# There’s more to life than O2: Simulating the detectability of a range of
molecules for ground-based high-resolution spectroscopy of transiting
terrestrial exoplanets

Miles H. Currie Department of Astronomy and Astrobiology Program, University
of Washington, Box 351580, Seattle, Washington 98195, USA  NASA Nexus for
Exoplanet System Science, Virtual Planetary Laboratory Team, Box 351580,
University of Washington, Seattle, Washington 98195, USA  Victoria S. Meadows
Department of Astronomy and Astrobiology Program, University of Washington,
Box 351580, Seattle, Washington 98195, USA  NASA Nexus for Exoplanet System
Science, Virtual Planetary Laboratory Team, Box 351580, University of
Washington, Seattle, Washington 98195, USA  Astrobiology Center, 2-21-1 Osawa,
Mitaka, Tokyo 181-8588, Japan  Kaitlin C. Rasmussen Department of Astronomy
and Astrobiology Program, University of Washington, Box 351580, Seattle,
Washington 98195, USA  NASA Nexus for Exoplanet System Science, Virtual
Planetary Laboratory Team, Box 351580, University of Washington, Seattle,
Washington 98195, USA

###### Abstract

Within the next decade, atmospheric O2 on Earth-like M dwarf planets may be
accessible with visible–near-infrared, high spectral resolution extremely
large ground-based telescope (ELT) instruments. However, the prospects for
using ELTs to detect environmental properties that provide context for O2 have
not been thoroughly explored. Additional molecules may help indicate planetary
habitability, rule out abiotically generated O2, or reveal alternative
biosignatures. To understand the accessibility of environmental context using
ELT spectra, we simulate high-resolution transit transmission spectra of
previously-generated evolved terrestrial atmospheres. We consider inhabited
pre-industrial and Archean Earth-like atmospheres, and lifeless worlds with
abiotic O2 buildup from CO2 and H2O photolysis. All atmospheres are self-
consistent with M2V–M8V dwarf host stars. Our simulations include explicit
treatment of systematic and telluric effects to model high-resolution spectra
for GMT, TMT, and E-ELT configurations for systems 5 and 12 pc from Earth.
Using the cross-correlation technique, we determine the detectability of major
species in these atmospheres: O2, O3, CH4, CO2, CO, H2O, and C2H6. Our results
suggest that CH4 and CO2 are the most accessible molecules for terrestrial
planets transiting a range of M dwarf hosts using an E-ELT, TMT, or GMT sized
telescope, and that the O2 NIR and H2O 0.9 $\mu$m bands may also be accessible
with more observation time. Although this technique still faces considerable
challenges, the ELTs will provide access to the atmospheres of terrestrial
planets transiting earlier-type M-dwarf hosts that may not be possible using
JWST.

astrobiology, planets and satellites: atmospheres, planets and satellites:
terrestrial planets

##  1 Introduction

With next-generation space- and ground-based observatories capable of
characterizing the atmospheres of terrestrial exoplanets coming online, we
enter a new era of searching for signs of habitability and life. For the best
targets, including the TRAPPIST-1 transiting planet system, spectral
characterization with the James Webb Space Telescope (JWST) may reveal novel
environments that contain signs of planetary evolution influenced by abiotic
or possibly biological processes (e.g. Lincowski et al., 2018; Lustig-Yaeger
et al., 2019a; Wunderlich et al., 2019; Pidhorodetska et al., 2020; Gialluca
et al., 2021; Meadows et al., submitted). The upcoming extremely-large ground-
based telescopes (ELTs) will provide an additional near-term opportunity to
understand how Earth-like planets evolve under a variety of conditions, and
search for signs of life. In particular, the ELTs have the potential to
complement the capabilities of space-based telescopes by accessing O2 and
other molecules that can give context to O2. While O2, the major byproduct of
oxygenic photosynthesis, and O3, the photochemical byproduct of O2, are
unlikely to be detectable with JWST (Lustig-Yaeger et al., 2019a; Wunderlich
et al., 2019; Pidhorodetska et al., 2020; Meadows et al., submitted), a large
body of previous work suggests that O2 may be detectable from the ground
(Rodler & López-Morales, 2014; Kawahara et al., 2012; Serindag & Snellen,
2019; López-Morales et al., 2019; Snellen et al., 2013). The next generation
of ground-based telescopes and their instruments have several advantages for
O2 detection, including the ability to spectrally resolve narrow features at
shorter wavelengths, where O2 more strongly absorbs, and a broader range of M
dwarf targets (Snellen et al., 2013; Rodler & López-Morales, 2014; López-
Morales et al., 2019). Consequently, ground-based observations will likely be
highly complementary to the near- and mid-infrared studies of terrestrial
exoplanet atmospheres possible with JWST.

The feasibility of detecting O2 in ground-based observations of terrestrial
exoplanets is relatively well studied. The main challenge of this approach is
separating exoplanet O2 absorption from Earth’s O2 absorption; however, it has
been argued that with well-constrained radial velocity measurements from high-
resolution (R $\sim$ 100,000) ELT spectrometers (Szentgyorgyi et al., 2014;
Marconi et al., 2022; Mawet et al., 2019), this challenge can be overcome.
Using this method, simulations suggest that strong atmospheric O2 features may
be detected through the Earth’s atmosphere by observing less than 40 transits
for nearby targets (Snellen et al., 2013; Rodler & López-Morales, 2014;
Serindag & Snellen, 2019; López-Morales et al., 2019), making O2 a prime
biosignature gas to be sought by upcoming ground-based telescopes. Snellen et
al. (2013) pioneered the use of cross-correlation analysis to simulate how to
untangle the telluric oxygen lines from exoplanet oxygen, and calculated that
O2 is detectable at 3.8$\sigma$ with the European–ELT (E-ELT) in 30 transits
for a hypothetical Earth twin in the GJ 1214 (M5V) system $\sim 14.6$ pc away.
Rodler & López-Morales (2014) later refined this study by adding red noise and
atmospheric refraction effects to the simulated observations, and predicted
about twice as many transits would be needed to detect O2 at the same
significance. Incorporating the effects of more realistic noise, Serindag &
Snellen (2019) injected an O2 signal into observations of Proxima Centauri
using the Ultraviolet and Visual Echelle Spectrograph (UVES) instrument on the
Very Large Telescope (VLT), and found that O2 would be detectable in 20–50
transits for an Earth twin orbiting an M5 star 7 pc away, which is consistent
with both Snellen et al. (2013) and Rodler & López-Morales (2014). López-
Morales et al. (2019) sought to optimize instrumentation and observing
strategies for detecting O2, and found that increasing the spectral resolution
to $R=300,000$ more than doubles the O2 line depths compared to $R=100,000$
spectra, and the higher spectral resolution lowers the number of transits to
detect O2 at 3$\sigma$ by 34%.

While the ground-based high-resolution technique is well explored for O2
detection, similar investigations for the detectability of molecules that can
help interpret O2 in the context of its planetary environment are relatively
sparse. Lovis et al. (2017) explored O2, CH4, and H2O detectability for
planetary reflected light observations using the SPHERE+ESPRESSO
instrumentation on the VLT and found that O2 and H2O may be accessible on an
Earth-like Proxima Centauri b, but the unavailability of high-accuracy line
list data in their wavelength region limited their ability estimate CH4
detectability. More recently, Lin & Kaltenegger (2020) suggested that in
addition to O2, ELTs may be used to search for potential biosignature or
climate-indicator gases like CH4, CO2, and H2O in Earth-like atmospheres
around late-type M dwarfs, but did not simulate observations to quantify
molecular detectability for high-resolution spectra. Wunderlich et al. (2020)
adopt a simple approach to estimate the detectability of a suite of molecules
including CO2, CH4, O2, H2O, and CO using an ELT, and finds that CO2 and CH4
may be readily accessible for TRAPPIST-1 e, but a rigorous cross-correlation
analysis of simulated observations was outside of the scope of their paper.

While O2 is undoubtedly an important biosignature for life on Earth, a
detection of even abundant O2 alone in an exoplanet atmosphere is not
necessarily a sign of life. Confidence that abundant O2 is in fact due to life
can be increased if abiotic mechanisms for its generation (so-called “false
positives”) can be ruled out (Meadows, 2017). Post ocean-loss worlds, for
example, can generate extremely abundant (up to thousands of bars),
potentially detectable levels of O2 on planets orbiting M dwarfs, and may be a
common outcome of atmospheric evolution (Luger & Barnes, 2015; Tian, 2015;
Schwieterman et al., 2016; Lustig-Yaeger et al., 2019a), but could potentially
be discriminated in high-resolution spectroscopic observations via detection
of shorter-wave O2 bands in addition to a non-detection of the 1.27$\mu$m
infrared band (Leung et al., 2020). CO2 photolysis could also be a source of
abiotic O2 in circumstances that inhibit CO and O recombination (Domagal-
Goldman et al., 2014; Tian et al., 2014; Harman et al., 2015; Gao et al.,
2015; Hu et al., 2012), including inhibiting NOx production (Harman et al.,
2018), in which case the concurrent presence of CH4, which is often low in
cases of abiotic O2 production (Domagal-Goldman et al., 2014), or CO, the
other byproduct of CO2 photolysis, may be false positive discriminants. The
interpretation of an O2 detection, therefore, is highly dependent on other
characteristics of the planetary environment, and will require the detection
of other gases or bands to help provide this context.

When modeling M dwarf planets, which are better suited to study in
transmission, it is also important to account for star-planet interactions
that can alter the atmosphere of habitable zone planets away from an Earth-
twin scenario. On M dwarf planets, even with the same assumed surface fluxes
of gases, the spectral energy distribution (SED) of the host star can alter
molecular destruction and production rates. These in turn alter photochemical
lifetimes of gas species, potentially enhancing the abundance of key gases in
the atmosphere such as CH4 (Segura et al., 2005). Planets in the habitable
zone may also receive different irradiances than Earth depending on their
orbital geometries (Kopparapu et al., 2013) which may act to cool or warm the
planet relative to Earth. However, the carbonate–silicate cycle, which is
presumed to be active on a planet with liquid water and plate tectonics, may
stabilize the surface temperature by buffering the CO2 abundance in the
planetary atmosphere (Walker et al., 1981). Thus planets receiving less
irradiance from their host star may have a buildup of CO2 in their atmospheres
to keep the surface temperate. These different abundances and distributions of
atmospheric species due to the action of photochemistry and climate buffers
can significantly alter their detectability, and thus these effects must be
accounted for when interpreting observations of terrestrial exoplanet
environments.

Expanding detectability predictions to include molecules other than O2 has
distinct advantages in helping us search for additional biosignatures and for
more comprehensively assessing any biosignatures that we may find, including
O2. The likelihood that the presence of O2 is a biosignature needs to be
assessed by searching for molecules that help us characterize the underlying
planetary environment and its history, rule out false positives for any
biosignatures detected, and potentially strengthen the biosignature
interpretation in other ways by identifying disequilibria that are more likely
to be driven by life (Meadows et al., 2018; Krissansen-Totton et al., 2016).
In particular, the detection of a strong CO2 feature immediately increases the
chance that a given planet is terrestrial, but may also suggest a possible
source for abiotic O2. Determining whether observed O2 is more or less likely
to be due to an O2-dominated atmosphere generated by H2O photolysis from past
ocean loss can potentially be achieved by searching at multiple wavelengths
for the presence of strong collisionally-induced absorption from O2 molecules
in a dense atmosphere (Lincowski et al., 2018; Lustig-Yaeger et al., 2019a;
Leung et al., 2020). However, there are several other possible false positive
discriminant molecules that can be used to indicate than an abiotic source may
be more likely, such as the simultaneous presence of CO2 and CO, its
photolytic byproduct (Schwieterman et al., 2016), and whether or not water
vapor is present (Wordsworth & Pierrehumbert, 2014; Gao et al., 2015). As well
as ruling out abiotic sources, additional molecules, especially in
disequilibrium pairs, can make a stronger case for a biological source.
Examples include the canonical O2/CH4 pair (Hitchcock & Lovelock, 1967) that
would reveal the gigayear-long impact of oxygenic photosynthesis on our planet
(Lyons et al., 2014), coupled with high fluxes of biogenic CH4 generated by
methanogens, that greatly exceed anticipated geological production rates
(Etiope & Sherwood Lollar, 2013). Even in more exotic planetary environments,
it is challenging to produce high abiotic CH4 fluxes without also revealing
the abiotic mechanism through other observable features (Thompson et al.,
2022). Finally, by expanding the types of molecules sought, we can also look
for alternative biosignatures that are potentially more detectable, such as
the CH4/CO2 disequilibrium pair (Krissansen-Totton et al., 2016) that may have
been present and potentially detectable for the majority of the Earth’s
history (Meadows et al., submitted). This biosignature could also be made more
robust by additionally searching for and ruling out the presence of CO, which,
if present, may indicate enhanced outgassing of abiotic CH4 from a more
reduced interior Krissansen-Totton et al. (2016, 2018b).

In this work, we explore the potential for using the ELTs to detect a number
of molecules expected in transiting terrestrial planetary atmospheres, and
their application for biosignature interpretation. We use previously-generated
simulations of self-consistent inhabited environments for Earth throughout its
history transiting five M dwarf spectral types from M2V-M8V. We apply cross-
correlation techniques to simulated spectra of these environments to determine
the number of transits needed to detect the molecules. Additionally, we
explore two lifeless, but habitable, atmosphere types that may also exhibit
abiotically-generated O2 from processes such as significant CO2 photolysis,
complete ocean loss, and ongoing ocean outgassing, to understand how we can
potentially discriminate these atmospheres from those influenced by life. In
Section 2, we outline how we constructed synthetic observed spectra and cross-
correlated them with a template spectrum to estimate the detectability of
molecular bands in the visible and near-infrared. In Section 3, we present the
results of our detectability calculations, which inform future observations of
transiting terrestrial exoplanets. In Section 4, we discuss the significance
of detections and non-detections of molecular bands in the context of
habitability and life, and propose an observational strategy for
characterizing terrestrial atmospheres in transit. We summarize our findings
in Section 5.

##  2 Methods

In this work, we develop a novel pipeline for estimating the detectability of
molecular bands in transiting terrestrial exoplanet atmospheres using cross-
correlation analysis (Figure 1). The model takes simulated high-resolution
(R=100,000) transit transmission spectra of a variety of terrestrial
environments as its input, adds appropriate noise for ground-based high-
resolution spectroscopy, and performs a cross-correlation analysis on the
synthetic observation and a model spectrum. We use the resulting cross-
correlation function to determine the detectability of a molecular band as a
function of the number of transits observed.

Figure 1: Overview of methods pipeline. The red round boxes represent the
start and end points of the pipeline. The solid green boxes represent models
or software pipelines, the white parallelograms represent input or output data
products, and the rhombuses represent user-specified metadata or parameters.
Cartoon examples of the data products are shown next to each data product
node.

###  2.1 Model inputs

Our novel detectability pipeline (Figure 1) requires high-resolution transit
transmission spectra as its input. We generate high-resolution spectra using a
radiative transfer model applied to atmospheric profiles that describe the
pressure, temperature and mixing ratio of key gases as a function of altitude
(pressure). To generate atmospheres that are self-consistent with the host
star SEDs, we use results from previous coupled climate–photochemical models.

####  2.1.1 System selection

We test the detectability of multiple absorption bands for seven molecules
(Table 1) in four atmosphere classes derived from the climate/photochemistry
models of other studies (Table 2) orbiting five different M dwarf host stars,
at 5 and 12 pc away from Earth. To inform the development of future
instrumentation, the detectability is calculated for individual absorption
bands of each molecule, as López-Morales et al. (2019) found no advantage to
combining molecular bands in observations when accounting for red noise. The
four atmosphere classes include pre-industrial Earth (PIE) and Archean Earth
(ARE) (Meadows et al., 2018; Davis et al., in prep.), a scenario with abiotic
O2 buildup from CO2 photolysis (referred to as CO2 photolysis) for both
lightning on and lightning off cases (Harman et al., 2018), and scenarios for
10 bar O2 atmospheres that result from both complete ocean loss (referred to
as ocean-loss) with internal desiccation, and ongoing ocean loss with
subsequent interior outgassing (referred to as ocean outgassing) (Meadows et
al., 2018; Leung et al., 2020). All planets are one Earth radius in size. We
place the PIE and ARE planets in orbit around M2V, M3V, M4V, M6V, and M8V
dwarf host stars, and use outputs from previous studies that produced self-
consistent photochemical and climatic simulations for calculating our spectra.
The CO2 photolysis and 10 bar O2 ocean loss/outgassing atmospheres are self-
consistent with only M4V and M6V dwarf hosts, respectively, and thus
detectability for these planets orbiting other M dwarf hosts is not
considered. For our pre-industrial Earth-like atmospheres, we include both
clear sky and cloudy scenarios. In total, there are $14$ unique planet/star
systems in this study, outlined in Table 2. As an example of a potential real
ELT target, we calculate the detectability of species in our Earth-sized PIE
and ARE atmospheres with TRAPPIST-1 e’s orbit (M8V host) at its canonical
distance of 12 pc (Gillon et al., 2016) using the atmosphere models of Davis
et al. (in prep).

Table 1: Molecular bands explored in this study Molecule | Bands [$\mu m$]
---|---
O2 | 0.69, 0.76, 1.27
CH4 | 0.89, 1.1, 1.3, 1.6
CO2 | 1.59, 2.0
H2O | 0.94, 1.1, 1.3
O3 | 0.63, 0.65, 3.2
CO | 1.55, 2.3
C2H6 | 3.33
Table 2: Atmosphere classes and host stars Atmosphere | Host Star(s) | Atm. Ref.
---|---|---
Pre-industrial Earth (PIE) | M2V, M3V, M4V, M6V, M8V | Davis et al. (in prep)
Archean Earth (ARE) | M2V, M3V, M4V, M6V, M8V | Davis et al. (in prep)
CO2 photolysis, lightning ON | M4V | Harman et al. (2018)
CO2 photolysis, lightning OFF | M4V | Harman et al. (2018)
10 bar O2 complete ocean loss | M6V | Meadows et al. (2018)
10 bar O2 ongoing ocean outgassing | M6V | Meadows et al. (2018)
Table 3: Planet orbital properties Host Spectral Type | Transit duration [hr] | Time between transits [hr] | Orbital Period [day] | Semi-major axis [AU]
---|---|---|---|---
M2 | 4.7 | 1153 | 48 | 0.24
M3 | 3.6 | 793 | 33 | 0.19
M4 | 3.4 | 692 | 29 | 0.16
M6 | 1.2 | 152 | 6.4 | 0.041
M8 | 0.99 | 96 | 4.0 | 0.027
Table 4: Host star properties

Spectral Type | Example star | Radius | Synthetic Spectrum Ref. | mI (5 pc) | mJ (5 pc) | mI (12 pc) | mJ (12 pc)
---|---|---|---|---|---|---|---
|  | [R⊙] |  | [mag] | [mag] | [mag] | [mag
M2 | GJ 832 | 0.499a | Peacock et al. (2019b) | 6.2 | 5.0 | 8.1 | 6.9
M3 | GJ 436 | 0.464b | Peacock et al. (2019b) | 6.8 | 5.4 | 8.7 | 7.3
M4 | GJ 876 | 0.3761c | Peacock et al. (2020) | 7.1 | 5.8 | 9.0 | 7.7
M6 | Proxima Centauri | 0.141d | Davis et al. (in prep) | 10.7 | 8.6 | 12.6 | 10.5
M8 | TRAPPIST-1 | 0.114e | Peacock et al. (2019a) | 12.2 | 9.3 | 14.1 | 11.2

Note. — Stellar radii references: aHoudebine (2010), bTorres (2007), cvon
Braun et al. (2014), dBonfils et al. (2005), eFilippazzo et al. (2015). The
magnitudes are calculated for each example star placed at 5 and 12 pc away
from Earth.

####  2.1.2 Planet atmosphere calculations

For the self-consistent planetary atmospheres used as input to the simulator,
we use previously generated results from a 1-D coupled climate–photochemistry
model—which includes the effects of the host star SED on the photochemistry
and climate of each planet atmosphere. The model couples the atmospheric
chemistry component of Atmos, a publicly available model based on Kasting et
al. (1979) and Zahnle et al. (2006), with VPL-Climate, a general-purpose, 1D
radiative–convective equilibrium, terrestrial planet climate model (Meadows et
al., 2018; Robinson & Crisp, 2018). The coupled model is described in detail
in Lincowski et al. (2018), including validations for both Earth and Venus.

Davis et al. (in prep) updated the coupled climate–photochemistry model to
include new H2O cross-sections, and ran globally-averaged PIE and ARE
atmospheres orbiting M2V, M3V, M4V, M6V, and M8V dwarf stars to convergence,
which we use for our PIE and ARE atmosphere classes in this study. Each planet
is placed in orbit around its host star such that it receives 0.66 times the
irradiance that Earth receives, and the accompanying orbital properties are
given in Table 3. This irradiance is approximately the same as that received
by the M dwarf habitable zone planets TRAPPIST-1e (Gillon et al., 2017) and
Proxima Centauri b (Anglada-Escudé et al., 2016). The host star properties and
spectra are given in Table 4, and their synthetic spectra are plotted in
Figure 2 (degraded to R = 50 for clarity). For each spectral type, we use
publicly available synthetic high-resolution stellar spectra for the stars
GJ832, GJ436, GJ876, Proxima Centauri, and TRAPPIST-1 (Peacock et al. (2019a,
b, 2020), Davis et al. in prep.). These spectra serve as analog examples for
M2V, M3V, M4V, M6V, and M8V dwarf stars, respectively. The vertical gas
abundance and temperature profiles for the self-consistent PIE and ARE
atmosphere calculations are shown as solid lines in Figures 3 and 4,
respectively; we include true Earth profiles in dashed lines as a comparison.
The habitable PIE and ARE atmospheres considered here contain 10% CO2, which
is required to raise the global mean surface temperatures above the freezing
point of water Meadows et al. (2018). This abundance is not unreasonable if we
assume that the surface temperature of a planet within the habitable zone is
buffered by a carbonate-silicate feedback, as proposed by (Walker et al.,
1981). With the carbonate-silicate feedback working, a planet may have an
atmospheric CO2 abundance of up to several bars of CO2 near the HZ outer edge
(Kopparapu et al., 2013).

The Harman et al. (2018) CO2 photolysis atmospheres, which, with lightning
turned off, can generate up to 6% O2 from CO2 photolysis are self-consistent
with our M4V stellar host, and the corresponding molecular and temperature
profiles are presented in Figure 5. The two red lines show the effects of
turning lightning off (dotted) and on (solid). NO produced by lightning
catalyzes the recombination of CO and O, decreasing oxidizing species and
increasing reducing species in the atmospheres. Again, true Earth is shown for
comparison in the blue dashed lines.

The 10-bar O2 atmospheres from Leung et al. (2020) are self-consistent with
our M6V stellar host, and the corresponding molecular and temperature profiles
are presented in Figure 6, with ocean-loss profiles as the solid lines and
ocean-outgassing profiles as the dashed lines. True Earth profiles are shown
for comparison as the blue dashed lines.

Figure 2: Upper panel: Spectral energy distributions of the M dwarf host stars
in this study incident at the top of the planets’ atmospheres. Lower panel:
The stellar fluxes as seen by an observer 12 pc away. The spectral resolution
is binned down to $R=50$ for clarity. The colored horizontal lines are
selected molecular absorption bands we investigate in our planetary
atmospheres. Figure 3: Mixing ratios for the major species in the pre-
industrial Earth-like (PIE) atmospheres and temperature profiles. Each line
represents a self-consistent Earth-like atmosphere orbiting a stellar host.
Earlier-type M dwarf hosts are lighter and later-type M dwarf hosts are
darker. The dashed blue line represents profiles for the Earth orbiting the
Sun for comparison.  Figure 4: Mixing ratios for the major species in the
Archean Earth-like (ARE) atmospheres and temperature profiles. Each line
represents a self-consistent Earth-like atmosphere orbiting a stellar host.
Earlier-type M dwarf hosts are lighter and later-type M dwarf hosts are
darker. The dashed blue line represents profiles for the Earth orbiting the
Sun for comparison.  Figure 5: Mixing ratios for the major species in the
abiotic O2 buildup from CO2 photolysis atmospheres and temperature profiles.
The solid and dotted red lines represent profiles for a CO2 photolysis
atmosphere with lightning turned on and off, respectively, orbiting an
M4V-type stellar host. The dashed blue line represents profiles for the Earth
orbiting the Sun for comparison.  Figure 6: Mixing ratios for the major
species in the 10-bar O2 ocean outgassing (dashed) and ocean-loss (solid)
atmospheres and temperature profiles. The dashed blue line represents profiles
for the Earth orbiting the Sun for comparison. There is no H2O in the ocean-
loss atmosphere.  Figure 7: Selected molecular bands in this work. Each
molecular band is plotted at $R=100,000$ and is continuum subtracted using a
high-pass filter (see Section 2). Note the scale in each row: planets
transiting later type M dwarf hosts typically have larger relative transit
depths than planets transiting early type hosts. Figure 8: Noise sources per
resolution element considered in this work for a band in the visible (top
panel) and a band in the NIR (bottom panel) for a 2 hr exposure at R =
100,000. The background shot noise is comprised of zodiacal light, telluric
emission, airglow, and telescope/instrument thermal emission, and the airglow
continuum dominates the background continuum. The shot noise is plotted for
all stars at 12 pc, and includes attenuation from the Earth’s telluric
transmission. Photon (shot) noise from the M2V and M8V stars exceeds the
combined readout and dark noise by a factor of 25 and 8, respectively, for the
visible, and 12 and 15, respectively, for the NIR.

####  2.1.3 High-resolution spectra

We calculated high-resolution spectra of our transiting planet atmospheres
using SMART, a 1D, line-by-line radiative transfer model (Stamnes et al.,
1988; Meadows & Crisp, 1996; Crisp, 1997). SMART calculates the spectra using
stellar sources, and solves the radiative transfer equation for each
atmospheric constituent in discrete layers in the atmosphere. In each layer,
SMART calculates extinction due to vibrational and rotational transitions, and
collisionally-induced absorption for each absorbing gas. It also calculates
the effects of aerosols, Rayleigh scattering, and wavelength-dependent surface
albedo. SMART outputs top-of-atmosphere planetary radiances and transmission
spectra, and has been validated for Earth in reflected light at low-resolution
in Robinson et al. (2011), and for Earth in transmission at high-resolution in
Lustig-Yaeger (in prep.). We calculated each spectrum at a resolution of
$R=1,000,000$ for both clear and cloudy sky scenarios, which we then convolve
with a Gaussian profile to achieve a resolution of $R=100,000$ for this study.
As in López-Morales et al. (2019), we find that increasing the resolution of
our spectra to $R=300,000$ roughly doubles (100% increase) the average line
depths for the O2 A-band and NIR band. However, this effect is dependent on
the molecular band, and we see different increases in line depth ranging from
50% (O3, 0.65 um) to nearly 200% (CO, 2.3 um). We simulate cloudy-sky
scenarios as 50% clear sky, 25% Earth-like cirrus clouds, and 25% and Earth-
like stratocumulus clouds for the pre-industrial Earth-like atmospheres. The
clouds are assumed to be uniformly-spaced around the planet, and the cirrus
and stratocumulus clouds are placed at 0.331 bar and 0.847 bar, respectively.
Figure 7 show clear-sky high-resolution spectra for selected molecular bands
in this study.

###  2.2 Simulated observations

We upgraded an existing, sophisticated noise model, coronagraph—which is
designed for simulating observations of space-based telescopes (Robinson et
al., 2016; Lustig-Yaeger et al., 2019b)—and modified it to produce synthetic
observations of ground-based ELTs. Because we are simulating transmission
spectra for the ELTs, we turn off the coronagraph mode in our ground-based
upgrade of coronagraph. To avoid confusion, we hereafter refer to our
coronagraph-free version of the coronagraph code as the Spectral Planetary ELT
Calculator for Terrestrial Retrieval (SPECTR)
pipeline111https://github.com/curriem/spectr. To estimate the wavelength-
dependent signal-to-noise ratio for a ground-based observation, we use SPECTR
to calculate the incoming photon count from the exoplanet/star system, and the
background photon count from zodiacal, exo-zodiacal, telescope, instrument,
detector, and atmospheric (telluric) sources.

####  2.2.1 Modeling the Sky

To simulate ground-based observations, SPECTR has a built-in interface to the
Cerro Paranal Advanced Sky Model (SykCalc Noll et al., 2012; Jones et al.,
2013), a highly customizable telluric atmosphere model built by the European
Southern Observatory (ESO) for observation planning. Within SPECTR, the user
can choose parameters that describe the observatory site, season, time, target
coordinates, and moon phase/location, which are passed to the SkyCalc command
line interface. SkyCalc returns the wavelength-dependent background and
telluric lines at the native resolution of the instrument. The background
component is comprised of moonlight, starlight, zodiacal light, telluric
emission, airglow, and telescope/instrument thermal emission. We find that the
dominant component of the background is the airglow continuum, which is
comprised of radiation from chemiluminsecent reactions between atmospheric
constituents (e.g. Khomich et al., 2008; Kenner & Ogryzlo, 1984) as well as
pseudo-continua from many closely spaced molecular lines (e.g. Saran et al.,
2011). We use Paranal as our observatory site, with precipitable water vapor
of 3.5 mm, and do not include scattered moonlight in these simulations.

####  2.2.2 Telescope and instrument properties

We simulate $R=100,000$ observations for European ELT (E-ELT), Thirty Meter
Telescope (TMT), and Giant Magellan Telescope (GMT) configurations, with
collecting areas of and 978 m2, 707 m2, and 368 m2, respectively. These
collecting areas correspond to a 39 m E-ELT with a central hole to accommodate
its secondary mirror222https://elt.eso.org/about/facts/, a 30 m diameter TMT,
and the total expected collecting area of all mirror segments of the
GMT333https://giantmagellan.org/explore-the-design/. We expect our E-ELT
aperture to yield a 1.39x higher flux than our TMT aperture. The required
equivalent observation time with our E-ELT configuration is $\sim 30$% less
than our TMT configuration to yield the same flux. The E-ELT aperture yields a
2.66x higher flux than our GMT aperture, and the required equivalent E-ELT
observation time is $\sim 60$% less than for the GMT. The ELTs will be
equipped with high-resolution spectrometers capable of R $=$ 100,000 in
visible and/or near-infrared wavelengths with estimated throughputs of 10%,
typical dark current values of 0.0002 e-/pix/s and 0.015 e-/pix/s for the
visible and NIR, respectively, and read noise of 3 e-/pix (Szentgyorgyi et
al., 2014, GMT) (Marconi et al., 2022, E-ELT) (Mawet et al., 2019, TMT). We
use these values for our E-ELT, TMT, and GMT configurations for this study,
and assume 100 detector pixels per resolution element. In particular, we note
that the term $T$ in Equation 1 is the 10% throughput multiplied by the
telluric absorption. One can approximate the effect of varying the total
efficiency by multiplying our detection significance calculation by the square
root of the ratio of a new efficiency and the 10% efficiency we use in our
calculations.

We briefly investigate the effect of varying the spectral resolution of our
simulated observations by calculating the number of transits required to
detect the O2 1.27 $\mu$m band for a PIE planetary atmosphere transiting
M2V–M8V host stars 5 pc away from Earth, and present the results in Figure 9.
We find that decreasing the spectral resolution to $R=50,000$ roughly doubles
the required number of transits, while increasing the resolution to $R\gtrsim
150,000$ only marginally decreases the required number of transits. We note
that while average line depth roughly doubles when increasing the spectral
resolution from $R=100,000$ to $R=300,000$, this only translates to a 35%
decrease in the number of transits required for a detection, which is
consistent with López-Morales et al. (2019). The dominant factor controlling
the number of transits required for a detection as a function of resolving
power is the average SNR of the resolution elements. Assuming the wavelength
range remains constant, an increase in spectral resolution requires an
increase in the number of pixels per observation, thus the signal is spread
over more pixels, and the average SNR of the individual resolution elements
decreases. We expect the other molecular bands to roughly follow this trend,
and we look forward to completing a more in-depth analysis in our future work.

Figure 9: Number of transits required to detect the O2 1.27 $\mu$m band as a
function of spectral resolution for a PIE atmospheres transiting M dwarf stars
5 pc away from Earth. The benefits of increasing the spectral resolution are
marginal for resolutions greater than $R\sim 150,000$.

####  2.2.3 Synthetic spectra

Our SPECTR pipeline generates synthetic observations by calculating the
incoming photon count from the planetary system for both in-transit and out-
of-transit observations, and the background photon count from atmospheric
molecular emission, airglow, scattered light, zodiacal light, dark current,
and read noise sources. We appropriately Doppler shift the planet spectrum
over the course of the transit, and multiply each exposure by the
corresponding high-resolution telluric transmittance spectrum obtained from
SkyCalc.

The stellar photon count rate in counts per unit time is given by:

$c_{s}=TF_{s}(\lambda)\frac{\lambda}{hc}d\lambda\pi\left(\frac{D}{2}\right)^{2}$
(1)

where $T$ is the total efficiency of the telescope/instrument/detector system
multiplied by the wavelength-dependent telluric absorption, $F_{s}(\lambda)$
is the stellar flux at the top of Earth’s atmosphere, $h$ is the Planck
constant, $c$ is the speed of light, $d\lambda$ is the spectral element width,
and $D$ is the diameter of the telescope. The stellar photon count rate is
scaled to simulate an in-transit observation by multiplying by one minus the
wavelength-dependent transit depth:

$c_{\mathrm{in-
transit}}=c_{s}\left(1-\left(\frac{R_{p}}{Rs}\right)^{2}\right)$ (2)

The atmospheric molecular emission, airglow, scattered light, zodiacal light
backgrounds are calculated using the ESO SkyCalc interface, and the dark
current and read noise are taken to be typical quoted values for ELT detectors
(see Section 2.2.2). The thermal contribution to photon count rate is the sum
of thermal emission from Earth’s atmosphere and the thermal emission of the
telescope and instrument, which we set as standard across both
telescope/instrument configurations: 273 K for the telescope mirror and 90 K
for the instrument/detector, typical standard values for a high-altitude
telescope/instrument setup. The noise sources are shown in Figure 8. To
minimize the contribution of readout noise to the total noise budget, we fix
the exposure time to equal the transit duration for each target. Our planets
have relatively short transit durations, and the planetary orbital velocities
relative to the host stars are sufficiently small such that any smearing
effects on the detector are negligible during the transit.

Finally, we simulate random Poisson noise in our spectra. We add the signal,
background, and dark current to obtain total signal for each resolution
element, and total shot noise follows by taking the square root of the total
signal. Total noise is then the total shot noise and readout noise added in
quadrature. We randomly draw values from Poisson distributions defined by the
total noise for each resolution element, and add this to our spectra to
simulate noise.

We assume all transit spectra in this study to have a system velocity shift of
$22$ km/s relative to Earth (including all sources of velocity shift) to shift
the target spectrum away from the telluric transmission lines. This is within
the optimal range for reducing blending between telluric and exoplanet O2
lines at the $0.76\mu m$ absorption feature (Rodler & López-Morales, 2014). A
non-optimal Doppler shift for the system may increase blending of the
planetary and telluric absorption lines by 50% or more (Rodler & López-
Morales, 2014).

####  2.2.4 A note on host star properties

Modeling how the properties of a host star affect exoplanet characterization
is complex and often unique to each individual system, thus we make
simplifications to generalize our study. In particular, we leave out the
effects of stellar rotation and star spots. We also do not model the effects
of stellar or planetary rotation, but we acknowledge that stellar rotation in
particular can make stellar line removal more difficult in transmission via
the Rossiter-McLaughlin effect (e.g. Brogi et al. (2016)).

While some studies have addressed the possibility of star spots interfering
with transit studies (Pont et al., 2007, 2008), Brogi et al. (2016) showed
that the appearance of star spots, at least in the scenario of the hot Jupiter
HD 189733 b, was negligible due to the small fraction of spectra which include
the spot. However, HD 189733 is a K dwarf, and is thus not as active as the
class of star we study in this work. In fact, other studies (e.g. Czesla et
al., 2009; Désert et al., 2011; Silva-Valio & Lanza, 2011; Bruno et al., 2016)
found that both occulted and non-occulted star spots can cause a wavelength-
dependent increase or decrease in transit depth, and that in some regions of
the spectrum, stellar features can actually be imprinted onto the transmission
spectrum (Bruno et al., 2020). This is especially problematic for cool and
low-mass stars (e.g. Rackham et al., 2017, 2018; Wakeford et al., 2018), which
are known to have H2O and CO in their spectra (Allard et al., 1997) that can
overlap with the spectral lines of identical molecules in the planetary
atmosphere. This molecular overlap effect is likely less problematic for O2,
CO2, CH4, which are weak or nonexistent in low-mass stellar spectra (Allard et
al., 1997). While this potential stellar contamination is a further
complication to consider when attempting to observe terrestrial exoplanet
atmospheres at high spectral resolution, the specific impact is not yet well
constrained, and we currently do not include these effects in our simulations.

###  2.3 Telluric line removal

To remove telluric lines from our spectra, we assume that there exists a
“perfect” corresponding out-of-transit observation at the same airmass for
each in-transit observation such that the ratio of the in-transit to out-of-
transit spectra leaves only the planetary transmission spectrum and noise. We
then construct an outlier mask to flag data in areas of extremely low signal-
to-noise (e.g. where the telluric transmittance falls to near zero) by
performing a running sigma clip with a width of 100 pixels. All values more
than 3$\sigma$ from the median were flagged and not considered in the cross-
correlation analysis. To remove low frequency variations associated with the
spectral continuum in the template and observed spectra, we apply a high-pass
filter with an arbitrarily chosen bin width of $100$ wavelength steps.

However, for real terrestrial exoplanet data, the removal of telluric
transmission lines using techniques initially developed for hot Jupiter
observations is less likely to be effective, although several other avenues
show promise. Although the radial velocity of the planetary system will
Doppler shift the planetary lines away from the telluric lines (López-Morales
et al., 2019), the high sensitivity required for these observations suggests
additional techniques may be needed to refine telluric subtraction. The
current state-of-the-art methods for hot Jupiter telluric transmission line
removal employ either a principal component analysis (PCA) algorithm (e.g.
Brogi et al., 2018), or a radiative transfer model (e.g. Allart et al., 2017)
to remove Earth’s transmission spectrum. However, for transiting habitable
zone terrestrial planets, PCA will be less effective because the planetary
velocity shifts during a transit are likely insufficient to effectively
isolate the planet spectrum from static telluric lines, resulting in the
subtraction of the planet spectrum itself. Therefore, blind analysis
techniques like PCA or PCA-like algorithms that do not use existing knowledge
of molecular absorption and the Earth’s atmospheric properties to subtract
tellurics will be less effective for observations of transiting Earth-like
planets.

More promising techniques relevant to terrestrial exoplanet observations
include applying radiative transfer tools like Molecfit (Smette et al., 2015)
or the online TAPAS service (Bertaux et al., 2014) to model the Earth’s
atmospheric transmission. Molecfit as a telluric line removal tool is
demonstrated in Allart et al. (2017), where it is used to fit and subtract the
telluric lines down to the noise level in high-resolution observations of
water lines in the atmosphere of HD 189733b. More recently, TAPAS has been
used on ESPRESSO data of HD 40307 to fit and remove telluric lines in
wavelength regions with significant telluric absorption traditionally excluded
by precision radial velocity surveys, improving the precision of the resulting
stellar spectrum by up to 25% (Ivanova et al., 2023). While using a radiative
transfer tool to fit the telluric lines of our synthetic data would be more
realistic, here we have chosen to present the ideal case with perfect
subtraction; however, we look forward to including a more realistic telluric
removal process in future iterations of this work.

###  2.4 Cross-correlation and detection significance

To estimate the detectability of the molecular absorption bands, we employ a
cross-correlation technique similar to that described in Brogi et al. (2016)
for its sensitivity to line locations, line shapes, and relative line depths,
and robustness against small perturbations to the radial velocity due to
stellar or planetary processes when applied to real data. With the cross-
correlation technique, it is not necessary to identify the precise wavelength
position of the band before co-adding the flux. We cross-correlate our
simulated transmission spectra with a model template of the molecular
absorption band based on the techniques tested and used by similar studies
(e.g. Snellen et al., 2010, 2013; Rodler & López-Morales, 2014; Brogi et al.,
2016; Serindag & Snellen, 2019; López-Morales et al., 2019; Spring et al.,
2022), and report detections as the significance at the expected signal
location in the resulting cross-correlation functions.

The cross-correlation technique works by comparing an observed spectrum
($f(n)$) to a range of Doppler-shifted template spectra ($g(n-s)$). We remove
the spectral features of molecules other than the one in question from the
template spectrum to reduce contamination, which, left in the spectrum, can
lead to inaccurate boosts in the detection significance. For each
observation–template comparison, the potential match is quantified with a
correlation coefficient, where a more positive coefficient indicates a better
match. The Doppler velocity of the template spectrum is allowed to vary over
$\pm 150$ km/s on an evenly spaced grid of 101 elements. For the grid of
velocity shifts, $s$, a cross-correlation function $C(s)$ is calculated from
the variance of the observed spectrum ($s_{f}^{2}$), the variance of the
template (model) spectrum ($s_{g}^{2}$), and the cross-covariance $R(s)$. To
remove low-frequency variations in the spectral continuum, it is crucial to
apply a high-pass filter to the model and observed spectra before the
variance, cross-covariance, cross-correlation function are calculated to
remove low frequency continuum variations. We adopt the notation of Brogi &
Line (2019) and define:

$s_{f}^{2}=\frac{1}{N}\sum_{n}f^{2}(n)$ (3)

$s_{g}^{2}=\frac{1}{N}\sum_{n}g^{2}(n-s)$ (4)

$R(s)=\frac{1}{N}\sum_{n}f(n)g(n-s)$ (5)

where n is the bin or pixel number, s is a bin or wavelength shift due to the
relative velocity, N is the total number of pixels, $f(n)$ is the synthetic
observed spectrum, and $g(n-s)$ is the template spectrum with a wavelength
shift of $s$. The cross-correlation function is then defined as

$C(s)=\frac{R(s)}{\sqrt{s_{f}^{2}s_{g}^{2}}}.$ (6)

This results in a cross-correlation function which peaks at a relative
velocity of zero if a signal can be detected through the noise. To estimate
the detection significance of the resulting cross-correlation function, we
follow the $\chi^{2}$ analysis of (Brogi et al., 2016): We define a non-
detection as zero planet signal and zero correlated noise, such that the
corresponding distribution of cross-correlation function values is a Gaussian
with a mean of zero. Thus, a non-detection is best fit with a flat line with
zero offset. To test for a signal, we compare our cross-correlation function
to a flat line with zero offset:

$\chi^{2}_{\mathrm{CCF}}=\sum_{s}\frac{(C(|s|<s_{0})-0)^{2}}{\sigma^{2}_{C(|s|>s_{0})}}$
(7)

In Equation 7, the sum is only calculated for velocity shifts $|s|<s_{0}$,
where $s_{0}$ is 5 km/s and chosen to reflect the width of a typical cross-
correlation peak in this study, to include only values that could correspond
to the planet signal, and exclude any aliasing patterns or spurious matches
with other spectral features. Similarly, $\sigma^{2}_{C(|s|>s_{0})}$ is the
variance excluding possible planet signal. We convert this
$\chi^{2}_{\mathrm{CCF}}$ value to a p-value using the cumulative distribution
function of a $\chi^{2}$ distribution, and finally convert the p-value to a
sigma interval using the inverse survival function of a normal distribution to
arrive at a measure of how much our cross-correlation function deviates from a
Gaussian distribution, $\sigma_{\mathrm{det}}$.

In this detection significance estimation scheme, a non-detection would be
consistent with a detection significance of $\sim 1$, which differs from a
traditional signal-to-noise ratio non-detection of $0$. To confirm this, we
calculated the detection significance of a “cross-correlation function”
consisting of randomly drawn values from a standard normal distribution. After
a million iterations of drawing random CCFs and estimating their detection
significances, the median detection significance was $1.04$. Therefore, we
expect non-detections in our results to have detection significances
consistent with 1.04, and this value is seen at a low number of observed
transits or for molecules that are challenging to detect in several of our
cases in e.g. Figure 12. Furthermore, there exists a fundamental upper limit
to the detection significance that is unique to each molecular band. Because
we estimate detection significance from a cross-correlation function, the
largest detection significance possible corresponds to the detection
significance of the cross-correlation of the template spectrum and a noiseless
observed spectrum. Therefore, instead of obeying a
$\sqrt{N_{\mathrm{transits}}}$ trend indefinitely as a traditional signal-to-
noise vs. number of transits observed curve would, we expect our detection
curves to “saturate” at the detection significance upper limit. Indeed, this
trend arises in strong, readily detectable molecular bands in Figure 12.

For N transits observed, we integrate N in-transit spectra with random noise
and N out-of-transit spectra with random noise, remove the telluric lines via
the procedure in Section 2.3, and calculate the cross-correlation detection
significance. We then repeat this process for 500 iterations. We report the
median detection significance with uncertainties corresponding to the standard
deviation of the detection significance iterations.

##  3 Results

Using our cross-correlation pipeline, we estimate the detectability of
molecular bands in terrestrial planet atmospheres transiting M2V through M8V
host star types. The atmosphere classes included in our study are self-
consistent PIE and ARE atmospheres which contain true biosignatures, and
abiotically-generated O2 via CO2 photolysis (Harman et al., 2018) and ocean-
loss/outgassing (Leung et al., 2020) atmospheres (see Table 2). Selected high-
resolution spectra of the most detectable molecular absorption bands are
presented in Figure 7, and the results for all molecular absorption bands are
available as an accompanying data product for this paper. We calculate
molecular detectability for these atmospheres for clear- and cloudy-sky
scenarios, for GMT, TMT, and E-ELT configurations, and at distances of 5 and
12 pc away from Earth. Although it is unlikely that we find a transiting
terrestrial planet at 5 pc, we include simulations of these systems at this
distance for direct comparison with previous results that have used 5 pc as a
point of reference.

A comparison of detectability for the most sensitive bands of specific
molecules in a given atmosphere is summarized in Figure 10, the effects of
clouds for the most sensitive bands are summarized in Figure 11. Detailed
results on molecular detectability as a function of stellar type, atmosphere
type, and number of transits are presented in Figure 12 for targets 5 pc away
observed using a TMT configuration and Figure 13 for targets 12 pc away using
an E-ELT configuration. Similarly, we present molecular detectability using an
E-ELT configuration for our simulated TRAPPIST-1 e clear-sky atmospheres in
Figure 14. Each plot shows the detection significance vs. number of observed
transits for a molecular absorption band in an atmosphere, orbiting M2V
through M8V host stars. Missing lines signify the molecule is not present in
the atmosphere, or that the atmosphere is only self-consistent with one host
star type, as in the case of the CO2 photolysis and ocean-loss/outgassing
atmospheres (M4V and M6V, respectively). Values for number of transits
required for 3- and 5-$\sigma_{\mathrm{det}}$ detections of molecular bands
are given in Tables 6 through 11. Although the collecting area of the GMT is
much smaller than the E-ELT and TMT, our simulations suggest that some
molecular targets, namely CO2, will still be feasible to observe with the GMT,
and we present these targets in Table 12 for systems 12 pc away. The addition
of clouds in our atmospheres increases the number of transits required by 2-4x
on average (Figure 11).

We apply the same noise properties to an 8.2 m diameter mirror telescope and
find that the number of transits required to achieve a similar detection
significance increases by at least an order of magnitude. The distance
dependence tracks with the inverse-square law: a target at 10 pc from Earth
requires 4 times the number of transits to obtain a 3-$\sigma_{\mathrm{det}}$
significance detection for a target 5 pc away from Earth.

The most detectable molecules are CH4 and CO2, with their most detectable
absorption features residing in the 1.6 $\mu$m region. We find that the
$1.27\mu$m, $0.9\mu$m, and $1.55\mu$m bands are the most detectable O2, H2O,
and CO features, respectively. O3 and C2H6 were not detectable in any case in
this study.

Figure 10: Total observed transits required for a 3-$\sigma_{\mathrm{det}}$
significance detection using an E-ELT sized telescope for selected bands in
all atmosphere classes orbiting a range of host stars at a distance of 12 pc
from the observer. A marker is missing either where the atmosphere does not
contain a molecule or $>300$ observed transits are required to detect the
molecular band. Figure 11: Ratios of the number of transits required to detect
the most sensitive molecular bands in our study for cloudy to clear sky pre-
industrial Earth-like atmospheres. Missing markers signify that cloudy spectra
require $>300$ transits to detect the molecular feature. Figure 12: Detection
significance as a function of total integration time for the most detectable
molecular bands in photochemically self-consistent pre-industrial Earth-like
(PIE) atmospheres, Archean Earth-like (ARE) atmospheres, CO2 photolysis
atmospheres (lightning on/off), and 10 bar O2 worlds (ocean-loss/outgassing)
(columns left to right) at distances of 5 pc from Earth observed with a TMT
sized telescope. The shaded regions are the 1-$\sigma_{\mathrm{det}}$
uncertainties on detection significance. The dark and light gray dotted lines
mark the 3- and 5-$\sigma_{\mathrm{det}}$ thresholds, respectively. Figure 13:
Detection significance as a function of total integration time for the most
detectable molecular bands in photochemically self-consistent pre-industrial
Earth-like (PIE) atmospheres, Archean Earth-like (ARE) atmospheres, CO2
photolysis atmospheres (lightning on/off), and 10 bar O2 worlds (ocean-
loss/outgassing) (columns left to right) at distances of 12 pc from Earth
observed with an E-ELT sized telescope. The shaded regions are the
1-$\sigma_{\mathrm{det}}$ uncertainties on detection significance. The dark
and light gray dotted lines mark the 3- and 5-$\sigma_{\mathrm{det}}$
thresholds, respectively. Figure 14: Detection significance as a function of
number of observed transits for four molecular bands in our simulated
TRAPPIST-1 e atmospheres observed with the E-ELT. The shaded regions are the
1-$\sigma_{\mathrm{det}}$ uncertainties on detection significance. The
horizontal dark and light gray dotted lines mark the 3- and
5-$\sigma_{\mathrm{det}}$ thresholds, respectively. The vertical dotted dark
and light gray lines, and corresponding labels to their right, denote the
precise integration time required for 3- and 5-$\sigma_{\mathrm{det}}$
detections. Note that the transit duration is 0.99 hr, so integration time
required is nearly the same as number of observed transits required.

###  3.1 Dependence on stellar type

In nearly all cases, the molecular detectability is dependent on the host star
type. We identify an interplay between physical and chemical effects of the
host star on the detectability of molecular bands in an exoplanet atmosphere.
In addition to photochemical effects in the exoplanet atmosphere, the
wavelength-dependent brightness and physical size of the stellar disk can
influence detectability.

####  3.1.1 Photochemistry

As discussed in Section 1, the SED of the host star affects both the abundance
and distribution of molecules in an exoplanet atmosphere through photochemical
processes. A higher molecular abundance absorbs more photons from the star,
creating deeper transits, and leading to stronger detectability. This effect
is clearly seen when comparing the CH4 profiles of self-consistent PIE to true
Earth (Figure 3). Variable UV activity of M dwarf stars allows for the buildup
of CH4, while CH4 in the true Earth atmosphere is photochemically destroyed
more rapidly (Segura et al., 2005).

####  3.1.2 Stellar brightness and size

Photochemical effects aside, the wavelength-dependent brightness of the host
star SED also impacts the detection significance of individual molecular
bands. The incident SED on the planet atmosphere can have wavelength-dependent
effects on detectability. For example, molecules that absorb in the region of
peak flux in the host star SED have the advantage of more available photons to
absorb when compared to an off-peak molecular absorption band, thus producing
deeper transits. This effect is illustrated in our results by comparing the
$0.76\mu m$ and $1.27\mu m$ bands of O2 in Figure 12. Since the earlier type M
dwarf host SEDs peak closer to the $0.76\mu m$ band, they have an
observational advantage over later-type stars: they require fewer transits to
achieve the same detection significance for this band (see Figure 12).
Conversely, later type M dwarf SEDs peak closer to the $1.27\mu m$ O2 band,
and O2 is equally or more detectable in this region for late-type hosts.
However, in many cases the flux of the host star at the observer due either to
intrinsic brightness or proximity (lower panel of Figure 2) can compete with
any wavelength-dependent effects due to the number of photons that are
incident on the detector. This effect can be seen in the 1.6 $\mu$m CH4 band
in Figure 13, where the larger, earlier-type hosts may require fewer transits
to achieve a 3- or 5-$\sigma_{\mathrm{det}}$ detection significance, even
though the later type host SEDs peak closer to this region.

Transit depth is also inversely proportional to the radius of the star
squared, and so larger stars can reduce the detectability of molecules in the
atmospheres of exoplanets transiting them, while smaller stars will enhance
the detectability of planetary atmospheric absorption. Deeper transits are
easier to distinguish from the surrounding noise, thus are more detectable.
This is an important factor working to increase the detectability of molecules
in planets transiting late-type hosts.

In summary, detectability is proportional to transit depth and stellar
luminosity, which both depend on the size of the host star. Additionally,
stellar luminosity depends on the effective temperature of the star.
Detectability is also highly variable for different wavelength regions in a
transit spectrum due to photochemical effects in the exoplanet atmosphere and
telluric effects in the Earth’s atmosphere.

###  3.2 Favorable targets for the ELTs

In terms of the fewest number of transits required to detect a molecule, the
best overall stellar host targets for characterizing terrestrial-sized
exoplanets for habitability and life are M dwarf stars earlier than M6V;
however, it is important to note that in terms of least total observing time
and opportunities to obtain transmission spectra, later-type M dwarf hosts
have the advantage (see Section 4.4 for a discussion). Since the transit
duration of a planet increases with earlier-type hosts, early-type systems
require fewer observed transits, while later-type hosts require less overall
observation time to achieve the same detection significance. The best targets
will depend on the priorities and limitations of the observer. Figure 10
provides a summary of the number of transits required to detect the most
accessible molecular bands for each self-consistent atmosphere transiting M
dwarf hosts in our study.

The CH4 $1.6\mu m$ and CO2 $1.59\mu m$ bands stand out as being the most
accessible bands for the ELTs (see Figures 12 and 13), requiring the fewest
number of observed transits (see also Figure 10). If a planet atmosphere
contains both species, such as our PIE and ARE atmospheres, a
$>$5-$\sigma_{\mathrm{det}}$ detection can be achieved for both CH4 and CO2 by
observing $\sim 20-30$ transits for all stellar host types at a distance of 12
pc for the E-ELT. If abundant O2 is present in an atmosphere, the band
requiring the fewest number of observed transits is the $1.27\mu m$ band for
all stellar host types (see Figure 12), and can be detected at a detection
significance of 5-$\sigma_{\mathrm{det}}$ for PIE atmospheres 12 pc away in
$>300$ transits for the GMT, 230 or more transits for the TMT, and 140–180
transits for the E-ELT. The $0.76\mu m$ A-band may be accessible with
significantly more observation time, requiring three or more times the number
of observed transits to detect at the same significance as the $1.27\mu m$
band. We note that this differs from the results of (e.g. Rodler & López-
Morales, 2014; López-Morales et al., 2019; Wunderlich et al., 2020), and
attribute the differences to their inclusion of red noise sources as 20% and
50% of the white noise in the visible and NIR, respectively. Here we choose
not to model red noise in this way to simulate an idealistic scenario. H2O may
also be accessible for planets around any M dwarf host, with the $0.9\mu$m
band as the most detectable feature in all cases, requiring 140–180 transits
for a 5-$\sigma_{\mathrm{det}}$ detection for a pre-industrial Earth-like
planet transiting an M6V 12 pc away observed with the E-ELT. CO may be
detectable for mid-type M dwarf hosts in the $1.55\mu m$ region if sufficient
CO is available in the atmosphere (i.e. only for the CO2 photolysis
atmospheres), and is accessible at a 3-$\sigma_{\mathrm{det}}$ level in 160
and 62 transits for scenarios with and without lightning, respectively, for
targets 12 pc away observed with the E-ELT. The next most detectable H2O and
CO bands may require an order of magnitude or more observed transits, and may
be inaccessible for targets at 12 pc. O3 and C2H6 are not detectable with our
simulated observational setups.

####  3.2.1 TRAPPIST-1 e with the ELTs

To better understand the detectability of molecular bands for a promising
known target, we simulate transit transmission observations of PIE and ARE
planets transiting TRAPPIST-1 at an orbital geometry analogous to the planet
TRAPPIST-1 e. We report the number of transits to 5-$\sigma_{\mathrm{det}}$
for the most detectable molecules in the atmosphere of TRAPPIST-1 e in Table
5, and include detectability estimates for an M4V at 12 pc in the table for
comparison. In Figure 14, we show that CO2 and CH4 are detectable in 34 and 33
observed transits, respectively, at a 5-$\sigma_{\mathrm{det}}$ significance
with the E-ELT for a PIE atmosphere, and 28 and 31 transits, respectively, for
an ARE atmosphere. Detecting O2 in a PIE atmosphere will be challenging,
requiring 200 observed transits for a 5-$\sigma_{\mathrm{det}}$ significance
detection, and 64 transits for a 3-$\sigma_{\mathrm{det}}$ significance
detection. H2O may be extremely challenging, requiring more than $300$
transits.

###  3.3 Molecular targets requiring other observation techniques

The most difficult molecules to detect with transit transmission spectroscopy
using ground-based ELTs are O3 for all atmospheres, CO for PIE and ARE
atmospheres, and C2H6 for ARE atmospheres. The only O3 bands with wavelength
accessibility for the ELTs are the Chappuis band and $3.2\mu m$ band, and the
features do not have sufficient high-resolution structure to use this
detection method. CO, which can be an indicator of abiotic generation of O2,
is most prominent in the CO2 photolysis atmosphere and may be possible to
detect in $<100$ transits in certain scenarios, but in all other cases is not
reliably detectable with the ELTs. These molecules may be better observed
using space-based telescopes, which can probe other molecular bands, or
ground-based reflected light spectroscopy, which can probe deeper into the
atmosphere.

##  4 Discussion

We have identified multiple molecular bands, including CO2, CH4, O2, H2O, and
CO, that could be detectable using the ELTs for terrestrial planet atmospheres
transiting a range of M dwarf host stars. The suite of molecules accessible to
ground-based observatories could aid in searching for environmental clues that
point to life, habitability, and biosignature false-positive mechanisms.
Additionally, the factors that affect the detectability of a molecule reach
beyond photochemistry and instrument sensitivity, and we note the importance
of the host star’s physical characteristics, such as size and wavelength-
dependent/overall brightness, for selecting ideal systems and molecular bands
to observe in the future. We present a recommended observing protocol for
discriminating possible terrestrial planet environments below in Section 4.1

Our results suggest that there are many molecular bands that may be accessible
with the ground-based ELTs, and these bands may be able to provide
environmental context for an O2 detection, or point to other outcomes of
atmospheric evolution. With our novel cross-correlation pipeline, we estimated
the detectability of O2, CH4, CO2, CO, H2O, O3, and C2H6 in the atmospheres of
pre-industrial Earth-like, Archean Earth-like, CO2 photolysis, and 10 bar
ocean-loss/outgassing atmospheres, all photochemically self-consistent with M2
through M8 host stars. We found that CO2 and CH4 are excellent targets to
search for in suspected habitable Earth-like atmospheres orbiting any type of
M dwarf host, taking just a few observed transits for initial discovery.
Additionally, O2 may be detectable for a pre-industrial Earth-like world at
both $0.76$ and $1.27\mu m$, with the $1.27\mu m$ band having a distinct
observational advantage. An atmosphere that builds up abiotic O2 from CO2
photolysis may not be able to accumulate detectable levels of O2 for the more
unrealistic case of no lightning in an atmosphere, therefore discrimination
from atmospheres influenced by life may not be a problem. However, if O2 is
detected, detection of both CO2 in 28 transits, and strong CO in 84 transits
would strongly suggest an abiotic buildup scenario from CO2 photolysis in the
case of a target 12 pc away observed with the E-ELT.

We compare our O2 results to previous literature (e.g. Rodler & López-Morales,
2014; López-Morales et al., 2019; Leung et al., 2020; Wunderlich et al.,
2020), and find that our detectability estimates for planets transiting late-
type hosts are in agreement, while our estimates for early-type M dwarf
planets differ. We overestimate the 1.27 um detectability, and underestimate
the 0.76 um detectability, for early type hosts. We agree on the late-type
detectability estimates for both the 0.76 and 1.27 O2 bands (López-Morales et
al., 2019). We hypothesize that this is due to a difference in how we added
noise to our simulations: we use ESO SkyCalc to generate realistic wavelength-
dependent background noise, while (Rodler & López-Morales, 2014) and (López-
Morales et al., 2019) approximate red noise as 20% and 50% of the white noise
in the visible and NIR, respectively, therefore differences in our
detectability estimates are expected. For planets with significant O2 buildup
due to H2O photolysis, Leung et al. (2020) predicts suppression in the 1.27
$\mu$m O2 band, which we do not see in our results. This is likely explained
by a difference in observational methods: we test transmission spectra only,
which are more sensitive to the upper layers of the atmosphere and less
susceptible to saturate, while Leung et al. (2020) tests reflected light
spectra, which probe to the surface and can saturate and suppress the 1.27
$\mu$m O2 feature. Wunderlich et al. (2020) investigate the detectability of a
similar suite of molecules in Earth-like TRAPPIST-1 e atmospheres with similar
abundances for CH4 and CO2 to our study using a simple SNR-estimation
approach, and they find CH4 and CO2 are detectable in 26 and 33 transits,
respectively, which are consistent with our estimates of 34 and 33 transits,
respectively. However, the O2 abundance for the Wunderlich et al. (2020) “wet
and alive” case is 35%, compared with our 21% O2 abundance. Therefore, we
cannot directly compare O2 detectability results, however our results suggest
that the $1.27\mu m$ O2 band can be detected in $200$ transits, a factor of
4.5 less than Wunderlich et al. (2020), and we attribute our enhanced O2
sensitivity to our more rigorous application of the cross-correlation
technique, and our “perfect” telluric line removal step.

Using the ground-based ELTs, we may have the capacity to search for two
biosignature pairs. O2, CH4, and CO2 are all detectable within the lifetime of
these telescopes at multiple absorption bands, and the O2–CH4 and CO2–CH4
biosignature pairs are potentially accessible. In the most optimistic scenario
of a transiting target 12 pc away, both biosignature pairs could be found in
as few as 140 observed transits with the E-ELT at 5-$\sigma_{\mathrm{det}}$
significance. The ELTs will require fewer transits to obtain both biosignature
pairs than than using JWST, which will require approximately 278 transits for
a 5-$\sigma$ detection of the O2 1.27 $\mu$m band on TRAPPIST-1 e at 12pc
using NIRSpec PRISM (Meadows et al., submitted). However, the GMT will require
$>$300 transits for the O2 1.27 $\mu$m, and in this case JWST NIRSpec PRISM
may be marginally better. However, the higher-resolution JWST/NIRSpec G140H
mode or the NIRISS spectrograph may enhance sensitive to O2 alone, albeit with
a truncated wavelength range, that may be compete with or exceed the
capabilities of ground-based observing.

Furthermore, we may be able to discriminate abiotic O2 buildup scenarios from
an Earth-like atmosphere influenced by biology. A detection of CO would
indicate that the abiotic O2 is due to CO2 photolysis by detecting CO. A non-
detection of CH4 could also reveal that detected O2 is abiotic from either CO2
photolysis or H2O photolysis in ocean-loss/outgassing scenarios.

The degree to which it is possible to constrain gas abundances in high-
resolution spectra depends on whether the chosen telluric line removal
technique also removes continuum information from the observed spectrum
(Birkby, 2018). See Section 2.3 for a discussion on telluric line removal
techniques. One consequence of applying a PCA-like routine is the loss of
continuum information, which can introduce degeneracies when comparing the
observed spectrum to a grid of atmosphere models to determine molecular
abundances. However, given simultaneous observations of multiple gases, it may
be possible to infer the relative abundances of the gases since they would
depend on the same pressure–temperature profile (de Kok et al., 2013). On the
other hand, knowledge of the Earth’s atmosphere during the observation can
inform radiative transfer tools which can model the Earth’s telluric
transmission, and remove it from an observed spectrum, preserving the
continuum and absolute line depths. Another proposed pathway for preserving
the continuum is to combine both low- and high-resolution observations of the
same target to break abundance degeneracies (De Kok et al., 2014). The power
of multi-resolution observations is demonstrated in Pino et al. (e.g. 2018),
where a more sophisticated atmosphere model for HD 189733 b was developed to
reconcile the sharp features seen in high-resolution observations with the
relatively flat spectrum seen at low-resolution. Abundance determination for
terrestrial planets, however, is still an open question to be explored in
future work.

###  4.1 Recommended observing protocol

To discriminate terrestrial atmosphere types and characterize planetary
environments, we recommend prioritizing specific molecular bands to maximize
the information gained in as little observation time as possible. Although
future designs for high-resolution spectrographs on the ELTs may allow for
simultaneous wavelength coverage in the visible (G-CLEF, 0.35-0.95 $\mu$m
Szentgyorgyi et al., 2014), NIR (MOHDIS, 0.95-2.4 $\mu$m Mawet et al., 2019),
or both (ANDES, 0.4-1.8 $\mu$m Marconi et al., 2022), some of the details of
these instruments have not yet been finalized; we therefore treat each
molecular band in this study individually in an effort to inform the
development of these instruments. In order from highest to lowest priority, we
recommend that observers target CO2, CH4, O2, H2O, and CO. In all cases, there
are no detectable O3 or C2H6 bands with the ELTs, and O3 will likely be best
detected using the lower-resolution methods of space-based missions. We
preface this discussion with the caveat that this study is limited to a
handful of planetary atmospheres, and there are many possible outcomes of
planetary evolution; detections and non-detections of molecules discussed
below should therefore be treated as additional pieces evidence that can
increase the probability of certain evolutionary outcomes. Below we synthesize
information about each molecule with its relative detectability to justify its
placement in our observing protocol priority list.

####  4.1.1 CO2

A detection of CO2 can help rule out larger H2-dominated atmospheres by
indicating that the atmosphere is primarily the result of planetary outgassing
rather than accretion during formation. CO2 is readily detectable for all of
the atmospheres in this work, requiring 20–30 observed transits for a target
12 pc away and 5–10 transits for a target 5 pc away to reach a
5-$\sigma_{\mathrm{det}}$ detection with the E-ELT.

Additionally, the presence of CO2 could help constrain planetary climate. CO2
is a greenhouse gas tightly coupled to geologic processes on our planet. A
planet with significant CO2 may have an active carbonate–silicate cycle
(Walker et al., 1981; Kasting et al., 1993; Kopparapu et al., 2013), which may
help buffer the planetary climate through geologic time (Walker et al., 1981).
CO2 could be a useful habitability indicator, however its presence alone is
not indicative of an inhabited planet.

####  4.1.2 CH4

A CH4 detection can help discriminate ocean-loss/outgassing and CO2 photolysis
scenarios from Earth-like atmospheres, and provide clues that can point to
habitability and life. If a CO2 detection is made with an accompanying CH4
detection, the resulting CH4/CO2 disequilibrium pair (Krissansen-Totton et
al., 2016) may be the most efficient way to search for biosignatures on both
PIE and ARE planets, requiring only $\sim 5$ and $\sim 20$ transits in the
most optimistic scenarios for transiting Earth-like planets 5 and 12 pc away,
respectively.

####  4.1.3 O2

Our simulations indicate that the NIR band requires fewer transits to detect
O2 than the A-band for most targets with O2 in their atmospheres by factors of
four or more. A non-detection of O2 could help rule out PIE and 10 bar O2
atmospheres, and, if CO2 is also present, could point to a CO2 photolysis
atmosphere or Archean Earth scenario, although a full suite of gases would be
needed to specify the atmosphere type with more certainty. Conversely, a
detection of O2 could provide evidence for a post-ocean-loss, ongoing ocean
outgassing, or PIE atmosphere. Furthermore, a simultaneous detection of CH4
would constitute the O2/CH4 disequilibrium biosignature in a PIE atmosphere.
Given that the Archean CH4/CO2 disequilibrium pair is also potentially
detectable (Meadows et al., submitted), this means that two potential
biosignature disequilibrium pairs, spanning early Earth to modern Earth
atmospheres, could be acquired in as few as $\sim 39$ transits in the most
optimistic scenario of an Earth-like planet transiting a star 5 pc away.

####  4.1.4 H2O

A detection of H2O can help strengthen the case for a PIE, ARE, or ocean
outgassing world, and, conversely, a non-detection can strengthen the case for
an ocean-loss or CO2 photolysis atmosphere. H2O detection typically requires a
comparable number of transits to the O2 NIR band to achieve a
3-$\sigma_{\mathrm{det}}$ detection, and a factor of 5–10 or more transits
than CO2 and CH4, and for that reason we recommend prioritizing H2O after CO2,
CH4, and O2. Although the 1-D atmosphere models in our study cannot self-
consistently account for 3D effects typically included in 3-D global
circulation models (GCMs), the H2O profiles are similar enough to first order
for reliable detectability estimates (Meadows et al., submitted).

The 0.9 um band is the most detectable H2O band despite the fact that it is
weaker than the other bands we tested. Although the H2O absorption at 1.1 and
1.3 $\mu$m may be stronger than the 0.9 $\mu$m band in the planet atmosphere,
they are also stronger in the telluric transmission spectrum. Compared with
the 0.9 $\mu$m region, these longer wavelength regions are dense with H2O and
overlapping CH4 lines that saturate to nearly 0% atmospheric transmission, and
effectively block our ability to observe these molecular bands, even at a
resolving power of $R=100,000$. Additionally, transmission spectroscopy is
limited in the regions of the atmosphere it can probe; namely, it is more
sensitive to upper regions of the atmosphere. To detect strongly absorbing
molecules that mainly reside near the surface of an Earth-like planet, like
H2O, the observer must also overcome this additional challenge, and different
techniques such as reflected light observations, which can probe deeper into
the atmosphere, may be better suited for detecting H2O.

Because an H2O detection would be crucial evidence for the presence of liquid
water on an exoplanet, we re-ran our analysis combining of all three H2O bands
in a single spectrum in an attempt to glean any information that may be
suppressed in the 1.1 and 1.3 $\mu$m bands. After examining the resulting
cross-correlation function, we find that H2O is marginally more detectable
with the combined bands, requiring 60 transits for a 3-$\sigma_{\mathrm{det}}$
detection, compared with 64 transits using only the 0.9 $\mu$m H2O band.
Additionally, the 1.1 and 1.3 $\mu$m bands are out of the nominal GMT G-CLEF
wavelength range (0.35-0.95 $\mu$m Szentgyorgyi et al., 2014), and the 0.9
$\mu$m band will be the most suitable target in that case.

####  4.1.5 CO

A CO detection can help identify a world with abiotic O2 buildup due to CO2
photolysis, requiring $42$ transits for a planet 5 pc away from Earth.
However, CO will not be present at significant levels on a planet with life,
and should only be prioritized for ruling out an O2 biosignature false
positive scenario.

####  4.1.6 O3 and C2H6

O3 and C2H6 are not accessible using ground-based high-resolution transit
transmission spectroscopy.

Table 5: Number of transits required for 5-$\sigma_{\mathrm{det}}$ detection
in modern Earth-like clear-sky atmospheres on TRAPPIST-1 e and around an M4V
at 12 pc with the ELTs

| TRAPPIST-1 e | M4V at 12 pc
---|---|---
| GMT | TMT | E-ELT | W20 E-ELT | GMT | TMT | E-ELT
CO2 (1.56 $\mu$m) | 240 | 62 | 34 | 33 | 60 | 33 | 25
CH4 (1.6 $\mu$m) | $>300$ | 56 | 33 | 26 | $>300$ | 14 | 11
H2O (0.9 $\mu$m) | $>300$ | $>300$ | $>300$ | 1224 | $>300$ | 180 | 130
O2 (1.27 $\mu$m) | $>300$ | 300 | 200 | 910 | $>300$ | 240 | 180

Note. — The W20 column presents ELT detectability results from Wunderlich et
al. (2020).

###  4.2 Effects of clouds

We investigate the effects of adding Earth-like clouds to our pre-industrial
Earth-like atmospheres. While in most cases we are still able to detect the
molecular features in a cloudy spectrum, we find that between two and four
times the transits required to detect molecules in the clear sky scenario are
required to achieve the same detection significance for cloudy sky scenarios.
This is an effect of the clouds suppressing some of the high-resolution
features by both raising the spectral continuum in altitude and reducing the
relative transit depths (Fauchez et al., 2019). The increase in the number of
transits required to detect a molecular feature is a direct consequence of the
level of suppression in the spectral features. Of particular note is the CH4
$1.6\mu$m band for the PIE atmosphere orbiting the M8V host, which requires
nearly 9 times the number of transits to detect this feature than for a clear-
sky scenario.

###  4.3 Observing protocol applied to TRAPPIST-1 e

To better understand how our observing protocol can be used in practice, we
apply it to PIE and ARE planets orbiting the M8V host star TRAPPIST-1,
simulating transit transmission spectroscopy of TRAPPIST-1 e. In Figure 14 and
Section 3.2.1, we show that CO2 and CH4 are potentially accessible for a
TRAPPIST-1 e planet with either a PIE or ARE atmosphere in 12 and 8 transits,
respectively, for a 3-$\sigma_{\mathrm{det}}$ detection, and 34 and 28
transits, respectively for a 5-$\sigma_{\mathrm{det}}$ detection. With
sufficient observation time, O2 may be accessible for PIE atmospheres, but
would likely require a multi-year observing strategy, even in the most ideal
conditions. Similarly, H2O would require significant observation time to
detect, and may be extremely challenging for the ELTs, even in the case of
water vapor transport due to synchronous rotation, as described above in
Section 4.1.4. Given the most favorable weather, telescope availability, and
seasonal observability, an observer could detect CO2 and CH4 in a relatively
short timescale, pointing to an Earth-like atmosphere and revealing a
biosignature pair.

####  4.3.1 Complementarities to space-based missions

The ELTs may be powerful tools for terrestrial exoplanet characterization, and
complementary to current and future space-based missions. JWST can be used to
characterize transiting rocky exoplanets for the biosignature gases CO2 and
CH4, but O2 will be more challenging (Lustig-Yaeger et al., 2019a;
Pidhorodetska et al., 2020; Krissansen-Totton et al., 2018a; Wunderlich et
al., 2019).

We compare our ELT detectability results to similar studies for an Earth-like
TRAPPIST-1 e observed with JWST, and find that JWST detectability results vary
depending on whether the atmospheric composition used was photochemically and
climatically self-consistent with the parent star. While it is not
straightforward to compare our results to current estimates for JWST
detectability due to the different specified atmospheres for these studies
(e.g. CO2 abundances that span 400 ppm to 10%) we can look at the broad range
of estimated detectability anticipated over a broad range of atmospheric gas
abundances. Studies with photochemically self-consistent TRAPPIST-1 e Earth-
like atmospheres (e.g. Lustig-Yaeger et al., 2019a; Krissansen-Totton et al.,
2018a; Pidhorodetska et al., 2020; Gialluca et al., 2021; Wunderlich et al.,
2019, 2020) use a range of CO2 abundances, and estimate that in atmospheres
with 400 ppm of CO2, it is detectable in $\sim 10$ transits (Lustig-Yaeger et
al., 2019a), while a more analogous atmosphere to our study with 10% CO2 may
be detectable in 5 transits (Wunderlich et al., 2020), roughly 7 times fewer
transits than for the ELTs. One consequence of studies that consider non-
photochemically self-consistent atmospheres is that even biological amounts of
CH4 may not reach high enough abundances to be readily detectable, requiring
$\sim 55$ or more transits to reach a detection (Fauchez et al., 2019;
Tremblay et al., 2020), compared with 34 transits for our self-consistent
E-ELT results. Therefore, while there are relatively few studies which provide
direct comparisons between JWST and ELT capabilities, it is apparent that the
ELTs will be powerful tools for followup or simultaneous observations to
confirm detections of CO2 or CH4, or to dedicate significant ELT resources to
search for molecules like O2 that may be out of reach for JWST.

Ground-based high-resolution spectroscopy is additionally more sensitive to
line cores higher in the atmosphere, and may be used to probe above the cloud
deck (Gandhi et al., 2020), while low-to-mid resolution space-based
instruments will be limited to relatively few spectral features for cloudy
atmospheres (Fauchez et al., 2019). Compared to low-resolution observations,
the cross-correlation technique is uniquely sensitive to both the relative
depths and spacing of individual absorption lines as well as the high-
resolution structure of the lines in the full molecular band, potentially
enhancing our ability to break degeneracies due to overlapping absorption
bands in low-resolution observations. Without a coronagraph designed for
terrestrial exoplanet characterization, JWST observations will be limited to
transmission spectroscopy only. Conversely, terrestrial exoplanets are being
considered in the design for the ELT coronagraphs, and the ELTs will have
access to a larger number of targets with the addition of reflected light
spectroscopy as a capability, a topic we leave for future work.

Looking ahead to the next generation of space-based instruments, the Astro2020
Decadal Survey recommended a 6 m class space mission capable of directly
imaging terrestrial-sized exoplanets as a top science priority (Decadal Survey
on Astronomy and Astrophysics 2021). Obtaining a direct spectrum of a
terrestrial exoplanet will require a coronagraph designed for high-contrast
imaging of habitable zone exoplanets, and the technology requirements set by
this goal can be implemented and tested with the current designs for ELT high-
contrast imaging capabilities in the near term. The lessons learned from the
ground will be crucial in the design process for a space-based direct imaging
mission, and may help inform future observing strategies.

###  4.4 Observing logistics and caveats

Although seasonal observability is a constraint for any observatory, space-
based telescopes are unencumbered by factors that limit ground-based observing
programs, including variation in the weather and the day-to-night cycle, and
may allow more opportunities to observe a transiting planet. However, Lovis et
al. (2017) estimate that despite the restrictions of ground-based observing,
it may be possible to detect O2 in the atmosphere of the non-transiting
Proxima Centauri b at 3.6$\sigma$ in 60 nights spread over three years,
assuming observations are obtained twice per orbit at approaching and receding
quadrature. For comparison with this non-transiting planet at 1.3 pc from
Earth using an 8.2 m telescope, we have calculated the feasibility of
detecting O2 from the ground for the transiting HZ planet, TRAPPIST-1 e at 12
pc with an ELT, where 64 transits are needed for a 3-$\sigma_{\mathrm{det}}$
detection. TRAPPIST-1 e undergoes transit once every 6.1 days, corresponding
to 60 transits per year; however, using the Exoplanet Archive transit
observability prediction tool with a maximum airmass of 3 predicts that only
$\sim 10$ of these (17%) are observable from the ground (Akeson et al., 2013).
If 35% of a semester’s observing time is also lost to weather444this statistic
was derived from Gemini-S (https://www.gemini.edu/observing/science-
operations-statistics), this means that only $\sim 6$ transits can be observed
per year. Thus a 3-$\sigma_{\mathrm{det}}$ detection of O2 in the PIE scenario
of TRAPPIST-1 e will take $\sim 10$ years, and $\sim 33$ years for a
5-$\sigma_{\mathrm{det}}$ detection. If this system was instead at a distance
of 5 pc, these estimates would improve to $\sim 2$ years and $\sim 6.5$ years
for 3- and 5-$\sigma_{\mathrm{det}}$ detections, respectively. This real-time
estimate potentially increases for habitable zone planets transiting early
type hosts, where orbital periods can reach 30 days or more, and the resulting
increased transit durations for early-type M dwarf planets may not fully
compensate for the decreased number of opportunities to observe a transit.
Consequently, while likely possible from the ground, high-resolution spectral
exploration of transiting HZ terrestrial exoplanet atmospheres for the known
planets at 12 pc will remain challenging, and will require a significant
investment in observing time over several years, and possibly decades. In
comparison, the closer, non-transiting planets may provide faster access to
adequate S/N for HZ exoplanet atmospheres, and we will explore these in a
subsequent paper.

##  5 Conclusion

The ELTs will be capable of detecting other molecules that can help place
detected O2 in its environmental context, including CO2, CH4, H2O, and CO. For
the terrestrial planets considered here, CO2 and CH4 stand out as the best
molecular targets for the ELTs. In particular, a CO2 detection can increase
the likelihood that a planet is terrestrial, and searching for CO2 may also
reveal biological levels of CH4 (if present), constituting a biosignature
pair. A non-detection of CH4 may point to an abiotic environment.
Additionally, detecting Earth-like levels of O2 in an atmosphere will be
challenging, but not impossible for the known transiting targets at 12 pc, and
more feasible for any transiting M dwarf planets found at closer distances.
However, since O2 is likely not accessible with JWST, ground-based searches
for O2 provide the best possible means of detecting this important molecule,
which can potentially identify an Earth-like planet with oxygenic
photosynthesis, and reveal a second biosignature pair (O2/CH4) if CH4 can also
be detected. This pair will help to rule out a scenario where abiotic O2 has
built up to detectable levels. A simultaneous non-detection of H2O or CH4 may
also help discriminate an abiotic O2 scenario from a planet with biogenic O2.
Furthermore, a detection of CO may help discriminate abiotic O2 buildup via
CO2 photolysis from O2 buildup via ocean loss or outgassing.

The ELTs have the potential to be used to reveal two biosignature pairs, and
detect the molecules necessary to discriminate biological and false positive
origins for O2. They will be even more powerful when techniques for telluric
line subtraction are improved, and if transiting targets are found closer than
the existing ones at 12 pc. In addition to testing and demonstrating
technology that will be crucial to the success of future space-based direct
imaging missions, their versatility and relatively near-term availability is
complementary to current space-based missions, and they will provide
opportunities to observe planetary targets and spectral features that are
otherwise inaccessible in the near term.

##  6 Acknowledgments

The authors thank our anonymous reviewers for their comments and suggestions
that substantially improved the clarity and robustness of the paper. We would
also like to thank Motohide Tamura (University of Tokyo) for reading an early
version of this paper and providing valuable comments that helped to
strengthen the paper. M.C. also thanks Michael Line (Arizona State University)
and Matteo Brogi (University of Warwick) for helpful discussions on the
capabilities and limitations of high-resolution spectroscopy. This work was
performed by the Virtual Planetary Laboratory Team, a member of the NASA Nexus
for Exoplanet System Science (NExSS), funded via NASA Astrobiology Program
Grant No. 80NSSC18K0829, and this work benefited from our participation in the
NExSS research coordination network. This work was also partly supported by
the Astrobiology Center, Japan. The simulations in this work were facilitated
though the use of advanced computational, storage, and networking
infrastructure provided by the Hyak supercomputer system at the University of
Washington.

##  Appendix A Appendix

Below we include detailed tables for the detectability estimates of each
atmosphere type in our study for the configurations of observing targets 5 pc
away with the TMT, and targets 12 pc away with the E-ELT. Tables 6 through 11
show exact values for the number of transits required to detect each molecule
at detection significances of 3-$\sigma_{\mathrm{det}}$ and
5-$\sigma_{\mathrm{det}}$. Table 12 shows the targets at 12 pc away that will
be accessible to the GMT. Ellipses are given for molecular bands not
detectable with the ELTs.

Table 6: Detectability results for clear sky pre-industrial Earths at 12 pc with an E-ELT sized telescope. Missing values indicate that the molecular band was not detectable at the missing significance level in less than 300 transits. Molecule | Band | M2V | M3V | M4V | M6V | M8V
---|---|---|---|---|---|---
|  | # transits to 3-$\sigma_{\mathrm{det}}$ (5-$\sigma_{\mathrm{det}}$) detection
O2 | 0.69$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
0.76$\mu m$ | 280 |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
1.27$\mu m$ | 40(160) | 68(240) | 58(180) | 37(140) | 38(150)
CH4 | 0.89$\mu m$ | 210 | 250 | 220 |  $\cdots$ |  $\cdots$
1.1$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
1.3$\mu m$ | 48(140) | 68(210) | 64(200) | 80(220) | 120
1.6$\mu m$ | 4(11) | 5(12) | 4(11) | 4(12) | 7(25)
CO2 | 1.59$\mu m$ | 7(20) | 8(24) | 7(25) | 9(25) | 11(32)
2.0$\mu m$ | 12(35) | 16(52) | 20(62) |  $\cdots$ |  $\cdots$
H2O | 0.9$\mu m$ | 64(180) | 54(160) | 41(130) | 42(140) |  $\cdots$
1.1$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
1.3$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
CO | 1.55$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
2.3$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
O3 | 0.63$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
0.65$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
3.2$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
Table 7: Detectability results for clear sky pre-industrial Earths at 5 pc with a TMT sized telescope. Missing values indicate that the molecular band was not detectable at the missing significance level in less than 300 transits. Molecule | Band | M2V | M3V | M4V | M6V | M8V
---|---|---|---|---|---|---
|  | # transits to 3-$\sigma_{\mathrm{det}}$ (5-$\sigma_{\mathrm{det}}$) detection
O2 | 0.69$\mu m$ | 180 | 280 | 230 |  $\cdots$ |  $\cdots$
0.76$\mu m$ | 68(260) | 88 | 78 | 290 |  $\cdots$
1.27$\mu m$ | 15(39) | 17(47) | 13(37) | 13(48) | 18(45)
CH4 | 0.89$\mu m$ | 49(150) | 62(180) | 50(160) | 96 |  $\cdots$
1.1$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
1.3$\mu m$ | 12(34) | 19(52) | 17(48) | 21(68) | 37(110)
1.6$\mu m$ | 1(3) | 2(4) | 2(3) | 1(4) | 2(4)
CO2 | 1.59$\mu m$ | 2(5) | 2(6) | 2(6) | 2(6) | 3(7)
2.0$\mu m$ | 3(7) | 3(9) | 3(9) |  $\cdots$ |  $\cdots$
H2O | 0.9$\mu m$ | 15(42) | 13(36) | 11(31) | 11(29) | 21(64)
1.1$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
1.3$\mu m$ | 120 | 130 | 140 | 58(180) | 140
CO | 1.55$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ | 170 |  $\cdots$
2.3$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
O3 | 0.63$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
0.65$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
3.2$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
Table 8: Detectability results for clear sky Archean Earths at 12 pc with an E-ELT sized telescope. Missing values indicate that the molecular band was not detectable at the missing significance level in less than 300 transits. Molecule | Band | M2V | M3V | M4V | M6V | M8V
---|---|---|---|---|---|---
|  | # transits to 3-$\sigma_{\mathrm{det}}$ (5-$\sigma_{\mathrm{det}}$) detection
O2 | 0.69$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
0.76$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
1.27$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
CH4 | 0.89$\mu m$ |  $\cdots$ |  $\cdots$ | 300 |  $\cdots$ |  $\cdots$
1.1$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
1.3$\mu m$ | 58(170) | 82(240) | 70(190) | 68(190) | 150
1.6$\mu m$ | 5(13) | 6(16) | 5(12) | 3(10) | 8(23)
CO2 | 1.59$\mu m$ | 6(18) | 7(23) | 7(21) | 8(22) | 9(29)
2.0$\mu m$ | 12(34) | 16(50) | 18(62) |  $\cdots$ |  $\cdots$
H2O | 0.9$\mu m$ |  $\cdots$ |  $\cdots$ | 66(210) | 64(240) |  $\cdots$
1.1$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
1.3$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
CO | 1.55$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
2.3$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ | 41
O3 | 0.63$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
0.65$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
3.2$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
C2H6 | 3.33$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
Table 9: Detectability results for clear sky Archean Earths at 5 pc with a TMT sized telescope. Missing values indicate that the molecular band was not detectable at the missing significance level in less than 300 transits. Molecule | Band | M2V | M3V | M4V | M6V | M8V
---|---|---|---|---|---|---
|  | # transits to 3-$\sigma_{\mathrm{det}}$ (5-$\sigma_{\mathrm{det}}$) detection
O2 | 0.69$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
0.76$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
1.27$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
CH4 | 0.89$\mu m$ | 78(220) | 130 | 74(220) | 160 |  $\cdots$
1.1$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
1.3$\mu m$ | 16(44) | 23(60) | 18(45) | 21(62) | 46(140)
1.6$\mu m$ | 2(4) | 2(5) | 2(4) | 1(3) | 2(5)
CO2 | 1.59$\mu m$ | 2(5) | 2(6) | 2(5) | 2(5) | 3(6)
2.0$\mu m$ | 3(7) | 3(8) | 3(9) | 36 |  $\cdots$
H2O | 0.9$\mu m$ | 24(72) | 30(90) | 17(48) | 17(45) | 31(98)
1.1$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
1.3$\mu m$ | 260 | 240 | 220 | 160 |  $\cdots$
CO | 1.55$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
2.3$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
O3 | 0.63$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
0.65$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
3.2$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
C2H6 | 3.33$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
Table 10: Detectability results for clear sky O2 false positive cases with an E-ELT sized telescope at 12 pc. Missing values indicate that the molecular band was not detectable at the missing significance level in less than 300 transits. Molecule | Band |  M4V host CO2 Phot. ltng. ON |  M4V host CO2 Phot. ltng. OFF |  M6V host 10 bar O2 ocean outgas |  M6V host 10 bar O2 ocean-loss
---|---|---|---|---|---
|  | # transits to 3-$\sigma_{\mathrm{det}}$ (5-$\sigma_{\mathrm{det}}$) detection
O2 | 0.69$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
0.76$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
1.27$\mu m$ |  $\cdots$ | 120 | 64(240) | 78(270)
CH4 | 0.89$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
1.1$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
1.3$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
1.6$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
CO2 | 1.59$\mu m$ | 7(25) | 8(28) | 14(40) | 11(34)
2.0$\mu m$ | 21(86) | 21(84) |  $\cdots$ |  $\cdots$
CO | 1.55$\mu m$ | 160 | 62(210) |  $\cdots$ |  $\cdots$
2.3$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
H2O | 0.9$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
1.1$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
1.3$\mu m$ |  $\cdots$ | 210 |  $\cdots$ |  $\cdots$
Table 11: Detectability results for clear sky O2 false positive cases with a TMT sized telescope at 5 pc. Missing values indicate that the molecular band was not detectable at the missing significance level in less than 300 transits. Molecule | Band |  M4V host CO2 Phot. ltng. ON |  M4V host CO2 Phot. ltng. OFF |  M6V host 10 bar O2 ocean outgas |  M6V host 10 bar O2 ocean-loss
---|---|---|---|---|---
|  | # transits to 3-$\sigma_{\mathrm{det}}$ (5-$\sigma_{\mathrm{det}}$) detection
O2 | 0.69$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
0.76$\mu m$ |  $\cdots$ | 170 |  $\cdots$ |  $\cdots$
1.27$\mu m$ |  $\cdots$ | 31(84) | 29(80) | 27(86)
CH4 | 0.89$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
1.1$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
1.3$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
1.6$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
CO2 | 1.59$\mu m$ | 2(6) | 2(6) | 3(10) | 3(8)
2.0$\mu m$ | 3(10) | 3(10) |  $\cdots$ |  $\cdots$
CO | 1.55$\mu m$ | 32(110) | 14(42) |  $\cdots$ |  $\cdots$
2.3$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
H2O | 0.9$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
1.1$\mu m$ |  $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$
1.3$\mu m$ | 78(270) | 52(170) |  $\cdots$ |  $\cdots$
Table 12: Detectability results for all favorable targets at 12 pc using a GMT sized telescope. Missing values indicate that the molecular band was not detectable at the missing significance level in less than 300 transits.  Host Star | Atmosphere | Molecule | Band [$\mu$m] | # transits to 3-$\sigma_{\mathrm{det}}$ | # transits to 5-$\sigma_{\mathrm{det}}$
---|---|---|---|---|---
M2V | ARE |  CO2 | 1.59 | 17 | 54
M2V | ARE |  H2O | 0.9 | 270 |  $\cdots$
M2V | PIE |  CH4 | 1.6 | 54 |  $\cdots$
M2V | PIE |  CO2 | 1.59 | 22 | 58
M2V | PIE |  H2O | 0.9 | 150 |  $\cdots$
M2V | PIE |  O2 | 1.27 | 120 |  $\cdots$
M3V | ARE |  CO2 | 1.59 | 16 | 62
M3V | PIE |  CH4 | 1.6 | 56 |  $\cdots$
M3V | PIE |  CO2 | 1.59 | 23 | 62
M3V | PIE |  H2O | 0.9 | 150 |  $\cdots$
M3V | PIE |  O2 | 1.27 | 190 |  $\cdots$
M4V | ARE |  CH4 | 1.6 | 54 |  $\cdots$
M4V | ARE |  CO2 | 1.59 | 16 | 60
M4V | ARE |  H2O | 0.9 | 200 |  $\cdots$
M4V |  CO2 phot. ltng ON |  CO2 | 1.59 | 18 | 60
M4V |  CO2 phot. ltng OFF |  CO2 | 1.59 | 22 | 70
M4V |  CO2 phot. ltng OFF |  CO | 1.55 | 150 |  $\cdots$
M4V | PIE |  CH4 | 1.6 | 37 |  $\cdots$
M4V | PIE |  CO2 | 1.59 | 23 | 60
M4V | PIE |  H2O | 0.9 | 130 |  $\cdots$
M4V | PIE |  O2 | 1.27 | 150 |  $\cdots$
M6V | ARE |  CH4 | 1.6 | 6 | 140
M6V | ARE |  CO2 | 1.59 | 21 | 62
M6V | ARE |  H2O | 0.9 | 270 |  $\cdots$
M6V | Ocean Loss |  CO2 | 1.59 | 30 | 96
M6V | Ocean Loss |  O2 | 1.27 | 150 |  $\cdots$
M6V | Ocean Outgassing |  CO2 | 1.59 | 29 | 140
M6V | Ocean Outgassing |  O2 | 1.27 | 110 |  $\cdots$
M6V | PIE |  CH4 | 1.6 | 8 |  $\cdots$
M6V | PIE |  CO2 | 1.59 | 20 | 60
M6V | PIE |  H2O | 0.9 | 180 |  $\cdots$
M6V | PIE |  O2 | 1.27 | 76 |  $\cdots$
M8V | ARE |  CH4 | 1.6 | 31 |  $\cdots$
M8V | ARE |  CO2 | 1.59 | 26 | 290
M8V | PIE |  CH4 | 1.6 | 10 |  $\cdots$
M8V | PIE |  CO2 | 1.59 | 30 | 240
M8V | PIE |  O2 | 1.27 | 58 |  $\cdots$

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