Amazonian Climate Modeling

Melinda Kahre NASA Ames Research Center CURRENT MARS + MODIFIED ORBIT PARAMETERS = AMAZONIAN MARS

CO2: Measured Surface Pressure Obliquity 11 50 10 40 CO CYCLE? 9 2 30

8 20 Pressure (mb)

7 Obliquity (Degrees) 10 WATER CYCLE? 6 0 60 120 180 240 300 360 0 Ls -10 -8 -6 -4 -2 0 Time (Myr) WATER: TES Column Water Vapor (pr-um) 90 Eccentricity 0.14 DUST CYCLE? 60 0.12 30 0.10 0 0.08 Latitude -30 0.06

Eccentricity NOTE: -60 0.04

-90 0.02 • Modeling the Amazonian climate is 0 90 180 270 360 L s 0.00 disnctly diﬀerent than modeling DUST: -10 -8 -6 -4 -2 0 TES IR Dust Optical Depth: MY 26 Time (Myr) 90 Noachian/Early Hesperian climate. 60 Longitude of Perihelion • 30 Early Mars GCMs generally include 300 0 physics of massive atmospheres, 200 Latitude -30 warm water cycle physics, and 100

-60 Perihelion (Degree) 0 simpliﬁed cloud microphysics. -6.0 -5.9 -5.8 -5.7 -5.6 -5.5 -90 Time (Myr) 0 90 180 270 360 L s Laskar et al. (2004) CURRENT MARS:

CO2 CYCLE

Measured Surface Pressure 11

VL 1 10 VL 2 MSL 9

8 Pressure (mb)

6 0 60 120 180 240 300 360 Ls CO2 Condensaon & Sublimaon:

CO2 Cycle Tuning: 1. Surface: • Governed by energy balance 1. Change parameters to reproduce VL 1 and 2. Atmosphere: VL 2 observed surface pressures: • Usually a simple scheme—no clouds! • CO2 ice albedo, emissivity • CO2 condenses when T

0 Latitude -30

-60

-90 0 90 180 270 360 Surface Water Ice Reservoirs: Ls Importance of Water Ice Clouds: 1. “Permanent” Source: North Cap • Deﬁned by observed T.I. or Tg 1. Radiave Inﬂuences • Simulaons show cap is not in equilibrium • Tropical clouds warm alo à Highest latudes gain annually • Feedbacks with circulaon à Lower latudes lose annually

à Hadley cell intensiﬁes 2. Seasonal/Diurnal Surface Thermal Inertia 180 800 2. Microphysical Processes • Seasonal CO2 cap -150 150

-120 120 • Clouds limit vercal extent of H2O • Nighme frosts 600 • N-S transport inﬂuenced: Clancy eﬀect -90 90 90 400 3. Regolith TI (MKS units) • 80 Dust needed for nucleaon! -60 60 • Oen not included 70 200 -30 30 3. GCMs now simulate H2O cycle with R.A. Clouds 0 60 Navarro et al., 2014; Haberle et al. (2017); Lee et al. (2016) 0 CURRENT MARS: DUST CYCLE (Part I)

TES and THEMIS Global Average Opacity

1.2 THEMIS MY 28

1.0

0.8

0.6

9 micron Opacity 0.4 OBSERVATIONS

0.2 Dust Liing Parameterizaons:

0.0 1. Wind Stress (Saltaon) 0 100 200 300 Ls • Liing occurs when a threshold wind stress is exceeded Global Mean Dust Opacity 1.2 • Provides localized sources and storms

1.0 2. Dust Devils • Liing depends on sensible heat ﬂux, PBL depth 0.8 • Provides background, low-level source GCM 0.6 GCMs predict low levels of dust during aphelion and Opacity increased dusness during perihelion 0.4

0.2

0.0 0 100 200 300 Ls CURRENT MARS: DUST CYCLE (Part I)

TES and THEMIS Global Average Opacity

1.2 TES MY 24 TES MY 25 TES MY 26 1.0 TES MY 27 THEMIS MY 25 THEMIS MY 26 THEMIS MY 27 0.8 THEMIS MY 28 THEMIS MY 29 THEMIS MY 30 0.6

9 micron Opacity 0.4 OBSERVATIONS

0.2 Dust Liing Parameterizaons:

0.0 1. Wind Stress (Saltaon) 0 100 200 300 Ls • Liing occurs when a threshold wind stress is exceeded Global Mean Dust Opacity 1.2 • Provides localized sources and storms

0.2

0.0 0 100 200 300 Ls CURRENT MARS: DUST CYCLE (Part I)

TES and THEMIS Global Average Opacity

1.2 TES MY 24 TES MY 25 TES MY 26 1.0 TES MY 27 THEMIS MY 25 THEMIS MY 26 THEMIS MY 27 0.8 THEMIS MY 28 THEMIS MY 29 THEMIS MY 30 0.6

9 micron Opacity 0.4 OBSERVATIONS

0.2 Dust Liing Parameterizaons:

0.0 1. Wind Stress (Saltaon) 0 100 200 300 Ls • Liing occurs when a threshold wind stress is exceeded Global Mean Dust Opacity 1.2 • Provides localized sources and storms

0.0 Kahre et al. (2005); Newman et al. (2015); Mulholland et al (2013) 0 100 200 300 • Coupling to the water cycle through clouds? Ls Kahre et al. (2011); Jha and Kahre (2017) CURRENT MARS: DUST CYCLE (Part II)

TES IR Dust Optical Depth: MY 26 90

0 Latitude -30

-60 Prescribed (or Semi-Prescribed) Dust

-90 0 90 180 270 360 1. Use observed dust maps to constrain model L s Smith (2008) • Vercal distribuon either prescribed or self- consistently determined Montabone et al. (2015) Is τ < τ ? GCM OBS 2. The best way to control the dust cycle for current Mars simulaons Yes No • Most realisc temperatures/circulaons Madeleine et al. (2011) 3. Not as useful for past climate simulaons Add τ = 0.1 / Sol to INSTEAD: Do Nothing Boom Layer i. Use simpliﬁed prescripons ii. Interacvely predicted liing CURRENT MARS + MODIFIED ORBIT PARAMETERS = AMAZONIAN MARS

CO2: Measured Surface Pressure Obliquity 11 50

10 40

9 30 CO2 CYCLE? 8 20 Pressure (mb)

7 Obliquity (Degrees) 10 6 0 60 120 180 240 300 360 0 Ls -10 -8 -6 -4 -2 0 Time (Myr) WATER: TES Column Water Vapor (pr-um) 90 Eccentricity 0.14 60 0.12 30 0.10 0 0.08 Latitude WATER CYCLE? -30 0.06 Eccentricity -60 0.04

-90 0.02 0 90 180 270 360 Ls 0.00 DUST: -10 -8 -6 -4 -2 0 TES IR Dust Optical Depth: MY 26 Time (Myr) 90

60 Longitude of Perihelion 30 300 0 200 Latitude -30 100 DUST CYCLE?

-60 Perihelion (Degree) 0 -6.0 -5.9 -5.8 -5.7 -5.6 -5.5 -90 Time (Myr) 0 90 180 270 360 L s Laskar et al. (2004) MODIFYING ORBIT PARAMETERS: STRATEGY

GCMs do not run fast enough to explicitly simulate changing orbit parameters.

Obliquity 50

Instead: 40

• Choose combinaons of: 20

• Obliquity (Degrees) obliquity, eccentricity, longitude of perihelion 10

0 • Goal: map out trends and branch points in behaviors -10 -8 -6 -4 -2 0 Time (Myr) Eccentricity 0.14 Consideraons: 0.12 0.10

0.08

• Surface reservoirs and total inventories 0.06 Eccentricity 1. CO : assume enre inventory starts in the atmosphere 0.04 2 0.02 à what about buried CO in south? 0.00 2 -10 -8 -6 -4 -2 0 2. Dust: inﬁnite availability (?) Time (Myr) 3. Water: surface source regions depend on study goals Longitude of Perihelion 300

200

100

Perihelion (Degree) 0 -6.0 -5.9 -5.8 -5.7 -5.6 -5.5 Time (Myr) Laskar et al. (2004) AMAZONIAN MARS:

CO2 CYCLE

Ward (1974)

Obliquity variaons have important consequences for the CO2 cycle As obliquity increases: 1. Annual mean insolaon at the poles increase • For obliquies > 54°: • Poles receive more insolaon than equator 2. Seasonal variaons are more extreme

• Seasonal CO2 ice caps more massive & latudinal extensive • Global average surface pressure decreases Mischna et al. (2003); Haberle et al. (2003); Newman et al. (2005) When obliquity is low (< ~20°):

• Permanent CO2 caps form and atmosphere collapses • Equilibrated atmospheric mass could be quite low (~30 Pa)

Newman et al. (2005) Newman et al. (2005) AMAZONIAN MARS: WATER CYCLE

Obliquity variaons largely control where water ice is stable on the surface

Low obliquity (<30°): • Water ice is stable at the pole à N or S likely depends on Ls of perihelion • Atmosphere is relavely dry (low polar insolaon) Mischna and Richardson (2003); Forget et al. (2006); Levrard et al. (2007) Moderate obliquity (30°-40°): • Water becomes stable in the mid-latudes • Atmosphere is considerably weer (higher polar insolaon) Madeleine et al. (2009/2012)

High obliquity (>40°): Madeleine et al. (2014) • Water ice destabilized at the poles • Water ice becomes stable at low latudes Mischna and Richardson (2003); Forget et al. (2006); Levrard et al. (2007) Radiave effects of water ice clouds have significant effects! • Warm surface; enhance circulaon, cloudiness and snowfall Madeleine et al. (2014); Haberle et al. (2012); Kahre et al (2015) Forget et al. (2006) AMAZONIAN MARS: DUST CYCLE

Increasing obliquity signiﬁcantly enhances predicted dust liing & dust loading

Increasing obliquity: • Enhances equator-to-pole temperature gradient • Drives an enhanced Hadley cell • Stronger return flow increases surface stresses • Increases dust liing Haberle et al. (2003) • Posive radiave-dynamic feedbacks à increases Hadley cell strength more à even more dust liing Newman et al. (2005) Newman et al. (2005) Notes and caveats: • Infinite surface dust reservoirs? • Dust-only simulaons: what would coupling to water cycle (cloud formaon) do? • Physics of liing (availability of sand, resoluon effects)? AMAZONIAN MARS: MODELING POLAR LAYER DEPOSITS

There have been a relavely limited number of GCM studies that directly relate to the PLDs

1. Newman et al., 2005: Polar deposion rates 2. Levrard et al., 2007: Net ice deposion/ of dust under different orbital configuraons sublimaon under different orbital • Interacve dust cycle simulaons configuraons • Dust deposion increases with obliquity • Water cycle model of Montmessin et al. (2004) • South pole gains more dust than north when • Loss of ice from pole increases with obliquity obliquity > ~30° (obliquity > ~30°) à Did not include a water cycle • Deposion of ice on pole increases with decreasing obliquity (obliquity < ~30°) à Did not include dust deposion/removal

Newman et al. (2005)

Levrard et al. (2007) AMAZONIAN MARS: MODELING POLAR LAYER DEPOSITS

There have been a relavely limited number of GCM studies that directly relate to the PLDs

Model that used GCMs (in part) for behaviors/parameterizaons: Hvidberg et al (2012): modeled PLD layering • Parameterized dust deposion based on NH summer equator to pole temperature gradient à proxy for obliquity à based on behavior from Haberle et al. (2003) and Newman et al. (2005). • Parameterized ice deposion based on computed saturaon vapor pressure (not GCM-based). • Deposing dust and ice together allow for lag formaon and layering • Otherwise, dust and water are uncoupled and higher order eﬀects are not included AMAZONIAN MARS: MODELING POLAR LAYER DEPOSITS

A logical next step for GCM work is to predict water and dust deposion/removal together

Jeremy Emme’s PhD project: Use the NASA Ames GCM with coupled dust and water cycles to invesgate the PLDs Dust and Water are coupled via: DUST: 1. Cloud microphysics • Scavenging, etc • Radiave/dynamic feedbacks 2. Lag deposit formaon • Throle sublimaon when thick enough ICE: Allows for the simultaneous, self- consistent tracking of dust and water ice in the polar regions for a range of orbital conﬁguraons à temporally and spaally

GOAL: Improve our understanding of how the PLDs relate to the climate of Mars over me AMAZONIAN MARS: FINAL THOUGHTS

1. We have learned a lot since GCMs were inially used to invesgate aspects of the PLDs • The radiave eﬀects of water ice clouds are signiﬁcant for the climate, parcularly at high obliquity • Coupling between the dust and water cycles is also likely quite important for both the water and dust cycles

2. GCMs are powerful, but complex • Interpreng results needs to be done with cauon

3. GCMs will be improved in mulple ways in the near future • Pole problem • Spaal resoluon • Computaonal speed