mmanuscript submitted to
JGR: Planets
Aerosols in Exoplanet Atmospheres
Peter Gao , , Hannah R. Wakeford , Sarah E. Moran , and VivienParmentier Department of Astronomy and Astrophysics, University of California, Santa Cruz, CA 95064, USA NHFP Sagan Fellow School of Physics, University of Bristol, HH Wills Physics Laboratory, Tyndall Avenue, Bristol BS8 1TL,UK Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, MD 21218, USA Department of Physics (Atmospheric, Oceanic and Planetary Physics), University of Oxford, Parks Rd,Oxford OX1 3PU, UK
Key Points: • Aerosols are common in the atmospheres of exoplanets of all temperatures, masses,and compositions. • Observations and models are painting a coherent picture of the nature of exoplanetaerosols. • Advances in laboratory work are essential for unveiling how exoplanet aerosols formand evolve.
Corresponding author: Peter Gao, [email protected] –1– a r X i v : . [ a s t r o - ph . E P ] F e b anuscript submitted to JGR: Planets
Abstract
Observations of exoplanet atmospheres have shown that aerosols, like in the So-lar System, are common across a variety of temperatures and planet types. The forma-tion and distribution of these aerosols are inextricably intertwined with the compositionand thermal structure of the atmosphere. At the same time, these aerosols also inter-fere with our probes of atmospheric composition and thermal structure, and thus a bet-ter understanding of aerosols lead to a better understanding of exoplanet atmospheresas a whole. Here we review the current state of knowledge of exoplanet aerosols as de-termined from observations, modeling, and laboratory experiments. Measurements ofthe transmission spectra, dayside emission, and phase curves of transiting exoplanets,as well as the emission spectrum and light curves of directly imaged exoplanets and browndwarfs have shown that aerosols are distributed inhomogeneously in exoplanet atmospheres,with aerosol distributions varying significantly with planet equilibrium temperature andgravity. Parameterized and microphysical models predict that these aerosols are likelycomposed of oxidized minerals like silicates for the hottest exoplanets, while at lower tem-peratures the dominant aerosols may be composed of alkali salts and sulfides. Particlesoriginating from photochemical processes are also likely at low temperatures, though theirformation process is highly complex, as revealed by laboratory work. In the years to come,new ground- and space-based observatories will have the capability to assess the com-position of exoplanet aerosols, while new modeling and laboratory efforts will improveupon our picture of aerosol formation and dynamics.
Plain Language Summary
For nearly two decades we have had the opportunity to probe the atmospheres ofplanets orbiting other stars (“exoplanets”). These efforts have revealed the existence ofclouds and hazes in these atmospheres, which prevent us from learning more about ex-oplanet atmospheres as a whole by blocking us from probing parts of the atmosphere be-low the cloud and haze layers. Here we summarize our current understanding of thesestructures. Using data from telescopes on the ground and in space, we have found thatexoplanet clouds are patchy and are distributed mostly according to the temperature ofthe local atmosphere. Using computer simulations we have surmised that these cloudsare likely made of materials that make up rocks on Earth, as the exoplanets we have probedthus far orbit their stars closely, resulting in very high temperatures in their atmospheres.At lower temperatures, but still several hundred degrees above room temperature, hazescomposed of organic material are possible. These hazes are likely formed from complexchemical reactions, which are the current focus of laboratory experiments. Future effortsin data collection, computer simulations, and lab work will lead to a better understand-ing of exoplanet clouds and hazes.
Aerosols are fundamental components of planetary atmospheres. Every perennialatmosphere in the Solar System, including that of Pluto, Saturn’s moon Titan, Neptune’smoon Triton, and every planet except Mercury possess some form of such small suspendedparticulates. The composition of these aerosols is extremely diverse, including sulfuricacid on Venus (Hansen & Hovenier, 1974), water on Earth, water, mineral dust, and car-bon dioxide on Mars (Montmessin et al., 2007), ammonia on Jupiter and Saturn (Brookeet al., 1998; Baines et al., 2009), and complex organics and condensed hydrocarbons andnitriles on Uranus, Neptune, Titan, Triton, and Pluto (Sromovsky et al., 2011; Romani& Atreya, 1988; Sagan et al., 1992; Brown et al., 2002; Rages & Pollack, 1992; Gladstoneet al., 2016; M. L. Wong et al., 2017; Lavvas et al., 2020; Ohno, Zhang, et al., 2020). Inaddition, these aerosols are inexorably tied to the chemistry, dynamics, and radiative en-vironments of their host atmospheres. Sulfuric acid clouds on Venus form a vital branch –2–anuscript submitted to
JGR: Planets of its sulfur chemical cycle and provide the planet its high albedo (Mills et al., 2007) whilewater clouds on Earth and dust on Mars are strong controls of their surface climates (Rosenfeldet al., 2014; Mart´ınez et al., 2017). In the outer solar system, moist convection on thegiant planets with the condensation of ammonia, water, and methane sculpts their globalatmospheric dynamics and trace gas distributions (Lunine, 1993; Hueso & S´anchez-Lavega,2006; Li & Ingersoll, 2015; Bolton et al., 2017); organic hazes on Titan and Pluto are theproducts of complex chemical networks and are major contributors to heating and cool-ing rates in their atmospheres (McKay et al., 1989; X. Zhang et al., 2017); and latentheat release from nitrogen condensation on Triton could control its atmospheric ther-mal structure (Rages & Pollack, 1992). Understanding the formation and impact of aerosolson solar system objects have thus been vital for understanding their atmospheres as awhole. The same applies to exoplanets.Aerosols were anticipated to exist in exoplanet atmospheres not long after the dis-covery of the first exoplanet orbiting a sun-like star (Guillot et al., 1996; Saumon et al.,1996). In the few years that followed, several works (Burrows et al., 1997; Seager & Sas-selov, 1998; Marley et al., 1999; Seager & Sasselov, 2000; Seager et al., 2000; Sudarskyet al., 2000; Hubbard et al., 2001; Barman et al., 2001; Sudarsky et al., 2003; Baraffe etal., 2003) considered the formation of mineral and metal aerosols, e.g. silicates and iron,in one-dimensional (vertical), globally averaged models, as inspired by earlier and con-temporary studies into equilibrium condensation and cloud formation processes in browndwarf atmospheres (Lunine et al., 1989; Marley et al., 1996; Allard et al., 1997; Tsuji etal., 1999; Lodders, 1999; Chabrier et al., 2000; Burrows et al., 2000; Allard et al., 2001;Ackerman & Marley, 2001; Helling et al., 2001; Marley et al., 2002; Lodders, 2002; Bur-rows et al., 2002; Tsuji, 2002; Cooper et al., 2003; Woitke & Helling, 2003), which pos-sess similar temperatures ( ≥ ≥ µ m (Lecavelier Des Etangs et al., 2008). Aerosols were also inferred in the atmospheresof some of the first directly imaged young giant exoplanets due to their red infrared col-ors (Marois et al., 2008). These initial observations were the first hints that aerosols werejust as ubiquitous in exoplanet atmospheres as in the atmospheres of our solar systemworlds.In this review, we will summarize our current understanding of exoplanet aerosols,focusing primarily on advancements in knowledge made in the 2010s. These advance-ments include (1) the proliferation of exoplanet transmission spectroscopy, reflected lightand emission photometry, and observations of exoplanet phase curves, which can all beused to probe exoplanet aerosols, (2) greater synergy between exoplanet and brown dwarfscience with a focus on photometry and spectroscopy of directly imaged exoplanets, (3)development of more rigorous aerosol models in 1D and the extension to 3D, and (4) theapplication of laboratory experiments to investigate exoplanet aerosol formation and cor- –3–anuscript submitted to JGR: Planets responding impact on observations. We refer the reader to Helling, Ackerman, et al. (2008);Marley et al. (2013) for comprehensive reviews of pre-2010 studies.We begin by defining specific types of aerosols based on their formation processesand describing their possible compositions in § § § §
5. Finally, in §
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Temperature (K) P r e ss u r e ( b a r ) CH NH H SNH SHH O KClZnSNa SMnSCrMgSiO Mg SiO FeTiO Al O CH /CONH /N U r a nu s J u p i t e r H R b H D b Figure 1.
Condensation temperatures of various cloud species as a function of atmosphericpressure, assuming solar metallicity, compared to temperature-pressure (TP) profiles of severalobjects. Condensation of a given species can occur when the planet TP profile becomes lowerthan its condensation temperature profile. TP profiles for Jupiter and Uranus are taken fromMoses and Poppe (2017) while those of HR 8799b and HD 209458b are generated by a thermalstructure model (Saumon & Marley, 2008) assuming appropriate planetary parameters. The con-densation curve for CH is computed by combining the CH saturation vapor pressure (Lodders& Fegley, 1998) with its mixing ratio in a solar metallicity gas (Lodders, 2010), assuming thatall carbon is in the form of CH . The condensation curves for NH , NH SH, and H O are takenfrom Lodders and Fegley (2002); that of H S is from Visscher et al. (2006); those of KCl, ZnS,Na S, MnS, and Cr are from Morley et al. (2012); those of MgSiO , Mg SiO , and Fe are fromVisscher et al. (2010); that of TiO is from Helling et al. (2001); and that of Al O is fromWakeford, Visscher, et al. (2017). The CH /CO and NH /CO transition curves are from Loddersand Fegley (2002). A number of terms have been used to refer to aerosols in planetary and exoplanetatmospheres in the literature, including clouds, hazes, and dust. For clarity, we will as-sign to them specific definitions based on their provenance in this review, inspired by H¨orst(2016). Where provenance is unclear, we will use the catch-all term, “aerosols.” –4–anuscript submitted to
JGR: Planets
Dust:
We define dust as particles lifted into the atmosphere from a planetary sur-face, such as sand and sea salt on Earth, fine regolith particles on Mars, and organic duneparticles and ices on Titan and Pluto.
Clouds:
We define clouds as collections of particles forming in the atmosphereunder thermochemical equilibrium. This definition includes both first order phase changes,such as H O(g) −−→←−− H O(s , l) (R1)as well as thermochemical reactions like2 Mg(g) + 3 H O(g) + SiO(g) −−→←−− Mg SiO (s , l) + 3 H (g) (R2)Thermochemical equilibrium arises from the minimization of Gibbs free energy given thelocal temperature, pressure, and elemental abundances. Because of this, cloud forma-tion is locally reversible, such that the loss of clouds through evaporation or chemicaldecomposition is in balance with condensation and synthesis. In the Solar System, cloudstend to form via condensation, a first order phase change, such as those of water, car-bon dioxide, ammonia, methane, and nitrogen. Meanwhile, ammonium hydrosulfide (NH SH)clouds, for which we have indirect evidence for in the atmospheres of the giant planets,form through chemical reactions between gaseous ammonia and hydrogen sulfide (e.g.,J. S. Lewis, 1969; Carlson et al., 1988; de Pater et al., 2014; M. H. Wong et al., 2015;Bjoraker et al., 2018).Thermochemical equilibrium models have predicted a myriad of cloud compositionsin exoplanet atmospheres under the assumption that the atmospheric gas compositionis 1 to only several times more enriched in metals than a solar composition gas. “Met-als” in this case refers to all elements heavier than hydrogen and helium (Figure 1; seee.g. Burrows & Sharp, 1999; Lodders, 1999; Lodders, 2002; Visscher et al., 2006, 2010;Wakeford, Visscher, et al., 2017). Some of the proposed clouds form via phase changes,e.g. iron, chromium, potassium chloride, while others form via chemical reactions, e.g.forsterite, enstatite, corundum, and various sulfides. These compositions can vary sig-nificantly at higher metallicities and/or different carbon-to-oxygen ratios, such as the for-mation of clouds of graphite and carbides at high C/O (Moses et al., 2013; Mbarek &Kempton, 2016; Helling et al., 2017). Additional cloud compositions can arise from thecondensation of gases produced from photochemistry, such as sulfuric acid on Venus, hy-drocarbons and nitriles on Titan and Pluto, and elemental sulfur in reducing atmospheres(e.g. Hu et al., 2013; Zahnle et al., 2016).
Hazes:
Clouds derived from gases originating from photochemisty are distinctfrom hazes, which we define as collections of particles formed directly from energy in-put via photochemistry and energetic particle bombardment. Hazes form through theseprocesses via the breakdown of simple molecules like methane, nitrogen, carbon monox-ide, hydrogen sulfide, etc. at low pressures to create radicals and ions, which then re-act to build more complex species, eventually forming small particles through succes-sive reactions (Trainer et al., 2013; Lavvas et al., 2013; Yoon et al., 2014). As a result,the exact compositions of hazes are highly uncertain, though their elemental make-upreflects the major gases in the atmosphere. Unlike clouds, haze formation is locally ir-reversible, tending towards complexity due to the external input of energy. Examplesof hazes in the Solar System include those of Titan, Pluto, and the giant planets, as wellas smog in Earth cities. Hazes in exoplanet atmospheres, particularly at high temper-atures, could be more complex since they can incorporate elements that would other-wise be hidden in deep clouds on cooler worlds, including sodium, potassium, chlorine,magnesium, and iron. –5–anuscript submitted to
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Our definitions of clouds, hazes, and dust are different from some of the previousand current usages of these terms in the exoplanet literature. For example, dust has beenused to refer to high temperature condensates like iron and silicates as they likely con-dense to solid particles (Seager & Sasselov, 1998; Ebel, 2006; Pont et al., 2013), but asthey form via condensation and thermochemical reactions of atmospheric gasses thesewould be referred to as clouds under our definition regardless of their phase. By spec-ifying dust as originating from a planetary surface due to mechanical processes like weath-ering, we relegate them to only thin atmospheres, such as those of rocky exoplanets. Inaddition, when interpreting observations, clouds and hazes have been used to refer to par-ticles of different sizes and/or structures of different optical depths, independent of theirformation processes. In transmission spectroscopy in the optical and near-infrared, largeparticles that act as gray absorbers/scatterers are often labeled as clouds, while smallparticles that preferentially scatter short wavelength visible light are labeled as hazes (Pontet al., 2008; Sing et al., 2016; Barstow et al., 2017; Goyal et al., 2018). Hazes have alsobeen used to describe low optical depth aerosol layers above more optically thick “cloud”layers (H. Yang et al., 2015). These uses are convenient for differentiating between theeffects different aerosols have on observations when aerosol provenance is unknown. How-ever, as we probe exoplanet atmospheres with more advanced instruments and more so-phisticated techniques (Wakeford & Sing, 2015; Kempton et al., 2017; Powell et al., 2019),aerosol definitions that are more connected to their formation mechanisms and under-lying atmospheric processes will become increasingly informative.
Transmission & Nightside EmissionDayside Emission & Reflection Phase CurveSecondaryEclipse
Figure 2.
The geometry of a transiting exoplanet as seen from Earth. When the exoplanetpasses in front of its star with respect to us, we can measure the transmission spectrum of thelimb of its atmosphere thanks to light from the host star filtering through the atmosphere on itsway to us, as well as thermal emission from the nightside. When the exoplanet passes behind itsstar during secondary eclipse, its dayside is blocked; comparison between the total brightness ofthe exoplanet-star system before/after and during the secondary eclipse then allows for the mea-surement of the dayside flux, which is a combination of reflected star light and thermal emission.During the rest of the exoplanet’s orbit, reflected star light and thermal emission as a function oflongitude can be measured by observing the exoplanet’s phase curve. The figure is not to scale.
Aerosols impact every method of exoplanet atmosphere characterization (Figure2). Aerosol opacity controls the pressure levels probed in transmission through the at-mosphere, emission from the atmosphere, and reflection by the atmosphere, suppress-ing the spectral signatures of molecular species at higher pressures. Heating and cool- –6–anuscript submitted to
JGR: Planets ing by aerosols change the atmospheric temperature profile, regulating the planet’s emit-ted flux. The reflectivity of aerosols, controlled by their optical constants, size, and shape,determines the albedo of a planet and thus the reflected light spectrum. Aerosol scat-tering and absorption also generate their own features in exoplanet spectra. In this sec-tion, we describe what observations of exoplanet atmospheres have revealed about ex-oplanet aerosols.
Transmission spectroscopy probes the day-to-night terminator of planetary atmo-spheres. Although it has become the most prolific method by which we probe exoplanetatmospheres (Kreidberg, 2018), it also leads to the most complex results to interpret,due to the large thermal and wind gradients across the atmospheric limb. The slant op-tical path through the atmosphere tangential to the target planet afforded by the ob-servational geometry allows for probes of minute abundances of both molecular species( ∼ ∼
50) of published transmission spectra have allowedfor population studies of exoplanet atmospheres. Several studies have focused on the am-plitude of the 1.4 µ m water absorption feature above its adjacent low opacity regions at ∼ µ m (equivalent to the J and H bands), modulated by the atmospheric scaleheight, as a measure of the vertical extent of the aerosols in the atmospheres of these plan-ets (Figure 4; Sing et al., 2016; Stevenson, 2016; Iyer et al., 2016; Fu et al., 2017; Cross-field & Kreidberg, 2017; Tsiaras et al., 2018; Wakeford, Wilson, et al., 2019). The scaleheight is typically computed using an atmospheric mean molecular weight correspond-ing to solar metallicity (2.3 g mol − ) and a temperature equal to the planets’ equilib-rium temperature ( T eq ). Higher metallicities are certainly possible, particularly for thelower mass planets, in which case assumptions of a solar metallicity scale height wouldunderestimate the number of scale heights spanned by the observed water feature. Theuse of T eq for defining the scale height is also approximate, since the temperature at thealtitudes probed by transits could be higher or lower. These studies have found that aerosolsdiminish the water feature amplitude with respect to a clear atmosphere by 50-70% onaverage. Some studies (Stevenson, 2016; Fu et al., 2017) have claimed that there maybe a trend in water feature amplitude with T eq , from ∼
600 to ∼ T eq < –7–anuscript submitted to JGR: Planets
Wavelength ( m) C h a n g e i n P l a n e t R a d i u s + O ff s e t ( H ) GJ 1214bGJ 3470bWASP-39bHD 189733bWASP-31bH ONa K M 0.1M J M > 0.1M J E q u ili b r i u m T e m p e r a t u r e ( K ) Figure 3.
Transmission spectra of several exoplanets showing the various impacts of aerosols.Planets with masses > − . We refer the reader tohttps://stellarplanet.org/science/exoplanet-transmission-spectra/ for an up to date database ofpublished exoplanet transmission spectra. At optical wavelengths, Heng (2016) measured the amplitude of the atomic sodiumand potassium absorption peaks and also claims a possible “cloudiness” trend with T eq ,with higher temperature planets being clearer. Sing et al. (2016) considered the relativeincrease in transit depths in the optical versus the mid-infrared as a measure of extinc-tion by small particles, and found that aerosols are the primary factor that shape trans-mission spectra rather than variable water abundance. Also, several studies (Pinhas etal., 2019; Welbanks et al., 2019; May et al., 2020; Alderson et al., 2020; Chen et al., 2021)have measured optical spectral slopes steeper than that of Rayleigh scattering. This wouldrequire an opacity source that varies with altitude, such as highly scattering aerosols withvariable particle size and/or vertical distribution (Lecavelier Des Etangs et al., 2008; Singet al., 2011; Wakeford & Sing, 2015).In addition to focusing on specific wavelength regions and spectral features, a num-ber of studies have performed uniform, homogeneous retrieval analyses on a large num-ber of planets’ complete transmission spectra from optical to mid-infrared wavelengths. –8–anuscript submitted to JGR: Planets
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Equilibrium Temperature (K) . m W a t e r B a n d A m p li t u d e ( H ) G20 (g = 10 m s )CK17 (modified)M 0.1M J M > 0.1M J G r a v i t y ( c m s ) Figure 4.
Amplitude of the 1.4 µ m water feature in transmission spectra of exoplanets inunits of atmospheric scale height H (defined as in Figure 3) as a function of gravity and equi-librium temperature. Planets with masses > H , 1.915 ± − and atmosphericmetallicity between 1 and 10 × solar is shown in pink. The best fit linear trend to the Crossfieldand Kreidberg (2017) data, modified from the original publication to take into account theslightly different definition of equilibrium temperature, is shown in the dotted line, and has thefunctional form of A = 0.0044T - 2.45, where A is the water feature amplitude in units of H andT is the temperature in K. Retrievals are data-model parameter estimation procedures commonly used to infer thestate properties (abundances, temperatures, cloud properties) of an atmosphere givena spectrum. Both Barstow et al. (2017) and Pinhas et al. (2019) performed a retrievalon the 10 planets presented in Sing et al. (2016). Although they both propose that non-monotonic trends with temperature exist in aerosol coverage at the limb of hot Jupiters,their results are incompatible. This highlights the sensitivity of retrieval studies to thedetails of the cloud parametrization (Barstow, 2020) and the many degeneracies presentbetween aerosol physical parameters such as altitude range, latitudinal coverage, par-ticle size distribution, etc. (see § Thermal emission has been detected from two distinct populations of exoplanets:directly imaged young giant exoplanets in wide orbits about their host stars and tran-siting worlds on tight orbits ranging from hot Jupiters to rocky planets. Emission froma handful of non-transiting exoplanets have also been detected (Harrington et al., 2006;Crossfield et al., 2010; Brogi et al., 2012; Lockwood et al., 2014; Piskorz et al., 2016, 2017;Birkby et al., 2017; Webb et al., 2020), but these observations have not yet been usedto infer aerosol properties. The nadir geometry of emission observations allow us to probedeeper into the atmosphere than transmission, with the emitted flux being a sensitive –9–anuscript submitted to
JGR: Planets function of atmospheric thermal structure in addition to chemical composition and aerosoldistribution. Thermal emission observations capture the average of the properties of afull hemisphere (often the dayside for transiting exoplanets), which makes them less sen-sitive to small variations in cloud properties and cloud coverage than transmission spec-tra. Thermal emission observed over a significant fraction of the rotation period of theobject also gives information on longitudinal heterogeneity in the atmosphere; for tran-siting exoplanets, this is accomplished by observing the orbital phase curve over a sig-nificant fraction or all of the orbital period, as they are tidally locked to their stars, i.e.their rotation and orbital periods are the same.
The large semi-major axes of directly imaged planets allow for analogies to be drawnbetween them and isolated field brown dwarfs, for which we have observations of higherquality and quantity (e.g., Helling & Casewell, 2014; Liu et al., 2016; Line et al., 2017).As Figure 5 shows, directly imaged companions and field brown dwarfs are similar in theirnear-infrared colors and luminosities, which in turn are controlled by the formation andevolution of clouds as brown dwarfs age (Kirkpatrick, 2005; Lodders & Fegley, 2006; Bai-ley, 2014): the M-L transition is characterized by the formation of titanium and vana-dium clouds, removing TiO and VO gas absorption from the spectrum (Lodders, 1999;Burrows & Sharp, 1999; Lodders, 2002). The reddening of the L dwarfs with decreas-ing luminosity is likely due to increasing cloud optical depth with the formation of sil-icate and iron clouds (Allard et al., 2001; Marley et al., 2002; Tsuji, 2002). The signif-icant spread in J-K colors of L dwarfs have been attributed to variations in metallicityand gravity, though the existence of high altitude aerosol layers composed of submicronparticles in addition to the mineral clouds have been suggested for the reddest field Ldwarfs (Hiranaka et al., 2016). The departure of objects towards bluer near-infrared col-ors at the L-T transition is partly due to increased methane absorption, but also the sink-ing of the clouds below the photosphere and/or breaking up of the clouds (Ackerman &Marley, 2001; Burgasser et al., 2002; Knapp et al., 2004; Tsuji & Nakajima, 2003; Stephenset al., 2009; Marley et al., 2010), though non-cloud explanations have also been proposed(Tremblin et al., 2016). The dimming and reddening of late T dwarfs is thought to bedue to condensation of sulfides and chlorides (Morley et al., 2012; Line et al., 2017; Za-lesky et al., 2019), while the transition to Y dwarfs occurs with the appearance of am-monia gas absorption, followed by water condensation for the coolest Y dwarfs discov-ered so far (Lodders & Fegley, 2002; Burrows et al., 2003; Hubeny & Burrows, 2007; Cush-ing et al., 2011; Morley, Marley, Fortney, Lupu, Saumon, et al., 2014; Leggett et al., 2015;Morley et al., 2018). Similar condensation sequences and chemical transitions should oc-cur in the atmospheres of directly imaged planets as they evolve.In addition to luminosity and color variations over cosmic timescales, brown dwarfsalso exhibit temporal variability in broadband emission and spectra as they rotate. Thisobserved variability is indicative of heterogeneous aerosol distributions, including holesin aerosol layers, multiple layers, and variable layer thicknesses, with a possible higherconcentration of variable objects at the L-T transition (Artigau et al., 2009; Radigan etal., 2012; B. A. Biller et al., 2013; Heinze et al., 2013; Radigan et al., 2014; Radigan, 2014;Crossfield et al., 2014; Faherty et al., 2014; Burgasser et al., 2014; Wilson et al., 2014;Buenzli et al., 2014; Buenzli, Marley, et al., 2015; Cushing et al., 2016; B. Biller, 2017;Artigau, 2018; Eriksson et al., 2019; Lew et al., 2020; Vos et al., 2020). This points tocloud breakup as potentially contributing to the increasingly blue near-infrared colorsof later spectral types. Furthermore, differences in variability amplitudes between atmo-spheric windows and absorption features can reveal the location of the aerosol layers. Forexample, the discovery that variability amplitudes in spectral windows are larger thanthose in wavelength-adjacent absorption features in several L-T transition objects showsthat an aerosol layer with spatially variable thickness likely exists between the opticaldepth unity altitudes (where optical depth equals 1) at these two wavelengths (Apai et –10–anuscript submitted to
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Figure 5.
Color-magnitude diagram of field dwarfs (M dwarfs: red; L dwarfs: dark red;T dwarf: blue; Y dwarfs: indigo) and directly imaged companions (orange). Here we only in-clude companions that may be exoplanets, which we take to be objects indicated by “Y”,“Y?”, and “N?” in the “exoplanet” column of Best et al. (2020), along with objects withthose designations that are part of binaries. We also include VHS J125601.92-125723.9 b andSDSSJ224953.46+004404.6A, which were stated to possess masses near the deuterium burninglimit by Bowler (2016). Data are taken directly from Best et al. (2020), which has been com-piled by Dupuy and Liu (2012); Dupuy and Kraus (2013); Liu et al. (2016); Best et al. (2018,2020). Only objects with near-infrared photometry available in MKO magnitudes are included.Annotations indicate our current understanding of cloud evolution on brown dwarfs. al., 2013; Buenzli, Saumon, et al., 2015; Buenzli, Marley, et al., 2015). Conversely, thevariability amplitudes of some mid-L dwarfs are linear with wavelength across absorp-tion bands, which is suggestive of an aerosol layer at high altitudes above the optical depthunity altitudes of the absorbers (H. Yang et al., 2015; Lew et al., 2016; Schlawin et al., –11–anuscript submitted to
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Near-infrared spectroscopy of directly imaged companions has shown that, like iso-lated brown dwarfs, aerosols are common in their atmospheres and that the distributionof aerosols appears to be heterogeneous, with some objects exhibiting temporal variabil-ity in photometry and spectra (Marois et al., 2008; Currie et al., 2011; Skemer et al., 2012;Marley et al., 2012; Bonnefoy et al., 2013; Skemer et al., 2014; Ingraham et al., 2014; Mac-intosh et al., 2015; Zhou et al., 2016; Bonnefoy et al., 2016; Rajan et al., 2017; Samlandet al., 2017; Delorme et al., 2017; Greenbaum et al., 2018; M¨uller et al., 2018; B. A. Biller& Bonnefoy, 2018; Manjavacas et al., 2019; Lew et al., 2020; Bowler et al., 2020; Zhouet al., 2020; J. J. Wang et al., 2020). Trends in wavelength-dependent variability withspectral type are also seen among companions, where L-type objects (those with spec-tra similar to L dwarfs) tend to have gray or linear dependence while L-T transition ob-jects show lower variability amplitudes in absorption features (Manjavacas et al., 2018;Zhou et al., 2018; Miles-P´aez et al., 2019). Importantly, these observations, along withthose of young, low gravity, isolated objects (e.g. Metchev et al., 2015; B. A. Biller etal., 2015; Gizis et al., 2015; Liu et al., 2016; Faherty et al., 2016; B. A. Biller et al., 2018;Vos et al., 2019, 2020), show that lower gravity objects tend to be more variable and pos-sess redder near-infrared colors (higher J-K) compared to higher gravity objects of thesame effective temperature (Figure 5).
While many transiting hot and warm Jupiters exhibit similar atmospheric temper-atures as isolated brown dwarfs and wide orbit companions, the intense stellar irradi-ation that they experience while tidally locked to their host stars mean that they pos-sess fundamentally different atmospheric thermal structures both vertically and horizon-tally. These differences have been revealed by observations of thermal emission from theirpermanent daysides (e.g. Deming et al., 2005; Charbonneau et al., 2005; Deming et al.,2007; Charbonneau et al., 2008; Knutson et al., 2008; Stevenson et al., 2010; Majeau etal., 2012; de Wit et al., 2012; Kreidberg et al., 2014; Arcangeli et al., 2018; Mikal-Evanset al., 2019; Wallack et al., 2019; Garhart et al., 2020) and phase curves (e.g. Knutsonet al., 2007, 2012; Cowan et al., 2012; N. K. Lewis et al., 2013; Zellem et al., 2014; Steven-son et al., 2014; I. Wong et al., 2015; Demory et al., 2016; I. Wong et al., 2016; Kreid-berg, Line, Parmentier, et al., 2018a, 2018b; Kreidberg et al., 2019). Meanwhile, com-plementary observations of reflected light in the optical probe the longitudinal distribu-tion of aerosols and the dayside albedo, as controlled by the reflectivity of aerosols andmolecular absorption (e.g. Rowe et al., 2008; Borucki et al., 2009; Snellen et al., 2009;Kipping & Spiegel, 2011; Esteves et al., 2013; Shporer et al., 2014, 2019; I. Wong, Sh-porer, et al., 2020; Bourrier et al., 2020; Beatty et al., 2020; von Essen et al., 2020; Jansen& Kipping, 2020). We refer the reader to Parmentier and Crossfield (2018); Alonso (2018)for comprehensive reviews.Combined optical and infrared observations have revealed significant longitudinalheterogeneity in the distribution of aerosols in transiting exoplanet atmospheres. For ex-ample, dayside near-infrared photometry and spectra can be explained without the needfor optically thick aerosols down to the pressure levels probed, suggesting either a lackof aerosols altogether or that aerosols form at pressures higher than the planets’ pho- –12–anuscript submitted to
JGR: Planets -60-40-2002040 P ha s e c u r v eo ff s e t ( ° ) H A T - P - b K e p l e r - b K e p l e r - b K e p l e r - b K e p l e r - b K e p l e r - b A ppa r en t A l bedo T eq (K) for A B =0 The lack of bright targets shows that daysides are not fully cloudyNegative phase shifts show that daysides are partially cloudy At high temperatures mostly thermal emission -60-40-2002040 P ha s e c u r v eo ff s e t ( ° ) H A T - P - b K e p l e r - b K e p l e r - b K e p l e r - b K e p l e r - b K e p l e r - b A ppa r en t A l bedo T eq (K) for A B =0 Data KeplerData TESSKepler cloudlessKepler MgSiO cloudsKepler MnS cloudsTESS cloudlessRe f ected part only-60-40-2002040 P ha s e c u r v eo ff s e t ( ° ) H A T - P - b K e p l e r - b K e p l e r - b K e p l e r - b K e p l e r - b K e p l e r - b A ppa r en t A l bedo T eq (K) for A B =0 Data KeplerData TESSKepler cloudlessKepler MgSiO cloudsKepler MnS cloudsTESS cloudlessRe f ected part only-60-40-2002040 P ha s e c u r v eo ff s e t ( ° ) H A T - P - b K e p l e r - b K e p l e r - b K e p l e r - b K e p l e r - b K e p l e r - b A ppa r en t A l bedo T eq (K) for A B =0 Data KeplerData TESSKepler cloudlessKepler MgSiO cloudsKepler MnS cloudsTESS cloudlessRe f ected part only Cloudless (Kepler)
MgSiO Clouds (Kepler)
MnS Clouds (Kepler)
Cloudless (TESS)
Reflected Light Only Data (TESS)
Data (Kepler) -60-40-2002040 P ha s e c u r v eo ff s e t ( ° ) H A T - P - b K e p l e r - b K e p l e r - b K e p l e r - b K e p l e r - b K e p l e r - b A ppa r en t A l bedo T eq (K) for A B =0 Data KeplerData TESSKepler cloudlessKepler MgSiO cloudsKepler MnS cloudsTESS cloudlessRe f ected part only Figure 6.
Optical phase curve offsets (top) and apparent albedos (bottom) of giant exo-planets in the Kepler (black points) and TESS (gray points) bandpasses compared to globalcirculation models assuming cloudless atmospheres (orange and gray curves for Kepler and TESSbandpasses, respectively) and atmospheres post-processed with MgSiO (green curve) or MnS(blue curve) clouds. The apparent albedo includes both reflected and emitted light; the reflectedlight-only albedo is shown in the dashed curves, indicating that most of the photons receivedfrom the daysides of ultra-hot giant exoplanets are emitted rather than reflected. Two planetsare not shown here, as they are situated beyond the plot limits: KELT-1b, which has an appar-ent albedo of 0.7 in the TESS bandpass (von Essen et al., 2020), and WASP-100b, which has acontroversial hot spot measurement in the TESS bandpass (see Jansen and Kipping (2020) vs.I. Wong, Shporer, et al. (2020)). The figure is updated from Parmentier et al. (2016). © AAS.Reproduced with permission. tosphere (J. M. Lee et al., 2012; Line et al., 2014; Barstow et al., 2014; Kataria et al.,2015). Similarly, the observed optical geometric albedoes are low ( ≤ ∼ T eq < –13–anuscript submitted to JGR: Planets may be clear. HD 189733b also exhibits a high albedo of 0.4 ± < TESS bandpass have yieldedhigh values ( > T eq > T eq ≥
600 K) Neptune-size, mini-Neptune, and rocky ex-oplanets in the
Kepler bandpass have been constrained to low values ( ≤ Spitzer , isa nearly constant ∼ T eq (Figure 7). This pattern persists up to at least T eq ∼ T eq ≤ The formation, evolution, and spatial and size distribution of aerosols depend oninteractions between the atmospheric thermal structure, wind patterns, and microphys-ical processes (Pruppacher & Klett, 1978). For clouds, these processes include nucleation,the conversion of condensate vapor into solid or liquid either directly (homogeneous) orwith the aid of a foreign surface (heterogeneous) often in the form of a “condensationnuclei”; condensation, the growth of cloud particles through the uptake of vapor; coag-ulation, the growth of cloud particles through collision and sticking; evaporation, the de-crease in particle mass due to loss of condensate molecules to the atmosphere; and trans- –14–anuscript submitted to
JGR: Planets B r i g h t n e ss T e m p e r a t u r e ( K ) NightsideDaysideData (B19):
Equilibrium Temperature (K) B r i g h t n e ss T e m p e r a t u r e ( K ) Models (P21):Dayside, CloudlessNightside, CloudlessDayside, CloudyNightside, Cloudy
Figure 7.
Observed brightness temperatures of the daysides (red) and nightsides (blue) of hotJupiters at the 3.6 (top) and 4.5 µ m (bottom) Spitzer bands from Beatty et al. (2019), whichprobe atmospheric temperature and opacity structures, including the effect of aerosols. Bright-ness temperatures computed by Parmentier et al. (2021) using a GCM for cloudless hot Jupiters(daysides: orange, nightsides: light blue) and hot Jupiters with cloudy nightsides (daysides: darkred, nightside: indigo) are shown for comparison. port by sedimentation, diffusion, and advection by winds. For hazes under our defini-tion ( § –15–anuscript submitted to JGR: Planets
Exoplanet aerosol models span a continuum in complexity, from single-variable pa-rameterizations to computationally expensive simulations of aerosol microphysics in 3D,with each type of model serving a different purpose. Highly parameterized models aretypically used for retrieval studies where rapid model execution is key. These models usea handful of variables (e.g., cloud top pressure) to capture only the first order impactsof aerosols on observations, such as changing/enhancing the spectral slope at optical wave-lengths and reducing the amplitude of molecular features in the infrared, without treat-ment of specific physical processes (e.g., Benneke & Seager, 2012; Greene et al., 2016;Line et al., 2016; Barstow et al., 2017; Burningham et al., 2017; Tsiaras et al., 2018; Goyalet al., 2018; Mai & Line, 2019; M. Zhang et al., 2019; Pinhas et al., 2019; Molli`ere et al.,2019; Barstow et al., 2020; Barstow, 2020). Some retrieval frameworks have included morecomplex aerosol models that consider various combinations of: Mie scattering by spher-ical particles (J.-M. Lee et al., 2014; M. Zhang et al., 2019; Benneke, Knutson, et al., 2019;B. I. Lacy & Burrows, 2020), aerosol layers that vary with altitude (Lupu et al., 2016;M. Zhang et al., 2019; Benneke, Knutson, et al., 2019; Damiano & Hu, 2020), aerosol com-position (J.-M. Lee et al., 2014; Fisher & Heng, 2018), and spatial heterogeneity (Line& Parmentier, 2016; MacDonald & Madhusudhan, 2017; Feng et al., 2018). These morecomplex models allow for more physical interpretations of how aerosols affect observa-tions, but they come at a price of a greater number of parameters, some of which maynot be well constrained by the data we currently possess (e.g., Fisher & Heng, 2018). Inaddition, Mai and Line (2019); Barstow (2020) showed that the retrieved atmospherictemperature and composition from transmission spectra are largely insensitive to the cho-sen aerosol parameterization, as long as aerosols are not ignored in the retrieval. In con-trast, the aerosol properties retrieved using the different parameterizations may be sub-stantially different from each other, suggesting that consistent constraints on exoplanetaerosols may be difficult to obtain through retrievals that use simple aerosol parameter-izations.Aerosol models that include some of the physical processes that control aerosol dis-tributions, but which are still parameterized to be computationally inexpensive are of-ten found as a part of radiative-convective equilibrium models. While these models aremore computationally expensive than retrievals, they are useful for generating model gridsthat elucidate the roles of specific parameters across populations of objects. In contrast,retrievals typically seek to extract physical parameters from observations by rapidly ex-ploring the parameter space for a single object. These more complex aerosol models typ-ically treat aerosol compositions computed from thermochemical equilibrium (see §
2) andassume either a mean particle size or a functional form for the aerosol size distribution(Figure 8), allowing them to compute aerosol optical properties assuming Mie scatter-ing. The differences between these models are due to how they parameterize aerosol mi-crophysics. The cloud model of Ackerman and Marley (2001), for example, computes clouddistributions by balancing particle sedimentation with vertical mixing, while the verti-cal extent of the clouds is controlled by a sedimentation efficiency parameter. In con-trast, the model of Cooper et al. (2003) computes the mean particle size by balancingthe timescales of microphysical processes following Rossow (1978); free parameters in-clude the supersaturation, which controls the nucleation and condensation timescales,and the sticking coefficient that controls the coagulation timescale. These two approachesboth allow for relatively fast computations of profiles of particle sizes, cloud mass mix-ing ratios, and cloud optical properties. While these models’ reliance on tunable param-eters hinders their predictive powers, it also allows them to explore a large range of cloudproperties and how they affect observations. Hu et al. (2012) describes an alternativeaerosol model coupled to a photochemical model where the particle size is a free param-eter, sedimentation is treated explicitly, and condensation and evaporation is computed –16–anuscript submitted to
JGR: Planets through associated timescales. This model considers cloud compositions produced by pho-tochemical reactions like sulfuric acid and sulfur ( §
2) and has been used mostly for ter-restrial planet atmospheres thus far. Particle Radius ( m) P a r t i c l e N u m b e r D e n s i t y ( c m ) Potential ExponentialLognormalCARMAK19
Figure 8.
Example particle size distributions from aerosol models. Shown are the parameter-ized potential exponential (dark blue; e.g. Helling, Woitke, & Thi, 2008) and lognormal (orange;e.g. Ackerman & Marley, 2001) functions, along with binned size distributions from the cloudsimulations of Gao et al. (2020) using
CARMA (red) and the haze simulations of Kawashima andIkoma (2019) (K19; light blue).
Table 1.
Properties of microphysical models of exoplanet aerosols
Model Moment/Bin Mixed/Pure C/H Nucleation Haze Formation Transport Reference
DRIFT
Moment Mixed C Comp. · · ·
Relaxation [1]O17 Moment Pure C&H Hybrid Param. Advection [2]L17 Bin Pure H · · ·
Hybrid Diffusion [3]
CARMA
Bin Mixed C&H Comp. Param. Diffusion [4]K18 Bin Pure H · · ·
Photo. Diffusion [5]
ARCiS
Moment Pure C Param. · · ·
Diffusion [6][1] Helling, Dehn, et al. (2008); [2] Ohno and Okuzumi (2017); [3] Lavvas and Koskinen(2017); [4] Gao et al. (2018); [5] Kawashima and Ikoma (2018); [6] Ormel and Min (2019)The most complex 1D aerosol models treat microphysical processes kinetically, pro-ducing particle distributions by balancing the rates of the individual processes. Thesemodels are used for exploring how different aerosol processes interact with each otherand how aerosol distributions form and evolve. Table 1 shows how current kinetic mod-els compare on several modeling techniques. Some of the models parameterize the par-ticle size distribution using moments of that distribution, such that the actual shape ofthe distribution must be user-prescribed, while other models are able to resolve the sizedistribution using mass bins. Parameterizing the size distribution saves computation power,but may fail to capture multiple particle size modes (Figure 8), leading to significant dif- –17–anuscript submitted to
JGR: Planets ferences in aerosol opacity and its wavelength dependence (Powell et al., 2019). The mod-els also differ in whether they treat particles as being composed of a single compositionor a mixture of multiple compositions. Given the large number of potential condensatesat high temperatures (Figure 1), the existence of mixed composition particles is likely.
DRIFT (not an acronym) and
CARMA (Community Aerosol and Radiation Model for At-mospheres) treat mixed particles differently, however:
DRIFT allows multiple species tocondense onto the same cloud particle at the same time via numerous thermochemicalreactions, forming well-mixed “dirty grains” (Helling & Woitke, 2006; Helling, Woitke,& Thi, 2008), a procedure originating from models of mixed dust grains in stellar winds(Gail et al., 1984; Gail & Sedlmayr, 1988). In contrast,
CARMA considers only layered par-ticles, such that only the outer most layer can grow by condensation at any one time (Gao& Benneke, 2018). This strategy originates from
CARMA ’s roots in Earth science (Turcoet al., 1979; Toon et al., 1979) where condensing water vapor can completely envelopea condensation nucleus, but may not suffice for high temperature exoplanet clouds wheremultiple condensates may interact.The considered kinetic models can be further divided between cloud models (C),haze models (H), and models capable of simulating both types of aerosols (C&H). Of themodels capable of simulating clouds, an important attribute to consider is how they treatnucleation, as that ultimately controls the depletion of condensate vapor and the cloudparticle number density and size. The
ARCiS (ARtful modeling Code for exoplanet Sci-ence) framework (Ormel & Min, 2019) parameterizes (Param. in Table 1) their parti-cle nucleation rate with a Gaussian profile and a user-defined column rate. The modelof Ohno and Okuzumi (2017) uses a hybrid approach where the heterogeneous nucleationrate is computed based on user-input number densities and sizes of condensation nuclei.
DRIFT (Helling, Dehn, et al., 2008) and
CARMA (Gao et al., 2018) both compute (Comp.in Table 1) homogeneous nucleation rates of condensation nuclei from classical nucleationtheory (or modified versions of it); for subsequent condensation of other cloud compo-sitions the former model considers grain chemistry while the latter model computes het-erogeneous nucleation rates. While consideration of nucleation theory is more physical,it has been shown to differ from experimentally determined rates by orders of magnitudefor some substances (see e.g. Oxtoby, 1992; Anisimov et al., 2009) and relies on mate-rial properties that may not have been measured at the appropriate temperatures, e.g.surface energies (Gao et al., 2020).Likewise, of the models capable of simulating hazes, a major source of uncertaintyis how they compute the haze formation rate profile in the atmosphere, which ultimatelydetermines the haze opacity. While no model has been able to fully simulate the chem-ical network from simple parent molecule to aerosol particles, some are more parame-terized than others.
CARMA and the Ohno and Okuzumi (2017) model (as described inOhno & Kawashima, 2020) both fully parameterize (Param. in Table 1) haze formationrates through user-chosen production rate profiles and initial particle sizes. The modelof Lavvas and Koskinen (2017) is similar except the column rate is computed from pho-tochemical models (Hybrid in Table 1) under the assumption that some percentage ofthe pertinent photochemical reactions (a “haze formation efficiency”), typically involv-ing the photolysis of hydrocarbons and nitriles, lead to haze formation. It is also the onlymodel thus far to explicitly treat thermal decomposition of haze particles. The modelof Kawashima and Ikoma (2018) takes this a step further by equating the production rateprofile to some percentage of the rate profiles of the chosen photochemical reactions (Photo.in Table 1), though the initial particle size is still a free parameter. Similar strategieshave also been adopted by other studies (Morley et al., 2013, 2015; Zahnle et al., 2016)that don’t consider kinetic models of aerosol microphysics. In addition, several modelshave simulated exoplanet haze particles as fractal aggregates (G. N. Arney et al., 2017;G. Arney et al., 2018; Adams et al., 2019; Lavvas et al., 2019), much like haze particleson Titan (Lavvas et al., 2011). Several cloud models have also considered aggregates com-posed of condensates (Ohno, Okuzumi, & Tazaki, 2020; Samra et al., 2020). –18–anuscript submitted to
JGR: Planets
The formation of clouds is intimately linked to the thermal structure of the atmo-sphere. For a given cloud species, too high of a temperature can prevent nucleation andcondensation while too low of a temperature might shift cloud formation to deeper lay-ers of the atmosphere that cannot be probed by current observations.The thermal structure of exoplanets can be either calculated a priori using radiative-convective equilibrium models or retrieved directly from the planets’ emission spectra.Radiative-convective models are often used in objects that experience no or little irra-diation such as brown dwarfs and directly imaged planets where horizontal advection ofheat is not significant. The radiative convective equilibrium profile can be further refinedby using a combination of cloud and cloud-free atmospheric patches (e.g., Marley et al.,2010; Morley, Marley, Fortney, & Lupu, 2014). Parameterized thermal structures are of-ten used for irradiated planets such as hot Jupiters (Guillot, 2010; Heng et al., 2012; Par-mentier & Guillot, 2014), where the presence of trace species that are difficult to char-acterize, such as metal oxides or metal hydrides, can lead to large uncertainty in the ex-pected thermal structures (Fortney et al., 2008; Gandhi & Madhusudhan, 2019). Fur-thermore, local radiative-convective equilibrium does not hold for specific parts of theplanet, such as the dayside and the limb that are probed by transmission and emissionspectra, as their local thermal structure is determined by the global atmospheric circu-lation.Aerosols have two main effects on the thermal structure of an atmosphere. First,they change the heat transport within the atmosphere: they warm up the atmospherebelow the cloud by their increased opacity and cool down the atmosphere above the cloudtop by efficiently radiating away heat. Second, aerosols change the albedo and thus theemissivity of the planet. The change in albedo leads to a change in the total energy re-ceived by the atmosphere and thus a change in the mean thermal structure of the planet.The change in emissivity leads to a change in the ability of the atmosphere to re-emitlight and thus a change in the relationship between thermal structure and observed spec-tra (J.-M. Lee et al., 2013; Lavie et al., 2017; Burningham et al., 2017; Molli`ere et al.,2020).
Atmospheric transport is needed both for aerosols to stay aloft in the atmosphereand for fresh gaseous species to replenish the depleted gas in the aerosol formation re-gion. If no vertical mixing were present, all condensable species would rain out of thevisible atmosphere. In 1D models, vertical mixing is usually assumed to be diffusive innature and parameterized by a eddy diffusion coefficient K zz , which is highly uncertain.As a diffusion coefficient, K zz has units of length-squared per unit time. The “zz” sub-script denotes motion in the z, i.e. vertical direction. In addition to being used in aerosolmodels to transport particles and condensate vapor, K zz has also been frequently usedin chemical kinetics/photochemistry models to approximate transport of gases (e.g. Moseset al., 2016). K zz is an approximation of all large scale transport in a planetary atmo-sphere, including atmospheric circulation, gravity waves, and convection, most of whichcannot be explicitly represented in 1D models. As some of these processes are not ac-tually diffusive (X. Zhang & Showman, 2018b, 2018a), the use of K zz to represent at-mospheric transport and how its profile in the atmosphere is calculated require caution.For objects that are mainly convective, mixing length theory is often used to estimatethe K zz profile (e.g., Gierasch & Conrath, 1985; Ackerman & Marley, 2001). This is notvalid for atmospheres that are predominantly radiative, such as those of transiting ex-oplanets.On tidally locked planets, advection by the atmospheric circulation is likely the mainsource of vertical mixing, as opposed to turbulence or wave breaking. As first shown by –19–anuscript submitted to JGR: Planets
Parmentier et al. (2013) using passive tracers in a 3D global circulation model (GCM),the mean vertical transport of particles in the atmospheres of hot Jupiters is surprisinglywell represented by a 1D diffusion approach, resulting in a K zz profile that increases withdecreasing atmospheric pressure as a power law. The magnitude of K zz is, however, ahundred times smaller than would be expected from extrapolating the mixing length pa-rameterization or by multiplying the root mean square of the vertical velocities by theatmospheric scale height. X. Zhang and Showman (2018b, 2018a) explored a wider rangeof atmospheric circulation patterns and showed that the K zz used in 1D models shouldbe different for different chemical and aerosol species. They further identified specific cases,such as when photochemical hazes form in the upper layers of a dayside updraft, whichwould require a negative K zz , representing local concentration rather than dilution. Lastly,Komacek et al. (2019) proposed an analytical formula for K zz that is based on the Earthstratosphere framework developed by Holton (1986); they showed that K zz should de-pend on both the strength of the circulation and the timescale at which a given speciesis lost. Their formalism, however, was developed for gaseous chemical species only, whichare not conserved. As such, it is not yet clear how to adapt it to aerosols that are usu-ally conserved when settling vertically. In the deeper atmosphere, other processes likelystart to dominate vertical mixing such as wave breaking (Fromang et al., 2016) or shearinstability driven turbulence (Menou, 2019).Not all 1D aerosol models treat transport as a diffusive process. The 1D versionof DRIFT considers a newtonian relaxation scheme for the chemical abundances insteadof solving an actual diffusion equation (Woitke & Helling, 2004), and assumes that theparticles are fully decoupled from the flow. In every atmospheric layer the gaseous com-position relaxes towards the initial conditions. Though this approach is more straight-forward numerically, it can lead to orders of magnitude differences in the cloud distri-bution compared to a model that uses a diffusion approach (Woitke et al., 2020). Ohnoand Okuzumi (2017) also does not consider diffusion; instead, chemical species and par-ticles are lifted upward by a constant advection along the 1D column. This approach iscorrect when modelling cloud formation in an updraft, but might be incorrect when mod-elling the spatially averaged atmosphere, where all updrafts are compensated by down-drafts.
Aerosol models of various complexities have been incorporated into GCMs in aneffort to understand the global aerosol distribution on exoplanets. On the more param-eterized end, cloud particles have been treated as radiatively passive (Parmentier et al.,2013; Charnay, Meadows, & Leconte, 2015; Komacek et al., 2019) and active (Charnay,Meadows, Misra, et al., 2015) tracers that typically have a user-defined particle size dis-tribution, are advected by the circulation, and may be removed through a parameter-ization of condensation and sedimentation. This technique has been useful in revealinghow aerosols are transported in an atmosphere, particularly whether they can be loftedto high altitudes to explain muted gas spectral features. Alternatively, parameterizedcloud distributions are prescribed onto the 3D grid of the GCM based on observations(M. Roman & Rauscher, 2017) or as 1D columns in which cloud formation is evaluatedbased on whether condensate vapor is locally supersaturated without advection of theclouds (Parmentier et al., 2016; Tan & Showman, 2017; Parmentier et al., 2018; M. Ro-man & Rauscher, 2019; Harada et al., 2019; Tan & Showman, 2020; Parmentier et al.,2021; M. T. Roman et al., 2020). More complex 1D cloud models, like
DRIFT (G. Leeet al., 2015; Helling et al., 2016; Helling, Gourbin, et al., 2019; Helling, Iro, et al., 2019;Helling et al., 2020) and the Ackerman and Marley (2001) model (Lines et al., 2019) havealso been incorporated into GCMs in this fashion. Both radiatively active and post-processedclouds (i.e. clouds added to the model atmosphere after a cloud-less GCM converged)have been considered. These studies have investigated how aerosols affect a planet’s ther-mal emission and albedo, particle heating and cooling, and the impact of local aerosol –20–anuscript submitted to
JGR: Planets formation on gas abundances. Several studies have more fully coupled
DRIFT to GCMs(G. Lee et al., 2016; G. K. H. Lee et al., 2017; Lines, Mayne, et al., 2018; Lines, Man-ners, et al., 2018), such that both particle advection, cloud microphysics, and cloud ra-diative feedback are considered simultaneously. While these models capture more of theinteractions between the different physical processes, running them until all modeled pro-cesses can converge is currently computationally prohibitive.
Exoplanet aerosol models have been used to interpret a variety of observations ofexoplanet atmospheres and also predict future observations. In particular, many stud-ies have focused on explaining observations of individual planets with aerosol models,either as part of retrieval frameworks (e.g. Kreidberg et al., 2014; MacDonald & Mad-husudhan, 2017; Wakeford et al., 2018; Benneke, Knutson, et al., 2019; Molli`ere et al.,2020) and/or more complex forward models (e.g. Fortney et al., 2005; Barman et al., 2011;Marley et al., 2012; Bonnefoy et al., 2013; G. Lee et al., 2015; Rajan et al., 2017; Chachanet al., 2019). These studies have revealed a diversity of exoplanet atmospheres across plan-etary parameter space. However, due to limited data these comparisons often run intodegeneracies and it is unknown how applicable their conclusions are to all exoplanets.Therefore, in this section we will mostly focus on studies that have attempted to explainor predict how aerosols impact whole populations of exoplanets, though we will also dis-cuss several benchmark objects.
As reviewed in §
3, the proliferation of exoplanet transmission spectroscopy, emis-sion photometry, and optical and infrared phase curves allow us to probe the vertical andhorizontal extent of aerosols in exoplanet atmospheres across a wide range of planetaryparameters. These efforts have yielded several important clues on how aerosol distribu-tions vary with planet T eq and longitude: (1) The daysides of giant transiting exoplan-ets are likely clear while the nightsides and western limbs likely host optically thick aerosolsand (2) the vertical extent of aerosols at the limbs, as probed by transmission spectroscopy,may correlate with planet temperature. Several modeling studies have tried to explainthese observations.Parmentier et al. (2016) computed the total thermal emission and reflected lightfluxes in the Kepler bandpass of a grid of hot giant exoplanets by adding post-processed,parameterized clouds to the output of a GCM and compared their results to observedoptical phase shifts and apparent albedos (Figure 6). They found that a transition incloud composition, as determined by local thermal stability of condensates predicted bythermochemistry models, could explain the data: at the highest temperatures, the day-side is devoid of aerosols with the flux dominated by thermal emission, which reachesa maximum towards the east limb; as temperatures decrease, silicate clouds form on thenightside and western limb, where the temperatures are the lowest, and begin extend-ing eastward over the dayside, shifting the brightest longitude in the
Kepler bandpassto the west and causing reflected light to dominate over thermal emission; at ∼ –21–anuscript submitted to JGR: Planets
Figure 9.
Visual appearance of the daysides of a set of exoplanet global circulation modelswith post-processed clouds (Parmentier et al., 2016), generated with the same approach as thoseof Harre and Heller (2021). Each column represents planets of a single equilibrium temperaturewhile each row shows different cloud species. For low equilibrium temperatures, the dayside isoften fully cloudy and the color of the cloud depends on its absorption bands in the optical. Forintermediate temperatures, the cloud coverage is concentrated on the western part of the dayside,where the planet is cooler. The eastern part, dominated by the hot spot, is cloudless and has adark blue appearance due to alkali absorption. At high equilibrium temperatures, the daysidesare mostly cloudless, with their visual appearance dominated by the thermal emission of the hotspot. Image credit: NASA/JPL-Caltech/University of Arizona/V. Parmentier. side temperatures of hot Jupiters (Figure 7; Beatty et al., 2019; Keating et al., 2019) andthermal phase curve shifts, though the decrease in radiative timescale with increasingequilibrium temperature also strongly contributes.The formation of spatially inhomogeneous clouds could also have a large impacton the spatial distribution of the gaseous species involved in cloud formation. For ex-ample, Helling, Gourbin, et al. (2019) found that the C/O ratio can vary from sub-solarto super-solar ( ∼ ∼ –22–anuscript submitted to JGR: Planets of SiO at the equator and larger particles dominated by Mg SiO at mid-latitudes. How-ever, these works also found that the aerosol distribution was much more longitudinallyhomogeneous, in contrast with observations. This may be due to non-convergence of someof the processes considered in the models.An important takeaway of the results of 3D models is that the aerosol distributionson the east and west limbs of hot Jupiters are unlikely to be the same, which could beobservable via transmission spectroscopy (Line & Parmentier, 2016; von Paris et al., 2016;Kempton et al., 2017). Using the aerosol microphysics model CARMA combined with tem-perature profiles extracted from a GCM, Powell et al. (2018) showed that higher tem-peratures on the east limb promote cloud formation at higher altitudes, making it ap-pear more cloudy than the cooler west limb, which hosts more massive but lower alti-tude clouds; this shifts abruptly above a critical temperature ( ∼ CARMA , Gao et al. (2020) computed the amplitude of the 1.4 µ m water feature of hotJupiters in transmission as a function of temperature, gravity, and atmospheric metal-licity. Their results compare well to the data compiled by Fu et al. (2017), though thereis more scatter in the data (Figure 4), which could be due to their usage of a 1D modelthat ignores east-west limb differences (e.g. Powell et al., 2018). The computed waterfeature amplitudes do not vary monotonically with temperature: CARMA predicts the for-mation of silicate, corundum, and titanium clouds at high temperatures, rapidly reduc-ing the water feature amplitude compared to hotter, cloudless (1D) atmospheres; thisis followed by the sinking of these clouds to lower altitudes at lower temperatures lead-ing to an increase in the water feature amplitude; finally, at ∼
950 K, photochemical hazesform from methane photolysis, reducing the water feature amplitude once more. Impor-tantly, Gao et al. (2020) predicts that optically thick iron and sulfide clouds, includingMnS clouds, are difficult to form due to energy barriers associated with nucleation. Theseresults are in contrast to those of Parmentier et al. (2016), who required silicate cloudsto disappear for T eq < ∼
950 K) and is between silicates and methane-derivedhazes rather than silicates and MnS clouds. A possible solution is if hazes could format higher temperatures (e.g. Lavvas & Koskinen, 2017) such that it could replace MnSas the main source of aerosol opacity at the limb, while remaining optically thin in emis-sion. An explanation for how silicate clouds disappear is also needed, as sequestrationat depth may be difficult (Thorngren et al., 2019). In addition, Gao et al. (2020) doesnot explain the diversity of spectral slopes in the optical. Ohno and Kawashima (2020)offers a possible cause for such slopes by appealing to photochemical hazes. They foundthat variations in haze formation rates at high altitudes and the rates with which hazeparticles are mixed downwards naturally lead to a diversity of optical spectral slopes.In particular, they showed that “super-Rayleigh” slopes (Pinhas et al., 2019; Welbankset al., 2019; May et al., 2020; Alderson et al., 2020; Chen et al., 2021) are possible whenmixing is strong and the haze formation rate is moderate. –23–anuscript submitted to
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Modeling efforts for cooler, lower mass exoplanets have focused on understandingwhy certain benchmark objects, e.g. the mini-Neptune GJ 1214b and the “super-puffs”Kepler-51b and d have extremely flat transmission spectra (Kreidberg et al., 2014; Libby-Roberts et al., 2020). A slew of studies have attempted to explain GJ 1214b’s transmis-sion spectra using 1D models, some relying on KCl and ZnS clouds (Gao & Benneke, 2018;Ohno & Okuzumi, 2018; Ohno, Okuzumi, & Tazaki, 2020), some relying on photochem-ical hazes (Kawashima & Ikoma, 2018; Adams et al., 2019; Kawashima et al., 2019; Lav-vas et al., 2019), and others relying on both (Morley et al., 2013, 2015). In general, amoderately high ( > × solar) atmospheric metallicity is needed in addition to aerosolopacity to suppress the amplitude of molecular features to match the data. Studies fo-cusing on clouds have required them to be extremely vertically extended, either due toextremely strong vertical mixing or low sedimentation velocities caused by high poros-ity. Charnay, Meadows, Misra, et al. (2015) showed using a GCM that cloud particlescan be lofted to low pressures by atmospheric circulation to explain the Hubble data (Kreidberget al., 2014), but the particle size required would be too small to explain the
Spitzer data,which extends GJ 1214b’s featureless transmission spectrum out to 5 µ m. Studies thatexplored the impact of hazes frequently faced the same issue: the production of hazesat low pressures leads to small particles that are unable to explain the full spectrum. Adamset al. (2019) was able to explain the full spectrum with aggregate haze particles, thoughthey relied on a parameterization of how the fractal dimension of aggregates scaled withthe number of monomers within the aggregate that may not be realistic (Ohno, Okuzumi,& Tazaki, 2020). Morley et al. (2015) was able to match the Hubble data for GJ 1214bwith photochemical hazes and predicted that their existence may lead to atmosphericheating, resulting in a temperature inversion that would generate emission features inGJ 1214b’s thermal infrared spectrum. In addition, Morley et al. (2015) showed that thehaze production rate, parameterized from a photochemical model, peaks at a planet T eq ∼
800 K with falling rates at higher temperatures due to lower methane abundances, andlower temperatures due to decreasing high energy UV photons. This could lead to in-creasing haze opacity with decreasing T eq for T eq < µ m water feature seen in Crossfield and Kreidberg(2017).The low gravities of super-puffs (e.g. Masuda, 2014) led to expectations of large( > < µ bar) where they must persist. L. Wang and Dai (2019); Gao and Zhang (2020) got aroundthis issue by taking into account the outward wind that could exist on super-puffs dueto ongoing atmospheric loss, which could entrain aerosol particles and push them to higheraltitudes. In particular, Gao and Zhang (2020) showed that such a phenomenon couldoccur on all young, low mass, temperate ( T eq < An important motivation of aerosol models of the current sample of directly im-aged exoplanets is to explain why they are redder than brown dwarfs of the same effec-tive temperature, given the general picture of cloud evolution on brown dwarfs outlinedin §
3. Marley et al. (2012) showed that the difference in gravity between field brown dwarfsand directly imaged exoplanets is the likely culprit: the atmospheric mass - and thus opac-ity - above a given pressure level is higher for a low gravity object than for a high grav-ity object, leading to the former object having higher temperatures at all pressures thanthe latter object for the same effective temperature. As such, the cloud base on the lower –24–anuscript submitted to
JGR: Planets gravity object would be situated at lower pressures, allowing clouds to persist above thephotosphere of the object to a lower effective temperature, leading to redder near-infraredcolors due to aerosol opacity. Charnay et al. (2018) reaffirms this result but also showsthat the location of the radiative-convective boundary is at lower pressures for lower grav-ity objects than for higher gravity objects of the same effective temperature, and thuslofting of cloud particles by convective turbulence may be more efficient for low grav-ity objects, further increasing cloud opacity. It is important to note, however, that thesestudies do not consider the kinetics of cloud formation, and thus how cloud opacity varieswith gravity on directly imaged exoplanets is still uncertain. Furthermore, interpretingspectra of the reddest objects still requires the inclusion of high altitude submicron aerosolsand/or highly vertically extended cloud layers (Hiranaka et al., 2016; Lew et al., 2016;Kellogg et al., 2017; Burningham et al., 2017; Schlawin et al., 2017; Manjavacas et al.,2018; Allart et al., 2020a; Stone et al., 2020).In addition to mineral clouds, the proximity of some directly imaged companionsto their young, UV-bright host stars coupled with relatively cool stratospheres could per-mit the formation of photochemical hazes. Griffith et al. (1998) hypothesized that ab-sorbing organic hazes could persist at several tens of bars in the atmosphere of the browndwarf companion Gl 229 B, which would explain its low flux at red optical wavelengths.Photochemical modeling of directly imaged exoplanets (Moses et al., 2016; Zahnle et al.,2016) showed that optically thick hydrocarbon hazes are difficult to form, though thereis great uncertainty in the chemical pathways involved. Interestingly, Zahnle et al. (2016)showed that H S photochemistry could produce elemental sulfur allotropes like S , whichmay condense to form optically thick sulfur clouds for planets with effective tempera-tures <
700 K. Such clouds would be highly reflective at red-optical and near-infrared wave-lengths, but highly absorbing at wavelengths < µ m (Gao et al., 2017).Spatial inhomogeneity and temporal variability of cloud distributions on brown dwarfsand directly imaged exoplanets have been used to explain the variability in rotationallight curves of these objects ( § –25–anuscript submitted to JGR: Planets
The numerous parameterizations made by models in simulating exoplanet aerosolsdemonstrate the complexity in aerosol formation and evolution in exoplanet atmospheres,complexity that can often only be unveiled by experimental work. The few laboratoryexoplanet studies that have been performed thus far have primarily focused on the for-mation and composition of hazes, as inspired by similar solar system studies such as in-vestigations of Titan’s hazes (see Cable et al., 2012, for a review), where haze formationin N /CH gas mixtures at <
300 K are considered. These works all involve exposing var-ious gas mixtures in a chamber under vacuum to an energy source, which dissociates andionizes molecules that can then recombine and grow into larger haze particles. Exper-iments cover a range of possible atmospheric compositions and temperatures, from thoseof hot Jupiters to terrestrial planets. Each experimental study is distinct in its choiceof temperatures, pressures, gas mixtures, gas flow mechanisms, and irradiation sourcesand thus drawing larger trends out of their results remains challenging at present. Sincethe actual atmospheric compositions of exoplanets are currently only loosely constrained,the choice of gas mixtures in many of these experiments is either based on equilibriummodel predictions or earlier solar system studies.Fleury et al. (2019) measured the photochemical output of a simple atmosphereof H with 0.3% CO between temperatures of 600 K and 1500 K exposed to UV (Ly α ,121.6 nm) photons to simulate photochemistry in hot Jupiters. No solid aerosol mate-rial was observed for most of their temperature range except at 1473 K and after verylong UV exposure times, though contamination by the ambient atmosphere may haveinfluenced their results. H¨orst et al. (2018); He et al. (2018b, 2020b, 2020a) conducteda series of experiments targeting hazes in mini-Neptunes and rocky planets with tem-peratures between 300 and 800 K incorporating gas mixtures dominated by H , H O,and CO , with varying amounts of CH , CO, NH , N , and H S, as determined by equi-librium chemistry calculations. Both plasma discharge and UV energy sources were used.These experiments showed that increasing H tended to decrease aerosol particle pro-duction, while the water-dominated atmospheres actually produced more haze than Ti-tan experiments, suggesting that some temperate terrestrial atmospheres may be extremelyhazy (H¨orst et al., 2018; He et al., 2018b). The visible appearance of these haze mate-rials are highly diverse, as shown in Figure 10, hinting at a similar diversity in opticalproperties and compositions. In addition, the inclusion of sulfur species were found todramatically increase haze production in terrestrial atmospheres and result in organosul-fur haze compositions (He et al., 2020b; Vuitton et al., 2021), instead of the elementalsulfur allotropes (e.g. S ) that have been predicted by some photochemical models (Hu& Seager, 2014; Zahnle et al., 2016). These conclusions are consistent with results froma recent, solar system-focused, experimental study of N /CH /H S gas mixtures (Reedet al., 2020). Furthermore, the gas phase compositions resulting from these mini-Neptuneand super-Earth experiments include a substantial abundance of organic species (He etal., 2019), which are then incorporated into the solids (Moran et al., 2020; Vuitton etal., 2021). Moran et al. (2020) demonstrated that oxygen is readily integrated into thehaze particles along with nitrogen and carbon when oxygen-carrying gas species are present,which is consistent with previous studies investigating oxidized solar system hazes (Traineret al., 2006; Hasenkopf et al., 2010; H¨orst & Tolbert, 2014; Ugelow et al., 2018). Finally,preliminary characterization of the results of these exoplanet experiments suggests theproduction of a plethora of prebiotic species, such as amino acids, sugars, and nucleotidebases (Moran et al., 2020).Critically, exoplanet aerosol experiments have demonstrated that methane, longused in the exoplanet literature as an essential component of haze formation (e.g. Mor-ley et al., 2015; Kawashima & Ikoma, 2018; Gao et al., 2020), is not always needed toproduce substantial amounts of haze (H¨orst et al., 2018; He et al., 2018b; Fleury et al.,2019; He et al., 2020b, 2020a) and that exoplanet hazes likely contain more than just hy- –26–anuscript submitted to
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Figure 10.
Laboratory hazes made from hydrogen-rich, water-rich, and carbon dioxide-richatmospheres from 300 K to 600 K have a range of colors at visible wavelengths, some unlike thoseseen in solar system hazes (He et al., 2018a). © AAS. Reproduced with permission. drocarbons or by-products of methane photolysis (Moran et al., 2020; Reed et al., 2020;Vuitton et al., 2021). While methane may be an intermediary gas product in some ofthe experiments that use CO and CO as the primarily carbon reservoir (e.g. Fleury etal., 2019), gas phase results show that it is not part of the chemical pathway in all cases,which instead seem more dependent on CO or CO photolysis (He et al., 2019, 2020b).Additionally, photochemical models are typically limited to hydrocarbon species contain-ing up to only five carbon atoms (e.g., G. Arney et al., 2016; Zahnle et al., 2016) or evenfewer (Kawashima & Ikoma, 2018), but laboratory work focusing on Titan hazes showsthat higher order reactions must be considered to realistically capture aerosol growth (Berryet al., 2019). Taken as a whole, laboratory results have clearly shown that the forma-tion of haze in exoplanet atmospheres is not nearly as simple as that assumed in pre-vious and current models (e.g. Gao et al., 2020), and that a greater appreciation for thechemistry and physics of haze formation at high temperatures is warranted.Measuring the optical properties of exoplanet aerosol materials allow for a directlink to observations of exoplanet atmospheres and facilitates calculations of aerosol opac-ity in exoplanet atmospheric models. While refractive indices of a variety of cloud com-positions exist (e.g., Wakeford & Sing, 2015), these measurements are not necessarily rep-resentative of exoplanet atmospheric conditions. Meanwhile, the most frequently used(e.g. Sudarsky et al., 2000, 2003; Howe & Burrows, 2012; Sing et al., 2013; Morley et al.,2015; Wakeford & Sing, 2015; Kitzmann & Heng, 2018; Kawashima & Ikoma, 2018, 2019;Adams et al., 2019; Ohno & Kawashima, 2020; Gao & Zhang, 2020) set of haze refrac- –27–anuscript submitted to JGR: Planets tive indices in exoplanet investigations have come from the work of Khare et al. (1984),who measured the optical properties of Titan haze analogs (“tholins”). Other less fre-quently used optical properties include that of soots (Morley et al., 2013; Lavvas & Kosk-inen, 2017; Gao et al., 2020), also made predominately of hydrocarbons.Gavilan et al. (2017, 2018) conducted spectroscopy and ellipsometry of solid ma-terial produced from essentially Titan-like atmospheres at 300 K, with the addition ofCO . They found that the aerosols they made were composed of complex organics, withprominent amide, hydroxyl, and carbonyl groups. In addition, the increased oxidationof the hazes were found to strongly increase their absorptivity in the UV and the mid-IR, particularly between 0.13 and 0.3 µ m and 6 and 10 µ m, as well as blueshift the ab-sorption edge from the visible to the UV, consistent with an early Earth experiment ofsimilar composition (Hasenkopf et al., 2010). In contrast, another similar compositionearly Earth-focused laboratory study, but which also contained molecular oxygen, foundno UV absorption from oxidized hazes, though their experimental set-up limited theirresults to discrete wavelength measurements at 405 and 450 nm (Ugelow et al., 2018).These works constitute the only measurements of spectra or refractive indices of exoplanethaze analogs thus far and likely represent only a tiny fraction of the potential diversityof haze optical properties. Solar system studies have shown that gas composition, pres-sure, temperature, and energy source all impact the spectral response of the resultinghaze particles (Imanaka et al., 2004; Brass´e et al., 2015).The particle size distribution of aerosols offer a glimpse of the microphysical pro-cesses involved in aerosol formation and growth. Size distributions measured by He etal. (2018a, 2018b, 2020b) for the temperate, high metallicity mini-Neptune and super-Earth atmospheres were unimodal and ranged between 20 and 200 nm for the temper-atures, initial gas mixtures, and energy sources considered (Figure 11), which would beable to produce spectral slopes in optical and near-infrared exoplanet transmission spec-tra. Size distributions were wider for experiments conducted with UV as the energy sourcethan for those conducted with plasma, but the plasma experiments generated more par-ticles. This variance in particle sizes likely results from the difference in energy densi-ties imparted by the UV versus the plasma discharge, but extrapolation to meaningfulproxies for diverse stellar types is unclear. High temperatures produced narrower sizedistributions than cooler temperatures, but the cooler temperatures bore the largest par-ticles. Higher metallicity atmospheres produce both more and larger particles, suggest-ing that the increased chemical complexity of the atmosphere is able to generate increas-ingly large, complex molecules. This is further displayed with the addition of sulfur inthe form of H S to the initial gas mixture, which resulted not only in increased parti-cle production (He et al., 2020b), but also in larger particle effective densities (Reed etal., 2020). Microscopy of the particles showed that not all of them are spherical, and thatsome particles clump into more aggregate structures, while some form linear chains. Thoughthis is qualitatively consistent with modeling studies that consider aggregate particles(G. Arney et al., 2016; Adams et al., 2019; Ohno & Kawashima, 2020), the specific growthmechanisms of exoplanet hazes made in the laboratory remains highly uncertain pasttheir initial formation, and the dynamics of haze particles in planetary atmospheres areunlikely to be fully captured by current experimental studies.
Aerosols are fundamental components of exoplanet atmospheres across a wide rangeof temperatures, gravities, compositions, and ages. The provenance and composition ofaerosols vary with planetary parameters, leading to differences in the planets’ emittedflux, geometric albedo, and transmission spectra. By combining state of the art obser- –28–anuscript submitted to
JGR: Planets E (cid:1) ect of Metallicity D i s t r i bu t i on ( pe r c en t t o t a l ) E (cid:1) ect of Metallicity D i s t r i bu t i on ( pe r c en t t o t a l ) E (cid:1) ect of Energy source D i s t r i bu t i on ( pe r c en t t o t a l ) E (cid:1) ect of Energy source D i s t r i bu t i on ( pe r c en t t o t a l ) E (cid:1) ect of Temperature D i s t r i bu t i on ( pe r c en t t o t a l ) E (cid:1) ect of Temperature D i s t r i bu t i on ( pe r c en t t o t a l )
800 K600 K400 K300 K100x solar1000x solar10000x solarUV lampPlasma
Figure 11.
Summary of particle size distributions from the laboratory haze experiments ofHe et al. (2018a, 2018b, 2020b) for 300–800 K (top), 100 × –10000 × solar metallicity (middle)atmospheres bombarded by UV photons and plasma discharges (bottom). vations with the latest theoretical models and laboratory data, we can summarize ourcurrent understanding of the nature of exoplanet aerosols.On tidally locked, transiting exoplanets with hydrogen/helium-dominated atmo-spheres, the longitudinal variation in instellation coupled with atmospheric circulationresult in daysides mostly devoid of aerosols for equilibrium temperatures ≥ ∼ ≥ –29–anuscript submitted to JGR: Planets photochemical hazes, dominate at lower temperatures, causing the observed variationsin the spectral slope and the amplitude of molecular features in transmission spectra.Exoplanet photochemical hazes are likely to possess diverse compositions, incorporat-ing atomic species like carbon, oxygen, hydrogen, nitrogen, and sulfur. Clouds on thedirectly imaged exoplanets discovered to date should be similar in composition to theirtransiting cousins, though the evolution of these clouds with planetary parameters shouldbe more akin to that on brown dwarfs. A major difference is that the lower gravity ofdirectly imaged exoplanets, as compared to brown dwarfs, leads to the persistence of cloudsabove the photosphere to lower effective temperatures. The sinking and breaking up ofclouds likely trigger the L-T transition in brown dwarfs and directly imaged exoplanetsand cause the observed temporal variability in emission.
While we are now able to construct a coherent picture of the formation and dis-tribution of aerosols in exoplanet atmospheres, there are still many holes in our under-standing. Below, we list a number of outstanding questions that will require detailed studyin the next decade and beyond:1. What are the compositions of exoplanet aerosols? Are they mixtures or mostlypure particles?2. How porous are exoplanet aerosol particles? Are they dense or fluffy aggregates?3. How do clouds initially form? What is the condensation sequence in exoplanet at-mospheres?4. What are the major parent molecules and chemical formation pathways of exo-planet photochemical hazes?5. What physical processes lead to the cloud evolution hypothesized at the browndwarf L-T transition and how do these change for directly imaged exoplanets?6. What level of model complexity is necessary to capture the aerosol processes in-ferred from current and future observations to high fidelity? How should exoplanetaerosols be parameterized in retrievals?7. How do aerosols respond to variations in planetary, atmospheric, and host star prop-erties? Conversely, how do aerosols affect the composition, thermal structure, anddynamics of exoplanet atmospheres?
A major goal of future observational investigations of exoplanet aerosols should beto unveil their compositions, which are currently unknown due to the lack of any spe-cific spectral features in current observations. Spectroscopy in the near-to-mid infrared(2-12 µ m) with the James Webb Space Telescope ( JWST ) will be a critical next step toexplore the composition of aerosols and their role in a 3D atmosphere. Many of the pro-posed aerosol species possess spectral features of their own, which are best measured inthe mid-infrared and correspond to the vibrational mode between the dominant atomsin the material (Figure 12). Silicates such as enstatite (MgSiO ) and forsterite (Mg SiO )have vibrational mode absorption dominated by the Si-O bond which produces promi-nent absorption at ∼ µ m. These vibrational modes have been measured in an arrayof astrophysical contexts, including in the atmospheres of brown dwarfs (Cushing et al.,2006; Looper et al., 2008). Wakeford and Sing (2015) showed using Mie calculations andthe optical properties of various cloud forming species that cloud vibrational-mode ab-sorption features could reach observable amplitudes in exoplanet transmission spectra,though the amplitude is a strong function of the mean particle size and the width andshape of the size distribution, with larger particles and wider size distributions leadingto smaller amplitudes. Follow up works investigated additional cloud species (Wakeford,Visscher, et al., 2017; Kitzmann & Heng, 2018), the impact of different sized particle pop- –30–anuscript submitted to JGR: Planets
Wavelength ( m) C h a n g e i n P l a n e t R a d i u s ( H ) Al O TiO FeMg SiO CrMnSNa SZnSNaClKClSootTholin
Figure 12.
Aerosol transmission spectra for a variety of proposed cloud and haze speciescomputed assuming monodisperse 0.1 µ m particles distributed with a constant mass mixing ra-tio profile in the atmosphere. The spectra are offset for clarity, normalised to the mean transitdepth, and shown in planetary scale heights. Optical constants for tholins are taken from Khareet al. (1984); those for soots are from Lavvas and Koskinen (2017); those for KCl, ZnS, Na S,MnS, and Cr are from Morley et al. (2012); those for NaCl are from Eldridge and Palik (1985);Querry (1987); those for Mg SiO , Fe, and Al O are from Wakeford and Sing (2015); and thosefor TiO are from Posch et al. (2003); Zeidler et al. (2011). The computed aerosol transmissionspectra can be accessed from Gao et al. (2021). ulations (Mai & Line, 2019), differences in cloud opacity at optical wavelengths (Pinhas& Madhusudhan, 2017), and the impact of taking into account cloud microphysics (Ormel& Min, 2019; Gao et al., 2020).The observability of cloud spectral features will also depend on whether the cloudparticles are pure, as predicted by equilibrium models, or mixtures, as predicted by ki-netic cloud models. Helling et al. (2006) showed that consideration of kinetic cloud for-mation and mixed grains could result in the condensation of cloud species that are sup-pressed in equilibrium models, such as SiO , which exhibit mid-infrared absorption fea-tures different from those of enstatite and forsterite, the major silicate clouds predictedby equilibrium models. As such, the extent to which exoplanet cloud formation followsequilibrium or kinetic models may be testable using observations. However, it is not yetknown how we can go a step further and, in the event that exoplanet clouds are betterreproduced using kinetic models, use observations to differentiate between well-mixedcloud particles, like those modeled in DRIFT , and layered cloud particles, like those mod- –31–anuscript submitted to
JGR: Planets eled in
CARMA . In addition, the porosity of the cloud particles will also impact the spec-tral features, with more aggregate-like particles exhibiting stronger absorption (Samraet al., 2020).Photochemical hazes may also exhibit spectral features in the near and mid-infrared(Figure 12; also see e.g., Wakeford & Sing, 2015; Kawashima & Ikoma, 2018; Gao & Zhang,2020), which could shed light on their complex compositions by revealing the types ofbonds that they contain. Many organic polymers that contain mixtures of carbon, oxy-gen, nitrogen, and hydrogen possess absorption features at wavelengths ∼ µ m and > µ m (e.g. Wang et al., 1998; Laskina et al., 2014) corresponding to the vibrational modesof the various single and double bonds between C, O, N, and H in various functional groups.However, additional laboratory work is needed to measure the optical constants of ex-oplanet haze analogues before we can predict the amplitude, width, and exact locationsof these features and interpret future exoplanet haze observations.In addition to composition, JWST ’s ability to continuously observe targets at hightime cadence, as opposed to
Hubble ’s staccato way of observing brought on by its orbitaround the Earth, will allow us to probe the east and west limbs separately. This willbe critical for deciphering the 3D distribution of aerosols and how asymmetric limbs im-pact transmission spectra (Fortney et al., 2010; Line & Parmentier, 2016; von Paris etal., 2016; Kempton et al., 2017; Powell et al., 2018, 2019).An alternative strategy for probing the composition of clouds is to look for the ex-istence or absence of gas species that have been hypothesized to condense. In particu-lar, as several groups of gasses are associated with clouds that condense at similar tem-peratures on hot Jupiters (e.g. TiO/VO, aluminum, and calcium at the highest temper-atures, iron, magnesium, silicon, chromium, and manganese at moderate temperatures,and potassium and sodium at lower temperatures, see Figure 1), measuring the abso-lute abundances of these gases and their ratios as a function of planetary temperatureand gravity could help constrain the condensation sequence in exoplanet atmospheres(Lothringer et al., 2020). However, while many species have been detected for ultra hotJupiters (e.g. Fossati et al., 2010; Haswell et al., 2012; Hoeijmakers et al., 2018; Yan etal., 2019; von Essen et al., 2019; Sing et al., 2019; Ben-Yami et al., 2020; Cabot et al.,2020; Nugroho et al., 2020), suggesting largely cloud-free atmospheres, efforts at lowertemperatures have yielded mixed results due to controversial detections that are diffi-cult to replicate (e.g. Vidal-Madjar et al., 2013; Cubillos et al., 2020; Sedaghati et al.,2017; Espinoza et al., 2019; Chen et al., 2018; Seidel et al., 2020; Sing et al., 2015; Gib-son et al., 2017, 2019; McGruder et al., 2020) and aerosol opacity at optical wavelengthsthat reduce the amplitudes of atomic and molecular absorption features (Charbonneauet al., 2002; Pont et al., 2008; Heng, 2016; Sing et al., 2016).Future high spectral resolution observations in the optical and near-ultraviolet bygroundbased extremely large telescopes and space-based telescopes will be essential foraccurate measurements of heavy element abundances in the upper atmospheres of ex-oplanets. The potential for constraining cloud and dynamical processes on hot Jupiterswith high spectral resolution observations was shown by Ehrenreich et al. (2020), whorecently detected the blue-shifted spectral signature of neutral iron on the eastern/dusklimb of the hot Jupiter WASP-76b but not on the western/dawn limb. This is sugges-tive of iron condensation on the nightside of the planet after it was transported thereby eastward winds. In addition, high spectral resolution data can reveal the vertical dis-tribution of aerosol layers, particularly for planets that exhibit low-resolution flat opti-cal or near-infrared transmission spectra, by revealing the cores of spectral lines that ex-tend above the aerosols (Pino et al., 2018; Hood et al., 2020; Gandhi et al., 2020); thiswas recently attempted for some hot Jupiters (S´anchez-L´opez et al., 2020; Allart et al.,2020b). –32–anuscript submitted to
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Future reflected light observations by groundbased extremely large telescopes andspace-based telescopes will also allow for probes of exoplanet aerosols. Morley et al. (2015);Charnay, Meadows, Misra, et al. (2015) showed that mini-Neptunes like GJ 1214b thatpossess flat near-infrared transmission spectra may exhibit a variety of optical geomet-ric albedo spectra that are diagnostic of aerosol compositions and particle sizes. Morley,Marley, Fortney, Lupu, Saumon, et al. (2014); MacDonald et al. (2018); Hu (2019) ar-gued that water clouds on cooler giant exoplanets can boost their albedo such that thewater and methane absorption bands in the red-optical become much more prominent,aiding retrievals of molecular abundances. However, degeneracies between retrieved cloudproperties and molecular abundances could arise unless the planet could be observed atmultiple orbital phases (Carri´on-Gonz´alez et al., 2020; Damiano & Hu, 2020; Damianoet al., 2020). Sulfur clouds sourced from H S photochemistry can also boost planets’ red-optical albedoes, though their blue-optical and near-UV albedoes would be much lower( ∼ TESS bandpass tend to be low( <
10 ppm) for a variety of cloud species, but that amplitudes increase towards bluer wave-lengths. In addition, measurements of the polarization of reflected light offer unique con-straints on the composition, size, shape, and spatial distribution of aerosol particles (Seageret al., 2000; Karalidi et al., 2013; Kopparla et al., 2016), though claims of detections sofar have been controversial (e.g. Berdyugina et al., 2011; Wiktorowicz et al., 2015; Bottet al., 2016). Polarization of the thermal emission of brown dwarfs and directly imagedexoplanets can also constrain aerosol properties and distributions in their atmospheres(e.g. Sengupta & Krishan, 2001; Marley & Sengupta, 2011; Stolker et al., 2017; Sang-havi & Shporer, 2018; Millar-Blanchaer et al., 2020).Further constraints on aerosol particle sizes and vertical distribution, importantfor shedding light on a myriad of microphysical and dynamical processes in the atmo-sphere, can be gleamed from current and future transmission and emission spectroscopy.Small particle sizes ( ≤ µ m) have already been estimated from the spectral slopes inoptical transmission spectra (e.g. Lecavelier Des Etangs et al., 2008; Wakeford & Sing,2015; Wakeford, Visscher, et al., 2017; I. Wong, Benneke, et al., 2020), while extensionsto the Spitzer bandpasses have yielded even tighter size constraints (Benneke, Knutson,et al., 2019). Observations by
JWST towards the mid-infrared will enhance these effortsby probing the decrease in opacity of larger particles, while the continuous wavelengthcoverage will allow for more precise measurements of the shape of the aerosol continuum,which contains information about the vertical distribution of aerosol particle sizes (Mai& Line, 2019). Meanwhile, tracking the wavelength-dependent light curve variability am-plitude of L dwarfs has allowed for the measurement of aerosol particle sizes (Lew et al.,2016; Schlawin et al., 2017), since aerosol opacity impacts shorter wavelengths more thanlonger wavelengths. Future spectroscopic light curve surveys of L-type objects may beable to constrain cloud particle sizes as a function of effective temperature and gravity.Finally, detections of weather/temporal variability in exoplanet atmospheres im-pose powerful constraints on dynamical processes therein, including how aerosol micro-physics is coupled to the atmospheric circulation pattern. Future long time baseline, highprecision observations will shed light on exoplanet weather in the same way rotationallight curve measurements have informed our understanding of weather on L and T typeobjects. Brown dwarf surveys are already delving into cooler temperatures with mea-surements of variability on Y dwarfs (Rajan et al., 2015; Esplin et al., 2016; Cushing etal., 2016; Leggett et al., 2016) that could be probing patchy sulfide and chloride cloudsand the onset of water clouds, thus bridging the gap between brown dwarfs and the gi-ant planets in our own solar system. GCMs have predicted hot Jupiter atmospheric vari-ability due to propagating waves and instabilities on spatial scales of thousands of kilo-meters to global scales (Dobbs-Dixon et al., 2010; Parmentier et al., 2013; Lines, Mayne, –33–anuscript submitted to
JGR: Planets et al., 2018; Komacek & Showman, 2020), which could lead to temporal variations in thespatial distribution of aerosols. Brightness variability has also been detected on hot Jupiters(Armstrong et al., 2016; Jackson et al., 2019), with interpretations ranging from cloudsreacting to changing winds and temperatures to the coupling of atmospheric circulationwith the planets’ magnetic field (Rogers & Komacek, 2014; Rogers, 2017).
The anticipated future exoplanet observations will necessitate the development ofmore sophisticated aerosol models and more detailed laboratory measurements. 3D mod-els of exoplanet atmospheres coupled with kinetic models of aerosol microphysics thatinclude cloud radiative and latent heat feedback will likely be required to fully under-stand these new observations due to the wavelength- and spatial-dependence of aerosolopacity. In particular, general, flexible models with the ability to simulate multiple par-ticle size modes and multiple aerosol species (e.g., both clouds and hazes) for various back-ground atmospheric compositions and thermal structures will need to be developed tointerpret new data from hundreds, perhaps thousands of exoplanets. At the same time,non-hydrostatic atmospheric models capable of resolving and studying moist convectionin H/He atmospheres (e.g. Freytag et al., 2010; Li & Chen, 2019; Ge et al., 2020) willallow for more rigorous investigations of cloud particle and condensate vapor transport,cloud formation, and cloud spatial inhomogeneity and temporal variability on giant ex-oplanets and brown dwarfs. Advances in computational efficiency will be essential forall of these innovations in modeling.At the same time, intercomparisons between existing models are needed for under-standing best modeling practices, evaluating model consistency, and placing interpreta-tions of observations by different models on a more equal footing. As discussed in § DRIFT and the Ackerman and Marley (2001) model, among oth-ers. They found that different models predicted different vertical and particle size dis-tributions of clouds, leading to variations in the predicted brown dwarf near-infrared fluxesof several tens of %. Given the recent proliferation of complex aerosol microphysics mod-els (Table 1), an update to Helling, Ackerman, et al. (2008) is warranted.For 3D aerosol models, Lines et al. (2019) juxtaposed the advection of cloud dis-tributions computed from
DRIFT by a GCM with stationary cloudy atmospheric columnscomputed by the Ackerman and Marley (2001) model coupled to the same GCM. Theyfound that the predominance of small, high altitude, scattering particles in
DRIFT ledto a decrease in global temperatures when compared to the larger, lower altitude par-ticles predicted by the Ackerman and Marley (2001) model. Showman et al. (2020) com-pared several different aerosol treatments in GCMs, including passive tracers and mi-crophysical models, and showed that all models found latitudinal variations in aerosoldistributions, but whether the aerosol abundance increased or decreased with latitudeis model dependent.In addition to comparisons of models of similar complexity, lessons learned fromcomplex aerosol models should be incorporated into simpler models used in retrievalsand radiative-convective equilibrium models so that they can be made more physical andpredictive, while allowing for connections between more subtle microphysical processesand exoplanet observations. Gao et al. (2018) attempted to place the sedimentation ef-ficiency parameter of the Ackerman and Marley (2001) model into the context of micro-physical processes by comparing it to
CARMA for different planetary and aerosol param-eters, but a more systematic and extensive effort is needed. –34–anuscript submitted to
JGR: Planets
The measurement of a variety of material properties and aerosol processes throughlaboratory experiments is critical for interpreting future observations and building morephysical models (Fortney et al., 2016). Many of the optical constants currently in usefor exoplanet aerosols are decades old and not measured for the composition and tem-perature regimes of exoplanet atmospheres (Morley et al., 2012; Wakeford & Sing, 2015;Kitzmann & Heng, 2018), and thus updates are needed. This issue is exacerbated in thecase of exoplanet hazes, which should have a diverse composition ( § The atmospheres of rocky exoplanets are undoubtedly more diverse than the H/He-dominated atmospheres we have explored thus far, suggesting similarly diverse aerosolcompositions. Importantly, aerosols in rocky exoplanets serve an additional role in theiratmospheres as compared to gas giants: a major control on the surface climate and thushabitability. No molecular features have been robustly observed in rocky exoplanet at-mospheres thus far (de Wit et al., 2016; Southworth et al., 2017; de Wit et al., 2018; Ducrotet al., 2018; Z. Zhang et al., 2018; Diamond-Lowe et al., 2018; Wakeford, Lewis, et al.,2019; Burdanov et al., 2019; Diamond-Lowe, Berta-Thompson, et al., 2020; Diamond-Lowe, Charbonneau, et al., 2020), which could be caused by aerosols, a high mean molec-ular weight atmosphere, a combination thereof (Moran et al., 2018), or a lack of an at-mosphere altogether (Kreidberg et al., 2019). We are therefore left with theoretical pre-dictions about the aerosols that may be present in these atmospheres and their impacts.The hottest rocky exoplanets, with T eq ≥ –35–anuscript submitted to JGR: Planets
Rocky exoplanets with temperatures more similar to those in the Solar System maypossess more familiar aerosols: clouds of water, sulfuric acid, and CO in temperate, ox-idizing atmospheres, clouds of hydrocarbons and nitriles, along with organic hazes in cool,reducing atmospheres, and dust elevated into the atmosphere by winds in a variety ofatmospheres. Temperate, reducing atmospheres may also host organic hazes (e.g. Pavlovet al., 2001; Trainer et al., 2004; Wolf & Toon, 2010; G. Arney et al., 2016; He et al., 2018b,2020b; Vuitton et al., 2021), as well as sulfur (S ) clouds (Hu et al., 2013). Many of theseaerosols are highly reflective at optical wavelengths, facilitating future reflected light ob-servations. In particular, the detection of bright sulfuric acid clouds may point to activevolcanism and the lack of significant oceans on the surface of temperate rocky exoplan-ets (Misra et al., 2015; Loftus et al., 2019).Of particular interest are rocky exoplanets orbiting near the habitable zone of Mdwarfs, as they are easier to characterize than those around Sun-like stars due to the morefavorable planet-to-star radius ratio and short orbital periods. In regards to possible aerosolsin their atmospheres, several differences between these planets and their solar cousinsneed to be considered: (1) they are most likely tidally locked to their host stars (Kastinget al., 1993), (2) they experience a prolonged period of high instellation during their hoststars’ pre-main sequence that could desiccate their upper mantles (Luger & Barnes, 2015),and (3) they are subjected to higher fluxes of high energy UV radiation and particle bom-bardment (France et al., 2013). A consequence of (1) is that a planet with a water oceancould exhibit vigorous convection at the subsolar point, leading to the formation of op-tically thick water clouds, with its high albedo acting as a stabilizing feedback againstincreasing instellation (Joshi, 2003; J. Yang et al., 2013; Way et al., 2018). Such cloudpatterns are predicted to be sensitive to planet rotation rate (J. Yang et al., 2014; Ko-macek & Abbot, 2019) and lead to significant muting of molecular features in transmis-sion (Suissa et al., 2020; Komacek et al., 2020). On the other hand, planets dried outfrom (2) could have clear or dusty atmospheres devoid of water clouds and sulfuric acidclouds (Lincowski et al., 2018; Lustig-Yaeger et al., 2019). Photochemical haze forma-tion due to (3) depends on how different links in the reaction web that leads from sim-ple parent molecules to haze particles react to the spectral energy distributions of M dwarfs.For example, G. Arney et al. (2018) showed that haze formation in temperate, anoxicatmospheres is more efficient for an M dwarf host star compared to a Sun-like host star,particularly in the presence of organic sulfur compounds. Lessons learned from studying aerosols in the atmospheres of solar system worldshave been and will continue to be vital for our understanding of exoplanet aerosols. Asshown in this review, the theoretical frameworks used to understand exoplanet aerosolsare heavily influenced by what we know of aerosols in the Solar System, and in fact sev-eral exoplanet aerosol models are derived directly from models of Earth and solar sys-tem aerosols (e.g.
CARMA and the Ackerman and Marley (2001) model). Laboratory in-vestigations of exoplanet aerosols are even more intimately linked to efforts to experi-mentally characterize aerosols closer to home, with many deriving from investigationsof Titan’s haze.As the quality of exoplanet observations improve in the coming decades, it wouldbe beneficial for analyses of these data to draw inspiration from efforts to analyze so-lar system data. For example, Irwin et al. (2015) retrieved the imaginary refractive in-dex of the aerosols in Uranus’s atmosphere from reflected light spectra in the near-infraredinstead of assuming any particular aerosol composition; this strategy may become nec-essary for future reflected light and thermal emission observations of exoplanets and browndwarfs (e.g. Taylor, Parmentier, Line, et al., 2020). In addition, several studies have fo-cused on treating observations of solar system worlds as analogues of future exoplanetdata. Mayorga et al. (2016); Dyudina et al. (2016) investigated how the brightness and –36–anuscript submitted to
JGR: Planets color of Jupiter and Saturn varied with phase and found significant deviations from aLambertian model, indicating complex vertical distributions of aerosol particles, withimplications for directly imaging exoplanets in reflected light. Simon et al. (2016); Geet al. (2019) analyzed rotational light curves of Neptune and Jupiter, respectively, as analo-gies of brown dwarf light curves, and discovered that light curve variability is controlledlargely by discrete features like the Great Red Spot, and that the shape of the light curvedepends strongly on the heterogeneous cloud cover and gas opacity. These efforts notonly provide cautionary tales of how complicated planetary atmospheres can be, but theyalso provide benchmarks to which exoplanet aerosol models can be compared. Karalidiet al. (2015), for example, applied their mapping code to both Jupiter and brown dwarfsand were able to retrieve several major atmospheric features on the former object. Sim-ilarly, Lupu et al. (2016) applied a multi-aerosol-layer retrieval code to reflected light ob-servations of Jupiter and Saturn in preparation for future observations of wide-orbit gi-ant exoplanets, and were able to retrieve methane mixing ratios and cloud single scat-tering albedos consistent with the observed values for those two planets. As we increas-ingly focus on cooler targets like Y dwarfs and temperate exoplanets with temperaturesand atmospheric compositions approaching that of our own giant planets (e.g. Morleyet al., 2018; Dalba & Tamburo, 2019; Benneke, Wong, et al., 2019; Vanderburg et al.,2020), forging connections with solar system science will become ever more important.As with giant planets, much of our predictions of rocky exoplanet aerosols are basedon the examples in our solar system (see § –37–anuscript submitted to JGR: Planets
Acknowledgments
Datasets for this research are available in these in-text data citation references: Fu etal. (2017); Crossfield and Kreidberg (2017); Libby-Roberts et al. (2020); Kreidberg etal. (2020); Best et al. (2020); Parmentier et al. (2016); Beatty et al. (2019); Gao et al.(2021). We are grateful for NASA’s Astrophysics Data System, without which this re-view would not have been possible. We thank X. Zhang and J. J. Fortney for valuablediscussions about the structure of this review. We also thank I. J. M. Crossfield, L. Krei-dberg, Y. Kawashima, and C. He for contributing to the production of several figures.P. G. acknowledges support from H. Zhang, W. Z. Gao, H. Y. Dai, H. P. Zhang, the 51Pegasi b Fellowship sponsored by the Heising-Simons Foundation, and NASA throughthe NASA Hubble Fellowship grant HST-HF2-51456.001-A awarded by the Space Tele-scope Science Institute, which is operated by the Association of Universities for Researchin Astronomy, Inc., for NASA, under contract NAS5-26555. S. E. M. acknowledges sup-port from NASA Earth and Space Science Fellowship Grant 80NSSC18K1109.
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