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Dr. David Crisp (Jet Propulsion Laboratory/California Institute of Technology Dr. Victoria Meadows (California Institu

Understanding the Remote-Sensing Signatures of Life in Disk-averaged Planetary Spectra: 1. Dr. David Crisp (Jet Propulsion Laboratory/California Institute of Technology Dr. Victoria Meadows (California Institute of Technology) . Rationale.

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Dr. David Crisp (Jet Propulsion Laboratory/California Institute of Technology Dr. Victoria Meadows (California Institu

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  1. Understanding the Remote-Sensing Signatures of Life in Disk-averaged Planetary Spectra: 1 Dr. David Crisp (Jet Propulsion Laboratory/California Institute of Technology Dr. Victoria Meadows (California Institute of Technology)

  2. Rationale • Understanding the origin and evolution of terrestrial planets, and their plausible diversity, will help inform the search and characterization of extrasolar terrestrial planets. • The emphasis is not only on understanding the likely planetary environments, but • Understanding their appearance to astronomical instrumentation • Understanding whether they are able to support life • As we search for habitable worlds, superEarths • Are likely to be the first extrasolar terrestrial planets that are characterized • represent a class of terrestrial planet that may also support life • And this could all happen in our lifetimes!!

  3. Characterizing Extrasolar Terrestrial Planets SIM – Launch 2011-? – Terrestrial Planet Masses and Orbits • TPF-I • MIR: 7.0-17 m • overall trace gas composition • greenhouse/metabolic byproducts • atmospheric and surface T TPF- Coronagraph Launch 2020-? Launch 2016-? TPF and Darwin Interferometers • TPF-C • Visible: 0.5-1.1 m • quantification of gases • surface composition

  4. NASA’s Life Finder Will search for chemical signatures of life at R~1000 The President’s Vision For Space Exploration

  5. Modeling Studies Supporting Extrasolar Planet Missions Spectral Simulation Information Retrieval Signal Extraction How well can we best detect Terrestrial planets? What kinds of information can we collect? What can we learn from this data?

  6. Remote Detection of Planetary Characteristics • We will not be able to “resolve” the extrasolar planet • Everything we learn about the planet will be obtained from disk-averaged data. • The signs of life must be a global phenomenon

  7. Detecting Distant Signs of Life Life can provide global-scale modification of: • A planet’s atmosphere • A planet’s surface • A planet’s appearance over time Astronomical Biosignatures • Photometric, spectral or temporal features indicative of life. • Must be global-scale to enable detection in a disk-averaged spectrum. • Must always be identified in the context of the planetary environment • e.g. Earth methane vs Titan methane • 0.72um, H2O/CH4/CO2 How will this information manifest itself in the planetary spectrum? H2O H2O

  8. Planetary Environmental Characteristics ? • Is it a terrestrial planet? (Mass, brightness, color) • Is it in the Habitable Zone? (global energy balance?) • Stellar Type - luminosity, spectrum • Orbit radius, eccentricity, obliquity, rotation rate • In general, moderate rotation rate, low obliquity and a near circular orbit stabilizes climate. • Bolometric albedo – fraction of stellar flux absorbed • Does it have an atmosphere? • Photometric variability (clouds, possibly surface) • Greenhouse gases: CO2,H2O vapor present? • UV shield (e.g. O3)? • Surface pressure • Clouds/aerosols • What are its surface properties? • Presence of liquid water on the surface • Surface pressure > 10 mbar, T> 273 K • Land surface cover • Interior: What is the geothermal energy budget?

  9. Energy Balance of a Planet r2S(1-A) r Planetary Energy balance is given by: σTe4 = S(1-A)/4 The effective radiating temperature Te denotes the average temperature that the planet’s effective emitting surface • The emitting surface is not necessarily the physical surface of the planet unless it has no atmosphere. 4r2T4 After Table 9.1, Bennet, Shostak, Jakosky, 2003 A planet’s greenhouse effect is at least as important in determining that planet’s surface temperature as is its distance from the star!

  10. Atmospheric Greenhouse Effects T4 T4 T4 T4 T4 - a a s a s The atmosphere is sufficiently transparent at solar wavelengths to allow some sunlight to penetrate to the surface • Ozone and water vapor are primary absorbers Fo (1-a) The atmosphere is (partially) opaque at thermal wavelengths, reducing escape of heat to space • Water vapor, CO2 and Ozone are the primary absorbers

  11. Carbonate-Silicate Cycle CO2 • Planetary processes can regulate the atmospheric greenhouse gas concentrations • Atmospheric CO2 dissolves in the ocean • Rainfall erodes silicate rocks and carries it to the oceans • The silicate minerals react with the dissolved CO2 to form carbonate minerals which fall to the ocean floor • The sea floor carbonates are eventually subducted • The subducted rocks melt to form CO2 rich magma, which is released to the atmosphere in volcanic eruptions.

  12. The CO2 Cycle as a Thermostat Rainfall Surface temperature Silicate weathering rate (-) Greenhouse effect Atmospheric CO2 • Carbonate mineral formation rate is sensitive to ocean temperature and silicate weathering is sensitive to surface temperature. • This allows the CO2 cycle to regulate our climate by setting up a negative feedback process • It takes 400,000 yrs after an increase in atmospheric CO2 for the carbonate-silicate cycle to return surface temperatures to their original value.

  13. Characterizing Planets by Remote-Sensing Net 60 Stratopause 50 Emission 40 Ozone Absorption 30 20 Tropopause 10 Absorption Water Vapor 0 200 250 300 In the visible, sunlight is reflected and scattered back to the observer or it is absorbed by materials on the planet’s surface and in its atmosphere. O3 • The planet emits this energy back to space as infrared radiation. As this radiation escapes to space, the atmosphere absorbs some of it producing • Spectral features • Greenhouse effects

  14. O3 O2 H2O H2O H2O H2O Simulating Planetary Spectra:Radiative Transfer Models Simulating the radiation field Resolve the spectral dependence of gases, cloud, aerosols, surface albedos and radiation sources. • Multiple Scattering Model • Gas Absorption • Line-By-Line model for IR vibration-rotation bands • Includes absorption by H2O, CO2, O3, N2O, CH4, and O2 • UV Absorption • Optical Properties of Clouds and Aerosols • Wavelength dependent albedos of the surface Atmospheric Composition Thermal Stellar

  15. Modeling Spectra of Alien Terrestrial Planets • 1. Input Database: • Stellar spectra • Gas absorption • Clouds/aerosol optical properties • R. Hasler • 2. Atmospheric structure and composition • Data or Climate/ Chemical Model. 3. Radiative Transfer Model: • 3. Surface Properties • Types/areal coverage • Optical properties

  16. The Effect of Surface Type in the Visible Crisp, Meadows

  17. The Earth From Space in the Infrared H2O N2O CH4 O3 CO2

  18. Effects of Clouds on Thermal Spectra O3 Ozone High Middle Low

  19. 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 GOT LIFE? CO2? O3? 14 16 8 10 12 Wavelength (m)

  20. 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

  21. 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 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

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