1. MULTI-LEVEL MODELING OF PLUTO'S SURFACE AND ATMOSPHERE Young, Buie, Young & Olkin

2. Multi-level modeling of Pluto's surface and atmosphere  Goal  Understand Pluto, make predictions for the New Horizons flyby, and position ourselves to capitalize on funding for Pluto research, which will be a hot topic for the next several years  Basic idea  We have developed a new SwRI model of Pluto’s seasons, which can be used in four papers with high impact.  Big Picture  This will establish the dominance of our group in seasonal modeling of Pluto and other icy bodies, and interpretation of their visible, thermal, and infrared data.

3. Technical background  Transport of Volatile N 2  Between surface and atmosphere  From summer to winter  Observables  Visible appearance  Temperatures  Infrared (IR) spectra  Time variation of these quantities  Context for New Horizons  Transport of Volatile N 2  Between surface and atmosphere  From summer to winter  Observables  Visible appearance  Temperatures  Infrared (IR) spectra  Time variation of these quantities  Context for New Horizons

4. Role of volatile transport on Pluto  About a meter of N 2 migrates each season.  Pressures vary by orders of magnitude over Pluto’s season. The history of atmospheric pressure depends critically on the location of the volatiles.  Volatile migration with albedo feedback probably explains why Pluto has extreme albedo contrasts.

5. VT3D example 1 of 3: Low thermal inertia, low N 2 inventory

6. VT3D example 2 of 3: Low thermal inertia, larger N 2 inventory

7. VT3D example 1 of 3: high thermal inertia, high N 2 inventory

8. Proposal background  2010/2011: began numeric framework for three- dimensional volatile transport (VT3D)  2012: submitted first model description paper  Young 2012: Volatile transport on inhomogeneous surfaces: I – Analytic expressions, with application to Pluto’s day  2012: submited first data-driven paper  Young 2012, Pluto’s Seasons: New Predictions for New Horizons  Mostly compares modeled pressured with stellar occultation constraints on time variability of Pluto’s atmosphere  This work used minor funds from NASA Planetary Atmospheres, NASA Planetary Astronomy, Spitzer, New Horizons.

9. Work proposed here for 2013/2014  Apr-Jun 2013: submit VT3D paper & release code  Jul-Sep 2013: compare with visible data  Oct-Dec 2013: compare with thermal data  Jan-Mar 2014: compare with infrared data

10. Task 1: Disseminate VT3D model  Model Strengths  Speed  Accuracy  Flexibility  Visualization  Wide applicability to Pluto, Triton, and Kuiper-belt objects (KBOs)  Publish Model  Release code  Model Strengths  Speed  Accuracy  Flexibility  Visualization  Wide applicability to Pluto, Triton, and Kuiper-belt objects (KBOs)  Publish Model  Release code

11. Task 2: Compare visible data & model  Model  Color & albedo depends on terrain, age  Variation with latitude & longitude  Constraining Observations  Albedo  Color  Model  Color & albedo depends on terrain, age  Variation with latitude & longitude  Constraining Observations  Albedo  Color

12. Task 3: Compare thermal data & model  Model  Emissivity depends on terrain, age, deposition rate  Variation with latitude & longitude  Observations  Thermal lightcurves vs. wavelength and year  Model  Emissivity depends on terrain, age, deposition rate  Variation with latitude & longitude  Observations  Thermal lightcurves vs. wavelength and year

13. Task 4: Compare infrared data & model  Model  Spectra depends on terrain, age, temperature, deposition rate  Variation with latitude & longitude  Observations  Spectra & band depth vs. longitude and year  Model  Spectra depends on terrain, age, temperature, deposition rate  Variation with latitude & longitude  Observations  Spectra & band depth vs. longitude and year

14. Timeline and milestones

15. Areas of risk and risk mitigation  Risk: Competition by other volatile transport models  C. Hansen: revival of 1996 Pluto seasonal models  F. Forget: volatile transport element in Global Climate Models  New work relating to the safety of New Horizons risks delaying the completion of our models, which opens us up to the danger of being outstripped by others  Mitigation  VT3D is faster and more flexible than competitors models.  Use a mix of junior and senior scientists. 1 hour of senior per 5.5 hour of junior personnel Four well-defined, focused projects  Quick publication of high impact papers  Make code publically available to dominate this field

16. Benefits to SwRI  Establish a competitive edge for SwRI Pluto scientists  Develop and demonstrate state-of-the-art models  Ensure high visibility  Capitalize on Pluto-related funding  A specific NASA Pluto Data Analysis Program is planned  Competition shows level of high importance and interest of Pluto volatile transport  Experience shows that spacecraft encounters generate interest (and funding). We expect NASA, NSF, and telescope allocation committees will be favorably disposed to proposals to study Pluto and related objects (Triton and Kuiper-belt objects).