Characterizing Extrasolar Terrestrial Planets

Using Remote Sensing NASA

Astrobiology Institute

General Meeting 2003

March 11, 2003

David Crisp and Vikki Meadows

(VPL/JPL/Caltech)

2

Remote Sensing of Extrasolar Planet Environments

Radio

Infrared

VisibleUltra-Violet X-Ray

Gamma Ray

Once an extrasolar terrestrial planet has been detected and resolved from its parent star – All information about its

environment will arrive as photons

– This information can be decoded using remote sensing methods

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Environmental Properties NeededExamples of factors affecting planetary habitability

• Global Energy Balance

– Stellar Type - luminosity, spectrum

– Orbital distance, eccentricity, obliquity, rotation rate

• In general, a planet with a moderately rapid rotation rate and low obliquity in a near circular orbit will have a more stable climate

– Bolometric albedo – fraction of stellar flux absorbed

• Presence of an atmosphere

– Surface pressure

– Bulk atmospheric composition

– Trace gases/greenhouse gases

– Clouds/aerosols

• Surface properties

– Presence of liquid water on the surface

• Surface pressure > 10 mbar

• Surface temperature > 273 K

– Land surface cover

How do we retrieve this information from planetary spectra?

?

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Planetary Remote Sensing

O3

CH4

?

• A broad range of remote sensing techniques have been developed for studying Earth and other planets in our solar system – Photometry– Spectroscopy

• Extrasolar planets will pose special challenges– The planet will appear as an

unresolved point source• No direct constraints on size• No spatial details• Limited signal-to-noise

– No prospects for ground truth

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Spectral Photometry

Photometric observations of a planetary disk in a few colors

• Can provide useful constraints on planetary properties, but …

• Sometimes yield ambiguous results– Not all pale blue dots are water worlds

with habitable environments– Not all red planets airless deserts– What does yellow-white mean?

Broad-band observations – provide constraints on the planetary

energy balance, but– Need independent constraints on the

SIZE of the body to quantify albedo, emissivity, and effective temperature

Twins?

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Spectral Photometry

Photometric observations of an unresolved planetary disk are most useful when you know what you are looking for– Surface properties

• Chlorophyll red edge

– Atmospheric constituents• 0.76 m O2 A-band• 0.63 m H2O band• 9.6 m O3 band• 15m CO2 band

– Atmospheric and surface temperature• 15m CO2 band or other well-

mixed absorbing gas

CAUTION: Terrestrial planets are NOT black bodies!!

H2O

O2

O3

CO2

H2O

7

Time-Resolved Photometry

What can we learn from Time-resolved full-disk photometry (light curves)– Rotation periods– Variations in surface physical

properties• reflectance • thermal inertia

– Weather, climate and other time-variable phenomena• Large scale cloud systems• Regional/global dust storms

– Occultations could yield constraints on size• Large satellites• Background stars

(Lellouch et al. 2000)

Pluto Lightcurve

East Longitude

Flu

x (J

y)R

elat

ive

Alb

edo

100 200 3000

1.0

1.2

1.4

Central Meridian Longitude

Neptune Lightcurve

Solar

Thermal

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Light Curves for the Earth

Issues:Clouds on an Earth-like terrestrial planet• Will reduce the

amplitude of the rotational lightcurve

• Can mask the rotation period

Goode et al. 2001: Earthshine Project

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Spectral Remote Sensing

• Reflected Stellar Radiation– “Color” of the reflecting

surface (cloud top/ground)– Atmospheric pressure at the

reflecting surface – Column abundance of trace

gases– Clouds/aerosols

• Thermal Emission– Surface and atmospheric

thermal structure– Vertical distribution of

atmosphere temperatures and trace gases above the emitting surface• H2O, O3, CH4, N2O

H2OH2O

H2OH2O

O2

O3

H2ON2OCH4

CO2

O3

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Characterizing Environments of Extrasolar Terrestrial Planets

• Once an extrasolar terrestrial planet has been detected (as an unresolved point source)– The first step will be to search for candidate

biosignatures in its spectrum

– If any are found, a more quantitative description of the planetary environment will be needed to determine whether they can be produced abiotically, or require a biological origin

8 10 12 14 16Wavelength (m)

O3?

GOT LIFE?

CO2?

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Planetary Remote Sensing Using Reflected Stellar Radiation

• Optical properties of the reflecting surface (cloud deck/ground)– Albedo/emissivity

• Pressure of reflecting surface – Need a well-mixed gas with a known

spectrum is needed (e.g. O2 or CO2)

• Detection and quantification of column abundance of key trace gases - UV/VIS/near-IR– H2O, O2, O3, N2O, CH4, NH3 …

• Limitations– Little information about surface or

atmospheric temperatures

– Clouds preclude full-column or surface measurements

– Independent constraints on planet size essential, since albedos vary greatly

Cloud

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Planetary Remote Sensing Using Thermal IR Emission

Thermal IR spectra can yield information about– Temperature of emitting surface

• Window regions– Atmospheric thermal structure

• Well-mixed gas: CO2 15 m band– Vertical distribution of temperatures and

trace gases above emitting surface• H2O, O3, CH4, N2O

Limitations– Atmospheric temperature information is

essential for retrieving trace gas amounts • requires a well-mixed gas with a known

spectrum

• Limited information on constituents near the surface – surface/atmosphere temperature gradient needed

– Thermal IR provides limited constraints on planetary surface composition

H2ON2OCH4

CO2

O3H2O

ThermalRadiation

Cloud

T(z)

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Spectral Remote Sensing Algorithms

• Typical spectral remote sensing retrieval methods perform a constrained non-linear least squares fit of a function (synthetic radiance spectrum) to an observed spectrum.

• The fitting coefficients are the unknown atmospheric and surface properties that we are trying to retrieve – Surface albedo– Surface temperature and pressure– Atmospheric temperature profiles– Trace gas abundances and distributions– Cloud/aerosol composition, phase, optical depths

• Typical retrievals include the following steps– Initialize model with assumed surface and atmospheric

state - Assume a planet …– Calculate a synthetic spectrum, and compare it to the

observed spectrum– Perform a non-linear least square fit, solving for

atmospheric/surface state vector• Repeat process until the computed spectrum matches the

observations to within the convergence criteria

Alti

tud

e

H2 O

XH2O

aer

Alti

tud

e

T

Alti

tud

e

T(K)

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Extracting Information from Spectral Remote Sensing Observations

• State Vector: Atmospheric and surface properties that affect the observed spectrum

• Forward Model: Computes the reflected or emitted spectrum for assumed state vector

• Instrument Model: Convolve results with Instrument Response function (spectral resolution, SNR, etc.)

• Inverse Model: Modify atmospheric and surface properties to improve fit

– “Radiance Jacobians” (weighting functions)• Give sensitivity of the spectral radiance at

each wavelength, i(), to variations in temperatures or optical properties in layer z

i(), i(), …. i()

Xj(z) Xj+1(z) T(z)– A priori Covariance Matricies: Provide Bayesian

constraints on the solution

FitTPF Obs

Alti

tud

e

H2 O aer T

Alti

tude

Xj Xj+1

H2 O

T(K)

aer T(K)

K = i/xj

Alti

tude

K = i/T

Typical remote sensing retrieval algorithms include:

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Retrieval Algorithm Schematic

Atmospheric/ Surface State

Vector[Co2](z), PS,

T(z), Q(z), Ai(z),

Cj(z), a0()

Simulate Spectral Radiance

Forward Model:Radiance Spectra

Adjust The Atmospheric /Surface State

Inverse Model: Update State Vector

Final Atmospheric/surface StateXH2O, PS, T(z), A

i(z), Cj(z), A0()

ObservedSpectra

Convergence

Iter

atio

n Instrument Model

Tabulated OpticalProperties Of Gases,

Aerosols, Clouds

Aerosols, A1, A2, A3

H2O , 1, 2,, 3,…

CO2 , 1, 2,, 3…

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Resolving Atmospheric Vertical Structure: Weighting Functions

Pre

ssu

re (

hP

a)

100

1000 0

10

5

15

Alt

itu

de

(km

)

GOES18 Channels

AIRS/CRIS>1000 Channels

Wavelength (m)5.010.015.0 3.4

300

260

220

Brig

htn

ess

Tem

per

atur

e (K

)

Different spectral regions are sensitive to different levels of the surface -atmosphere system. The vertical resolution for temperature and trace gas retrievals increases with spectral resolution and signal-to-noise.

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Effects of Spectral Resolution on Retrieval Accuracy

The accuracy of Temperature and trace constituent retrievals increases as the spectral resolution and measurement signal-to-noise ratio increases.

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Special Challenges Posed by Extrasolar Terrestrial Planets

While reliable remote sensing retrieval methods exist for studying terrestrial planets in our solar system, extrasolar terrestrial planets pose unique challenges

– Spatial variations: • Most existing remote sensing retrieval methods

can be applied only to soundings acquired over a spatially homogeneous scene

• The first generation observations of terrestrial planets will provide only disk integrated results that mix viewing geometries, surface types, clear and cloudy scenes, etc.

– Additional (ad-hoc) constraints will be needed to define the spatial variability within each sounding

– Modest spectral resolution and signal / noise• resolving power < 100

• Signal-to-noise ratios <<100

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Unresolved Spatial Variability

Spatial variability over the disk of an unresolved planet introduces challenges– Signal comes primarily from the

brightest areas - not a true disk average

• Reflected Stellar Radiation:– Highest surface albedos at visual

and near infrared wavelengths (clouds, polar caps)

• Thermal wavelengths:– Warmest regions

– Observations of variability over diurnal and seasonal cycles will help to constrain spatial variations

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Unresolved Spatial Variability

Contributions from different parts of the disk traverse different atmospheric paths

• Thermal: Longer paths near limb• Solar: path increases with solar

incidence or emission zenith angles

• Absorption by gases and airborne particles is proportional to the product of the optical pathlength and the absorber amount:

= N(z) (,z) dz

– if the optical pathlength is unknown, we can’t retrieve unique estimates of the trace gas abundances.

– Ad-hoc constraints may be needed (especially if temperatures and absorber amounts vary)

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Impact on Retrieval Algorithms

• Unresolved spatial variability introduces two specific challenges for existing remote sensing retrieval algorithms

– Forward model: Even though only disk-averaged observations are available, synthetic radiances must be generated for an array of points on the planet’s disk to:• Resolve spatial variability in surface or

atmospheric properties• Accommodate variations is atmospheric

pathlengths over different parts of disk

– Inverse Method: Radiance Jacobians also vary spatially across the disk, and must be computed on a spatially resolved grid because

½ i(,,) / Xj(z,,) sin d d ≠

½ / Xj(z) i( ,,) sin d d

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Boot-Strap Method

• Assume a spatially-varying description of planetary properties– Couple the retrieval model to a climate model and other ad hoc

assumptions• Calculate spatially-resolved radiances and radiance Jacobians

– Integrate radiances over the disk and compare to observations– Integrate radiance Jacobians over disk to predict 0th order

correction to assumed atmospheric and surface properties• Derive new estimates of state structure variables

– Constrained by climate model or ad-hoc assumptions• Repeat until the retrieval converges

This approach is underconstrained and will result in a family of equally-likely solutions…..

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Implications for TPF and DarwinVIS Coronagraphs• Most trace gas information is at UV and

near-IR wavelengths– Currently ignored in TPF designs

• Time dependent photometric or spectroscopic data may be essential to detect/discriminate biosignatures

IR Nulling interferometers• Atmospheric temperatures must be

measured to quantify trace gas amounts from thermal radiances– A well mixed gas with a well known

spectrum is essential for this– The CO2 15 micron band is the best

candidate for terrestrial planets in our solar system (Venus/Earth/Mars)

• Moderate spectral resolution and high signal-to-noise are essential for characterizing environments

• The problem is still underconstrained