_trap, yellow). Right: The brown dwarf $T(P)$ profile (black) overlaid on the NH$_3$ abundances from the updated 2121-point equilibrium chemistry table with [M/H] = +0.0 and C/O = 0.55.https://github.com/natashabatalha/picaso/blob/4944bc1d0924ecac071872def9600a87eacb3428/docs/notebooks/B_chemistry/1_ChemicalEquilibrium.py
• Figure 4: A depiction of different combinations of patchy clouds in the atmosphere tuning the parameters of fhole, the fraction of clear atmosphere in the model. F$_{\rm clear}$ and F$_{\rm cloudy}$ represent the total flux from the clear and cloudy column, respectively (Eq \ref{['eq:patchy']}).
• Figure 5: Thermal emission spectra of a clear Sonora Bobcat model (black), fully cloudy model (blue), and a 30% cloudy model (orange). These are for a 200 K brown dwarf with log(g)=4. For the cloudy models, only H$_2$O clouds are included with $f_{\rm sed}$ = 8.https://github.com/natashabatalha/picaso/blob/4944bc1d0924ecac071872def9600a87eacb3428/docs/notebooks/D_climate/5_CloudyBrownDwarf_PreW.py