Atmospheric Modeling and Retrieval

We seek to learn about the atmospheres of exoplanets both to understand their physical and chemical processes at work, as well as to potentially tie atmospheric abundances to theories of planet formation and evolution. Such work relies on spectroscopic investigation. The interpretation of spectra of exoplanetary atmospheres is heavily dependent on atmospheric models. The accuracy and reliability of these models is especially critical in the absence of in situ measurements that in the Solar System provide us with the “ground truth” to anchor remote sensing observations. Atmospheric models for exoplanets vary in type and complexity (Figure 1). Models attempt to understand the state of an atmosphere, including the temperature structure, atmospheric abundances, and cloud composition and distribution. “Self-consistent” models, where a wide range of atmospheric physics and chemistry are specified, are typically either 1D and iterate to radiative-convective equilibrium, (e.g. Fortney et al., 2008), or 3D, general circulation models (GCMs), which use the equations of fluid dynamics (or some simplifications thereof) to describe the atmospheric dynamics, (e.g. Showman et al., 2009; Heng & Showman, 2015). Either in 1D or 3D, such models need to include sub-models that treat chemical abundances as a function of pressure and temperature (Lodders & Fegley, 2002), the chemical effects of incoming stellar UV flux and vertical mixing (Moses et al., 2011), as well as cloud microphysics and opacities (Ackerman & Marley, 2001; Helling & Casewell, 2014). With all physical effects either explicitly treated within some framework (or explicitly ignored) one calculates a solution for a given atmosphere’s state, and may then generate an emission, reflection, or transmission spectrum, which is then compared to observations. By contrast, parametric models are normally incorporated into a data-driven “retrieval” approach (Madhusudhan, 2018). A variety of atmospheric parameters that control, for instance, the temperature structure, atmospheric abundances, or a small number of cloud parameters, can be tuned in order to provide the best match to a spectrum, typically with minimal physical assumptions. Within this framework one performs a wide exploration of a range of possible atmosphere models that can yield a best-fit to an observed spectrum. Such models can find solutions outside the confines of self-consistent models. They are most important in providing a sound statistical assessment of retrieved parameters, including mixing ratios of atmospheric gases and the shape of the atmospheric temperature profile. These models have mostly been developed in the 1D context, but recent work has shown the importance of 2D and 3D frameworks for inhomogeneous atmospheres Caldas et al. (2019); MacDonald et al. (2020); Taylor et al. (2020). These families of different model types represent a compromise between computation speed/flexibility and physical accuracy; as such they have different uses. For example, highly physics-based GCMs are typically used to simulate detailed conditions on a specific planet, with a pre-determined set of inputs. 1D selfconsistent models can be calculated across large “grids” of parameter space (as is often done in classic stellar atmospheres modeling) to investigate the role of surface gravity, metallicity, and intrinsic fluxes (Burrows et al., 1997; Fortney et al., 2008; Mollière et al., 2015; Gandhi & Madhusudhan, 2017). Retrievals can be run on individual planets, or larger samples of spectra for many planets, to search for trends (Barstow et al., 2017; Line et al., 2017; Welbanks et al., 2019), which can be compared to expectations from self-consistent models.