Free Access
Issue
A&A
Volume 647, March 2021
Article Number A48
Number of page(s) 19
Section Planets and planetary systems
DOI https://doi.org/10.1051/0004-6361/202039663
Published online 05 March 2021

© ESO 2021

1 Introduction

The nearby temperate super-Earth LHS 1140 b (Dittmann et al. 2017; Ment et al. 2019) is an exciting target for atmospheric characterization. Morley et al. (2017) assumed Venus-, Titan-, and Earth-like atmospheres for LHS 1140 b. Their results suggest that atmospheric characterization with the James Webb Space Telescope (JWST) could be possible, although challenging.

A recent study of the temporal radiation environment of the LHS 1140 system suggests that the planet receives relatively constant near-ultraviolet (NUV; 177–283 nm) flux <2% compared to that of Earth (Spinelli et al. 2019). The results of Chen et al. (2019) suggest that LHS 1140 b might be stable against complete ocean desiccation due to the low UV activity of the host star, which would bode well for its habitability. However, Chen et al. (2019) assumed a rather low UV flux for the star, which might lead to an underestimation of the water loss. Furthermore, due to the extended pre-main-sequence phase of M dwarfs (see, e.g., Baraffe et al. 2015; Luger & Barnes 2015), LHS 1140 b may have experienced extreme water loss before the star entered the main-sequence phase (see, e.g., Luger & Barnes 2015).

Assuming an Earth-like atmosphere with updated sea-ice parameterization, the 3D model study of Yang et al. (2020) suggestsa reduced surface ocean on LHS 1140 b (from 12 to 3% surface coverage). Diamond-Lowe et al. (2020) observed two transits of LHS 1140 b with the twin Magellan Telescopes, but their analysis suggests that a precision increased by a factor of about 4 was needed for the detection of a cloudless hydrogen atmosphere, for example, present at amounts consistent with the bulk density. Recently, Edwards et al. (2021) presented spectrally resolved observations of LHS 1140 b using the G141 grism of the Wide Field Camera 3 (WFC3) on the Hubble Space Telescope (HST). Their results suggest that the planet may host a clear H2 -dominated atmosphere and may show evidence of an absorption feature at 1.4 μm.

The processes affecting the climates and compositions of super-Earths such as LHS 1140 b are not well known. Evidence has been found that there is a dip in the radius distribution of extrasolar planets at 1.5–2.0 R (see, e.g., Owen & Wu 2013; Fulton et al. 2017; Van Eylen et al. 2018; Hardegree-Ullman et al. 2020). With a radius of ~1.7 R, LHS 1140 b lies within this “radius valley” (Ment et al. 2019), which is interpreted as the transition between predominantly rocky planets and volatile-rich planets. A number of studies have investigated the origin of the radius valley (see, e.g., Owen & Wu 2013, 2017; Lee et al. 2014; Lopez & Rice 2018; Ginzburg et al. 2018; Gupta & Schlichting 2019). LHS 1140 b is not expected to have a large H2 –He envelope due to its high bulkdensity of ρ, of 7.5 ± 1.0 cm−3 (Ment et al. 2019). However, massive super-Earths might retain small residual H2 atmospheresat the end of the core-powered mass loss (see, e.g., Ginzburg et al. 2016; Gupta & Schlichting 2019).

In H2-dominated atmospheres significant heating could be induced by self- and foreign H2 collision induced absorption (CIA; e.g., Pierrehumbert & Gaidos 2011; Ramirez & Kaltenegger 2017). Regarding the composition, lessons from the solar system gas giants (Yung & DeMore 1999, and references therein) suggest ammonia (NH3) and phosphine (PH3) chemistry, as well as pathways starting with methane (CH4) forming long-chain hydrocarbons that can condense to form hazes, and pathways destroying long-chain hydrocarbons driven mainly by initial reaction with atomic hydrogen (H) from extreme UV (EUV) photolysis of H2. In exoplanetary science Hu & Seager (2014), among others, studied the photochemical responses of hydrogen atmospheres on super-Earths; Line et al. (2011) discussed processes controlling the partitioning between CH4 and CO on GJ 436 b; and recently Lavvas et al. (2019) studied the effects of photochemistry, mixing, and hazes on GJ 1214 b.

Clouds and hazes can obscure the observed spectrum of the planetary atmosphere below the top of the haze or cloud layer. Arney et al. (2016, 2017) used a 1D climate–chemistry model to simulate the photochemically driven formation of organic hazes in the atmosphere of early Earth and exoplanets located in the habitable zones (HZs) of their host stars. They concluded that the concentration of CH4 has a large impact on haze formation, and propose that a hydrocarbon haze may be a biosignature on planets with substantial levels of CO2.

The detection of potential biosignature gases like oxygen (O2), nitrous oxide (N2 O), or chloromethane (CH3 Cl) in an Earth-like or CO2-dominated atmosphere will be challenging using transmission or emission spectroscopy (see, e.g., Schwieterman et al. 2018; Batalha et al. 2018; Wunderlich et al. 2019; Lustig-Yaeger et al. 2019). The characterization of an H2 atmosphere is more favorable due to the lower mean molecular weight leading to larger spectral features. In such an atmosphere several potential biosignatures might be detectable including NH3, dimethyl sulfide (DMS), CH3Cl, PH3, and N2 O (Seager et al. 2013a,b; Schwieterman et al. 2018; Sousa-Silva et al. 2020).

In this work we apply the steady-state, cloud-free, radiative-convective photochemistry model 1D-TERRA (Scheucher et al. 2020; Wunderlich et al. 2020) together with the theoretical spectral model GARLIC (Schreier et al. 2014) to simulate a range of CO2, H2 –He atmospheres(and mixtures thereof), as well as atmospheric spectra for LHS 1140 b. The central aim of our work is to investigate potential atmospheres of this super-Earth and determine the detectability of key atmospheric features, in particular potential biosignatures, in the context of the forthcoming JWST and Extremely Large Telescope (ELT).

Section 2 introduces the climate–photochemistry model 1D-TERRA, the line-by-line spectral model GARLIC, and the signal-to-noise ratio (S/N) models for JWST and ELT. In Sect. 3 we first show the results of the atmosphericmodeling and the resulting transmission spectra, followed by the results of the S/N calculations. We summarize and conclude our results in Sect. 4.

2 Methodology

2.1 System parameter and stellar input spectrum

LHS 1140 is a close-by M4.5-type main-sequence red dwarf 14.993 ± 0.015 pc away from the Earth (Gaia Collaboration 2018) with an effective temperature (Teff) of 3219 ± 39 K, a radius (R) of 0.2139 ± 0.0041 R, and a mass (M) of 0.179 ± 0.014 M (Ment et al. 2019). The star is known to host two rocky planets, LHS 1140 b and LHS 1140 c (Dittmann et al. 2017; Ment et al. 2019). In this study we simulate the potential atmosphere of the habitable zone planet LHS 1140 b by using a radius of 1.727 ± 0.032 R, a mass of 6.98 ± 0.89 M, and a surface gravity (g) of 23.7 ± 2.7 m s−2 (Ment et al. 2019). We do not expect that our results would change significantly when using the slightly lower planetary mass of 6.48 ± 0.46 M suggested by Lillo-Box et al. (2020). The planet receives an incident flux of 0.503 ± 0.03 S and orbits its host star in ~24.7 days.

The stellar spectrum has not been measured for LHS 1140. However, the FUV1344−1786Å/NUV1711−2831Å ratio was determined to be 0.303 (Spinelli et al. 2019). In the UV range up to 400 nm, we use the adapted panchromatic spectral energy distribution (SED) of Proxima Centauri from the MUSCLES database version 22 (France et al. 2016; Loyd et al. 2016) with an FUV1344−1786Å/NUV1711−2831Å value of 0.313. In the visible and near-infrared (NIR) we take the SED from GJ 1214 with stellar parameters similar to LHS 1140 (Teff = 3252 ± 20 K, R = 0.211 ± 0.011 R, M = 0.176 ± 0.009 M; Anglada-Escudé et al. 2013).

2.2 Model description and updates

In this study we use the radiative-convective photochemistry model 1D-TERRA. The code dates back to early work by Kasting & Ackerman (1986), Pavlov et al. (2000), and Segura et al. (2003) and has been considerably extended by Grenfell et al. (2007, 2013), Rauer et al. (2011), von Paris et al. (2011, 2015), and Gebauer et al. (2017), among others. Recently, a major update of the climate radiative transfer module (called REDFOX; Scheucher et al. 2020) and chemistry module (called BLACKWOLF; Wunderlich et al. 2020) enabled CO2 - and H2 -dominated atmospheres to be consistently simulated.

REDFOX includes absorption values of 20 molecules1 using spectroscopic cross sections from the HITRAN 2016 line list (Gordon et al. 2017) and 81 molecules using UV and VIS cross sections mainly taken from the MPI Mainz Spectral Atlas (Keller-Rudek et al. 2013) as described in Scheucher et al. (2020) and Wunderlich et al. (2020). Additionally, Rayleigh scattering of eight molecules2 and Mlawer-Tobin-Clough-Kneizys-Davies absorption (MT_CKD; Mlawer et al. 2012) and CIAs of H2 –H2, H2 –He, CO2–H2 CO2–CH4, and CO2–CO2 are considered (see Scheucher et al. 2020, for details).

The globally averaged zenith angle is set to 60° in the climate module and 54.5° in the chemistry module in order to fit the observed O3 column of ~300 Dobson Units (DU) on Earth (see, e.g., de Grandpré et al. 2000; Thouret et al. 2006). The atmosphere in the climate module is divided into 101 pressure levels and the chemistry module into 100 pressure layers. The eddy diffusion profile can be calculated according to the parameterization shown in Wunderlich et al. (2020), or set to a given profile. Unless indicated otherwise, we use a parameterized eddy diffusion profile. The photochemical module accounts for dry and wet deposition, as well as surface emission fluxes and atmospheric escape (see details in Wunderlich et al. 2020). For wet deposition we use the parameterization of Giorgi & Chameides (1985) and the tropospheric lightning emissions of nitrogen oxides, NOx (here defined as NO + NO2), which are based on the Earth lightning model of Chameides et al. (1977).

In the current paper we additionally introduced some minor updates compared to the photochemical model described in Wunderlich et al. (2020). Recently, the water (H2O) cross section between 186–230 nm has been measured by Ranjan et al. (2020) for a temperature of 292 K. We use this new cross section datain the current study. However, the weak NUV flux of M dwarfs suggests that the water photolysis is not affected significantly by the use of the new measurements (see, e.g., Wunderlich et al. 2019).

Recently, Greaves et al. (2020a) found evidence of phosphine (PH3) absorption in the atmosphere of Venus. The presence of detectable amounts of PH3 is still debated in the literature (Snellen et al. 2020; Thompson 2021; Encrenaz et al. 2020; Villanueva et al. 2020; Mogul et al. 2020; Greaves et al. 2020b,c), and the chemical and biological processes leading to its production are not well known (Greaves et al. 2020a; Bains et al. 2020; Lingam & Loeb 2020). However, Sousa-Silva et al. (2020) suggest that in H2 - and CO2-dominated atmospheres chemical sinks of PH3 are reduced compared to Earth, favoring a potential detection in such an environment. In the atmosphere of gas giants, where the pressure and temperature and the concentration of H2 are sufficiently high, the thermodynamical formation of PH3 is favored (see, e.g., Visscher et al. 2006). For rocky, potentially habitable planets these conditions are not expected making PH3 a reasonable candidate biosignature gas in a reduced atmosphere.

The chemical network of BLACKWOLF, as presented in Wunderlich et al. (2020), did not include the chemical production and destruction of PH3. Hence, we consider in the present work 16 additional phosphorous containing reactions (see Table 1). To calculate the wet deposition of PH3 we use the Henry’s law constant from Fu et al. (2013). We do not consider a sink reaction for tetraphosphorus (P4). At low temperatures P4 is expected to sublimate without undergoing chemical reaction or photolysis (see, e.g., Kaye & Strobel 1984). Hence, we assume that all P4 is deposited or removed from the atmosphere in order to avoid a runaway effect.

With the new chemical reactions from Table 1 we repeated the validation of modern Earth with 1D-TERRA shown in Wunderlich et al. (2020). The additional consideration of PH3 has no significant impact on the concentration of key species in the atmosphere of modern Earth. PH3 was measured locally on Earth with concentrations ranging between 1 × 10−15 (ppq) and 1 × 10−9 (ppb) (see Pasek et al. 2014; Bains et al. 2019; Sousa-Silva et al. 2020, and references therein). 1D-TERRA suggests a global and annual mean surface mixing ratio of 1 × 10−12 (1 ppt) when using an assumed surface emission flux of 1 × 108 molecules cm−2 s−1.

In addition to the validation of 1D-TERRA against modern Earth, in Scheucher et al. (2020) and Wunderlich et al. (2020) the climate and chemistry modules were validated against Mars- and Venus-like conditions to show that the model is able to predict consistently N2 -O2 and CO2-dominated atmospheres. In this work we validate the model against H2-dominated and CH4-rich atmospheres; the simulation of the atmosphere of Neptune can be found in Appendix A and that of Titan in Appendix B.

2.3 Climate-only runs

We perform climate-only runs of N2-dominated, CO2, and H2–He atmosphereswith the radiative transfer module REDFOX, and vary the surface pressures in order to investigate for which atmosphericconditions LHS 1140 b could be habitable at the surface (see Table 2). The mixing ratios of the species are constant over height. We consider pressures leading to surface temperatures between 220 K (approximated limit of open water with ocean heat transport in climates of tidally locked exoplanets around M dwarf stars; see Hu & Yang 2014; Checlair et al. 2017, 2019) and 395 K (see Clarke 2004; McKay 2014). In addition, we limit our calculation to 100 bar surface pressure since massive envelopes are not expected due to the high bulk density of the planet (Ment et al. 2019).

We include absorption by the major radiative species (Scheucher et al. 2020). For the H2O profile we use a constant relative humidity of 80% up to the tropopause. Above the tropopause the H2O profile is set to a constant abundance based on its value at the cold trap. For the N2 atmospheres we assume an Earth-like CO2 level of 400 ppm (see, e.g., Monastersky 2013) and for the H2-dominated atmosphereswe use 80% H2 and 20% He.

Table 1

Phosphorous containing reactions added to the photochemical reaction scheme.

Table 2

Climate-only scenarios: surface pressure range (p0, in bar) and mixing ratios (f) of N2, CO2, H2, and He considered for LHS 1140 b.

2.4 Coupled climate–chemistry runs

Here we apply the coupled version of 1D-TERRA to simulate the potential atmospheric temperature and composition profiles of LHS 1140 b. All simulations assume a constant relative humidity of 80% from the surface to the cold trap. The surface albedo is set to 0.255,which is the value needed to achieve a mean surface temperature of 288.15 K for the Earth around the Sun (see Scheucher et al. 2020; Wunderlich et al. 2020).

Table 3 shows the 30 scenarios performed with the coupled-climate model. All scenarios assume a constant surface pressure of 2.416 bar, corresponding to the atmospheric mass of the Earth assuming a surface gravity g of 23.7 ms−2 for LHS 1140 b (Ment et al. 2019). We chose this moderate surface pressure for the following reasons. LHS 1140 b has a high bulk density (ρ) of 7.5 ± 1.0 cm−3 (Ment et al. 2019). Hence, it is unlikely that the planet has a thick H2 or He envelope. However, the enhanced gravity compared to Earth results in reduced H2 escape rates (see, e.g., Pierrehumbert & Gaidos 2011). This is supported by theoretical studies showing that cool and/or massive super-Earths can retain small residual H2 –He envelopes at the end of the core-powered mass loss (Misener & Schlichting, in prep.; Gupta & Schlichting 2019; Ginzburg et al. 2016). The secondary outgassing of CO2 is expected to be small for a super-Earth like LHS 1140 b with a mass of ~7 M (see, e.g., Dorn et al. 2018; Noack et al. 2017). Hence, we do not consider thick CO2 atmospheresas on Venus.

In this study we vary the atmospheric mixtures of H2 –He and CO2 in ten steps (see Table 3). For each of the steps we also consider three different boundary conditions for CH4. The CH4 abundance can have a large impact on surface temperature and habitability (see, e.g., Pavlov et al. 2000; Ramirez & Kaltenegger 2018). The detectability of atmospheric spectral features on exoplanets can also largely depend on the CH4 inventory due to haze formation or CH4 absorption (see, e.g., Arney et al. 2016, 2017; Lavvas et al. 2019; Wunderlich et al. 2019). We vary the boundary conditions of CH4 as follows:

  • a:

    The “low CH4” scenarios assume the volume mixing ratio (vmr) of CH4 to be constant at 1 × 10−6 at the surface, corresponding roughly to the surface CH4 concentration in the pre-industrial era on Earth (see, e.g., Etheridge et al. 1998).

  • b:

    The “medium CH4” scenarios use a constant CH4 vmr of 1 × 10−3 at the surface. Model studies such as Rugheimer et al. (2015) and Wunderlich et al. (2019) suggest a CH4 vmr of roughly 1 × 10−3 for Earth-like planets in the HZ around mid-M dwarfs using a surface emission of 1.4 × 1011 molec. cm−2 s−1, as measured on Earth (e.g., Lelieveld et al. 1998).

  • c:

    The “high CH4” scenarios assume that the surface vmr of CH4 is constant at 3%. This is consistent with the observed main composition of the lower atmosphere of Neptune with 2–4% CH4 at 2 bar (see, e.g., Irwin et al. 2019).

We assume the same boundaries for all 30 scenarios (except for CO2, H2, He, and CH4). For O2, CO, H2 S, NO, N2 O, OCS, HCN, CS2, CH3OH, C2 H6, C3 H8, and HCl weassume pre-industrial Earth-like biogenic fluxes (see Table 4). Regarding NH3, PH3, and CH3Cl we use higher biogenic fluxes than measured on Earth, assuming that an H2 -dominated atmosphere could favor the biogenic production of these species, since H2 can act as a nutrient. We assume a biogenic NH3 flux of 8.38 × 1010 molecules cm−2 s−1 corresponding to mean global emissions on a hypothetical cold Haber world (Seager et al. 2013a). This NH3 flux is ~100 times higherthan that observed on pre-industrial Earth (Bouwman et al. 1997). The assumed biogenic emissions of CH3Cl are assumed to be 100 times greater than on pre-industrial Earth (Seinfeld & Pandis 2016). The biogenic surface flux of PH3 is taken from Sousa-Silva et al. (2020). Additionally, we apply biogenic and volcanic emissions as measured on Earth (see Table 4 and Wunderlich et al. 2020, for references). We assume a non-zero dry deposition velocity (νdep) for all species to reduce a potential runaway effect (see, e.g., Hu et al. 2020). For CH4, O2, CH3Cl, PH3, NH3, and N2 O we assume a νdep of 1 × 10−8 cm s−1. For O2 this value was used by other model studies (e.g., Arney et al. 2016; Hu et al. 2020; Wunderlich et al. 2020) and provides an upper estimation of how much of these gases could be accumulated for the scenarios assumed. For all other species we use a νdep of 0.02 cm s−1, following Hu et al. (2012) and Zahnle et al. (2008).

Table 3

Climate–chemistry scenarios: surface mixing ratio of CO2, H2, He, and CH4.

Table 4

Assumed surface emissions (in molecules cm−2 s−1) and dry deposition velocities (in cm s−1) for model runs.

2.5 Transmission

The simulated atmospheres serve as input to compute transmission spectra with the Generic Atmospheric Radiation Line-by-line Infrared Code (GARLIC; Schreier et al. 2014, 2018). Line parameters and CIAs are taken from the HITRAN database (Gordon et al. 2017; Karman et al. 2019). We consider the CKD continuum model for H2O (Clough et al. 1989) and Rayleigh extinction for H2, He, CO2, H2O, N2, CH4, CO, N2 O, and O2 (Murphy 1977; Sneep & Ubachs 2005; Marcq et al. 2011). In the visible we use the cross sections at room temperature (298 K) listed in Table 3 of Wunderlich et al. (2020).

We assume cloud-free conditions for all simulated transmission spectra. We do, however, consider extinction from uniformly distributed aerosols with an optical depth (τA) at wavelength λ (μm) following Ångström (1930), Allen (1976), and Yan et al. (2015): (1)

with the column density (Nc) in molecules cm−2. For clear sky conditions with weak scattering by haze or dust we set α to 1.3, representing an average measured value on Earth following the Junge distribution (see, e.g., Ångström 1961) and we set the coefficient β to 1.4 × 10−27 following Allen (1976). For hazy conditions we assume α to be 2.6 and set β to 6.0 × 10−25, representing the best fit to extinction by hazes on Titan (see Appendix C). It should be noted that we do not consider the production of hazes in H2- and CO2-dominated atmospheres in our model. The assumed impact of hazes on the spectral appearance of the simulated atmospheres should therefore only serve as a rough estimation, and further investigation is needed to test the validity of this assumption.

We express the transmission spectra as wavelength-dependent atmospheric transit depth, which contains the contribution of the atmosphere to the total transit depth without the contribution from the solid body (see more details in Schreier et al. 2018; Wunderlich et al. 2020).

2.6 Signal-to-noise ratio (S/N)

An important aim of this study is to determine whether molecular absorption features are detectable with the JWST and ELT (see Table 5). For low-resolution spectroscopy (LRS) we calculate the required number of transits with JWST NIRSpec PRISM and MIRI LRS with the method presented in Wunderlich et al. (2019). LHS 1140 exceeds the brightness limits of NIRSpec PRISM leading to a saturation of the detector between 1 and 2 μm. Hence, we consider partial saturation of the detector, as proposed by Batalha et al. (2018), and exclude the wavelength range between 1 and 2 μm from our analysis. The results do not significantly differ when we use medium-resolution filters such as NIRSpec G235M and G395M (Birkmann et al. 2016), which do not saturate for LHS 1140. However, the two filters cannot be used simultaneously because it would decrease the wavelength coverage compared to NIRSpec PRISM, and the additional binning of the spectral data with higher resolving power might lead to enhanced red noise.

With high-resolution spectroscopy (HRS) we investigated the potential detection of spectral lines from NH3, PH3, CH3Cl, and N2O with the cross-correlation technique using the HIRES and METIS instruments on ELT. We used the same approach to estimate the number of transits necessary to detect the molecules with the cross-correlation method, as described in Wunderlich et al. (2020). The S/N of the star per transit is calculated with the European Southern Observatory Exposure Time Calculator3 (ESO ETC) Version 6.4.0 from November 2019 (see updated documentation4 from Liske 2008). As input for the ETC we use the stellar spectrum described in Sect. 2.1 and scale it to the K-band magnitude of 8.821 (Cutri et al. 2003) in order to obtain the input flux distribution. Equivalent to Wunderlich et al. (2020) we used a mean throughput of 10% for ELT HIRES and METIS. Previous studies investigating the feasibility of detection of O2, for example,in Earth-like atmospheres assumed a more optimistic throughput of 20% (Snellen et al. 2013; Rodler & López-Morales 2014; Serindag & Snellen 2019). The sky conditions are set to a constant airmass of 1.5 and a precipitable water vapor (PWV) of 2.5. For each wavelength band we changed the radius of the diffraction limited core of the point spread function according to the recommendation in Liske (2008).

Table 5

Wavelength coverage and resolving power (R) of the instruments on JWST and ELT used to calculate the number of transits required to detect spectral features in the atmosphere of LHS 1140 b.

thumbnail Fig. 1

Surface temperature in Kelvin against surface pressure in bar for simulated climate-only N2 -, CO2 -, and H2 -dominated atmospheresof LHS 1140 b (see Table 2). The horizontal dashed line indicates 273 K.

3 Results and discussion

3.1 Surface habitability

Figure 1 shows the surface temperatures of LHS 1140 b for N2 -, CO2-, and H2 -dominated atmospheres with varying surface pressures, for the climate-only runs (without coupling to the photochemistry module, see Sect. 2.3). The results suggest that thick N2 -dominated atmosphere on LHS-1140 b or substantial amounts of greenhouse gases such as CO2 are required to reach habitable surface temperatures. The simulations show that a CO2 atmosphere requires a surface pressure of ~2.5 bar to reach a global mean surface temperature above 273 K. This is comparable to the results of Morley et al. (2017) who found that a 2 bar Venus-like atmosphere on LHS 1140 b would lead to a surface temperature of around 280 K.

For an H2 atmosphere CIA by H2 -H2 extends the outer edge of the HZ (see, e.g., Pierrehumbert & Gaidos 2011) compared to CO2 atmospheres. The simulated H2 atmospheres have up to 80 K warmer surface temperatures than the CO2 atmospheres. Our results suggest that a surface pressure of 0.6 bar leads to a surface temperature of about 273 K on LHS 1140 b.

3.2 Atmospheric profiles

To simulate the atmospheric scenarios as described in Sect. 2.4 we use the coupled version of 1D-TERRA. Figure 2 shows the temperature and composition profiles of selected species for H2 -dominated atmospheres with low concentrations of CO2 (scenarios 1a, 1b, and 1c; see Sect. 2.4 and Table 3) and CO2-dominated atmospheres with low concentrations of H2 (scenarios 10a, 10b, and 10c). Figure 3 shows the surface temperature, the atmospheric height at the top of atmosphere (ToA) at ~0.01 Pa, the surface vmr of CO2, H2O, and O2, and the CH4 vmr at the ToA with decreasing concentrations of H2 (corresponding to increasingconcentrations of CO2; see Table 3).

3.2.1 Temperature

Figure 2 suggests that the H2-dominated atmosphereswith low and medium CH4 concentrations (corresponding to scenarios 1a and 1b, respectively) show a similar temperature profile from the surface up to ~200 hPa with a strong warming towards the ToA.

The H2-dominated atmosphere with a high CH4 concentration (scenario 1c) results in a warm stratosphere due to CH4 short-wave absorption and accordingly a cool troposphere, similar to the shape of the temperature profile behavior on Titan (see, e.g., Fulchignoni et al. 2005; Serigano et al. 2016). The high concentration of CH4 absorbs most of the stellar energy in the stratosphere, leading to reduced stellar irradiation reaching the troposphere (see also Ramirez & Kaltenegger 2018). We note that 1D-TERRA does not consider the effect of hazes that might be formed in such an environment (see, e.g., He et al. 2018; Hörst et al. 2018).

In CO2-dominated atmospheres we simulate similar temperature profiles for low CH4 concentrations (scenario 10a) compared to medium CH4 concentrations (scenario 10b). There is no temperature inversion from O3 absorption in the middle atmosphere due to the weak UV emission of M dwarfs (see also, e.g., Segura et al. 2005; Grenfell et al. 2014; Wunderlich et al. 2019, 2020). The CO2-dominated atmosphere with high abundances of CH4 (scenario 10c) shows weak temperature variations in the range of ~40 K through the simulated atmospheric profile. The high concentration of CH4 leads to a warming of the atmosphere compared to the runs with lower CH4 abundances, except near the surface, where the anti-greenhouse effect cools the atmosphere.

In Fig. 3 the low and medium CH4 scenarios (blue and green lines, respectively) feature similar responses in surface temperatures with decreasing H2. The high CH4 scenarios (orange line) first show a warming effect of about 50 K due to the decreased partial pressure of CH4 with increasing molecular weight towards CO2-rich atmospheres (scenarios 1c to 4c). We note that the surface mixing ratio of CH4 is kept constant at 3% for all the high CH4 scenarios. For CO2-dominated atmospheres the surface temperature decreases when reducing the abundances of H2 (scenarios 6c to 10c) due to the weaker warming from the H2 -H2 CIA.

3.2.2 H2O

The water profile in the lower atmosphere depends mainly on the assumed relative humidity and the temperature profile near the surface. For all simulations we assume the relative humidity to be constant at 80% up to the tropopause. In the middle and upper atmosphere the H2O profile is determined mainly by chemical production or loss and to a lesser extent by eddy mixing. H2O is mainly destroyed by photolysis at wavelengths below 200 nm forming the hydroxyl radical (OH) and atomic hydrogen (H): (R1)

The H2-dominated atmospheres show weaker FUV absorption compared to the CO2-dominated atmospheres. This weaker shielding effect leads to enhanced photolysis and less stratospheric water content for the scenarios with H2 -dominated atmospheres (Fig. 2). For the high CH4 scenarios the water photolysis is weak due to strong FUV absorption from CH4. In CO2-dominated atmospheres H2O is significantly reformed via HOx-driven (HOx = H + OH + HO2) oxidation of CH4 at pressures less than 0.1 hPa (see also Segura et al. 2005; Grenfell et al. 2013; Rugheimer et al. 2015; Wunderlich et al. 2019).

3.2.3 O2

All runs assume an Earth-like surface O2 flux from photosynthesis (see Table 4). However, in H2 -dominated atmospheres the oxygen content near the surface is only ~1 ppm for low and medium CH4 scenarios (blue and green solid line in Fig. 2). Grenfell et al. (2018) suggest that the catalytic cycles including HOx and NOx leads to oxidation of H2 into water. O2 is mainly destroyed via photolysis or the three-body reaction with atomic hydrogen:

The atomic hydrogen required for reaction (R3) is mainly produced via (R4)

OH can be formed via the reactions (R5)

and by water photolysis (reaction (R1)), which increases the concentration of H in the middle atmosphere. The atomic hydrogen can be removed via escape or it can recombine to form H2 (see Hu et al. 2012). For the H2 -dominated atmosphere with high CH4 concentrations (scenario 1c) the surface mixing ratio of O2 is two orders of magnitudes higher than for scenarios 1a and 1b due to the lower concentrations of NOx and H in the lower atmosphere.

For increasing mixing ratios of CO2 the destruction of O2 via H2 oxidation is less dominant, and O2 can accumulate in the atmosphere (see Fig. 3). The CO2-dominated atmospheres with less than 10% H2 feature high abundances of O2 of up to 30% for the atmosphereswith low and medium CH4 concentrations (scenario 8a–10a and 8b–10b). For scenarios 8a and 8b the concentration of O2 might be limited to about 10% (see gray shaded region in Fig. 3) due to the potential combustion of the atmosphere (Grenfell et al. 2018).

The high concentrations of O2 are related to the lower UV flux of M dwarfs (hence weaker O2 photolysis) as well as strong abiotic production of O2 from CO2 photolysis at wavelengths below 200 nm (see also, e.g., Domagal-Goldman et al. 2014; Harman et al. 2015; Wunderlich et al. 2020). The lower FUV/NUV value of LHS 1140 compared to a solar-type star favors the production of abiotic O2 in CO2-rich atmospheres via

(see also, e.g., Selsis et al. 2002; Segura et al. 2007; Tian et al. 2014; France et al. 2016; Wunderlich et al. 2020). The production of O2 is further enhanced by the presence of OH via (R10)

CO and O can recombine to CO2 via a HOx catalyzed reaction sequence, forming CO2: CO + O CO2 (see, e.g., Domagal-Goldman et al. 2014; Gao et al. 2015; Meadows 2017; Schwieterman et al. 2019). For the CO2-dominated atmosphere with high CH4 concentrations we find that atomic oxygen (O) quickly reacts with CH3 via

forming H2, H, CO, and H2CO. Part of the H2CO separates into H2 and CO via

Hence, the results suggest low concentrations of O2 and high abundances of CO for the high CH4 scenarios with CO2-dominated atmospheres.

thumbnail Fig. 2

Simulated temperature and composition profiles of selected species of LHS 1140 b. Different colors represent the three types of scenarios considered: blue for low CH4, green for medium CH4, and orange for high CH4. The solid lines represent H2-dominated atmospheres(scenarios 1a, 1b, and 1c), whereas the dashed lines show CO2-dominated atmospheres(scenarios 10a, 10b, 10c).

thumbnail Fig. 3

Change in surface temperature in K; in atmospheric height at ToA (~0.01 Pa) in km; and in surface volume mixing ratio (vmr) of H2O, O2, CO, and CH4 with decreasing concentrations of H2 for scenarios with low (blue lines), medium (green lines), and high CH4 scenarios. The concentrations of CO2 corresponding to H2 vmrs of 0.75,0.5, 0.25, and 0.0 are shown in green on the top x-axis for the medium CH4 scenarios. The colored dots represent the ten individual main scenarios described in Table 3. The dashed black line shows T = 273 K. The gray shaded region represents the limits of combustion for H2-CO2 and H2 -O2 gas mixtures (see Grenfell et al. 2018). The assumed surface mixing ratios of H2 and CO2 for the main scenarios 5–8 are at the edge of the H2-CO2 combustion limit.

3.2.4 O3

A main source of O3 largely depends on the amount of O2 available for photolysis inthe atmosphere. O2 is split into atomic oxygen via photolysis between 170 and 240 nm (reaction (R2)) and then reacts with O2 to form O3 via a fast three-body reaction (see, e.g., Brasseur & Solomon 2006). The main O3 sinks are photolysis at wavelengths less than ~200 nm and catalytic cycles involving HOx and NOx, which convert O3 into O2 in the middle atmosphere (see, e.g., Brasseur & Solomon 2006; Grenfell et al. 2013).

In our scenarios the low FUV/NUV environment compared with the Earth favors weak release of HOx and NOx from their reservoir molecules, which leads to weak O3 catalytic loss. The low and medium CH4 scenarios with CO2-dominated atmospheres show an ozone layer with peak abundances (~20–50 ppm) up to about five times higher than on Earth (compare to Fig. 2 in Wunderlich et al. 2020). Grenfell et al. (2014) suggest that the smog mechanism is an important O3 source for late M dwarfs since UV levels are not sufficient to efficiently drive the Chapman mechanism. All scenarios with H2 -dominated atmospheres and CO2-dominated atmosphere with high concentrations of CH4 are however low in O2, leading overall to weak production of O3.

3.2.5 CO

All simulations assume emissions of CO from volcanoes and biomass based on pre-industrial Earth values. An important in situ sink for CO is OH via (R16)

Our results suggest a decreased OH due to a slowing of its main source reaction, (R17)

since production of O1D (e.g., from O3 photolysis) is disfavored by the stellar UV emission. The weak OH favors an increase in CO and CH4 by several orders of magnitude compared with modern Earth. Similar effects have been noted by several studies in the literature for Earth-like planets (see, e.g., Segura et al. 2005; Grenfell et al. 2007; Rugheimer et al. 2015; Wunderlich et al. 2019).

The tropospheric temperature of the H2-dominated atmosphere with high concentrations of CH4 (scenario 1c) is much lower compared to the other scenarios due to the strong CH4 anti-greenhouse effect (see above), leading to water condensation and hence less water photolysis in the troposphere. We note that the OH radical can also be formed via HOx re-partitioning (reaction (R7)), which can be driven by enhancements in NO via incoming cosmic rays (see, e.g., Airapetian et al. 2016, 2020; Scheucher et al. 2018).

In the atmosphere with high concentrations of CH4 results suggest that the recombination of CO and O into CO2 is weakened due to the additional sinks of atomic oxygen via reactions (R11) and (R12) as discussed in Sect. 3.2.3. High CH4 generally favors lowered OH, which weakens the HOx catalyzed combination of CO and O into CO2. This leads to higher abundances of CO for high CH4 scenarios compared to the other scenarios.

3.2.6 CH4 and hydrocarbons

Understanding how CH4 forms higher-order hydrocarbons (Cn) and how these species are subsequently removed to reform CH4 is a central issue because higher-order hydrocarbons can readily condense to form hazes, which could strongly impact climate and observed spectra. The high CH4 scenarios feature a large production of hydrocarbons such as C2 H2, C2 H4, and C2 H6. Figure 2 shows the atmospheric profiles of C2 H6, but we note that C2 H2 and C2 H4 (not shown) have concentrations similar to C2 H6 with the highest mixing ratios of up to 0.1% at the ToA.

Our results suggest that in H2-dominated atmospheres the main pathway for initiating ascent of the homologous chain from C1 → C2 (CH4 → C2 H6) pathway is as follows:

The above pathway is an established route for ascending the hydrocarbon chain (see, e.g., Yung & DeMore 1999, Chapter 5 and references therein). It is initiated by CH4 photolysis to form reactive methyl radicals (CH3), which participate in a three-body self-reaction to form ethane (C2 H6).

Our results suggest that C2 H4 is mainly formed by the reactions

where CH is produced via

and C2 H2 is formed via photolysis of C2 H4 and the reaction (R26)

(see also Yung & DeMore 1999, and references therein).

In CO2-dominated atmospheres our results suggest that the reaction (R19) is slower due to reduced H2, and because part of the CH2 reacts with CO2 forming H2CO and CO. This effect disfavors the pathway producing C2 H6. Additionally, however, the destruction of CH3 via (R27)

is weakened due to lowered H2. This effect favors enhanced CH3, hence the pathway producing C2 H6. The overalleffect is slightly lower concentrations of CH4 in the upper atmosphere but larger amounts of C2 H6 as well as C2 H2 and C2 H4 (not shown) for CO2-dominated atmospheres compared toH2-dominated atmospheres when assuming high concentrations of CH4.

The higher abundances of C4 H2 shown in Figs. 2 and 3 for scenario 10c compared to scenario 1c suggests more haze production by hydrocarbons in CO2-dominated compared with H2 -dominated atmospheres. This effect would be reinforced assuming CO2-dominated atmospheres have cooler mid to upper atmospheres compared with H2 -dominated atmospheres, as suggested by Fig. 2. We note, however, that the above result could be reversed in atmospheres where CH2 (reaction (R19)) becomes more important for C2 H6 production since H2-dominated atmospheres favor reduced CH2, as discussed above. For the low and medium CH4 scenarios the concentrations of C4 H2 decrease with decreasing CH4/CO2 values, as suggested by Arney et al. (2018), among others, for N2 -dominated atmospheres.

3.2.7 SO2

We assume volcanic outgassing of SO2 as measured on modern Earth. Due to its high solubility in water-forming sulfate, SO2 is deposited easily over wet surfaces leading to SO2 surface mixing ratios below 1 ppb. The main in situ chemical sink of SO2 is photodissociation below 400 nm (e.g., Manatt & Lane 1993), and its oxidation via reaction with OH or O3 to ultimately form SO3, which quickly reacts with water to form sulfate (see, e.g., Burkholder et al. 2015; Seinfeld & Pandis 2016). In CO2-dominated atmospheres with low and medium CH4 concentrations (scenarios 10a and 10b), the results suggest that shielding associated with high UV absorption from CO2, O2, and O3 enables the concentrations of SO2 to reach up to 1 ppm in the stratosphere.

In H2-dominated atmospheres the high UV environment leads to low abundances of SO2 over the entire atmosphere. We note that strong SO2 abundances in moist, warm tropospheres (see Fig. 2) would favor significant sulfate aerosol formation, although a strong hydrological cycle would quickly wash out the sulfate formed (see, e.g., Loftus et al. 2019).

3.2.8 Potential biosignatures NH3, PH3, CH3Cl, and N2O

The chemical destruction of NH3, PH3, and CH3Cl is controlled by reactions with OH, O1D, and H and by photodissociation in the UV (see, e.g., Segura et al. 2005; Seager et al. 2013a; Sousa-Silva et al. 2020). In the middle atmosphere NH3 photodissociates into NH2 and H. In H2 -dominated atmospheres the NH2 reacts quickly with H and reforms as NH3. In the high CH4 atmosphere this recombination process is slower due to enhanced HO2 from water photolysis in the stratosphere leading to more destruction of atomic hydrogen by reaction (R5) (see also Sect. 3.2.3). Our results suggest that an assumed emission of 8.38 × 1010 molecules cm−2 s−1 would lead to NH3 surface mixing ratios between 0.1 and 1 ppm. This is consistent with the results from Seager et al. (2013a), who obtain an NH3 mixing ratio of 0.1 ppm with surface flux of 5.1 × 1010 molecules cm−2 s−1 for a planet with an H2-dominated atmosphere around an active M dwarf.

For PH3 we assume an emissions flux of 1 × 1010 molecules cm−2 s−1, and obtain a surface mixing ratio of about 200 ppb for the H2 -dominated atmosphere with low CH4 concentrations. Sousa-Silva et al. (2020) find similar mixing ratios of PH3 when assuming a ten times higher surface flux for a planet with an H2 -dominated atmosphere around an active M dwarf. However, they did not consider that PH3 may be recycled via chemical reactions with H or H2.

For CO2-dominated atmospheres with low and medium concentrations of CH4 (scenarios 10a, 10b) the destruction of PH3 from H is weaker compared to H2-dominated atmospheres, and we obtain higher surface mixing ratios of several ppm. Given the weak recycling of PH3 from H or H2, our results are consistent with Sousa-Silva et al. (2020) who obtain a mixing ratio of 15 ppm for a ten times higher surface flux. For the high CH4 scenario(10c) our results suggest that high abundances of OH are produced near the surface via (R28)

leading to enhanced destruction of PH3 compared tothe other scenarios. The CH3Cl and N2O loss processes are dominated by photolysis in the UV below 240 nm and reaction with O1 D (see also, e.g., Grenfell et al. 2013). Due to the low UV environment in CO2-dominated atmospheres, the destruction by photolysis is weaker and the abundances of CH3Cl and N2 O are higher compared to the H2-dominated atmospheres.

thumbnail Fig. 4

Simulated transmission spectrum of the medium CH4 scenario with the H2-dominated atmosphere (scenario 1b) binned to a constant resolving power of R = 300 (maximum resolving power of JWST NIRSpec PRISM at 5 μm). The black line represents the spectrum that includes the contribution from all gases which are considered in 1D-TERRA and also available in HITRAN, Rayleigh scattering, aerosol extinction, H2O CKD, and CIAs. Individual gaseous absorption of selected molecules are represented by different colors.

3.2.9 Atmospheric height

The upper right panel of Fig. 3 shows atmospheric height at the ToA for all simulated scenarios. A central aim of this paper isto investigate whether it is feasible to detect atmospheric molecular features on LHS 1140 b with future telescopes. A large atmospheric height leads to stronger absorption features in transmission spectroscopy and hence to improved detectability of the corresponding species. Due to the low mean molecular weight hence larger scale height of the H2 -dominated atmospheres, the ToA at ~0.01 Pa occurs at a height of up to 570 km, whereas the CO2-dominated atmospheres only reach an altitude of about 35 km. The higher stratospheric temperatures for the high CH4 scenarios furthermore lead to an expansion of the atmosphere compared to the scenarios with less CH4.

3.3 Transmission spectra

Figure 4 shows the simulated transmission spectrum of LHS 1140 b for the H2 -dominated atmosphere with medium CH4 concentrations (scenario 1b). The contribution from individual molecular absorption bands is color-coded. To detect a spectral band with the JWST, for example, it is important to identify a wavelength range that is not obscured by the absorption of other molecules or haze extinction. The strongest spectral features are due to absorption by CH4. Below ~2 μm molecular features of H2O and NH3 are obscured by haze extinction, which increases the transit depth to ~90 ppm. Between 2 and 2.5 μm H2 -H2 CIA contributes significantly to the transmission spectrum (see Abel et al. 2011).

Around 3.0 μm we obtain two absorption features mainly produced by NH3, N2 O, and PH3. Absorption by CH4 and H2O is rather weak at these wavelengths. However, between 3.0 and 3.1 μm C2 H2 contributes significantly to the spectral feature (not shown). The absorption by CO2 around 3.0 μm is significant for atmospheres with mixing ratios of ~1% or more CO2 (not shown). The spectral features of CH3Cl around 3.3 and 7 μm overlap with absorption by CH4 and H2O. For CO2-poor atmospheres the features from N2O and PH3 dominate the spectrum between 4.1 and 4.6 μm. However, Earth-like CO2 levels lead to a strong absorption feature around 4.3 μm (see, e.g., Rauer et al. 2011; Wunderlich et al. 2019).

Between 9 and 12 μm we find strong absorption by PH3, NH3, and CH3Cl. For H2 -dominated atmospheres there is a significant contribution from H2 -H2 CIA (not shown) to the transmission spectrum (Abel et al. 2011; Fletcher et al. 2018). In CO2 atmospheres these features might overlap with absorption by O3 and CO2. Many of the simulated atmospheric spectral features of N2O, NH3, PH3, or CH3Cl overlap with other molecular bands, for example CO2 or CH4 (see Table 6). However, at some spectral bands these potential biosignatures contribute significantly to the full feature offering the possibility to detect the additional absorption if the abundances of CO2 and CH4 are known. Figure 5 shows the simulated transmission spectra of H2 -dominated atmospheres (scenarios 1a, 1b, and 1c) and CO2-dominated atmospheres (scenarios 10a, 10b, and 10c). We take into account the effect of weak extinction from thin hazes (solid line) and from thick Titan-like hazes (dashed line).

For the high CH4 scenarios the mean transit depth is increased compared to the other two scenarios due to strong absorption by CH4 and the warm stratospheric temperature which leads to an expansion of the atmosphere. Due to the low molecular weight of the H2 -dominated atmospheres the spectral features are generallylarger than in CO2-dominated atmospheres. The extinction by thick haze significantly increases the transit depth at atmospheric windows up to 6 μm. Hazes at large altitudes are considered to be the main reason for the observed flat spectrum of GJ 1214 b (Bean et al. 2010; Désert et al. 2011; Kempton et al. 2011; Kreidberg et al. 2014). However, HST observations of the atmosphere of LHS 1140 b suggest a clear atmosphere (Edwards et al. 2021). In the following text we compare the observed spectrum with our simulations.

thumbnail Fig. 5

Simulated atmospheric features of the LHS 1140 b for all three scenarios with H2 - (upper panel) and CO2-dominated atmospheres(lower panel), represented by cloud-free transit transmission spectra and binned to a constant resolving power of R = 300. The scenarios are color-coded: blue for low concentrations of CH4 (scenario 1a for H2- and scenario 10a for CO2-dominated atmospheres), green for medium concentrations of CH4 (scenario 1b for H2- and scenario 10b for CO2-dominated atmospheres), and orange for high concentrations of CH4 (scenario 1c for H2- and scenario 10c for CO2-dominated atmospheres). Important atmospheric molecular absorption are highlighted with horizontal lines in the color of the scenario with a strong absorption feature or in gray when all scenarios show a strong feature. Solid lines include weak extinction from hazes, whereas dashed lines consider extinction by thick hazes.

Table 6

Central wavelength, λc (μm), of molecular bands from NH3, PH3, CH3 Cl, and N2 O and spectral features that might overlap or obscure the bands at the corresponding λc.

thumbnail Fig. 6

Simulated spectral features compared to HST observations taken from Edwards et al. (2021), assuming H2 -dominated atmospheres. The scenarios are color-coded: solid blue line for the low CH4 scenario (1a), as shown in Fig. 5, and solid purple line for scenario 1a, but with an assumed constant H2O mixing ratio of 1%; solid green line for the medium CH4 scenario (1b); solid and dashed orange lines for the high CH4 scenario (1c) with thin and thick hazes, respectively. All spectral data were binned to R = 300 and subtracted from the mean transit depth between 1.1 and 1.7 μm.

3.4 Comparison to observations

Diamond-Lowe et al. (2020) combined two spectrally resolved transit observations of LHS 1140 b from the optical to the NIR (610–1010 nm). Their median uncertainty of the transit depth was 260 ppm. They concluded that about a factor of 4 higher precision would be needed to detect a clear hydrogen-dominated atmosphere. The strongest spectral feature of CH4 at ~900 nm has a strength of about 130 ppm assuming weak extinction by hazes (solid orange line in Fig. 5). The slope from extinction by thick hazes between 610 and 1010 nm leads to a decrease in the transit depth of ~50 ppm (dashed orange line in Fig. 5, upper panel). Hence, our results confirm that the precision of transit observations between 610 and 1010 nm shown by Diamond-Lowe et al. (2020) is not large enough to draw a conclusion on the atmosphere of LHS 1140 b.

Recently, Edwards et al. (2021) presented HST transit observations between 1.1 and 1.7 μm. They concluded that a maximum in the spectrum around 1.4 μm might suggest evidence of water vapor absorption in a clear H2–He atmosphere. However, due to the large stellar contamination and the low overall S/N further observation time is required to confirm the detection of a planetary atmospheric feature. Figure 6 compares the spectrally resolved HST observations with simulated spectra assuming H2-dominated atmospheres (scenarios 1a, 1b, and 1c). The low CH4 scenario shows a mixing ratio of H2O below 1 ppm in the middle atmosphere (see Fig. 2) and the resulting spectrum shows only a weak feature at 1.4 μm (blue line). In Edwards et al. (2021) the water vapor abundance has been retrieved to log10(V) = −2.94. When assuming an H2O mixing ratio of 1%, constant over height, we find a difference in the transit depth of about 150 ppm between 1.4 and 1.6 μm. However, such large abundances of H2O are not consistent with the results of our photochemical model simulations for thin H2-dominated atmospheres with habitable surface temperatures. Large abundances of H2O in thick H2-atmospheres, which were not considered here, might be consistent with our model but would lead to surface temperatures above 395 K (see Fig. 1), which are unlikely to sustain life (see, e.g., Bains et al. 2015).

Figure 6 suggests that a large spectral feature of ~200 ppm at 1.4 μm can be also obtained by strong CH4 absorption in a thin H2-dominated atmosphere containing several percent of CH4 and limited haze production. High abundances of CH4 might favor the formation of hydrocarbon haze (He et al. 2018; Hörst et al. 2018; Lavvas et al. 2019). However, Fig. 2 suggests that the haze production is lower compared to CO2-dominated atmospheres with high CH4 abundances (see Sect. 3.2.6).

Similarly to our finding regarding the atmosphere of LHS 1140 b, the model studies by Bézard et al. (2020) and Blain et al. (2021) suggest that the detected spectral feature at 1.4 μm in the atmosphere of K2-18 b (Benneke et al. 2019; Tsiaras et al. 2019) might be produced by CH4 rather than H2O. They concluded that the H2O-dominated spectrum interpretation is either due to the omission of CH4 absorption or to a strong overfitting of the data. Further observations with the Very Large Telescope (VLT) or the JWST are expected to confirm or rule out the existence of large abundances of CH4 in the atmosphere of LHS 1140 b or K2-18b (see, e.g., Edwards et al. 2021; Blain et al. 2021). In the following we further determine the capabilities of the upcoming generation of telescopes with increased sensitivity and larger wavelength coverage to characterize the atmosphere of LHS 1140 b.

3.5 Detectability of spectral features with JWST and ELT

To determine the number of transits that are required to detect a spectral feature (SN = 5) we subtract the full transmission spectra (including the absorption from all species, CIAs, H2O CKD, Rayleigh extinction, and extinction from hazes) from the spectrum excluding the contribution from the corresponding species. Thus, we consider only the contribution of these species to the full spectrum. This method assumes that we know the concentration of other main absorbers, such as CH4 or CO2, that may overlap with the spectral bands (see Table 6). We note that other molecules could mimic large-scale features of the molecule in question. Hence, retrieval analysis (see Barstow & Heng 2020, for a recent overview) orthe detection of the molecules at multiple wavelengths would be required to exclude any ambiguity.

Second, for LRS with JWST NIRSpec we bin the spectral data until the optimal value is found, leading to the lowest required number of transits. On the one hand binning the data decreases the noise contamination, but on the other hand large wavelength ranges can lead to interfering overlaps of absorption bands and atmospheric windows. Due to the unknown systematic error when binning the synthetic spectral data we assume only white noise for the binning. This gives an optimistic estimation on the detection feasibility of the JWST. It should be noted that we do not consider the wavelength range between 1 and 2 μm due to the saturation of the detector for NIRSpec PRISM. However, due to extinction by hazes, the detection of the spectral features of CH4 and CO2 at this wavelength range would require similar or more observation time than the features at longer wavelengths. For the high-resolution spectra we use a constant resolving power of R = 100 000, as planned for the HIRES and METIS on ELT, and apply the cross-correlation technique with the same method as presented in Wunderlich et al. (2020). We do not show results from emission spectroscopy in this study due to the much longer required observation time compared to transmission spectroscopy for planets in the habitable zone (see, e.g., Rauer et al. 2011; Lustig-Yaeger et al. 2019).

At most wavelengths the simulated spectra are dominated by major absorbing species (here defined as H2, He, CO2, CH4, and H2O). To identify suitable wavelength ranges for the detection of minor absorbers (here defined as all non-major species), the spectrum including only major absorbers was subtracted from the full spectrum. The remainder, representing the contribution of the minor absorbers to the full spectrum, is shown in the shaded region in Fig. 7. The expected error bars of the simulated H2 -dominated atmospheres observed by JWST NIRSpec PRISM or MIRI LRS suggest that a detection of minor absorbers will be challenging within a single transit (upper panel of Fig. 7). However, multiple transits might improve the S/N sufficiently to detect these features.

In CO2-dominated atmospheres the larger molecular weight decreases the features in the transmission spectra compared to H2 -dominated atmospheres. The error bars in the lower panel of Fig. 7 arise from 30 co-added transit observations with JWST, which would correspond to a period of two years if each transit of LHS 1140 b were to be observed or ~120 h observing time. The results suggest that it will be very challenging to detect minor absorbers in a CO2-dominated atmosphere during the lifetime of JWST. The detection of major absorbers such as CO2 or CH4 might be feasible, but also challenging. This is consistent with the results of Morley et al. (2017), who suggest that the detection of a Venus-like atmosphere on LHS 1140 b would require over 60 transits with JWST.

The upper panel of Fig. 8 suggests that CO2 is detectable at 4.3 μm for mixing ratios between 1 × 10−3 and 0.1 within a few transits. For the scenarios 1a, 1b, and 1c with only 1 ppb CO2 the spectral features are too weak to allow the detection of CO2 (see also Fig. 4). The detection of CO2 is only weakly dependent on the amount of CH4 in the atmosphere. The high CH4 cases require fewer transits due to the larger atmospheric heights from CH4 heating in the middle atmosphere (see Fig. 3). The detection of CH4 in a clear H2 -dominated atmosphere requires only one transit observation with CO2 concentration of less than 10% for the high CH4 scenarios and with less than 1% CO2 for the medium CH4 scenarios. When assuming thick hazes the detection of CH4 would require about ten times more transits for the medium and low CH4 scenarios. For the high CH4 scenarios the impact of haze extinction on the detectability of CH4 is weaker. For CO2-dominated atmosphere the detection of the 3.4 μm CH4 feature will be challenging with transmission spectroscopy. The detectability of CH4 and CO2 is not significantly improved when applying the cross-correlation technique with observations by ELT HIRES or METIS (not shown; see, e.g., Wunderlich et al. 2020).

For each of the atmospheric scenarios we assume a strong dry deposition of CO (see Table 4) leading to weak accumulation of CO from CO2 photolysis in CO2-rich atmospheres compared to previous model studies (Schwieterman et al. 2019; Hu et al. 2020; Wunderlich et al. 2020). Hence, the detection of CO will be challenging in the atmospheres we consider. Hydrocarbons such as C2 H2 are formed in large abundances for the high CH4 case and could be detectable with fewer than ten transits in CO2-poor, H2 -dominated atmospheres.

The number of transits required to detect H2O is smallest for the H2-dominated atmosphere with high CH4 concentration (scenario 1c). This scenario has the lowest surface water vapor, but large amounts of chemically produced H2O in the stratosphere. Hence, the detection of water in transmission spectroscopy is only weakly related to the presence of liquid surface water in these cases.

In the middle panel of Fig. 8 we show the number of transits required to detect spectral features from NH3, PH3, CH3Cl, N2 O, and the CIA from H2 -H2 using JWST NIRSpec PRISM. The lower panel of Fig. 8 shows the number of transits required to detect spectral features from NH3, PH3, CH3Cl, and N2 O using ELT HIRES. We show only the scenarios with H2 mixing ratios higher than 50%. For CO2-poor atmospheres with low or medium concentrations of CH4 (scenarios 1a–3a and 1b–3b) the results suggest that the detection of NH3 would require tens to hundreds of transits with JWST NIRSpec PRISM, and the spectral lines of NH3 around 1.5 μm might be detectable with about five transits with ELT HIRES. The detection of NH3 around 2.3 μm would require around eight transits (not shown). However, due to the possibility of a simultaneous detection of spectral lines from CH4, H2O, and CO, this wavelength region might be favorable for HRS (Brogi & Line 2019). Strong extinction by hazes or large absorption by CH4 for the high CH4 scenarios would prevent the detection of NH3.

The spectral feature of PH3 at 4.3 μm might be detectable within 10–30 transits for the H2-dominated atmosphereswith 1 ppb CO2 (scenarios 1a, 1b, and 1c) and weak extinction by hazes. For the other scenarios the PH3 feature is obscured by the CO2 absorption band around 4.3 μm (see also Sousa-Silva et al. 2020). The detectability of PH3 is improved using high-resolution spectra of ELT METIS compared to JWST observations for CO2-rich atmospheres. However, for more than 1% of CO2 mixing ratios the detection of PH3 would require tens to hundreds of transits.

For H2-dominated atmospheres with high concentrations of CH4 (scenario 1c)the mixing ratios of CH3Cl are higher compared to scenarios 1a and 1b (see Fig. 2). However, the strongest spectral band of CH3Cl in the wavelength range of JWST NIRSpec PRISM overlaps with absorption by CH4 (see Fig. 4) owing to a better detectablility of CH3Cl in CH4-poor atmospheres. A similar observation time is required to detect CH3Cl with JWST and with ELT for the low CH4 scenarios. However, the cross-correlation technique is less sensitive to the increase in CH4 compared to LRS. We note that ten transit observations with JWST would be feasible within one or two years (given a 24.7-day orbital period of LHS 1140 b, Dittmann et al. 2017), but ground-based facilities would require a much longer observation period because fewer transits could be captured per year. N2 O might be detectable within 10 to 20 transits in CO2-poor atmospheres with thin hazes. Results suggest weak dependence of the detectability of N2 O on the concentration of CH4.

Our paper suggests that the detection of potential biosignatures with JWST or ELT is feasible for clear, H2 -dominated atmospheres, but would require several transit observations. If such a molecule were detected, retrieval analysis might find it challenging to constrain its abundance with low uncertainties, due to sparse knowledge on broadening coefficients (see, e.g., Tennyson & Yurchenko 2015; Hedges & Madhusudhan 2016; Barton et al. 2017; Fortney et al. 2019), and hence it would be difficult to rule out a potential abiotic origin.

thumbnail Fig. 7

Simulated transmission spectra of LHS 1140 b with weak extinction from hazes in solid lines as shown in Fig. 5. Upper panel: H2-dominated atmosphereswith low, medium, and high CH4 concentrations corresponding to scenarios 1a (blue), 1b (green), and 1c (orange), respectively. Lower panel: CO2 -dominated atmosphereswith low, medium, and high CH4 concentrations corresponding to scenarios 10a (blue), 10b (green), and 10c (orange), respectively. The shaded region shows the contribution of the minor species to the full spectrum. Expected error bars for a single transit observation assuming scenario 1c (upper panel) and 30 co-added transits assuming scenario 10c (lower panel) using JWST NIRSpec PRISM (0.7–5 μm) and JWST MIRI LRS (5–12 μm), binned to R = 30. Strongest contribution of minor atmospheric molecular absorption bands are indicated by the color of the scenario or in gray when all scenarios have a significant contribution.

thumbnail Fig. 8

Number of transits required to reach a S/N of 5 for the corresponding spectral features in a cloud free atmosphere with thin (solid lines) and thick (dashed lines) hazes using low-resolution transmission spectroscopy with JWST NIRSpec PRISM (upper and middle panels) and the cross-correlation technique with high-resolution transmission spectroscopy using ELT HIRES or METIS (lower panels). The x-axis of the upper panel is different from the middle and lower panels. Each colored circle corresponds to one scenario from Table 3. Blue lines and circles show low CH4 scenarios, green lines and circles show medium CH4 scenarios, and orange lines and circles show high CH4 scenarios.

4 Summary and conclusion

In this study we used a self-consistent model suite to simulate the atmosphere and spectral appearance of LHS 1140 b. First, we performed climate-only runs to determine the surface pressures for which the super-Earth LHS 1140 b would have habitable surface conditions in N2, H2, and CO2 atmospheres. Our results suggest that a thick N2-dominated atmosphere on LHS-1140 b or substantial amounts of greenhouse gases such as CO2 would be required to reach habitable surface temperatures. A ~2.5 bar CO2 atmosphere or a ~0.6 bar H2 –He atmosphere would lead to a surface temperature of ~273 K. In the second step we used these results and assumed a fixed surface pressure of 2.416 bar (corresponding to the atmospheric mass of the Earth) to simulate potential CO2- and H2-dominated atmospheresof LHS 1140 b with our coupled climate-photochemistry model, 1D-TERRA. We simulated the possible compositionof the planetary atmospheres, assuming fixed biomass emissions and varying boundary conditions for CH4.

The results suggest that the amount of atmospheric CH4 can have a large impact upon the temperature and composition of H2 -dominated atmospheres. A few percent of CH4 may be enough to lower the surface temperatures due to an anti-greenhouse effect. In H2 -dominated atmospheres with high concentrations of CH4 this effect dominates, leading to a cooling of up to 100 K, and the stratosphere is pronounced with temperatures up to 70 K warmer than those at the surface. Although we did not consider the effect of hydrocarbon hazes in the climate-chemistry model, Arney et al. (2017), among others, have shown that this is expected to warm the surface temperature by only a few degrees. For CO2 atmospheres the temperature profile is lessaffected by CH4 absorption due to CO2 cooling in the stratosphere.

In H2-dominated atmospheres O2 is efficiently destroyed preventing significant concentrations of O2 and O3 in such environments. Hence, even if O2 and O3 were biosignatures they would not be detectable in the atmosphere of such a habitable planet dominated by H2. In CO2-dominated atmospheresO2 and O3 can be produced abiotically, which might lead to a false-positive detection (see also Selsis et al. 2002; Segura et al. 2007; Harman et al. 2015; Meadows 2017; Wunderlich et al. 2020). However, results suggest that large amounts of CH4 would also lead to low concentrations of O2 and O3.

We consider NH3, PH3, CH3Cl, and N2 O to be potential biosignatures in H2 and CO2 atmospheres. Here the main constituent of the atmosphere has a weak impact on the concentrations of these potential biosignatures assuming that the emission flux is the same for both H2 and CO2 atmospheres (see also Seager et al. 2013a; Sousa-Silva et al. 2020). However, the detectability of molecules with transmission spectroscopy largely depends on the main composition of the atmosphere due to the difference in mean molecular weight.

Observations of the planet suggest that the atmosphere of LHS 1140 b has a low mean molecular weight (Edwards et al. 2021). In a thin, H2 -dominated atmosphere our results suggest that the tentative spectral feature at 1.4 μm might be produced by CH4 rather than H2O. If the feature at 1.4 μm were produced by water vapor absorption, surface temperatures are unlikely to be habitable. Our results suggest that the molecular features of CH4 and CO2 for habitable surface conditions might be detectable within one transit using JWST NIRSpec observations around 3.4 and 4.3 μm, respectively. At these wavelengths the absorption cross section of H2O is weak, and at large wavelengths the extinction by hazes has only a weak impact on the detectability of spectral features. The detection of NH3, PH3, CH3Cl, or N2 O would require about 10–50 transits (~40–200 h) with JWST, assuming clear conditions. The molecular bands of these species overlap with absorption by CO2 or CH4 in most cases, making a detection more challenging.

With high-resolution spectroscopy using ELT HIRES or METIS individual absorption lines are distinguishable which might improve the detectability of potential biosignatures. Our results suggest that NH3 might be detectable with less than 20 h of ELT observing time in H2 -dominated atmospheres with low or medium CH4 mixing ratios. A thick haze layer in the atmosphere would, however, prevent the detection of any potential biosignature. Strong spectral lines of PH3, CH3Cl, and N2O feature in the wavelength range of ELT METIS with overall lower sensitivity compared to ELT HIRES (see, e.g., Wunderlich et al. 2020).

Results suggest that a single transit observation of LHS 1140 b with JWST NIRSpec would be enough to confirm or rule out the existence of a clear H2-dominated atmosphere, as suggested by recent observations (Edwards et al. 2021). Such an observation would help more accurately constrain the atmospheric CH4. If future observations suggest a thin H2-dominated atmosphere on LHS 1140 b, the planet is one of the best currently known targets to find potential biosignatures such as NH3 or CH3Cl in the atmosphere of an exoplanet in the habitable zone with a reasonable amount of JWST or ELT observation time.

Acknowledgements

This research was supported by DFG projects RA-714/7-1, GO 2610/1-1, SCHR 1125/3-1 and RA 714/9-1. We acknowledge the support of the DFG priority programme SPP 1992 “Exploring the Diversity of Extrasolar Planets (GO 2610/2-1)”. J.L.G. thanks ISSI Team 464 for useful discussion. We thank Michel Dobrijevic for providing their simulated chemical profiles of Neptune and Titan and discussions on the CH4 chemistry on Neptune. We also thank Billy Edwards for providing the spectral resolved HST WFC3 data of LHS 1140 b.

Appendix A Neptune validation

Figure A.1 shows stratospheric composition profiles of selected species in the Neptunian atmosphere, simulated with the photochemistry model BLACKWOLF (Wunderlich et al. 2020). We use the atmospheric temperature profile from Fletcher et al. (2010), inferred from infrared measurements. The profiles are compared to observations and results from Dobrijevic et al. (2020)5 and Moses & Poppe (2017). The observations are taken from numerous studies as follows: CH4 from Yelle et al. (1993), Fletcher et al. (2010), and Lellouch et al. (2015); CH3 from Bezard et al. (1999); C2 H2, C2 H4, and C2 H6 from Yelle et al. (1993), Fletcher et al. (2010), and Greathouse et al. (2011).

thumbnail Fig. A.1

Neptune composition profiles for CH4 (blue), CH3 (orange), C2H2 (green), C2H4 (red), and C2H6 (purple) calculated with our photochemistry model (solid lines), compared to the model results of Dobrijevic et al. (2020; dashed lines), Moses & Poppe (2017; dotted lines), and a range of multiple observations (dots and corresponding error bars; see text for details).

The lower boundaries at 100 hPa are set to a constant mole fraction (f) for He, CH4, CO, CO2, and H2O respectively to fHe = 0.19 (Williams et al. 2004), = 9.3 × 10−4 (Lellouch et al. 2015), fCO = 1.1 × 10−6 (Luszcz-Cook & de Pater 2013), and = 5 × 10−10 (Feuchtgruber et al. 1999). H2 is set to be the fill gas in each layer to make up the total volume mixing ratio to unity. For all other species we assume a downward flux given by the maximum diffusion velocity, v = K0H0, where K0 is the eddy diffusion coefficient and H0 the atmospheric scale height at the lower boundary. The eddy diffusion coefficient over height (Kzz, in cm2s−1) is adapted from Moses et al. (2018): (A.1)

Results suggest that the Neptunian atmosphere, as simulated by the photochemistry model, compares well both with the observations and with the results from Dobrijevic et al. (2020) and Moses & Poppe (2017). Observations for pressures below 0.1 hPa, however, suggest a depletion of CH4, which is not predicted in our model. In the stratosphere of Neptune molecular diffusion is the main process that controls the relative abundance of CH4 above the methane homopause. The model version applied here includes Eddy diffusion, but not molecular diffusion, consistent with an overestimation of the CH4 concentrations below 0.1 hPa.

Appendix B Titan

Figure B.1 shows the composition profiles of selected species in the atmosphere of Titan, calculated with BLACKWOLF and compared to the results from Loison et al. (2019) and Krasnopolsky (2014). The observations are taken from Nixon et al. (2013), Kutepov et al. (2013), and Koskinen et al. (2011).

thumbnail Fig. B.1

Titan composition profiles for CH4 (blue), C3H8 (orange), C2H2 (green), C2H4 (red), and C2H6 (purple) calculated with our photochemistry model (solid lines), compared to the results from Loison et al. (2019; dashed lines), Krasnopolsky (2014; dotted lines), and a range of multiple observations (dots and corresponding error bars; see text for details).

At the surface (1.45 bar) we set a constant mole fraction (f) for CH4, CO, and H2, respectively, to = 0.015 (Niemann et al. 2010), fCO = 4.7 × 10−5 (de Kok et al. 2007), and = 1 × 10−3 (Niemann et al. 2010). N2 is set to be the fill gas in each layer. For all other species we assume a dry deposition velocity of 0.02 cm s−1. The eddy diffusion profile is taken from Krasnopolsky (2014). The temperature profile is taken from Loison et al. (2019).

Our simulated atmosphere of Titan compares reasonably well with the results of Krasnopolsky (2014) and Loison et al. (2019) and is consistent with the observations. We simulate annual and global mean conditions with the model, whereas the measurements do not represent the full range of temporal and spatial variations in the atmosphere of Titan. Profiles of latitudinal variations in the atmospheric composition of Titan are shown in Vinatier et al. (2010), among others.

Appendix C Representation of thick hazes in GARLIC

We simulate the transmission spectrum of Titan with GARLIC using the output of BLACKWOLF (see Appendix B). Since the ToA for Titan in our model is at ~500 km, we use the data from Loison et al. (2019) to extend the atmosphere up to 1500 km. GARLIC represents the extinction by hazes using Eq. (1). We vary α and β to fit the transmission spectrum from Robinson et al. (2014) observed by the Visual and Infrared Mapping Spectrometer (VIMS) from Brown et al. (2004) aboard the Cassini spacecraft. The best fit is presented in Fig. C.1 using α = 2.6 and β = 6.0 × 10−25. We use these values to simulate the impact of extinction from thick hazes in the atmosphere of LHS 1140 b.

thumbnail Fig. C.1

Transit transmission spectrum for Titan represented by effective height (in km, blue solid line). The shaded region indicates deviations from four individual transit spectra taken from Robinson et al. (2014). The black dashed line shows the best fit haze model.

The CH4 absorption features are reproduced well by GARLIC. We underestimate the absorption of the C-H stretching mode of aliphatic hydrocarbon chains near 3.4 μm (see Bellucci et al. 2009; Maltagliati et al. 2015). This discrepancy is likely due to incomplete line lists or cross sections in the HITRAN 2016 database for several hydrocarbons such as the allyl radical (C3H5; Uy et al. 1998; DeSain & Curl 1999), butane (C4H10; Abplanalp et al. 2019) and methylacetylene (CH3CCH; Abplanalp et al. 2019). Furthermore, our chemical network lacks some of the higher hydrocarbons for which absorption cross sections exists, such as isoprene (C5H8; Brauer et al. 2014).

References

  1. Abel, M., Frommhold, L., Li, X., & Hunt, K. L. C. 2011, J. Phys. Chem. A, 115, 6805 [Google Scholar]
  2. Abplanalp, M. J., Góbi, S., & Kaiser, R. I. 2019, Phys. Chem. Chem. Phys., 21, 5378 [Google Scholar]
  3. Airapetian, V. S., Glocer, A., Gronoff, G., Hébrard, E., & Danchi, W. 2016, Nat. Geosci., 9, 452 [Google Scholar]
  4. Airapetian, V. S., Barnes, R., Cohen, O., et al. 2020, Int. J. Astrobiol., 19, 136 [Google Scholar]
  5. Allen, C. W. 1976, Astrophysical Quantities (London: The Athlone Press) [Google Scholar]
  6. Anglada-Escudé, G., Rojas-Ayala, B., Boss, A. P., Weinberger, A. J., & Lloyd, J. P. 2013, A&A, 551, A48 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  7. Ångström, A. 1930, Geograf. Ann., 12, 130 [Google Scholar]
  8. Ångström, A. 1961, Tellus, 13, 214 [Google Scholar]
  9. Arney, G., Domagal-Goldman, S. D., Meadows, V. S., et al. 2016, AsBio, 16, 873 [Google Scholar]
  10. Arney, G. N., Meadows, V. S., Domagal-Goldman, S. D., et al. 2017, ApJ, 836, 49 [Google Scholar]
  11. Arney, G., Domagal-Goldman, S. D., & Meadows, V. S. 2018, AsBio, 18, 311 [Google Scholar]
  12. Arthur, N. L., & Cooper, I. A. 1997, J. Chem. Soc., Faraday Trans., 93, 521 [Google Scholar]
  13. Bains, W., Xiao, Y., & Yu, C. 2015, Life, 5, 1054 [Google Scholar]
  14. Bains, W., Petkowski, J. J., Sousa-Silva, C., & Seager, S. 2019, Sci. Total Environ., 658, 521 [Google Scholar]
  15. Bains, W., Petkowski, J. J., Seager, S., et al. 2020, ArXiv e-prints, [arXiv:2009.06499] [Google Scholar]
  16. Baraffe, I., Homeier, D., Allard, F., & Chabrier, G. 2015, A&A, 577, A42 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  17. Barstow, J. K., & Heng, K. 2020, Space Sci. Rev., 216, 82 [Google Scholar]
  18. Barton, E. J., Hill, C., Czurylo, M., et al. 2017, 203, 490 hITRAN2016 Special Issue [Google Scholar]
  19. Batalha, N. E., Lewis, N. K., Line, M. R., Valenti, J., & Stevenson, K. 2018, ApJ, 856, L34 [Google Scholar]
  20. Bean, J. L., Kempton, E. M.-R., & Homeier, D. 2010, Nature, 468, 669 [Google Scholar]
  21. Bellucci, A., Sicardy, B., Drossart, P., et al. 2009, Icarus, 201, 198 [Google Scholar]
  22. Benneke, B., Wong, I., Piaulet, C., et al. 2019, ApJ, 887, L14 [NASA ADS] [CrossRef] [Google Scholar]
  23. Bezard, B., Romani, P. N., Feuchtgruber, H., & Encrenaz, T. 1999, ApJ, 515, 868 [Google Scholar]
  24. Bézard, B., Charnay, B., & Blain, D. 2020, ArXiv e-prints [arXiv:2011.10424] [Google Scholar]
  25. Birkmann, S. M., Ferruit, P., Rawle, T., et al. 2016, Proc. SPIE, 9904, 99040B [Google Scholar]
  26. Blain, D., Charnay, B., & Bézard, B. 2021, A&A, 646, A15 [EDP Sciences] [Google Scholar]
  27. Bosco, S. R., Brobst, W. D., Nava, D. F., & Stief, L. J. 1983, J. Geophys. Res. Oceans, 88, 8543 [Google Scholar]
  28. Bouwman, A., Lee, D., Asman, W., et al. 1997, Global Biogeochem. Cycles, 11, 561 [Google Scholar]
  29. Brandl, B. R., Agócs, T., Aitink-Kroes, G., et al. 2016, Proc. SPIE, 9908, 990820 [Google Scholar]
  30. Brasseur, G. P., & Solomon, S. 2006, Aeronomy of the middle atmosphere: chemistry and physics of the stratosphere and mesosphere (New York: Springer Science & Business Media), 32 [Google Scholar]
  31. Brauer, C. S., Blake, T. A., Guenther, A. B., Sams, R. L., & Johnson, T. J. 2014, Atm. Sci., 7, 4163 [Google Scholar]
  32. Brogi, M., & Line, M. R. 2019, ApJ, 157, 114 [Google Scholar]
  33. Brown, R. H., Baines, K. H., Bellucci, G., et al. 2004, Space Sci. Rev., 115, 111 [Google Scholar]
  34. Burkholder, J. B., Sander, S. P., Abbatt, J. P. D., et al. 2015, JPL publication, 18 http://hdl.handle.net/2014/45510 [Google Scholar]
  35. Chameides, W., Stedman, D., Dickerson, R., Rusch, D., & Cicerone, R. 1977, J. Atmos. Sci., 34, 143 [Google Scholar]
  36. Checlair, J., Menou, K., & Abbot, D. S. 2017, ApJ, 845, 132 [Google Scholar]
  37. Checlair, J. H., Olson, S. L., Jansen, M. F., & Abbot, D. S. 2019, ApJ, 884, L46 [Google Scholar]
  38. Chen, F., Judge, D. L., Robert Wu, C. Y., et al. 1991, J. Gephys. Res. Planets, 96, 17519 [Google Scholar]
  39. Chen, H., Wolf, E. T., Zhan, Z., & Horton, D. E. 2019, ApJ, 886, 16 [Google Scholar]
  40. Clarke, A. 2004, Funct. Ecol., 18, 252 [Google Scholar]
  41. Clough, S., Kneizys, F., & Davies, R. 1989, Atmos. Res., 23, 229 [Google Scholar]
  42. Cutri, R. M., Skrutskie, M. F., van Dyk, S., et al. 2003, 2MASS All Sky Catalog of point sources [Google Scholar]
  43. de Grandpré, J., Beagley, S. R., Fomichev, V. I., et al. 2000, J. Geophys. Res. Atmos., 105, 26475 [Google Scholar]
  44. de Kok, R., Irwin, P., Teanby, N., et al. 2007, Icarus, 186, 354 [Google Scholar]
  45. DeSain, J., & Curl, R. 1999, J. Mol. Spectr., 196, 324 [Google Scholar]
  46. Désert, J.-M., Kempton, E. M.-R., Berta, Z. K., et al. 2011, ApJ, 731, L40 [NASA ADS] [CrossRef] [Google Scholar]
  47. Diamond-Lowe, H., Berta-Thompson, Z., Charbonneau, D., Dittmann, J., & Kempton, E. M.-R. 2020, AJ, 160, 27 [Google Scholar]
  48. Dittmann, J. A., Irwin, J. M., Charbonneau, D., et al. 2017, Nature, 544, 333 [Google Scholar]
  49. Dobrijevic, M., Loison, J., Hue, V., Cavalié, T., & Hickson, K. 2020, Icarus, 335, 113375 [Google Scholar]
  50. Domagal-Goldman, S. D., Segura, A., Claire, M. W., Robinson, T. D., & Meadows, V. S. 2014, ApJ, 792, 90 [Google Scholar]
  51. Dorn, C., Noack, L., & Rozel, A. B. 2018, A&A, 614, A18 [EDP Sciences] [Google Scholar]
  52. Edwards, B., Changeat, Q., Mori, M., et al. 2021, AJ, 161, 44 [Google Scholar]
  53. Encrenaz, T., Greathouse, T. K., Marcq, E., et al. 2020, A&A, 643, L5 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  54. Etheridge, D. M., Steele, L. P., Francey, R. J., & Langenfelds, R. L. 1998, J. Geophys. Res., 103, 979 [Google Scholar]
  55. Feuchtgruber, H., Lellouch, E., Bézard, B., et al. 1999, A&A, 341, L17 [NASA ADS] [Google Scholar]
  56. Fletcher, L. N., Drossart, P., Burgdorf, M., Orton, G. S., & Encrenaz, T. 2010, A&A, 514, A17 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  57. Fletcher, L. N., Gustafsson, M., & Orton, G. S. 2018, ApJS, 235, 24 [Google Scholar]
  58. Fortney, J., Robinson, T. D., Domagal-Goldman, S., et al. 2019, Astro2020: Decadal Survey on Astronomy and Astrophysics, 146 [Google Scholar]
  59. France, K., Loyd, R. O. P., Youngblood, A., et al. 2016, ApJ, 820, 89 [Google Scholar]
  60. Fritz, B., Lorenz, K., Steinert, W., & Zellner, R. 1982, Phys Chem Behav Atmos Pollut Proc Eur Symp. [Google Scholar]
  61. Fu, M., Yu, Z., Lu, G., & Song, X. 2013, Chin. J. Oceanol. Limnol., 31, 860 [Google Scholar]
  62. Fulchignoni, M., Ferri, F., Angrilli, F., et al. 2005, Nature, 438, 785 [Google Scholar]
  63. Fulton, B. J., Petigura, E. A., Howard, A. W., et al. 2017, AJ, 154, 109 [Google Scholar]
  64. Gaia Collaboration (Brown, C., et al.) 2018, A&A, 616, A1 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  65. Gao, P., Hu, R., Robinson, T. D., Li, C., & Yung, Y. L. 2015, ApJ, 806, 249 [Google Scholar]
  66. Gebauer, S., Grenfell, J., Stock, J. W., et al. 2017, AsBio, 17, 27 [Google Scholar]
  67. Ginzburg, S., Schlichting, H. E., & Sari, R. 2016, ApJ, 825, 29 [Google Scholar]
  68. Ginzburg, S., Schlichting, H. E., & Sari, R. 2018, MNRAS, 476, 759 [Google Scholar]
  69. Giorgi, F., & Chameides, W. 1985, J. Geophys. Res. Atmos., 90, 7872 [Google Scholar]
  70. Gordon, I., Rothman, L., Hill, C., et al. 2017, J. Quant. Spectr. Rad. Transf., 203, 3 [Google Scholar]
  71. Greathouse, T. K., Richter, M., Lacy, J., et al. 2011, Icarus, 214, 606 [Google Scholar]
  72. Greaves, J. S., Richards, A. M. S., Bains, W., et al. 2020a, Nat. Astron., in press, [arXiv:2009.06593] [Google Scholar]
  73. Greaves, J. S., Richards, A. M. S., Bains, W., et al. 2020b, ArXiv e-prints [arXiv:2011.08176] [Google Scholar]
  74. Greaves, J. S., Bains, W., Petkowski, J. J., et al. 2020c, ArXiv e-prints [arXiv:2012.05844] [Google Scholar]
  75. Grenfell, J. L., Stracke, B., von Paris, P., et al. 2007, Planet. Spece Sci., 55, 661 [Google Scholar]
  76. Grenfell, J., Gebauer, S., Godolt, M., et al. 2013, AsBio, 13, 415 [Google Scholar]
  77. Grenfell, J. L., Gebauer, S., Paris, P. v., Godolt, M., & Rauer, H. 2014, Planet. Space Sci., 98, 66 [Google Scholar]
  78. Grenfell, J. L., Gebauer, S., Godolt, M., et al. 2018, ApJ, 861, 38 [Google Scholar]
  79. Gupta, A., & Schlichting, H. E. 2019, MNRAS, 487, 24 [Google Scholar]
  80. Hamilton, P. A., & Murrells, T. P. 1985, J. Chem. Soci., Faraday Trans. 2: Mol. Chem. Phys., 81, 1531 [Google Scholar]
  81. Hardegree-Ullman, K. K., Zink, J. K., Christiansen, J. L., et al. 2020, ApJS, 247, 28 [Google Scholar]
  82. Harman, C. E., Schwieterman, E. W., Schottelkotte, J. C., & Kasting, J. F. 2015, ApJ, 812, 137 [Google Scholar]
  83. He, C., Hörst, S. M., Lewis, N. K., et al. 2018, ApJ, 156, 38 [CrossRef] [Google Scholar]
  84. Hedges, C., & Madhusudhan, N. 2016, MNRAS, 458, 1427 [Google Scholar]
  85. Hörst, S. M., He, C., Lewis, N. K., et al. 2018, Nat. Astron., 2, 303 [NASA ADS] [CrossRef] [Google Scholar]
  86. Hu, R., & Seager, S. 2014, ApJ, 784, 63 [NASA ADS] [CrossRef] [Google Scholar]
  87. Hu, Y., & Yang, J. 2014, PNAS, 111, 629 [Google Scholar]
  88. Hu, R., Seager, S., & Bains, W. 2012, ApJ, 761, 166 [Google Scholar]
  89. Hu, R., Peterson, L., & Wolf, E. T. 2020, ApJ, 888, 122 [Google Scholar]
  90. Irwin, P. G., Toledo, D., Braude, A. S., et al. 2019, Icarus, 331, 69 [Google Scholar]
  91. Iyer, R. S., Rogers, P. J., & Rowland, F. 1983, J. Phys. Chem., 87, 3799 [Google Scholar]
  92. Karman, T., Gordon, I. E., van der Avoird, A., et al. 2019, Icarus, 328, 160 [CrossRef] [Google Scholar]
  93. Kasting, J. F., & Ackerman, T. P. 1986, Science, 234, 1383 [Google Scholar]
  94. Kaye, J. A., & Strobel, D. F. 1984, Icarus, 59, 314 [Google Scholar]
  95. Keller-Rudek, H., Moortgat, G. K., Sander, R., & Sörensen, R. 2013, Earth Syst. Sci. Data, 5, 365 [Google Scholar]
  96. Kempton, E. M.-R., Zahnle, K., & Fortney, J. J. 2011, ApJ, 745, 3 [Google Scholar]
  97. Kendrew, S., Scheithauer, S., Bouchet, P., et al. 2015, PASP, 127, 623 [Google Scholar]
  98. Koskinen, T., Yelle, R., Snowden, D., et al. 2011, Icarus, 216, 507 [Google Scholar]
  99. Krasnopolsky, V. A. 2014, Icarus, 236, 83 [Google Scholar]
  100. Krasnopolsky, V. A., & Feldman, P. D. 2001, Science, 294, 1914 [Google Scholar]
  101. Krasnopolsky, V. A., & Gladstone, G. R. 2005, Icarus, 176, 395 [Google Scholar]
  102. Kreidberg, L., Bean, J. L., Désert, J.-M., et al. 2014, Nature, 505, 69 [NASA ADS] [CrossRef] [Google Scholar]
  103. Kutepov, A., Vinatier, S., Feofilov, A., Nixon, C., & Boursier, C. 2013, AAS/Div. Planet. Sci. Meeting Abstracts, 45, 207.05 [Google Scholar]
  104. Lavvas, P., Koskinen, T., Steinrueck, M. E., García-Muñoz, A., & Showman, A. P. 2019, ApJ, 878, 118 [Google Scholar]
  105. Lee, E. J., Chiang, E., & Ormel, C. W. 2014, ApJ, 797, 95 [Google Scholar]
  106. Lelieveld, J., Crutzen, P. J., & Dentener, F. J. 1998, Tellus B, 50, 128 [Google Scholar]
  107. Lellouch, E., Moreno, R., Orton, G. S., et al. 2015, A&A, 579, A121 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  108. Lillo-Box, J., Figueira, P., Leleu, A., et al. 2020, A&A, 642, A121 [CrossRef] [EDP Sciences] [Google Scholar]
  109. Line, M. R., Vasisht, G., Chen, P., Angerhausen, D., & Yung, Y. L. 2011, ApJ, 738, 32 [Google Scholar]
  110. Lingam, M., & Loeb, A. 2020, ArXiv e-prints [arXiv:2009.07835] [Google Scholar]
  111. Liske, J. 2008, E-ELT Spectroscopic ETC: Detailed Description, Tech. rep. [Google Scholar]
  112. Loftus, K., Wordsworth, R. D., & Morley, C. V. 2019, ApJ, 887, 231 [Google Scholar]
  113. Loison, J., Dobrijevic, M., & Hickson, K. 2019, Icarus, 329, 55 [Google Scholar]
  114. Lopez, E. D., & Rice, K. 2018, MNRAS, 479, 5303 [Google Scholar]
  115. Loyd, R. O. P., France, K., Youngblood, A., et al. 2016, ApJ, 824, 102 [Google Scholar]
  116. Luger, R., & Barnes, R. 2015, AsBio, 15, 119 [Google Scholar]
  117. Lustig-Yaeger,J., Meadows, V. S., & Lincowski, A. P. 2019, AJ, 158, 27 [Google Scholar]
  118. Luszcz-Cook, S., & de Pater, I. 2013, Icarus, 222, 379 [Google Scholar]
  119. Maltagliati, L., Bézard, B., Vinatier, S., et al. 2015, Icarus, 248, 1 [Google Scholar]
  120. Manatt, S. L.,& Lane, A. L. 1993, J. Quant. Spectr. Rad. Transf., 50, 267 [Google Scholar]
  121. Marconi, A., Di Marcantonio, P., D’Odorico, V., et al. 2016, Proc. SPIE, 9908, 990823 [Google Scholar]
  122. Marcq, E., Belyaev, D., Montmessin, F., et al. 2011, Icarus, 211, 58 [Google Scholar]
  123. McKay, C. P. 2014, PNAS, 111, 12628 [Google Scholar]
  124. Meadows, V. S. 2017, AsBio, 17, 1022 [Google Scholar]
  125. Meadows, V. S., Orton, G., Line, M., et al. 2008, Icarus, 197, 585 [Google Scholar]
  126. Ment, K., Dittmann, J. A., Astudillo-Defru, N., et al. 2019, AJ, 157, 32 [Google Scholar]
  127. Mlawer, E. J., Payne, V. H., Moncet, J.-L., et al. 2012, Philos. Trans. R. Soc. A Math. Phys. Eng. Sci., 370, 2520 [Google Scholar]
  128. Mogul, R., Limaye, S. S., Way, M. J., & Cordova, Jamie A., J. 2020, ArXiv e-prints [arXiv:2009.12758] [Google Scholar]
  129. Monastersky, R. 2013, Nature, 497, 13 [Google Scholar]
  130. Morley, C. V., Kreidberg, L., Rustamkulov, Z., Robinson, T., & Fortney, J. J. 2017, ApJ, 850, 121 [Google Scholar]
  131. Moses, J. I., & Poppe, A. R. 2017, Icarus, 297, 33 [Google Scholar]
  132. Moses, J. I., Fletcher, L. N., Greathouse, T. K., Orton, G. S., & Hue, V. 2018, Icarus, 307, 124 [Google Scholar]
  133. Murphy, W. F. 1977, J. Chem. Phys., 67, 5877 [Google Scholar]
  134. Nava, D. F., & Stief, L. J. 1989, J. Phys. Chem., 93, 4044 [Google Scholar]
  135. Niemann, H. B., Atreya, S. K., Demick, J. E., et al. 2010, J. Geophys. Res. Planets, 115, E12006 [Google Scholar]
  136. Nixon, C. A., Jennings, D. E., Bézard, B., et al. 2013, ApJ, 776, L14 [Google Scholar]
  137. Noack, L., Rivoldini, A., & Van Hoolst, T. 2017, Phys. Earth Planet. Inter., 269, 40 [Google Scholar]
  138. Owen, J. E., & Wu, Y. 2013, ApJ, 775, 105 [Google Scholar]
  139. Owen, J. E., & Wu, Y. 2017, ApJ, 847, 29 [Google Scholar]
  140. Pasek, M. A., Sampson, J. M., & Atlas, Z. 2014, PNAS, 111, 15468 [Google Scholar]
  141. Pavlov, A. A., Kasting, J. F., Brown, L. L., Rages, K. A., & Freedman, R. 2000, J. Geophys. Res. Planets, 105, 11981 [Google Scholar]
  142. Pierrehumbert, R., & Gaidos, E. 2011, ApJ, 734, L13 [Google Scholar]
  143. Ramirez, R. M., & Kaltenegger, L. 2017, ApJ, 837, L4 [Google Scholar]
  144. Ramirez, R. M., & Kaltenegger, L. 2018, ApJ, 858, 72 [Google Scholar]
  145. Ranjan, S., Schwieterman, E. W., Harman, C., et al. 2020, ApJ, 896, 148 [Google Scholar]
  146. Rauer, H., Gebauer, S., von Paris, P., et al. 2011, A&A, 529, A8 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  147. Robinson, T. D., Maltagliati, L., Marley, M. S., & Fortney, J. J. 2014, PNAS, 111, 9042 [Google Scholar]
  148. Rodler, F., & López-Morales, M. 2014, ApJ, 781, 54 [Google Scholar]
  149. Rugheimer, S., Kaltenegger, L., Segura, A., Linsky, J., & Mohanty, S. 2015, ApJ, 809, 57 [Google Scholar]
  150. Scheucher, M., Grenfell, L., Wunderlich, F., et al. 2018, ApJ, 863, 1 [Google Scholar]
  151. Scheucher, M., Wunderlich, F., Grenfell, J. L., et al. 2020, ApJ, 898, 44 [CrossRef] [Google Scholar]
  152. Schreier, F., Gimeno García, S., Hedelt, P., et al. 2014, J. Quant. Spectr. Rad. Transf., 137, 29 [Google Scholar]
  153. Schreier, F., Städt, S., Hedelt, P., & Godolt, M. 2018, Mol. Astrophys., 11, 1 [Google Scholar]
  154. Schwieterman,E. W., Kiang, N. Y., Parenteau, M. N., et al. 2018, AsBio, 18, 663 [Google Scholar]
  155. Schwieterman,E. W., Reinhard, C. T., Olson, S. L., et al. 2019, ApJ, 874, 9 [Google Scholar]
  156. Seager, S., Bains, W., & Hu, R. 2013a, ApJ, 777, 95 [NASA ADS] [CrossRef] [Google Scholar]
  157. Seager, S., Bains, W., & Hu, R. 2013b, ApJ, 775, 104 [Google Scholar]
  158. Segura, A., Krelove, K., Kasting, J. F., et al. 2003, AsBio, 3, 689 [Google Scholar]
  159. Segura, A., Kasting, J. F., Meadows, V., et al. 2005, AsBio, 5, 706 [Google Scholar]
  160. Segura, A., Meadows, V., Kasting, J., Crisp, D., & Cohen, M. 2007, A&A, 472, 665 [NASA ADS] [CrossRef] [EDP Sciences] [MathSciNet] [Google Scholar]
  161. Seinfeld, J. H., & Pandis, S. N. 2016, Atmospheric Chemistry and Physics: From Air Pollution to Climate Change (New York: John Wiley & Sons) [Google Scholar]
  162. Selsis, F., Despois, D., & Parisot, J.-P. 2002, A&A, 388, 985 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  163. Serigano, J., Nixon, C. A., Cordiner, M. A., et al. 2016, ApJ, 821, L8 [Google Scholar]
  164. Serindag, D. B., & Snellen, I. A. G. 2019, ApJ, 871, L7 [Google Scholar]
  165. Sneep, M., & Ubachs, W. 2005, J. Quant. Spectr. Rad. Transf., 92, 293 [Google Scholar]
  166. Snellen, I. A. G., de Kok, R. J., le Poole, R., Brogi, M., & Birkby, J. 2013, ApJ, 764, 182 [NASA ADS] [CrossRef] [Google Scholar]
  167. Snellen, I. A. G., Guzman-Ramirez, L., Hogerheijde, M. R., Hygate, A. P. S., & van der Tak, F. F. S. 2020, A&A, 644, L2 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  168. Sousa-Silva, C., Seager, S., Ranjan, S., et al. 2020, AsBio, 20, 235 [Google Scholar]
  169. Spinelli, R., Borsa, F., Ghirlanda, G., et al. 2019, A&A, 627, A144 [EDP Sciences] [Google Scholar]
  170. Tennyson, J., & Yurchenko, S. N. 2015, MNRAS, 40, 563 [Google Scholar]
  171. Thompson, M. A. 2021, MNRAS, 501, L18 [Google Scholar]
  172. Thouret, V., Cammas, J.-P., Sauvage, B., et al. 2006, ACP, 6, 1033 [Google Scholar]
  173. Tian, F., France, K., Linsky, J. L., Mauas, P. J., & Vieytes, M. C. 2014, Earth Planet. Sci. Lett., 385, 22 [Google Scholar]
  174. Tsiaras, A., Waldmann, I. P., Tinetti, G., Tennyson, J., & Yurchenko, S. N. 2019, Nat. Astron., 1 [Google Scholar]
  175. Uy, D., Davis, S., & Nesbitt, D. J. 1998, .J. Chem. Phys., 109, 7793 [Google Scholar]
  176. Van Eylen, V., Agentoft, C., Lundkvist, M. S., et al. 2018, MNRAS, 479, 4786 [Google Scholar]
  177. Villanueva, G., Cordiner, M., Irwin, P., et al. 2020, Nat. Astron., submitted [arXiv:2010.14305] [Google Scholar]
  178. Vinatier, S., Bézard, B., Nixon, C. A., et al. 2010, Icarus, 205, 559 [Google Scholar]
  179. Visscher, C., Lodders, K., & Bruce Fegley J. 2006, ApJ, 648, 1181 [Google Scholar]
  180. von Paris, P., Cabrera, J., Godolt, M., et al. 2011, A&A, 534, A26 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  181. von Paris, P., Selsis, F., Godolt, M., et al. 2015, Icarus, 257, 406 [Google Scholar]
  182. Wang, W., Wang, W., Luo, Q., et al. 2005, J. Mol. Struct., 716, 93 [Google Scholar]
  183. Williams, D. R. et al. 2004, National Space Science Data Center, NASA [Google Scholar]
  184. Wunderlich, F., Godolt, M., Grenfell, J. L., et al. 2019, A&A, 624, A49 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  185. Wunderlich, F., Scheucher, M., Godolt, M., et al. 2020, ApJ, 901, 126 [Google Scholar]
  186. Yan, F., Fosbury, R. A., Petr-Gotzens, M. G., et al. 2015, IJA, 14, 255 [Google Scholar]
  187. Yang, J., Ji, W., & Zeng, Y. 2020, Nat. Astron., 4, 58 [Google Scholar]
  188. Yelle, R. V., Herbert, F., Sandel, B. R., Vervack, R. J., & Wentzel, T. M. 1993, Icarus, 104, 38 [Google Scholar]
  189. Yung, Y. L., & DeMore, W. B. 1999, Photochemistry of Planetary Atmospheres (Oxford: Oxford University Press) [Google Scholar]
  190. Zahnle, K., Haberle, R. M., Catling, D. C., & Kasting, J. F. 2008, J. Geophys. Res. Planets, 113, E11004 [Google Scholar]

1

CH3Cl, CH4, CO, CO2, H2, H2O, HCl, HCN, HNO3, HO2, HOCl, N2, N2O, NH3, NO, NO2, O2, O3, OH, and SO2.

2

CO, CO2, H2O, N2, O2, H2, He, and CH4.

All Tables

Table 1

Phosphorous containing reactions added to the photochemical reaction scheme.

Table 2

Climate-only scenarios: surface pressure range (p0, in bar) and mixing ratios (f) of N2, CO2, H2, and He considered for LHS 1140 b.

Table 3

Climate–chemistry scenarios: surface mixing ratio of CO2, H2, He, and CH4.

Table 4

Assumed surface emissions (in molecules cm−2 s−1) and dry deposition velocities (in cm s−1) for model runs.

Table 5

Wavelength coverage and resolving power (R) of the instruments on JWST and ELT used to calculate the number of transits required to detect spectral features in the atmosphere of LHS 1140 b.

Table 6

Central wavelength, λc (μm), of molecular bands from NH3, PH3, CH3 Cl, and N2 O and spectral features that might overlap or obscure the bands at the corresponding λc.

All Figures

thumbnail Fig. 1

Surface temperature in Kelvin against surface pressure in bar for simulated climate-only N2 -, CO2 -, and H2 -dominated atmospheresof LHS 1140 b (see Table 2). The horizontal dashed line indicates 273 K.

In the text
thumbnail Fig. 2

Simulated temperature and composition profiles of selected species of LHS 1140 b. Different colors represent the three types of scenarios considered: blue for low CH4, green for medium CH4, and orange for high CH4. The solid lines represent H2-dominated atmospheres(scenarios 1a, 1b, and 1c), whereas the dashed lines show CO2-dominated atmospheres(scenarios 10a, 10b, 10c).

In the text
thumbnail Fig. 3

Change in surface temperature in K; in atmospheric height at ToA (~0.01 Pa) in km; and in surface volume mixing ratio (vmr) of H2O, O2, CO, and CH4 with decreasing concentrations of H2 for scenarios with low (blue lines), medium (green lines), and high CH4 scenarios. The concentrations of CO2 corresponding to H2 vmrs of 0.75,0.5, 0.25, and 0.0 are shown in green on the top x-axis for the medium CH4 scenarios. The colored dots represent the ten individual main scenarios described in Table 3. The dashed black line shows T = 273 K. The gray shaded region represents the limits of combustion for H2-CO2 and H2 -O2 gas mixtures (see Grenfell et al. 2018). The assumed surface mixing ratios of H2 and CO2 for the main scenarios 5–8 are at the edge of the H2-CO2 combustion limit.

In the text
thumbnail Fig. 4

Simulated transmission spectrum of the medium CH4 scenario with the H2-dominated atmosphere (scenario 1b) binned to a constant resolving power of R = 300 (maximum resolving power of JWST NIRSpec PRISM at 5 μm). The black line represents the spectrum that includes the contribution from all gases which are considered in 1D-TERRA and also available in HITRAN, Rayleigh scattering, aerosol extinction, H2O CKD, and CIAs. Individual gaseous absorption of selected molecules are represented by different colors.

In the text
thumbnail Fig. 5

Simulated atmospheric features of the LHS 1140 b for all three scenarios with H2 - (upper panel) and CO2-dominated atmospheres(lower panel), represented by cloud-free transit transmission spectra and binned to a constant resolving power of R = 300. The scenarios are color-coded: blue for low concentrations of CH4 (scenario 1a for H2- and scenario 10a for CO2-dominated atmospheres), green for medium concentrations of CH4 (scenario 1b for H2- and scenario 10b for CO2-dominated atmospheres), and orange for high concentrations of CH4 (scenario 1c for H2- and scenario 10c for CO2-dominated atmospheres). Important atmospheric molecular absorption are highlighted with horizontal lines in the color of the scenario with a strong absorption feature or in gray when all scenarios show a strong feature. Solid lines include weak extinction from hazes, whereas dashed lines consider extinction by thick hazes.

In the text
thumbnail Fig. 6

Simulated spectral features compared to HST observations taken from Edwards et al. (2021), assuming H2 -dominated atmospheres. The scenarios are color-coded: solid blue line for the low CH4 scenario (1a), as shown in Fig. 5, and solid purple line for scenario 1a, but with an assumed constant H2O mixing ratio of 1%; solid green line for the medium CH4 scenario (1b); solid and dashed orange lines for the high CH4 scenario (1c) with thin and thick hazes, respectively. All spectral data were binned to R = 300 and subtracted from the mean transit depth between 1.1 and 1.7 μm.

In the text
thumbnail Fig. 7

Simulated transmission spectra of LHS 1140 b with weak extinction from hazes in solid lines as shown in Fig. 5. Upper panel: H2-dominated atmosphereswith low, medium, and high CH4 concentrations corresponding to scenarios 1a (blue), 1b (green), and 1c (orange), respectively. Lower panel: CO2 -dominated atmosphereswith low, medium, and high CH4 concentrations corresponding to scenarios 10a (blue), 10b (green), and 10c (orange), respectively. The shaded region shows the contribution of the minor species to the full spectrum. Expected error bars for a single transit observation assuming scenario 1c (upper panel) and 30 co-added transits assuming scenario 10c (lower panel) using JWST NIRSpec PRISM (0.7–5 μm) and JWST MIRI LRS (5–12 μm), binned to R = 30. Strongest contribution of minor atmospheric molecular absorption bands are indicated by the color of the scenario or in gray when all scenarios have a significant contribution.

In the text
thumbnail Fig. 8

Number of transits required to reach a S/N of 5 for the corresponding spectral features in a cloud free atmosphere with thin (solid lines) and thick (dashed lines) hazes using low-resolution transmission spectroscopy with JWST NIRSpec PRISM (upper and middle panels) and the cross-correlation technique with high-resolution transmission spectroscopy using ELT HIRES or METIS (lower panels). The x-axis of the upper panel is different from the middle and lower panels. Each colored circle corresponds to one scenario from Table 3. Blue lines and circles show low CH4 scenarios, green lines and circles show medium CH4 scenarios, and orange lines and circles show high CH4 scenarios.

In the text
thumbnail Fig. A.1

Neptune composition profiles for CH4 (blue), CH3 (orange), C2H2 (green), C2H4 (red), and C2H6 (purple) calculated with our photochemistry model (solid lines), compared to the model results of Dobrijevic et al. (2020; dashed lines), Moses & Poppe (2017; dotted lines), and a range of multiple observations (dots and corresponding error bars; see text for details).

In the text
thumbnail Fig. B.1

Titan composition profiles for CH4 (blue), C3H8 (orange), C2H2 (green), C2H4 (red), and C2H6 (purple) calculated with our photochemistry model (solid lines), compared to the results from Loison et al. (2019; dashed lines), Krasnopolsky (2014; dotted lines), and a range of multiple observations (dots and corresponding error bars; see text for details).

In the text
thumbnail Fig. C.1

Transit transmission spectrum for Titan represented by effective height (in km, blue solid line). The shaded region indicates deviations from four individual transit spectra taken from Robinson et al. (2014). The black dashed line shows the best fit haze model.

In the text

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.