CARMApy: An Open-Source Python Framework for Simulating Microphysical Clouds in Planetary Atmospheres
Abstract
CARMApy is a Python wrapper of the Community Aerosol and Radiation Model for Atmospheres (CARMA), originally developed by Turco et al. (1979), Toon et al. (1988), and Bardeen et al. (2008). CARMApy specifically wraps ExoCARMA, a version of CARMA developed by Gao et al. (2018) and Powell et al. (2018) to model clouds on exoplanets. This code is still under development: while we believe there are no significant errors in the code, the CARMApy wrapper is still young. If you encounter any issues or bugs, please open an issue or email Wolf Cukier (wcukier@uchicago.edu). The documentation, including detailed tutorials and installation instructions, are available here: https://carmapy.readthedocs.io Features Detailed microphysical modeling including Homogeneous and heterogeneous nucleation Particle growth and evaporation Vertical transport including eddy diffusion, falling, and bulk winds Coagulation The ability to use user-defined condensates by specifying their physical properties A beginner-friendly, opinionated API that makes the code easy to learn Easy reading of output data and plotting of results Helper functions with reasonable physical assumptions to supplement input data Installation CARMApy is available on PyPI: pip install carmapy CARMApy requires Python 3.10 or newer. Pre-built wheels bundle a standalone Fortran binary, so no compiler is needed for a standard install. See the documentation for source builds and details. Quick Start import carmapy # Create a simulation (the name becomes the output directory) carma = carmapy.Carma("my_first_run") # Set the timestep [s], output cadence, and number of steps carma.set_stepping(dt=100, output_gap=100, n_tstep=24000) # Load the example atmosphere (a 2000 K Sonora Diamondback profile). # All inputs are cgs: P in barye, T in K, kzz in cm^2/s. P_levs, T_levs, kzz_levs, mu_levs = carmapy.example.example_levels() carma.add_P(P_levs) carma.add_T(T_levs) carma.add_kzz(kzz_levs) # Surface gravity [cm/s^2] and mean molecular weight carma.set_physical_params(surface_grav=31600, wt_mol=mu_levs[0]) carma.set_atmospheric_parameters_from_defaults("Pure H2") # H2-dominated carma.calculate_z(mu_levs) # derive altitudes from P/T/mu # Add cloud species: TiO2 nucleates homogeneously, Mg2SiO4 grows on it. # The second argument is the minimum particle radius [cm]. carma.add_hom_group("TiO2", 1e-8) carma.add_het_group("Mg2SiO4", "TiO2", 1e-8 * 2**(1/3)) # Initialize gas abundances (via FastChem) and run carmapy.chemistry.populate_abundances_at_cloud_base(carma) carma.run() results = carma.read_results() See the tutorials for complete, runnable examples.
