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 Visible Ultra- 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

3 Environmental Properties Needed Examples 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? ?

4 Planetary Remote Sensing O3O3 CH 4 ? 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

5 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?

6 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 O 2 A-band 0.63  m H 2 O band 9.6  m O 3 band 15  m CO 2 band –Atmospheric and surface temperature 15  m CO 2 band or other well- mixed absorbing gas CAUTION: Terrestrial planets are NOT black bodies!! H 2 O O2O2 O3O3 CO 2 H2OH2O

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 Flux (Jy) Relative Albedo Central Meridian Longitude Neptune Lightcurve Solar Thermal

8 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

9 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, O 3, CH 4, N 2 O H2O H2O H2OH2O H2OH2O H2OH2O O2O2 O3O3 H 2 O N 2 O CH 4 CO 2 O3O3

10 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 Wavelength (  m) O3?O3? GOT LIFE? CO 2 ?

11 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. O 2 or CO 2 ) Detection and quantification of column abundance of key trace gases - UV/VIS/near-IR –H 2 O, O 2, O 3, N 2 O, CH 4, NH 3 … 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

12 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: CO 2 15  m band –Vertical distribution of temperatures and trace gases above emitting surface H 2 O, O 3, CH 4, N 2 O 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 H 2 O N 2 O CH 4 CO 2 O3O3 H2OH2O Thermal Radiation Cloud T(z)

13 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 Altitud e H 2 O X H2O  aer Altitude  T T(K)

14 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( )  X j (z)  X j+1 (z)  T(z) –A priori Covariance Matricies: Provide Bayesian constraints on the solution Fit TPF Obs Altitude H 2 O  aer T Altitude XjXj X j+1 H 2 O T(K)  aer T(K) Altitude K =  i/  T Typical remote sensing retrieval algorithms include:

15 Retrieval Algorithm Schematic Atmospheric/ Surface State Vector [Co 2 ](z), P S, T(z), Q(z),  A i (z),  C j (z), a 0 ( ) Simulate Spectral Radiance Forward Model: Radiance Spectra Adjust The Atmospheric /Surface State Inverse Model: Update State Vector Final Atmospheric/surface State X H2O, P S, T(z),  A i (z),  C j (z), A 0 ( ) Observed Spectra Convergence Iteration Instrument Model Tabulated Optical Properties Of Gases, Aerosols, Clouds Aerosols, A 1, A 2, A 3 H 2 O,  1,  2,,  3,… CO 2,  1,  2,,  3 …

16 Resolving Atmospheric Vertical Structure: Weighting Functions Pressure (hPa) Altitude (km) GOES 18 Channels AIRS/CRIS >1000 Channels Wavelength (  m) Brightness Temperature (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.

17 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.

18 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

19 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

20 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)

21 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(, ,  ) /  X j (z, ,  ) sin  d  d  ≠ ½  /  X j (z)   i(, ,  ) sin  d  d     

22 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 0 th 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…..

23 Implications for TPF and Darwin VIS 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 CO 2 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