Earth as an Exoplanet. II. Earths Time-variable Thermal Emission and Its Atmospheric Seasonality of Bioindicators

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Earth as an Exoplanet. II. Earth’s Time-variable Thermal Emission and Its Atmospheric

Seasonality of Bioindicators

Jean-Noël Mettler

1,2

, Sascha P. Quanz

, Ravit Helled

, Stephanie L. Olson

, and Edward W. Schwieterman

ETH Zurich, Institute for Particle Physics and Astrophysics, Wolfgang-Pauli-Strasse 27, CH-8093 Zurich, Switzerland; jmettler@phys.ethz.ch

Center for Theoretical Astrophysics & Cosmology, Institute for Computational Science, University of Zurich, CH-8057 Zurich, Switzerland

Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, IN 47907, USA

Department of Earth and Planetary Sciences, University of California, Riverside, CA 92521, USA

Received 2022 October 7; revised 2023 February 3; accepted 2023 February 21; published 2023 April 3

Abstract

We assess the dependence of Earth’s disk-integrated mid-infrared thermal emission spectrum on observation

geometries and investigate which and how spectral features are impacted by seasonality on Earth. We compiled an

exclusive data set containing 2690 disk-integrated thermal emission spectra for four different full-disk observing

geometries (North and South Pole-centered and Africa and Paciﬁc-centered equatorial views)over four consecutive

years. The spectra were derived from 2378 spectral channels in the wavelength range from 3.75–15.4 μm(nominal

resolution ≈1200)and were recorded by the Atmospheric Infrared Sounder on board the Aqua satellite. We

learned that there is signiﬁcant seasonal variability in Earth’s thermal emission spectrum, and the strength of

spectral features of bioindicators, such as N

O, CH

, and CO

depends strongly on both season and viewing

geometry. In addition, we found a strong spectral degeneracy with respect to the latter two indicating that multi-

epoch measurements and time-dependent signals may be required in order to fully characterize planetary

environments. Even for Earth and especially for equatorial views, the variations in ﬂux and strength of absorption

features in the disk-integrated data are small and typically 10%. Disentangling these variations from the noise in

future exoplanet observations will be a challenge. However, irrespectively of when the planet will be measured

(i.e., day or night or season)the results from mid-infrared observations will remain the same to the zeroth order,

which is an advantage over reﬂected light observations.

Uniﬁed Astronomy Thesaurus concepts: Astrobiology (74);Earth (planet)(439);Infrared spectroscopy (2285);

Biosignatures (2018);Exoplanet atmospheric variability (2020);Space vehicle instruments (1548)

1. Introduction

Spatially resolved ﬂyby data from the Pioneer 10/11

(Bender et al. 1974; Baker et al. 1975; Gehrels 1976; Ingersoll

et al. 1976; Kliore & Woiceshyn 1976)and Voyager 1/2

(Hanel et al. 1977; Kohlhase & Penzo 1977)missions in the

1970s initiated the exploration of key concepts for the

characterization of planetary bodies other than Earth in our

solar system. The photometric and spectroscopic observations,

ranging from the ultraviolet to infrared (IR), allowed planetary

scientists to infer unprecedented details for these worlds such

as planetary energy balance, surface, and atmospheric chemi-

cal, thermal, and composition properties including cloud and

aerosol formation and distribution (for an extensive review see

Robinson & Reinhard 2018). In 1993, Sagan et al. (1993)and

Drossart et al. (1993)constructed a control experiment by

applying remote sensing tools and techniques to search for life

on Earth by analyzing Galileo spacecraft (Johnson et al. 1992)

Earth-ﬂyby data. Their data indicated a habitable world with

water, carbon, and chemical energy. The data also showed signs

of biological activity that modulates surface and atmospheric

properties. Among these biosignatures, the coexistence of O

and

is a particularly strong indication of life (e.g., Love-

lock 1965; Krissansen-Totton et al. 2016,2018; Schwieterman

et al. 2018).

The impact of life on the geochemical environment and the

composition of the atmosphere throughout billions of years of

coevolution led to the suggestion that alien biospheres should

be detectable remotely via spectroscopy (Lovelock 1965;

Lovelock et al. 1975; Olson et al. 2018a). Today, the advances

made in instrumentation and observing techniques allow us to

peak and discover planets beyond our solar system, resulting in

a total of 5118 detected exoplanets.

Among these discoveries,

potentially habitable exoplanets have been found orbiting in the

so-called habitable zone (HZ)of their host stars, which sparked

interest in spectroscopic studies of exoplanet surfaces and

atmospheres for signs of life (e.g., Montet et al. 2015; Anglada-

Escudé et al. 2016; Dittmann et al. 2017; Gillon et al. 2017;

Gilbert et al. 2020). Thus, over the next decades, the long-run

goal of exoplanet science will be the characterization of the

atmospheres of temperate terrestrial exoplanets in order to

assess their habitability and search for indications of biological

activity, which requires the direct detection of their signals over

interstellar distances.

The ﬁrst generation of such terrestrial exoplanet detection

and characterization missions will not be capable of spatially

resolving the planets due to the large distances of at least

several parsecs at which the exoplanets typically will be

observed. Even with the most powerful telescopes conceived

today, including the recently launched JWST (Gardner et al.

2006), they will remain spatially unresolved point sources.

Moreover, the relatively low planet-to-star contrast ratio

signiﬁcantly limits the temporal sampling and the provided

The Astrophysical Journal, 946:82 (21pp), 2023 April 1 https://doi.org/10.3847/1538-4357/acbe3c

Original content from this work may be used under the terms

of the Creative Commons Attribution 4.0 licence. Any further

distribution of this work must maintain attribution to the author(s)and the title

of the work, journal citation and DOI.

http://exoplanet.eu (visited 2022 September 27).

spectral information will be averaged over the observable disk

and integration time. The latter may vary between several days

and weeks to build up an adequate signal-to-noise ratio to

detect biosignatures, depending on the target and mission

concept. For example, in the speciﬁc case of JWST, which

pushes the limits from detecting toward characterizing Jovian

to super-Earth exoplanets, the accumulation of transmission

spectra from hundreds of transits is required in order to reach a

signal-to-noise ratio high enough to potentially conﬁrm the

presence of biosignature pairs like O

and CH

or O

and N

(e.g., Krissansen-Totton et al. 2016; Fauchez et al. 2019;

Lustig-Yaeger et al. 2019; Wunderlich et al. 2019; Tremblay

et al. 2020). Hence, considering the mission’s lifetime and the

telescope time necessary for the detection of atmospheric

biosignatures, probably only a few attempts will be made on

speciﬁc targets. Therefore, JWST as well as other current

technologies are not yet capable of detecting and characterizing

the atmospheres of temperate, terrestrial exoplanets in a

statistically meaningful sample and the community has to wait

until space-based direct imaging is realized in future missions

like the Habitable Exoplanet Observatory (Gaudi et al. 2020),

Large Ultraviolet Optical Infrared Surveyor (Tan et al. 2019)or

Large Interferometer For Exoplanets (Quanz et al. 2018).

During the integration time of such direct imaging missions,

the spectral appearance and characteristics of a planet change as

it rotates around its spin axis and as spatial differences from

clear and cloudy regions, contributions from different surface

types as well as from different hemispheres evolve with time.

In addition, 20 yr of exoplanet discovery have revealed a vast

diversity of planets regarding their masses, sizes, and orbits

(e.g., Batalha 2014; Burke et al. 2015; Paradise et al. 2022)and

it is thought that this diversity also extends to their atmospheric

mass and composition, making the characterization of the

planetary environment even more difﬁcult. Speciﬁcally, the

interpretation of the spectrum is not unique and a plethora of

solutions exist to describe the planet’s surface and atmospheric

characteristics.

To achieve the fundamental goal of detecting signs of life on

planets beyond our solar system, we will need to be able to

interpret this space and time-averaged data. Ideally, an

exoplanet candidate with the potential of harboring life would

be observed by multiple observing techniques in both the

reﬂected and thermal emission spectrum in order to attempt to

fully characterize the planet’s nature. Yet, especially for

biosignatures, the potential for both false positives and false

negatives remains (e.g., Selsis 2002; Meadows 2006; Reinhard

et al. 2017; Catling et al. 2018; Krissansen-Totton et al. 2022).

One way to break this degeneracy and narrow down the set of

possible solutions is by adding information coming from time-

dependent signals such as atmospheric seasonality.

The phenomenon of planetary seasonality generally arises

for nonzero obliquity or orbital eccentricity planets, and the

extent of the atmospheric response is governed by stellar ﬂux

incident as well as planetary and atmospheric characteristics

(e.g., Kopparapu et al. 2013; Guendelman & Kaspi 2019).In

our solar system, seasonal variations were observed for the gas

giant planets such as Uranus, Saturn, and Jupiter (e.g., Nixon

et al. 2010; Fletcher et al. 2015; Shliakhetska & Vidmachenko

2019; Fletcher 2021)as well as for Mars, which is prone to the

most diverse seasons in the solar system, due to its 25°. 2 tilt of

the spin axis and eccentricity of 0.093 (e.g., Lefﬂer et al. 2019;

Trainer et al. 2019).

On Earth, the seasonal variation in atmospheric composition,

for example of carbon dioxide (CO

), is a well-documented and

mechanistically understood biologically modulated occurrence

(e.g., Keeling 1960)that is driven by the time-variable

insolation and the reacting biosphere. Net ﬂuxes of methane

and other trace biological products evolve seasonally, respond-

ing to temperature-induced changes in biological rates, gas

solubility, precipitation patterns, density stratiﬁcation, and

nutrient recycling (e.g., Khalil & Rasmussen 1983; Olson

et al. 2018b; Schwieterman 2018).

Since atmospheric seasonality arises naturally on Earth, it is

very likely to occur on other inhabited planets as well. Hence,

the search for seasonality as a biosignature on exoplanets is

particularly promising and has been proposed by Olson et al.

(2018b). Yet, the discussion of time-varying biosignatures has

remained qualitative (e.g., Tinetti et al. 2006a,2006b;

Meadows 2006,2008; Schwieterman et al. 2018)and the ﬁeld

of exoplanet research lacks a comprehensive understanding of

which spectral features are impacted by observable seasonality

on inhabited worlds and how these impacts would be

modulated by stellar, planetary, and biological circumstances.

Earth offers a unique opportunity to study this aspect, yet it

requires investigating our planet from a remote vantage point.

Although there are several methods to study Earth from afar

such as Earth-shine measurements or spacecraft ﬂybys (for a

recent review see, e.g., Robinson & Reinhard 2018, and

references therein), we chose a remote sensing approach, which

offers the extensive temporal, spatial, and spectral coverage

needed to investigate the effect of observing geometries on

disk-integrated thermal emission spectra and time-varying

signals. However, for Earth-orbiting spacecraft it is impossible

to view the full disk of Earth and the spatially resolved satellite

observations have to be stitched together to a disk-integrated

view (e.g., Tinetti et al. 2006a; Hearty et al. 2009; Gómez-Leal

et al. 2012).

In a previous paper (Mettler et al. 2020), we analyzed 15 yr

of thermal emission Earth observation data for ﬁve spatially

resolved locations. The data was collected by the Moderate

Imaging Spectroradiometer on board the Aqua satellite in the

wavelength range of 3.66–14.40 μm in 16 discrete thermal

channels. By constructing data sets with a long time baseline

spanning more than a decade and hence several orbital periods,

we investigated ﬂux levels and variations as a function of

wavelength range and surface type (i.e., climate zone and

surface thermal properties)and looked for periodic signals.

From the spatially resolved single-surface-type measurements,

we found that typically strong absorption bands from CO

(15 μm)and O

(9.65 μm)are signiﬁcantly less pronounced

and partially absent in data from the polar regions. This implies

that estimating correct abundance levels for these molecules

might not be representative of the bulk abundances in these

viewing geometries. Furthermore, it was shown that the time-

resolved thermal emission spectrum encodes information about

seasons/planetary obliquity, but the signiﬁcance depends on

the viewing geometry and spectral band considered. In this

paper, we expand our analyses from spatially resolved

locations to disk-integrated Earth views and present an

exclusive data set of 2690 disk-integrated mid-infrared (MIR)

thermal emission spectra (3.75–15.4 μm: R≈1200)derived

from remote sensing observations for four full-disk observing

geometries (North and South Pole, Africa and Paciﬁc-centered

equatorial view)over four consecutive years at a high temporal

The Astrophysical Journal, 946:82 (21pp), 2023 April 1 Mettler et al.

resolution (see Figure 1and Table 1). Using the data set, we

assess the dependency of Earth’s disk-integrated thermal

emission spectrum on observing geometries, phase angles,

and integration times much longer than Earth’s rotation period

as well as investigate which spectral features of habitability and

life are impacted by observable seasonality. In Section 2,we

describe the input data and our method to derive the disk-

integrated spectra, in Section 3we present and discuss our

results.

In Section 4we put our ﬁndings in context with previous

works on this matter and close with the conclusions in

Section 5.

2. Observations and Data Reduction

We leveraged the extensive temporal, spatial, and spectral

coverage of the Atmospheric Infrared Sounder (AIRS)(Chahine

et al. 2006)aboard the Earth-monitoring satellite Aqua. Every

day, AIRS obtains 2,916,000 Earth spectra in 2378 spectral

channels in the MIR wavelength range between 3.75 and

15.4 μm(nominal resolution: λ/δλ ≈1200). Due to the

satellite’s operation height of 705 km and due to its Sun-

synchronous, near-polar, and circular orbit, it revolves around

Earth in 99 minutes, providing a rich set of spectra consisting of

day, night, land, and ocean scenes at all latitudes. However, for

orbiting spacecraft like this, it is impossible to view the full disk

of Earth, which is why the observations have to be tailored to

show a spatially resolved, global map of Earth, which can then be

disk integrated in order to study Earth’s characteristics by means

of exoplanet characterization techniques (see Figure 2). For our

analysis, we have compiled an exclusive data set of such disk-

integrated Earth thermal emission spectra at a high temporal

resolution for four different observation geometries over four

consecutive years. In total, the data set comprises 2690 spectra.

In order to derive the spectra, we have used radiance

measurements from an AIRS IR level 1C product (V6.7)called

AIRICRAD (AIRS Science Team/Larrabee Strow

2019),

containing calibrated and geolocated radiances given

in physical units of W m

−2

μm

−1

(Manning et al. 2019).

These measured AIRS radiances were then mapped onto the

globe at high spatial resolution, and subsampled at spatial grid

points with Nside =128 (196,608 pixels)using the Hierarch-

ical Equal Area and Iso-Latitude Pixelization (HEALPIX)

approach (Gorski et al. 2005), which allowed us to easily

simulate how Earth would look from different perspectives.

The chosen Nside allowed us to sample the data with the best

possible resolution. While higher spatial resolution grids would

not portray the data correctly, lower Nside value grids resulted

in differences in the disk-integrated spectra compared to the full

resolution average due to the larger pixel sizes.

For our analysis, we deﬁned four speciﬁc observing

geometries as shown in Figure 1: North and South Pole,

Africa, and Paciﬁc-centered equatorial views. Since Earth-

monitoring instruments observe in the nadir viewing geometry,

we applied a simple empirical limb correction function adapted

from Hodges et al. (2000)to our disk views, where the limb-

adjusted radiance, R(θ), with the zenith angle θ, is calculated

from the radiance at nadir, R(0), as follows:

RR0,qlq=´() () ()



where λ(θ)is the MIR limb correction function as a function of

the satellite zenith angle given as

10.09lncos .

qq=+ ´() ( ())

This weighting function progressively down weights off-nadir

pixels with their cosine of satellite zenith angle in favor of near-

nadir pixels, fully taking account of the geometric effects.

Furthermore, due to the swath geometry of satellites, daily remote

sensing data contain gores, which are regions with no data points,

between orbit passes near the equator. These regions are ﬁlled

within 48 hr as the satellite continues scanning Earth while

orbiting it. However, in order to create snapshots of Earth’sfull

disk on a daily basis, one has to consider the missing data.

For the purpose of investigating the impact of missing

thermal emission data on the disk-integrated mean, we

analyzed 2000 randomly selected AIRS observation frames

Figure 1. The four observing geometries studied in this work. From left to right: North Pole (NP), South Pole (SP), Africa-centered (EqA), and Paciﬁc-centered

equatorial view (EqP). In section 3.3, we study integration times longer than the Earth’s rotation period. Due to the continuously evolving view of low latitude viewing

geometries as the planet rotates, we combine the two equatorial views EqA and EqP to a combined observing geometry, EqC.

Table 1

Data Set Overview

Year Temporal Resolution Total Days Day Night

2016 Every 3rd day 121 X X

2017 Every 3rd day 121 X X

2018 Daily 365 X L

2019 Every 3rd day 121 X X

Effective number of spectra #

NP 405 X X

SP 408 X X

EqA_day 765 X

EqA_night 311 X

EqP_day 391 X

EqP_night 410 X

https://cmr.earthdata.nasa.gov/search/concepts/C1675477037-GES_

DISC.html

The Astrophysical Journal, 946:82 (21pp), 2023 April 1 Mettler et al.

from which up to 12% of data was cut out and compared the

results of ﬁve different interpolation methods (linear, nearest,

and cubic python SciPy griddata, nearestND, and python

NumPy linear interpolator)to the not interpolated frames. The

results showed that the deviation from the original frames to the

not interpolated frames with 12% missing data was ≈0.2% and

less for the interpolated frames. Thus, the effect of missing

thermal emission data on the disk-integrated mean is negligible,

if the Earth-view disk contains gores of 12% or less missing

data. Hence, due to these results and the fact that AIRS daily

coverage is more than 95% of Earth’s surface, we have not

applied any interpolation methods and refrained from adding

artiﬁcial data to the scenes. The entire data set can be shared

upon request.

3. Results

In the following sections, we analyze the data for the four

viewing geometries presented in Figure 1. These viewing

geometries evolve throughout the year due to Earth’s nonzero

obliquity. Figure 3illustrates how the phases change for the

equatorial and pole-on viewing geometries. Whereas the former

view blends seasons and has a diurnal cycle, the polar view

shows one season but blends day and night. Furthermore, some

of the observing geometries discussed in Section 3.3 represent

idealized scenarios as they cannot be readily observed for

exoplanets by future observatories.

3.1. Seasonal Variability of Earth’s Thermal Emission

Spectrum for Different Viewing Angles

Here, we investigate the annual variability of Earth’s MIR

thermal emission spectrum due to obliquity as a function of

viewing geometry. For the analysis, the measured spectra are

considered to be snapshots, i.e., the integration time is a lot less

than Earth’s rotation period. The results are shown in Figure 4,

which displays the time-variable change of ﬂux over one full

year for both polar and equatorial viewing geometries. For each

speciﬁc viewing geometry, the annual average spectrum was

calculated from all disk-integrated measurements taken over 4

yr. The plots also show the minimum and maximum measured

spectrum within that time period as well as an average summer

and winter spectrum. To determine the latter, the months with

the highest/lowest ﬂux measurement per year at Earth’s

peaking wavelength of 10.4 μm were averaged over 4 yr to

get an accurate average spectrum for that season. For the

northern hemisphere this turned out to be January and July for

the winter and summer seasons, respectively, and vice versa for

the southern hemisphere. To facilitate the quantitative analysis

of Figure 4,wedeﬁne the following three atmospheric

windows: window 1: 10.2–11 μm, window 2: 8–9μm, and

window 3: 3.9–4.1 μm, which either lie in the IR window

(8–14 μm)or show a maximum absorption of up to ∼10%. The

results are summarized in Table 2.

The North Pole-centered view (NP), shown in Figure 1,

contains a large landmass fraction and latitudes spanning from

the arctic circle down to ∼20°N. Hence, it comprises three out

of the four main climate zones found on Earth, including the

arctic, temperate, and tropical zone as well as the subpolar and

subtropical transition zones in between them. While the former

three climate zones are dominated throughout the year by the

same air masses, the subpolar and subtropical transition zones

change with seasons as the air masses from neighboring zones

enter at various times of the year. This leads, in combination

with the surface characteristics of the continental mass, to a

larger expected variability. In the NP view, the arctic zone is

dominating the scene and contributes therefore the most to the

disk-integrated measured ﬂux, followed by the temperate

climate zone. The hottest visible climate zone, tropical, is

located close to the edge of the scene and its contribution to the

overall disk-integrated average is therefore affected by the

limb-darkening effect of 5%–10%. The equatorial and sub-

equatorial climate zone is not visible for that viewing geometry.

Figure 2. An illustration of the method: for every observation geometry the corresponding AIRS radiances were mapped onto the globe and subsampled using the

HEALPIX approach. To account for the fact that Earth-monitoring instruments are recording their data in the nadir viewing geometry, a limb-darkening

parameterization adapted from Hodges et al. (2000)was applied to the data before disk averaging the disk view. The whole process was repeated for all 2644 MIR

thermal emission spectral channels from the AIRICRAD level 1C satellite product, producing the disk-integrated spectra used in this work.

Figure 3. Annual change in the position of Earth on its orbit around the Sun.

The graphic in the top panel illustrates a view on Earth from a position in space

that is at an increased angle with respect to the ecliptic plane in order to show

the different phases of illumination during the solstices and equinoxes. The

bottom panel shows the phases for the two polar observing geometries at the

same time.

The Astrophysical Journal, 946:82 (21pp), 2023 April 1 Mettler et al.

As expected, NP shows the largest seasonal variability out of

the four viewing geometries. At longer wavelengths, the ﬂux

increases between an average winter and summer by 33% and

42% in the atmospheric windows 1 and 2, respectively. At

shorter wavelengths in the MIR (window 3), the relative

change in ﬂux increases by more than a factor of 2.

Like NP, the South Pole-centered view (SP)is dominated by

the arctic climate zone and the tropical zone lies close to the

edge, meaning that the ﬂux coming from this region is affected

by the limb-darkening effect. However, due to the inhomoge-

neous land distribution of our home planet, the pole-on view of

the southern hemisphere can be considered as an ocean-

dominated view and due to the high thermal inertia of oceans in

combination with the dominating arctic climate, the seasonal

variability is expected to be less than for the northern

hemisphere pole-on view. Comparing the seasonal ﬂux

variation of NP to SP shows that the variability is in the order

of a third less for longer wavelengths in windows 1 and 2 and a

factor of 2 less for shorter wavelengths in window 3. This

results in an annual effective temperature change at Earth’s

peaking wavelength of only 9 K for SP whereas for NP it turns

out to be 16 K. In terms of seasonal variability, SP shows a

similar variability in the two longer wavelength band

atmospheric windows 1 and 2 (11% and 13%, respectively)

than the Paciﬁc-centered, ocean-dominated, equatorial view

EqP view. However, at shorter wavelengths, e.g., window 3,

SP shows a 15% larger variability than EqP. The increased

variability at the shorter wavelengths in window 3 could be

assigned to a reﬂected light component and the difference in

landmass fraction and its surface characteristics contained in

these two observing geometries. The pole-on view of the

southern hemisphere is centered on the antarctic continent

Antarctica and its ice shelves.

The dominating climate zones for the Africa-centered

equatorial viewing geometry (EqA)are the equatorial and

tropical zones as well as the subequatorial and subtropical

transition zones linking them. The EqA view includes an

extended equatorial zone as both the northern part of South

America as well as central Africa lie in the view, yet, radiances

from the latter contribute more to the disk-integrated mean.

Fluxes coming from the temperate, and especially, the arctic

climate zones are progressively down-weighted by the limb-

darkening parameterization. As expected, the EqA view shows

the highest ﬂux readings of all the viewing geometries during

both day and night time in the summer season, reaching an

effective temperature of 288 K at Earth’s peaking wavelength.

Figure 4. A comparison of the disk-integrated thermal emission spectrum for the four different observing geometries (NP, SP, EqA, and EqP). The mean represents

the annual spectrum averaged over 4 yr of data. The shaded area corresponds to the standard deviation of all measurements for that particular observing geometry. The

average summer and winter were deﬁned as the months with the highest and lowest ﬂux levels at 10.4 μm, respectively. For the northern hemisphere this turned out to

be July and January and vice versa for the southern hemisphere. The dashed lines represent blackbody curves at three different temperatures: 288, 270, and 260 K.

The Astrophysical Journal, 946:82 (21pp), 2023 April 1 Mettler et al.

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EarthasanExoplanet.II.Earth’sTime-variableThermalEmissionandItsAtmosphericSeasonalityofBioindicatorsJean-NoëlMettler1,2,SaschaP.Quanz1,RavitHelled2,StephanieL.Olson3,andEdwardW.Schwieterman41ETHZurich,InstituteforParticlePhysicsandAstrophysics,Wolfgang-Pauli-Strasse27,CH-8093Zurich,Switzerland;jmett...

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Earth as an Exoplanet. II. Earths Time-variable Thermal Emission and Its Atmospheric Seasonality of Bioindicators

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