Thermophysical Analysis of Gale Crater Using Observations from TES, THEMIS and the Mars Science Laboratory Curiosity Rover

Home , Climate of Mars, Gale (crater), Hutton (Martian crater), LK (spacecraft), Peace Vallis, Pollack (Martian crater)

Thermophysical Analysis of Gale Crater Using Observations from

TES, THEMIS and the Mars Science Laboratory Curiosity Rover

Edward M. Barratt BSc (Hons), University of Edinburgh, 2011

December 5, 2013

A thesis submitted to the

Faculty of the Graduate School of the

University of Colorado in partial fulﬁllment

of the requirement for the degree of

Master of Science

Department of Astrophysics and Planetary Science

2013 This thesis entitled

Thermophysical Analysis of Gale Crater Using Observations from

TES, THEMIS and the Mars Science Laboratory Curiosity Rover

written by Edward Mark Barratt

has been approved for the Department of Astrophysical and Planetary Science

Prof. Brian Hynek

Signature Date

Dr. Nathaniel Putzig

Signature Date

Prof. Shijie Zhong

Signature Date

Dr. Mike Mellon

Signature Date

The ﬁnal copy of this thesis has been examined by the signatories, and we

ﬁnd that both the content and the form meet acceptable presentation standards

of scholarly work in the above mentioned discipline.

ii Edward Mark Barratt (Masters of Science, Department of Astrophysics and Planetary Science)

Thermophysical Analysis of Gale Crater Using Observations from

TES, THEMIS and the Mars Science Laboratory Curiosity Rover

Thesis directed by Professor Brian Hynek of the department of Geological Sciences.

Abstract I created a web-based interface to the MARSTHERM one-dimensional numerical thermal model, which calculates surface and atmospheric temperatures of Mars for a set of user-speciﬁed conditions. The website also provides access to tools which I adapted to allow users to automatically derive thermal inertia from images taken by the Mars Odyssey Thermal Emission Imaging System (THEMIS), and access to existing maps of thermal inertia derived from Mars Global Surveyor Thermal Emission Spectrometer (TES) observations. To demonstrate the capabilities of the tools provided on the website, I conducted a case study investigating the thermal inertia within Gale Crater, using observations from TES, THEMIS, and the Mars Science Lab- oratory (MSL) Ground Temperature Sensor (GTS). Seasonal variations in TES-derived thermal inertia in the vicinity of the MSL landing site are consistent with a layer of sand or dust over rock, an interpretation supported by diurnal temperature variations recorded by GTS on MSL sol 30. However, diurnal temperature variations from elsewhere along the MSL traverse route could not be modeled by simple two-component structures. Seasonal variations in TES-derived thermal inertia support the hypothesis that the thickness of a surface layer of dust likely increases with elevation on Mount Sharp.

iii Acknowledgments None of this work would have been accomplished without the advice, patience and assistance of Than

Putzig at the Southwest Research Institute, who generously entrusted me with the construction of the MARS-

THERM website whilst supporting and encouraging my research eﬀorts. I am also grateful for the guidance and hard work of Brian Hynek and Fran Bagenal, who both assisted me in navigating the complications of educational bureaucracy and were quick to oﬀer advice when I needed it. Thanks are also due to the other members of my committee, Mike Mellon and Shijie Zhong. And also to my good friends and housemates at the Mackshack for making the last two years in Boulder so enjoyable.

iv Contents

List of Figures vii

List of Tables viii

1 Introduction 1

2 The MARSTHERM Thermal Model 5

2.1 Thermal Inertia Derivation ...... 7

2.1.1 Single Point Thermal Inertia Derivation ...... 7

2.2 Surface Heterogeneity ...... 8

2.3 Instruments ...... 11

2.3.1 TES ...... 11

2.3.2 THEMIS ...... 11

2.3.3 REMS ...... 12

2.3.4 MOLA ...... 13

2.3.5 Imaging Instruments ...... 13

3 The MARSTHERM Website 14

3.1 Project Designation ...... 16

3.1.1 Project Maps ...... 16

3.1.2 Dust Opacity ...... 16

3.1.3 Two Component Modeling ...... 17

3.2 THEMIS Image Processing ...... 17

3.2.1 Download and Pre-Processing ...... 17

3.2.2 Thermal Inertia Derivation ...... 19

3.2.3 Output Files ...... 20

3.2.4 Known Issues ...... 21

4 Gale Crater 26

4.1 Mount Sharp ...... 26

4.1.1 Thermal Inertia of Mount Sharp ...... 28

4.2 Northern Crater Floor ...... 29

v 4.2.1 Peace Vallis and Peace Vallis Fan ...... 32

4.3 Diurnal Temperature Cycles ...... 42

4.3.1 MSL sol 30 ...... 43

4.3.2 MSL sol 100 ...... 46

5 Conclusions 50

6 References 52

vi List of Figures

1 Seasonal atmospheric pressure within Gale Crater...... 3

2 Thermal inertia and particle-size scaling...... 4

3 Model diurnal surface temperatures at lookup-table nodes...... 9

4 THEMIS image times and seasons for Gale Crater...... 12

5 MARSTHERM website and model temperatures in Gale Crater...... 15

6 TES dust opacity history within Gale Crater...... 19

7 THEMIS thermal inertia products for Gale Crater from the MARSTHERM website...... 22

8 Histogram of global TES-derived thermal inertia values...... 23

9 Simulated Histogram showing Shark-ﬁn Artifacts...... 25

10 Gale Crater THEMIS visible mosaic...... 27

11 THEMIS brightness temperature and derived thermal inertia for Gale Crater...... 30

12 THEMIS brightness temperature and derived thermal inertia for a proﬁle across Mount Sharp. 30

13 TES-derived thermal inertia of Mount Sharp...... 31

14 Seasonal variations in apparent thermal inertia on Mount Sharp...... 31

15 Peace Vallis thermal inertia...... 33

16 Peace Vallis elevation from HRSC DTM...... 34

17 Histograms of Peace Vallis thermal inertia...... 35

18 The high thermal inertia area of the Peace Vallis fan...... 36

19 Histograms of thermal inertia in the Peace Vallis fan...... 37

20 HiRISE images of the high thermal inertia area of the Peace Vallis fan...... 38

21 Annual-median TES-derived thermal inertia on the Peace Vallis fan...... 39

22 Seasonal variations in apparent thermal inertia on the Peace Vallis fan...... 41

23 HiRISE image of the Curiosity traverse...... 42

24 Mosaic of MSL sol 30 site...... 44

25 GTS diurnal temperature cycle at MSL sol 30 site...... 45

26 Left navigation camera image of MSL sol 100 site at Rocknest...... 47

27 GTS diurnal temperature cycle at MSL sol 100 site, with single component model curves. . . 48

28 GTS diurnal temperature cycle at MSL sol 100 site, with two component model curves. . . . 49

vii List of Tables

1 Thermal model parameters...... 6

2 Thermal model lookup-table properties...... 8

3 Idealized surface materials...... 10

4 Apparent thermal inertia of Peace Vallis Fan...... 40

5 MSL diurnal temperature cycles...... 43

viii 1 Introduction

Since 1996, when Mars Global Surveyor (MGS) began the first successful mission to Mars since the Viking era, Mars has been the target of 9 successful missions. Two rovers are currently exploring the surface of the planet, and three orbiters are in place continually gathering and returning data. With still more missions planned by NASA, ESA, and the Russian and Indian space agencies, no other planet apart from our own has ever been subject to such intense and active exploration. These missions have revealed clear evidence that the past climate of Mars was very different from how it is now. With newer data the sparse valley networks observed by the Viking orbiters (e.g. Masursky et al. 1977) have been revealed as integrated and mature drainage systems indicative of flowing surface water (Hynek et al. 2010). Spectral data indicates that clay minerals, which only form in aqueous conditions, occur in sedimentary rocks on the surface (e.g. Michalski et al. 2005; Milliken et al. 2010). The global distribution of deltas and valleys even suggests the possibility of an ancient Martian ocean (Di Achille & Hynek 2010). All of this evidence provides proof that Mars was at one time warmer and wetter than it is now. Understanding more about these early conditions can help us to theorize about the habitability of the early planet. Could Mars have once hosted life? And if so, are there signs that it did? Continued research on the geology of Mars can help to answer these questions, while answers to them will also help us to understand the nature of life on our own planet. This knowledge will provide us with a better idea of past or present conditions that may support life on other planets in our own solar system and beyond.

While rovers provide a means of investigating the geology of the surface in situ at a few small sites, investigations over broader regions are predominantly limited to using data from remote sensing instruments on the Martian orbiters or Earth based telescopes. Surface temperature is one measurement that can be derived from remote-sensing observations. The temperature of a planetary surface is controlled by a balance of the outward radiated heat from the surface with the inward heat ﬂux due to solar insolation (SM ) and

− atmospheric radiation (FIR), while also accounting for heat exchange with the subsurface by conduction, and any other heat sources. In the case of Mars, another heat source that must be considered is latent heat due to seasonal CO2 condensation and sublimation. This balance provides an equation for the surface temperature (Ts) (e.g. Mellon et al. 2008):

r 4 − ∂m π ∂T σTs = SM (1 − A) cos i + FIR + L + I 0 (1) ∂t Γ ∂Z Z0=0 where is the surface emissivity, σ is the Stefan-Boltzmann constant, A is the albedo, i is the solar incidence

1 angle and L is the latent heat of CO2 sublimation or condensation, and m is the mass of CO2 frost. I is the thermal inertia, Γ is the seasonal (or diurnal) period, and T is the subsurface temperature while Z0 is depth normalized by the seasonal (or diurnal) thermal skin depth δ, where δ is given by:

r I Γ δ ≡ (2) ρc π

where ρ is the density and c is the heat capacity of the surface materials. For two frost-free regions in

close proximity, the ﬁrst three terms on the right-hand side of Eq. 1 will be approximately equal, so that

temperature diﬀerences between the two regions will be controlled by thermal inertia. Thermal inertia is a

bulk property that controls how a volume of material stores and conducts heat. The surface temperature of

an ideal object with negligible thermal inertia would respond instantaneously to radiative forcing, depending

solely on incident radiation and the object’s albedo. Real materials have non-zero conductivity (k) that

allows heat to pass into their interiors, while their density and heat capacity allow them to store heat.

Together, these three properties comprise the thermal inertia:

I ≡ pkρc (3)

In this work I will use the SI derived unit of thermal inertia, tiu:

tiu ≡ Jm−2K−1s−1/2 (4)

For geologic materials under Martian surface conditions, thermal inertia generally increases with grain size (Mellon et al. 2008). Presley & Christensen (1997) used laboratory measurements to show that, under

Martian conditions, the thermal conductivity can be estimated by:

0.6 (−0.11 log (P/c2)) k = (c1P )d 10 (5)

where P is atmospheric pressure in Pa, and d is the grain diameter in µm, yielding k in Js−1m−1. The

constants c1 and c2 depends on other properties of the material, especially the bulk density. For a medium well-packed density analogous to loose sediments on a planetary surface (as described by Presley & Chris-

−5 7 tensen (1997)), c1 = 7.97 × 10 and c2 = 1.08 × 10 Pa. The seasonal pressure variations on Mars can be taken from the ﬁt to the ﬁrst year of Viking Lander 1

measurements (Tillman et al. 1993) and scaled for elevation using a scale height of 10.8 km. Figure 1 shows

2 the range of pressures that this method produces for Gale Crater, with the atmospheric pressures ranging from 4.36–5.76 mbar on the south crater rim (elevation 0.1 km) to 7.41–9.78 mbar at the crater’s low point

(elevation -4.5 km).

Figure 1: The seasonal atmospheric pressure in Gale Crater, as calculated using the Tillman et al. (1993) ﬁt and scaled to elevations within the crater. The southern autumn equinox occurs at Solar Longitude 0◦.

Figure 2 shows a model scaling between particle size and thermal inertia within these pressure ranges, demonstrating that changes in pressure within the crater and through the year will have a modest eﬀect on the surface thermal inertia, compared to changes due to material properties such as grain size itself. To derive Eq. 5, Presley & Christensen (1997) used spheres of glass in a CO2 gas atmosphere, so it should be relevant to the analysis of sand dunes, dust, and other dry unbonded agglomerates. In such materials under

Mars surface conditions, conduction between grains is dominated by the conductivity of the gas in the pore spaces (Mellon et al. 2008). Conductivity may be increased by physically bonding particles, thereby filling the pore space with a material of greater conductivity than gas. A duricrust of material bonded in this way was observed at the Viking Lander 1 site (Shorthill et al. 1976), and its effect on the thermal inertia of the soil was described by Kieffer (1976). Duricrust was also observed at the Pathfinder landing site (Moore et al. 1999). Putzig & Mellon (2007) showed that large regions of the low to mid latitudes of Mars had thermophysical responses that could be indicative of a layer of duricrust overlying dust. Observations of Gale

Crater from orbit suggest that large regions of the crater ﬂoor are characterized by duricrust (e.g. Fergason et al. 2012; Anderson & Bell 2010). The likelihood of duricrust layers and other forms of heterogeneity

3 means that the use of thermal inertia to infer particle size should be considered with caution.

Figure 2: Relation between thermal inertia and eﬀective particle size for minimum and maximum expected pressures in Gale Crater, assuming ρ = 1600 kg m−3 and c = 625 J kg−1K−1. Derived using Eq. 5 (Presley & Christensen 1997). Grain-size designations are standard Wentworth scale. Adapted from Kieﬀer (2013)

In Section 2, I will describe the numerical model that calculates surface and atmospheric temperatures and the derivation algorithm that is used to derive thermal inertia from temperature measurements of Mars.

In Section 3, I will describe the structure and the capabilities of the MARSTHERM website that I have developed to provide access to these and other thermophysical analysis tools. In Section 4, I will discuss a case study where I have used these and other tools to analyze surface properties within Gale Crater.

4 2 The MARSTHERM Thermal Model

The MARSTHERM one-dimensional numerical thermal model was adapted by Putzig & Mellon (2007)

from a previous model developed by Mellon et al. (2000). It calculates surface and brightness temperature variations for seasonal and diurnal periods, under a deﬁned set of conditions. Brightness temperature is surface temperature scaled by surface emissivity and attenuated by atmospheric opacity, and it is the temperature that one would calculate by measuring the infrared radiance from space and assuming a black body radiator. MARSTHERM calculates brightness and surface temperatures using user-speciﬁed values of surface properties (thermal inertia, albedo, emissivity, atmospheric pressure) and atmospheric dust opacity

(τD); water-ice-cloud opacity is not included in the current version of the model. Models can be run for a single latitude, or simultaneously for several regularly spaced latitudes. Longitude is not parameterized since outputs are for local solar times. Topography can be accounted for by adjusting slope azimuths and angles and surface pressure. The model can be accessed on the MARSTHERM website described in Section 3.

To calculate the temperature of a surface on Mars using Eq. 1 still poses some problems. It is possible to measure from orbit, or to make educated guesses about, the emissivity, albedo, thermal inertia and the frost covering for a particular time and place. The solar irradiation can be easily modeled from the eccentricity of the Martian orbit, while the solar incidence angle is a simple function of location, time of day and season.

But the subsurface temperature gradient must be calculated, and depends on the past variation of the temperature. The model must therefore be “spun up” over a few seasonal (or diurnal) cycles to eliminate the eﬀects of initial conditions.

The atmospheric model incorporates solar and thermal-infrared radiative transfer in a dusty CO2 atmosphere (Pollack et al. 1990), as described by Mellon et al. (2000). Improvements to the near-surface and atmospheric portions of the model are being undertaken and once this work is completed, the revised model will replace the current one on the website.

Table 1 shows all of the parameters that may be speciﬁed for the thermal model, and those that are pre-programmed. In addition to physical properties, users may specify the number of times per day at which surface ground temperature is calculated, the number of iterations of the subsurface model per iteration of the atmospheric model, the interval in subsurface iterations at which diurnal temperatures are output, and the interval in days at which seasonal temperatures are output.

5 Table 1: Thermal Model Input Parameters. More details are on the website documentation pages.

Name Units Range/Value User specified parameters Thermal Inertia (I) tiu 5–5000 Albedo of Bare Ground 0–1 Albedo of Bare CO2 Surface Ice 0–1 −2 Effective Semi-Infinite CO2 kg m 0–1000 Bare Ground Emissivity @ 15 µm 0–1 Bare Ground Emissivity elsewhere 0–1 Surface Slope Angle ◦ from horizontal 0–90 Surface Slope Azimuth ◦ clockwise from north 0–360 Surface Pressure (Γ) mbar 0–1000 Visible Dust Opacity (τD) 0–1000 Southern Most Latitude ◦ north from equator −90–90 Latitudes Per Run 1–37 Latitude Increment ◦ north 0–180 1 ◦ Solar Longitude (Ls) 0–360 Ground Time Steps per Day 1–10000 Ground Time Steps per Atmospheric...... Time Step 1–99 Seasonal Output Interval 2 sols 1–668 Diurnal Output Interval time steps 1–99 Pre-programmed parameters Soil Heat Capacity J kg−1 K−1 627.900 IR/Visible Dust Opacity @ 15µm 0.500 IR/Visible Dust Opacity Elsewhere 0.500 CO2 Ice Emissivity at 15µm 0.800 CO2 Ice Emissivity Elsewhere 0.800 Soil Density kg m−3 1.5×103 Initial Temperature K 200 Wind Speed m s−1 0.000 Seasonal Spin Up Time 2 Martian Years 3 Diurnal Spin Up Time 1 sols 10 1. Diurnal model only. 2. Seasonal model only.

6 2.1 Thermal Inertia Derivation

Early work to measure the thermal inertia of the Martian surface involved adjusting the albedo and thermal

inertia inputs for a thermal model similar to the one used here, in order to produce the best ﬁt to acquired

temperature data for the surface (e.g. Palluconi & Kieﬀer 1981; Hayashi et al. 1995). This technique is only

feasible when temperature observations sample the same region frequently at multiple stages of a diurnal

cycle. The Viking Infrared Thermal Mapper data used by Palluconi & Kieﬀer (1981) and Hayashi et al.

(1995) was collected in such a way that this was possible (Kieﬀer 1976), but the MGS and the Mars Odyssey

spacecraft are is sun-synchronous orbits, which means that they sample regions away from the poles at nearly

the same local time of day on every pass, so that repeat observations of the same point occur at two times

of day at best and therefore the diurnal temperature variation is not well sampled by these spacecraft.

2.1.1 Single Point Thermal Inertia Derivation

Mellon et al. (2000) developed a method that allows thermal inertia to be derived from single-point temper-

atures for use with Mars Global Surveyor Thermal Emission Spectrometer (TES) observations. First, the

thermal model is used to compute temperature cycles for values across the expected range of albedo, thermal

inertia, surface pressure, dust opacity and latitude conditions. Model temperatures are stored for ranges of

time of day and season, producing a 7-dimensional lookup table. Each single-point temperature observation

is then correlated with modeled or measured values for each of the 6 dimensions apart from thermal inertia.

Thermal inertia is then interpolated from the lookup table for the speciﬁc set of parameters.

Derivation and mapping of thermal inertia by Mellon et al. (2000) and Putzig et al. (2005) found values

at the limits of the lookup table, which allowed values between 24 and 800 tiu. Among other updates to the

derivation algorithm, Putzig & Mellon (2007) extended the lookup table to allow thermal inertia between

5 and 5000 tiu, and used it to derive separate 2am and 2pm global maps of Martian thermal inertia at 20

pixels per degree (ppd) resolution. The current derivation code for both TES and THEMIS thermal inertia

uses the lookup table of Putzig & Mellon (2007). Table 2 provides details of the lookup table, including the

data source for interpolation along each dimension.

◦ For the latitude of Gale Crater and Ls = 0 , Figure 3 presents modeled surface temperatures at each node value for four dimensions of the lookup table, together with two panels showing the eﬀect of surface slopes. The parameter with the largest eﬀect on modeled surface temperatures is thermal inertia. A surface with lower thermal inertia will have colder nighttime temperatures and higher daytime temperatures. In this example, CO2 frost forms and limits the minimum temperature to ∼148 K at certain times of night for

7 Table 2: Thermal Model Lookup-table Properties

Dimension Nodes Range Data Source Mars Day 84 0–668 Spacecraft Ephemeris Mars Hour 24 1–24 Spacecraft Ephemeris Latitude 37 −90–90 Spacecraft Ephemeris Dust Opacity 3 0.0–0.8 TES data (Smith 2004) Pressure 5 0.8–15.0 Scaled from (Tillman et al. 1993) fit using a 20-ppd MOLA elevation map. Albedo 3 0.15–0.35 20-ppd TES annual maps Thermal Inertia 21 5–5000 Interpolated from temperature. surfaces with lower values of thermal inertia (Fig. 3a). Note that the temperature curves cross one another near dawn and dusk; the derived thermal inertia is non-unique at those times. Figure 3b shows that for ranges typically found on Mars, albedo can affect the surface temperature by ∼20 K, with higher albedo reflecting more solar irradiation and leading to lower temperatures. Atmospheric pressure changes on Mars can affect surface temperature by ∼10 K, with a lower pressure leading to lower temperatures (Fig. 3c).

Martian dust opacities can aﬀect the temperature by ∼10 K, an aﬀect similar in form to that of thermal inertia, where lower dust opacity leads to a larger diurnal temperature range (Fig. 3d).

A north-south slope has a similar eﬀect to changing the latitude in the same direction; e.g. a slope tilted towards the equator will show a similar temperature response to one that is closer to the equator (Fig. 3e).

An eastward facing slope warms up sooner in the morning and heats up more than a westward facing slope

(Fig. 3f).

Mellon et al. (2000) estimated uncertainties of 6% for thermal inertia derived from TES bolometer measurements using the earlier model, noting that this value is an upper limit. The major sources of uncertainty come from observations, particularly those of albedo and dust opacity, and how past measurements of these values relate to conditions at the time of subsequent temperature observations.

2.2 Surface Heterogeneity

Each value of thermal inertia derived will be accurate so long as the observation on which it is based sampled a horizontal thermally homogeneous region. In this case, the derived thermal inertia should be identical regardless of whether the observation was taken during the day or night, and regardless of the season during which the observation was taken. However, if the observation footprint covered a region composed of a horizontal mixture of different thermal inertia values, or if it covered a layered region with the upper layer thinner than a seasonal skin depth, then different surface components may have different temperatures at any

8 Figure 3: Model diurnal surface temperatures for (a-d) node values from the lookup table and (e, f) a few ◦ ◦ illustrative sloped surfaces. All models are computed for 0 Ls at 4.6 S, the latitude of the Curiosity landing site. In each panel the dashed line represents an identical model with I = 223 tiu, A = 0.25, P = 5 mbar, τD = 0.0, and a level surface. Thermal inertia nodes are 5, 7, 10, 14, 20, 28, 40, 56, 79, 112, 158, 223, 316, 446, 630, 889, 1256, 1774, 2506, 3540, and 5000 tiu. Albedo nodes are 0.15, 0.25, and 0.35. Surface atmospheric pressure nodes are 0.8, 2, 5, 10, and 15 mbar. Dust opacity nodes are 0.0, 0.4, and 0.8. Sloped models are calculated for (e) an angle of 20◦ with a south-facing azimuth and (f) angles of 0, 10◦ with east and west facing azimuths. Adapted from Putzig & Mellon (2007).

9 given time. The observed brightness temperature will be a mixture of the temperatures of the components within the sample area. Due to the non-linear dependence between temperature and thermal inertia, the derived thermal inertia will not be a linear mixture of component thermal inertias. Evidence from imaging at high resolution indicates that the surface of Mars is heterogeneous on the scales of TES observations and

THEMIS images, and therefore it is generally more appropriate to refer to the derived thermal inertia as the apparent thermal inertia, which is expected to be time and season dependent. Similar eﬀects are expected for surfaces with varying slopes.

The derivation of apparent thermal inertia, rather than the true averaged value for the surface, may appear to represent a drawback in the derivation technique. However, the apparent thermal inertia’s seasonal and diurnal dependence provide a tool which can be used to analyze the sub-pixel and sub-surface structure of the near-surface. Putzig & Mellon (2007) developed a technique that allows seasonal and diurnal variations in apparent thermal inertia to be compared to simple two-component situations. They deﬁned four idealized surface materials (dust, sand, duricrust and rock) with thermal inertia on nodes of the lookup table (to minimize interpolation errors). Typical basaltic values were assigned for density and heat capacity, adjusted to account for a sulfate cement in the case of the duricrust. Albedo values were taken from modal values for Mars (Putzig et al. 2005). The parametrization of the materials is described in Table 3. Seven layered models (dust over sand, crust, and rock; sand over rock; rock over sand; and crust over dust and sand), at a range of upper layer thicknesses, and six horizontal mixes (all possible combinations) at a range of component ratios were produced. The apparent thermal inertia for each of these models was compared with that derived from TES measurements over all available seasons and times of day to infer likely structures for a number of locations on Mars (Putzig & Mellon 2007). I use the same techniques to analyze locations within Gale Crater in Section 4.

Table 3: Idealized Surface Materials

Type Inertia Albedo Density Heat cap. Skin Depth (m) (tiu) (kg m−3) (J kg−1 K−1) Diurnal Seasonal Dust 56 0.26 1375 837 0.008 0.212 Sand 223 0.16 1650 837 0.027 0.702 Duricrust 889 0.23 1875 854 0.093 2.413 Rock 2506 0.16 2500 837 0.201 5.206

10 2.3 Instruments

Observations by the following instruments have been used to provide data for the thermal model during this study; and a description of key instrument parameters are included below.

2.3.1 TES

The Thermal Emission Spectrometer onboard the Mars Global Surveyor was designed to determine the surface mineralogy, volatile abundance and history, and atmospheric dynamics of Mars (Christensen et al.

2001). It made thermal infrared spectral observations between 5.8 and 50 µm, bolometric thermal radiance observations between 5.1 and 150 µm, and visible/near-infrared solar reﬂectance measurements. It had a spatial resolution of 3 km at the surface of Mars. MGS arrived at Mars on the 11th of September 1997, TES began taking measurements during the aerobraking phase of its mission, and it began the formal mapping phase on the 1st of April 1999. MGS was placed into a ﬁxed-local-time orbit of Mars with equator crossing times at ∼2am and ∼2pm local time. Contact was lost with the spacecraft in November 2006, though the spectrometer instrument on TES failed previously in April 2004. Dust opacity extractions (Smith et al.

2001b; Smith 2004) used in the thermal inertia derivation are accessed from a database of TES data. Albedo is read from global maps produced from TES observations by Putzig & Mellon (2007) and Putzig et al.

(2013). I also used maps of seasonal and annual mean thermal inertia derived from TES measurements which were produced by Putzig & Mellon (2007).

2.3.2 THEMIS

The Thermal Emission Imaging System onboard the Mars Odyssey spacecraft was designed to allow global mapping of unique compositional units at resolutions that permit mineral and rock distributions to be related to geological features (Christensen et al. 2004). It takes visible-spectrum images at 18 m per pixel resolution with up to 5 different color filters, and it takes infrared images at 100 m per pixel resolution with up to 9 different wavelength filters between 6.2 and 15.3 µm. I used radiance data from THEMIS IR band 9, at

12.57 µm, to estimate the brightness temperature of the surface as a ﬁrst step in calculating thermal inertia.

Figure 4 shows the times of day and the seasons when THEMIS images sampled Gale Crater. Before mid-2009, the Mars Odyssey spacecraft passed over Gale Crater at around 5am and 5pm, with a seasonal oscillation about these times of around 1 hour. After mid-2009, the spacecraft’s orbit was altered and it began passing overhead about 1.5 hours earlier, but with the same oscillation. From Figure 3 it is clear that the majority of THEMIS daytime observations occur near dusk when thermal inertia is non-unique, and for

11 this reason daytime THEMIS images are of little value for the derivation of thermal inertia. Most of the

◦ ◦ THEMIS images that sample Gale Crater were taken when Mars was between 330 and 50 Ls, around the southern autumn equinox, with very little coverage around the spring equinox.

Figure 4: Top: The times of day for THEMIS images which sample Gale Crater. Times for daytime and nighttime images are hours after local noon and midnight, respectively. Before mid-2009 the Mars Odyssey spacecraft passed over Gale Crater at around 5am and 5pm, with a seasonal oscillation about these times of about an hour. After mid-2009 the spacecraft’s orbit was altered and it began passing overhead around 1.5 hours earlier, but with the same oscillation. Bottom: Count of THEMIS images that sampled Gale Crater, binned by season. More observations of Gale Crater have occurred close to the the southern autumn equinox. Nighttime bars are plotted behind partially transparent daytime bars; e.g. during the ﬁrst 10◦ of the Martian year there have been 3 nighttime and 4 daytime observations.

2.3.3 REMS

The Rover Environmental Monitoring Station (REMS) onboard the Mars Science Laboratory Curiosity rover is designed to investigate the environmental factors that relate to the habitability of the Martian surface

(Gomez-Elvira et al. 2012). It measures air and ground temperature, pressure, relative humidity, wind speed and ultraviolet radiation. The REMS Ground Temperature Sensor (GTS) is located on Curiosity’s mast, and it uses infrared radiometry to measure the temperature in a roughly 100-m2 field of view. The large field of view was chosen to avoid small-scale temperature effects caused by, for example, shadowing by individual boulders. The field of view was designed to minimize the direct heating of the area by the rover

12 itself, though signiﬁcant thermal contamination still occurs (Gomez-Elvira et al. 2012).

2.3.4 MOLA

In order to scale the Tillman et al. (1993) ﬁt for atmospheric pressure at the Viking Lander 1 site to the appropriate value of locations elsewhere on the planet, it is necessary to know the surface elevation. The

Mars Orbiter Laser Altimeter (MOLA) onboard MGS provided measurements of the elevation of the surface to a vertical accuracy of 1 m (Smith et al. 2001a). The surface spot size for the laser beam from the mapping orbit was 168 m, along-track shot spacing was 300 m, and cross track shot spacing was 4 km at the equator.

I used MOLA elevations binned at 20-ppd in a map created by Putzig & Mellon (2007) for the same purpose.

Maps of slope also created from MOLA data by Putzig & Mellon (2007) are used to adjust the latitude and time of day corresponding to each observation before it is processed using the thermal inertia derivation routine.

2.3.5 Imaging Instruments

For analysis of individual sites within Gale Crater, I used imagery provided by the High Resolution Imaging

Science Experiment (HiRISE) onboard the Mars Reconnaissance Orbiter (MRO), which provides visible- spectrum images at sub-meter resolution (McEwen et al. 2007). Digital terrain models (DTM) created from stereo image pairs provided elevation models with 25-cm vertical precision and horizontal resolution half as detailed as the original images (McEwen et al. 2007). HiRISE monochrome images are taken using red light, and span 6 km with 30-cm resolution at the surface of Mars. The central part of each red image is also sampled by blue-green and near-infrared sensors to provide false-color images with a width of 1.2km at the same resolution. HiRISE images do not cover more than a few percent of the planet’s surface, and DTMs cover even less, but where available they have proven to be invaluable.

The MRO Context Camera (CTX) has a lower resolution and a larger ﬁeld of view than HiRISE, with which it typically provides complementary observations. It has a resolution of ∼6 m per pixel at the surface

(Malin et al. 2007) and is used to view areas larger than those imaged by HiRISE.

The High Resolution Stereo Camera (HRSC) onboard the Mars Express orbiter provides images and

DTMs at 10–20 m per pixel at the surface (Albertz et al. 2004; Neukum & Jaumann 2004; Jaumann et al.

2007). For this work it was primarily used in areas where HiRISE DTMs were not available.

13 3 The MARSTHERM Website

The MARSTHERM website is located at https://marstherm.boulder.swri.edu. A summary of its capabilities is included on the home page, and public access to the rest of the site will begin on December 12th 2013. The website is written in HTML, PHP and JavaScript, and requested jobs are written to a MySQL database.

The database is monitored by a backend python script to control the processing of individual jobs. The website provides access for registered users to a number of thermophysical analysis tools, described below.

Numerical Thermal Model The ﬂagship component of the website is an interface providing access to the MARSTHERM numerical model described in Section 2. All of the user-deﬁned parameters listed in

Table 1 can be speciﬁed in an HTML form. Figure 5a provides a view of the MARSTHERM website that shows the options available for the thermal model. Output can be downloaded as ASCII data ﬁles, and in graphical form. Examples of the diurnal and seasonal temperature graphs that are generated automatically by the MARSTHERM website are shown in Figures 5b and 5c.

Global maps Global 20-ppd maps of MOLA elevation, TES-derived albedo, and annual-median and ∆10◦ seasonal TES-derived thermal inertia (Putzig & Mellon 2007) are available to view or download from the website.

Project Area Creation Users can designate an area of interest by creating a project, as described in

Section 3.1. Access to the tools for deriving thermal inertia from THEMIS images and for heterogeneity modeling is provided within the bounds of each project.

THEMIS Processing The MARSTHERM website provides an interface that allows users to specify

THEMIS infrared (IR) images, which are automatically downloaded and processed to derive thermal inertia for each pixel, providing output in the form of Hierarchical Data Format 5 (HDF5) data files and georeferenced tiff files. This process is described in Section 3.2.

Documentation Comprehensive HTML documentation is provided to help users understand how to use the tools provided. These documents describe the tools available on the website, explain how to use them, and list the options available for customizing each model. Some example scripts demonstrating how the output HDF5 ﬁles can be read using python, MATLAB and IDL are also provided.

14 (a)

(b) (c)

Figure 5: (a) View of the MARSTHERM website showing the thermal-model interface. (b) Diurnal and (c) seasonal temperature variations for assumptions suitable for the ﬂoor of Gale Crater. Times are local solar times. These graphs were automatically generated by the thermal model attached to the MARSTHERM website.

15 3.1 Project Designation

In order to process THEMIS IR images, MARSTHERM users are ﬁrst required to designate a project area.

Each THEMIS image is then trimmed to the bounds of that project to ensure that pixels outside of the

researcher’s region of interest are not unnecessarily processed. Designating a project instructs the system

to generate a number of ancillary data ﬁles and plots, some of which are utilized to process the THEMIS

image, and some of which are generated as stand-alone products.

3.1.1 Project Maps

Global maps at 20-ppd of TES albedo, MOLA elevation and TES-derived thermal inertia (Putzig & Mellon

2007) are trimmed to the project bounds. Interpolation from the elevation and albedo maps to THEMIS

pixel locations is carried out during THEMIS image processing.

Three annual albedo maps are generated for each project, since global dust storms changed the albedo of

large regions twice during the period when THEMIS and TES were both operational. These albedo maps are

extracted from those used by Putzig & Mellon (2007) and Putzig et al. (2013) to derive apparent thermal

inertia from TES observations.

A ﬁnal step in THEMIS image processing is to calculate comparisons between the median thermal

inertia derived from the THEMIS image and from TES measurements for the same region. This is done by

interpolating from the TES-derived maps to each THEMIS pixel location. Comparisons are made between

◦ the THEMIS results and both the (day or night) annual-median maps and ∆40 Ls seasonal maps. The

◦ ◦ ∆40 Ls seasonal maps are generated because data within the ∆10 Ls maps is typically too sparse to allow sensible interpolation to THEMIS pixels.

◦ ◦ Project maps are made available on the website, including all seasonal (both ∆10 Ls and ∆40 Ls) thermal inertia maps.

3.1.2 Dust Opacity

Infrared atmospheric dust opacity was derived from TES daytime observations and stored in the TES

database (Smith et al. 2001b; Smith 2004). Upon creation of a new project these data are used by the

MARSTHERM website to generate a history of dust opacity within the project area. The dust-opacity

values are saved in an array together with the solar longitude and the date and time of the observation. If

the project area is so small that fewer than 2000 suitable observations occurred within the project bounds

during the TES mission, then the project bounds for this purpose alone are expanded automatically. There

16 is no attempt to ensure that the dust-opacity data are evenly distributed by season or time of day, though

ensuring 2000 observations typically results in a reasonable spread throughout all seasons. To approximate

visible dust opacities needed as input by the MARSTHERM model, the TES infrared dust opacities are

multiplied by 2 (Mellon et al. 2000).

3.1.3 Two Component Modeling

The techniques for modeling two-component apparent thermal inertia described in Section 2.2 are used

to produce two-component model curves for the central latitude of the project area, and these curves are

◦ displayed together with median TES-derived thermal inertia values from the ∆40 Ls maps. The comparison will allow users to observe similarities between the models and the TES-observed behavior. The model curves

are also available to download as an ASCII data ﬁle.

3.2 THEMIS Image Processing

In the following discussion, the Courier Font is used to denote individual programs. Unless otherwise

stated, all programs are routines available within United States Geological Survey (USGS) Integrated Soft-

ware for Imagers and Spectrometers (ISIS3).

3.2.1 Download and Pre-Processing

The MARSTHERM website automatically downloads georeferenced THEMIS infrared images from the repos-

itory at Arizona State University (http://static.mars.asu.edu). Each image is initially processed to ISIS3

cube format using THMPROC, which projects the camera image to a uniform map, using Mars Odyssey

ephemeris data. The image is then trimmed to the project bounds using MAPTRIM and CROPSPECIAL. The

header of the ﬁle is read at this stage to ﬁnd its spatial extents, solar longitude, exposure date and time, and

the bands available within the image. THEMIS has 10 IR bands, though frequently only 3 bands are included

in the downloaded images. Band 9 centered on 12.57 µm is used for brightness temperatures. Thermal drift correction also requires band 10, centered on 14.88 µm, since the Martian atmosphere is opaque at this wavelength (Christensen et al. 2004). Bands 9 and 10 are always included in the downloaded images. Drift correction is conducted by THMDRIFTCOR. After this step, only the drift-corrected band-9 image remains.

PHOCUBE is used to supplement each pixel in the image with meta data. This process expands the ﬁle to

5 bands, which include the drift-corrected radiance value from band 9, the latitude, the longitude, the solar

incidence angle, and the sub-solar ground angle (that is, the bearing from North to the point on the planet

17 which has the Sun at its zenith). PHOCUBE is the ﬁnal segment of the processing within the ISIS3 software

suite.

The atmospheric dust opacity is taken from the project dust-history ﬁle. If the THEMIS image was

taken before the failure of the TES spectrometer in April 2004, then a weighted mean of the 5 dust opacity

measurements that occurred nearest to the THEMIS exposure time is used (weighted by time proximity). If

the THEMIS image was taken after the failure of the TES spectrometer, then the median value for all dust

◦ opacity measurements taken within 0.5 Ls of the exposure solar longitude are used, ignoring measurements taken during the global dust storm of Mars year 25. To emphasize:

• For early THEMIS images, actual measurements of TES opacity at or near to that time are used.

• For later THEMIS images, it is assumed that opacity is seasonally dependent, and a median TES value

for that season is used.

Figure 6 shows an example dust opacity history for a project centered on Gale Crater. There is considerable

scatter around a seasonal trend. The option to override the infrared dust opacity selected using this method

with a custom value is available on the web interface.

Elevation and albedo are interpolated from the MOLA and TES project maps to each THEMIS pixel

position. The atmospheric pressure at the Viking Lander 1 elevation for the image Ls is found using the ﬁt from Tillman et al. (1993), and is then scaled to the elevation of each pixel. Dust opacity for each pixel

is then scaled to the appropriate pressure. The TES albedo map used depends on the date of exposure for

the THEMIS image. If the image was taken before the end of the MGS TES mission, then the albedo map

for the corresponding Mars year is used. Otherwise the newest albedo map is used. The eﬀects of any dust

storms since the failure of the TES instrument are not considered.

THEMIS band-9 radiance is converted to brightness temperature (Tb) by interpolation from a lookup table generated using the make temp rad routine from the Da Vinci software suite, as described by Christensen

(2003). Latitude is known for each pixel, and Mars day is derived from Ls. Mars hour must be calculated from the pixel latitude and longitude (φpix, θpix), the solar incidence angle (λ), and the sub-solar ground azimuth (Ψ). First the sub-solar latitude and longitude (φsol, θsol) are calculated:

sin (φsol) = sin (φpix) cos (λ) + cos (φpix) sin (λ) cos (Ψ) (6)

θsol = θpix + atan2 cos(φpix)sin(λ)sin(Ψ), cos(I) − sin(φpix)sin(φsol) (7)

18 Figure 6: Dust opacity derived from TES observations within the bounds of the Gale Crater project. Mars Years follow the convention of Clancy et al. (2000).

This formulation assumes that the radius of Mars is insigniﬁcant compared to the distance to the Sun, and that Mars is spherical. Once the sub-solar longitude is calculated it is trivial to ﬁnd the Mars hour as the local solar time; with all angles in degrees the formula is:

24 LST = 12 − (θ − θ ) (8) 360 sol pix

3.2.2 Thermal Inertia Derivation

The next processing step is the creation of an ASCII data ﬁle with one line for each pixel in the THEMIS image, where each line contains all of the necessary data to search the MARSTHERM lookup table. The

ISIS3 cube ﬁle is read line by line using modules of the Geospatial Data Abstraction Library (GDAL). Aside from scaling pressure and dust opacity, topography is also accounted for using a global slope map, generated

19 from the 20-ppd MOLA map, to correct for regional slope and the non-spherical shape of the planet by

adjusting the input latitude and time of day (Putzig & Mellon 2007).

The albedo, pressure, and dust opacity dimensions of the lookup table are interpolated quadratically,

hour and latitude are interpolated using a bi-cubic spline, and season is interpolated linearly. The observed

temperature is interpolated linearly in the log of thermal inertia (Mellon et al. 2000). A quality factor is

assigned to each pixel, where quality factors 0, 1, 2 and 3 represent how close the derived thermal inertia is

to one of the lookup table node values (0 closer, 3 further). While temperature is not precisely a function

of the log of thermal inertia, the approximation provides a reasonably close ﬁt that is better closer to the

nodes. A quality factor of 5 is assigned when the observed temperature falls outside the range of values in

the lookup table for the observing conditions. A value of 6 is assigned when model temperatures do not

change throughout the day, typically due to the presence of CO2 frost. In general pixels with quality factors

0–3 represent valid results, but with increasing uncertainty, while pixels with other quality factors should

be disregarded when interpreting results. The derived thermal inertia and quality factors for each pixel are

returned in an ASCII ﬁle.

The website code reads the new ASCII ﬁle line by line, and thermal inertia and quality factor values

are written to their appropriate places in the image array. The arrays are then written to an HDF5 data

ﬁle, together with pertinent information including the ﬁle name, the spacecraft clock time, and the fraction

of pixels having each quality factor (a full list of ﬁle attributes is available on the website documentation

pages).

3.2.3 Output Files

The median value of valid derived thermal inertia is calculated for each THEMIS image. The TES-derived

◦ thermal inertia from the appropriate (day/night) annual median map and from the appropriate ∆40 Ls seasonal map are interpolated to those same pixel locations, and the median of those values is calculated.

These three median values are also saved as attributes in the HDF5 ﬁles.

The results are also saved as a series of georeferenced tiff (GTIFF) images. The projection and georef- erencing is read from the mapped ISIS3 cube file, and re-assigned to the tiff images using GDAL modules.

GTIFF images are produced from the original radiance values, the interpolated elevation, the derived thermal inertia and quality factors, and the logarithm of the derived thermal inertia (since it is often desirable to display thermal inertia with a logarithmic scale bar). The logarithms of thermal inertia normalized by TES median values (both annual and seasonal) are also saved to GTIFF images. Other arrays included in the

20 data ﬁle but not output as GTIFF images are the interpolated albedo and the brightness temperature. A python script capable of creating GTIFF ﬁles from these arrays is made available on the website to facilitate additional processing steps using the output data.

As a ﬁnal step, sample, quality, and context images are produced to be viewed online. The sample image shows the derived thermal inertia, the quality image shows the distribution of quality factors, and the context image shows the outline of the trimmed THEMIS image over the project MOLA elevation map.

These images provide a ﬁrst look on the web site to judge the results of the processing. Figure 7 shows examples of these automatically generated images.

3.2.4 Known Issues

Cartoony Images Figure 7b shows the derived thermal inertia for daytime THEMIS image I18044001, which was taken at 16:14 local time, close to dusk. The image is composed of blocks of single colors which represent thermal-inertia node locations. Due to their appearance, I refer to images of this nature as cartoony images. Almost all of the processed THEMIS daytime images appeared cartoony. Near dusk, modeled temperature curves cross one another (see Fig. 3a) and the measured temperature yields non-unique values of thermal inertia; the interpolation code places the thermal inertia on one of the node values and

ﬂags the pixel as having a quality factor of 5, as illustrated in Figure 7d.

THEMIS Offsets Another issue is described in detail in Section 4.2.1. Large offsets in the absolute value of thermal inertia derived for the same location from one THEMIS image to another do not appear to depend on season or time of day. Relative differences in thermal inertia within a THEMIS image are reasonable but the offsets make meaningful analysis of thermal inertia differences from one image to another difficult.

Shark-ﬁn Artifacts Another issue was found with TES derived values of thermal inertia, and is illustrative of some of the issues arising from interpolation of thermal inertia from the lookup table. Figure 8 shows a histogram of thermal inertia values derived from all suitable TES observations between MGS orbits 1583 and 8000, ignoring values assigned a quality factor of 5 or 6. Jumps occur in the histograms at thermal inertia values corresponding to node values in the lookup table. For values assigned a quality factor of 0, corresponding to the derived thermal inertia values which are assumed to have the least uncertainty, these jumps can be close to an order of magnitude across the node value. There is no physical reason why the thermal inertia distribution on Mars should be discontinuous across arbitrarily chosen nodal positions, so these jumps must be an artifact of the derivation routine. I refer to these artifacts as shark-ﬁn artifacts.

21 (a) (b)

(e) (f)

Figure 7: The automatically generated preview images for two THEMIS images sampling Gale Crater; (left) I18262008, a nighttime image taken at 04:22, and (right) I18044001, a daytime image taken at 16:14. (a, b) Derived thermal inertia, which appears ’cartoony’ in the daytime image. (c, d) Assigned quality factors. The night-side image are almost entirely of the highest quality whereas the day-side image is almost entirely bad. (e, f) Context map of these images over MOLA elevations.

22 Figure 8: Histogram of thermal inertia values derived from all suitable TES-observations between MGS orbits 1583 and 8000, ignoring values assigned a quality factor of 5 or 6. The solid black line shows the total histogram for all measurements, other lines show individual histograms for quality factors 0, 1, 2 and 3. Vertical dashed lines show thermal inertia node values. Jumps occur in the histograms at node values.

The shark-ﬁn artifacts are largest for thermal inertia values assigned a quality factor of 0, but become much smaller when quality factors 0–3 are combined. This indicates that a great part of the artifact is quality factor dependent; when values of thermal inertia are derived on the upper side of lookup table nodes they tend to be assigned lower quality factors than values on the lower side of nodes. This demonstrates the importance of not restricting analysis to derived thermal inertia values assigned a quality factor of

0. However, smaller shark-ﬁn artifacts still exist when quality factors 0–3 are considered together. The remaining artifacts are believed to be caused by the derivation method which interpolates for thermal inertia using the logarithm of observed temperature, an imperfect approximation to a more complex dependence.

To better analyze the source of the non-quality factor dependent artifacts I conducted a sensitivity test on the thermal-inertia derivation routine. I ﬁrst used the MARSTHERM model to generate surface temperature using thermal inertia values ranging from 5–5000 tiu, keeping all other model input attributes the same. I then took the output from these forward model runs and inverted for thermal inertia using the lookup table

23 derivation routine. Figure 9a shows the results of this test for a latitude of 10◦N. Nighttime results below

∼50 tiu are aﬀected by CO2 frost. Elsewhere, deviations are zero at node values, tend to be negative for moderate thermal inertia, and are positive for high thermal inertia. I used the results of this sensitivity test to perturb 2 million thermal inertia values selected at random from a Gaussian distribution spanning the range 5–800 tiu. Figure 9b shows the histogram returned using this method, with shark-ﬁn artifacts existing at lookup table node values.

The sensitivity test produced similar results at all latitudes. The artifacts produced for daytime model runs tended to be larger than for nighttime model runs. The test confirms that the shark-fin artifacts in the TES-derived data are an implicit effect of the interpolation technique used. They could be minimized by using a lookup table with a finer node spacing in thermal inertia, though this would result in slower interpolation times. Shark-fin artifacts are not apparent in the histograms of THEMIS-derived thermal inertia discussed in later sections, probably because only nighttime THEMIS observations were used.

24 (a)

(b)

Figure 9: (a) Results of the sensitivity test, showing the change in thermal inertia compared to the initial input value. Output thermal inertia is the mean over all seasons for the appropriate time of day. The lower pane shows a close up of the upper pane. (b) A simulated histogram generated using the results of the sensitivity test for 2,000,000 thermal inertia values randomly generated from a Gaussian distribution. Upper pane shows the total histogram, lower pane shows deviation between input count and output count. Results were generated for a latitude of 10◦N and typical surface conditions.

25 4 Gale Crater

In order to test and demonstrate the functionality of the MARSTHERM website, I conducted a case study

using the website to investigate the thermophysical properties of Gale Crater.

Figure 10 shows a mosaic of THEMIS visible images of Gale Crater. Gale is a Late Noachian (∼3.5–

3.8 Ga) impact crater located on the Martian dichotomy boundary between the heavily cratered southern

highlands and the smoother northern lowlands (Thomson et al. 2011). Gale is centered at 5.3◦S, 237.7◦E,

and has a diameter of ∼155 km. Gale Crater is the target for the Mars Science Laboratory (MSL) Curiosity

rover, which landed on the northern crater ﬂoor in August 2012, it’s landing location is marked by a star in

Figure 10. A mound of layered material known as Mount Sharp occupies the center of the crater and stands

∼5 km above the lowest point on the crater floor, and ∼ 3km above the northern crater rim. Many origins for the layered material have been suggested, including lava flows, impact ejecta, pyroclastic deposits and aeolian or fluvial deposits. Malin & Edgett (2000) have suggested that Gale fits within a family of craters which follow a process from being entirely filled with deposits to a point where the deposits have been entirely eroded; they suggest that the thickness and rhythmic nature of many layers within the mound, and within similar layered deposits elsewhere on the planet, imply a lacustrine origin for the layered material.

Numerous valleys incised into the crater wall and mound show evidence of aqueous processes eroding the layered surfaces. However, there are no outflow channels from the crater, so ultimately any parts of the layered deposit that have been removed must have been removed by aeolian processes. Evidence for recent aeolian processes includes dunes on the floor of Gale which have been interpreted as compositionally similar to olivine basalt (Rogers & Bandfield 2009).

4.1 Mount Sharp

The International Astronomical Union named the central mound within Gale Crater as Aeolis Mons in May

2012, keeping to their custom of naming mountains after the classical albedo feature upon which it is located

(Blunck 1982). NASA and the MSL science team named the mound informally in March 2012 as Mount

Sharp, after Robert P. Sharp (1911-2004), a geologist and member of NASA’s ﬁrst few Mars missions (Agle

2012). I will refer to the mound as Mount Sharp, since this is the name most commonly recognized from

MSL press releases.

Mount Sharp measures approximately 45 km by 90 km, and from the lowest point in the crater ﬂoor to the highest point on the peak, it has a relief of 5.2 km. The mound’s average relief with respect to the ﬂoor

26 Figure 10: Mosaic of THEMIS visible images showing Gale Crater. Dashed box indicates bounds of Figure 15. Star indicates landing location of the MSL rover Curiosity. Mosaic prepared by Jonathon Hill at Arizona State University, available at http://jmars.mars.asu.edu/maps/gale/gale.html (Hill 2013, Pers. Comm.).

materials is 3.8 km, approximately twice that of the exposed strata in the Grand Canyon. Thomson et al.

(2011) provide age estimates of the mound. They suggest that most of the exposed surfaces can be dated

to the Late Hesperian/Early Amazonian boundary (∼3 Ga), but that this age is likely to represent a period of resurfacing or exposure due to recent erosion. Placing constraints on the mound’s depositional age was more difficult. Some craters in the lower formation appear to have been exhumed from beneath subsequently deposited layers, but Thomson et al. (2011) do not provide an age estimate from these craters. Instead they use basic superposition relationships. Noting that units on the crater floor overlap deposits on the mound itself, they argue that even though these units are topographically lower, they are stratigraphically higher than the lowest mound units. These crater-floor units are dated to the Early Hesperian period (∼3.5 Ga), so the lower portions of the crater floor must be at least that age. Thomson et al. (2011) take the age of Gale itself as the maximum age of the mound. They date Gale using crater counts on Gale’s ejecta. The results

27 are somewhat ambiguous. The largest craters on the ejecta suggest a Late Noachian origin, while smaller

craters are too infrequent to concur with this analysis, suggesting an age closer to the Late Noachian/Early

Hesperian boundary (∼3.7 Ga), around 200-300 million years later.

Mount Sharp can be divided into two formations. The lower formation is composed of beds that dip

gently to the northwest at 2–4◦ and are heterogeneous in thickness, albedo and surface texture (Milliken et al. 2010). The upper formation dips more steeply towards the northeast, appears more homogeneous, and has fewer impact craters. An erosional unconformity appears to separate the two formations (Milliken et al. 2010). Using data from CRISM, Milliken et al. (2010) show that the lower formation has a transition

from clay/sulfate assemblages in its lower layers to sulfate/iron oxide assemblages in its higher layers. The

upper formation lacks the signatures of clay and sulfates. They argue that the observed stratigraphy records

a global transition from a climate favorable to clay formation to one favorable to salt formations. Curiosity

will be investigating these strata for information of the early Mars climate once it has completed its drive

to Mount Sharp.

4.1.1 Thermal Inertia of Mount Sharp

Thomson et al. (2011) use THEMIS nighttime temperature images within Gale to show that the nighttime

temperatures decrease with elevation on Mount Sharp. They inferred that thermal inertia is lowest at the

top of the mound, and is indicative of a dust cover in excess of a diurnal thermal skin depth of a few

centimeter thickness. But even a simple argument considering only the adiabatic lapse rate within the

Martian atmosphere would also suggest that the temperature should decrease with altitude on the mound;

an atmospheric lapse rate of −4.5 K km−1 in the lower atmosphere is commonly assumed (e.g. Heavens et al.

2010). To test the hypothesis that the nighttime temperature’s decrease with height is due to the thermal properties of the surface, I investigated both the measured temperature and derived apparent thermal inertia on the mound.

Figure 11a shows the brightness temperatures for a number of nighttime THEMIS images. In agreement with Thomson et al. (2011) the coldest temperatures do occur at the highest elevations on Mount Sharp.

Figure 11b presents apparent thermal inertia derived using the MARSTHERM web interface for the same images. Results support the hypothesis of Thomson et al. (2011) that the region’s cooler nighttime temperatures are caused at least in part by a surface with the lowest thermal inertia in the region. Figure 12 shows temperature and thermal inertia proﬁles across the mound, and also a modeled atmospheric temperature proﬁle at ground level, assuming constant adiabatic lapse rate of −4.5 K km−1. That the trend in the

28 temperature profile closely follows that of the thermal inertia profile and deviates significantly from that of the modeled atmospheric temperature further supports the hypothesis.

The minimum thermal inertia shown in Figure 12 is below 50 tiu, which supports the suggestion that the surface is composed of dust. If, as Thomson et al. (2011) suggest, this dust is at least as thick as several diurnal skin depths, then the derived thermal inertia for a daytime image of the same approximate season should be roughly the same, and the temperature map should show very high values for that surface. As mentioned previously, thermal inertia values derived from daytime THEMIS images are often low quality, and in fact none of the images sampling this area were of suﬃcient quality to analyze for thermal inertia.

The daytime THEMIS brightness temperature images do show the same region being warmer than its surroundings, but the diﬀerence is not as marked as it is for the nighttime images. This may be because

THEMIS daytime images were mostly taken after 16:00, when low thermal inertia surfaces would have begun to cool down. √ The ratio between the diurnal skin depth and the annual skin depth is 1/ 668.6 = 1/26, 668.6 being the number of sols in a Martian year. If the dust is fewer than 26 diurnal skin depths in thickness then the layering should result in seasonal changes to the derived apparent thermal inertia. Figure 13 shows the annual median TES-derived thermal inertia on Mount Sharp, for both day and nighttime measurements.

Figure 14 shows annual-median TES-derived values from the area around the summit of Mount Sharp. The scatter in the plotted points, which might be caused by the variable surface slopes in the area, or the changing thickness of the dust layer within the region, is too great to claim a ﬁt with any model. However, the low values found, together with the general trend of higher daytime and lower nighttime thermal inertia values in the second half of the year compared to the ﬁrst, can only be reproduced with a layered system that has a large thermal inertia contrast between the surface and lower layers. Of the four idealized components which are considered, an upper layer of dust is required to produce the general low thermal inertia values, and it must sit over a layer of rock to produce the large seasonal variation in derived thermal inertia. The upper pane of Figure 14 shows that an idealized structure consisting of up to 1cm of dust overlying rock is broadly consistent with the observed trends and values, while the lower pane demonstrates that another plausible structure, dust-over-sand, does not produce the magnitude of seasonal variation observed.

4.2 Northern Crater Floor

Pelkey & Jakosky (2002) investigated the thermal properties of Gale using TES-derived thermal inertia that employed the precursor to the MARSTHERM model (Mellon et al. 2000). They found that the

29 (a) (b)

Figure 11: (a) THEMIS band-9 nighttime brightness temperatures within Gale Crater, showing that the coldest nighttime temperatures on Mount Sharp are located on the summit. (b) Thermal Inertia derived from the same images, indicating that the low temperature region is associated with the lowest thermal inertia values in Gale Crater. MOLA elevation contour interval is 400m. Mosaics use THEMIS images I18574010, I35195003, I18262008, I17376009, I34284008, I17613014, I42932006 and I01325006. All images were taken between Ls 320–020. The dashed line indicates the location of the proﬁle shown in Figure 12 and the dashed box indicates the bounds of Figure 13.

Figure 12: The brightness temperature (dashed line) and derived thermal inertia (solid line) from THEMIS image I35195003. The dotted line indicates a model adiabatic temperature proﬁle, which has a smaller amplitude compared to the observed temperatures. The close correlation between the derived thermal inertia and the brightness temperature conﬁrms the hypothesis of Thomson et al. (2011) that the cold nighttime areas on Mount Sharp are caused by lower thermal inertia.

30 (a) (b)

Figure 13: Annual median thermal inertia on Mount Sharp derived from (a) TES daytime measurements, and (b) TES nighttime measurements. The dashed outline shows the area within which seasonal maps were used in Figure 14.

Figure 14: TES-derived apparent thermal inertia on the summit of Mount Sharp, plotted against solar ◦ longitude. Blue and red dots represent mean derived thermal inertia from ∆10 Ls seasonal maps within the area shown in Figure 13. Dashed lines are model curves for layers of dust over rock (top pane) or dust over sand (bottom pane); δ signiﬁes the seasonal skin depth, 20 cm in the case of dust.

31 northern crater ﬂoor, including the MSL landing site, was characterized by high thermal inertia (315–635 tiu) and high albedo (0.200–0.250). Using images taken by the Mars Orbiter Camera (MOC) onboard MGS and information on elevation and surface roughness from MOLA, they suggested that the region’s subdued topography is characteristic of mantles formed from the deposition of airfall dust. Normally, the thermal inertia of such materials would be expected to be lower than 100 tiu. They suggested that the higher thermal inertia found in the area was likely caused by induration of the mantle material.

4.2.1 Peace Vallis and Peace Vallis Fan

The MSL landing ellipse occupies the lower part of an alluvial fan deposited at the mouth of Peace Vallis, which runs south from the northern crater rim. The valley and fan are shown in Figure 15. A detail of the valley in Figure 16 shows that Peace Vallis has a width of around 700 m, and a depth of tens of meters.

The alluvial fan shows two distinct regions distinguished by their diﬀerent thermal properties. The northern part has a thermal inertia around 300 tiu, while the southern part of the fan has a thermal inertia around

450 tiu. These two units are referred to as the low and high thermal inertia fans respectively, following the convention of Anderson & Bell (2010).

Figure 15b shows derived thermal inertia for the region, while the top pane in Figure 17 shows a histogram of all THEMIS-derived pixels from within the region. No daytime THEMIS-derived thermal inertia images were of suﬃcient quality, so only thermal inertia derived from nighttime images are included in the histogram.

Each image that sampled the region has an individual histogram shown in color, while a histogram of all images together is shown as a dashed black line. No single THEMIS image sampled the entire Peace Vallis and Fan region shown in Figure 15; in fact some only clip the corner of the region, so the shapes of histograms would not be expected to be identical from one image to the next as some images may not sample a region that contributes to the distribution of others. However, a similar distribution ought to be evident between images that sample large portions of the region.

The histograms for individual images within the region show two distinct peaks, representing two modal values for thermal inertia within the region. There is an initial peak between 100 and 300 tiu, representative of low thermal inertia units lying in depressions. A second peak between 300 and 500 tiu represents the yellowish areas of Figure 15b which appear to represent the bulk thermal inertia of regions on the crater wall.

In most cases this second peak falls away gradually to higher values, but in some images a third distinct peak or shoulder in the range of 500 to 600 tiu is apparent. This is representative of the high thermal inertia regions in the crater in the south east corner of Figure 15b.

32 (a) (b)

Figure 15: (a) Detail of Peace Vallis from MRO Context Camera image B07 012340 1750 XN 05S222W. Dashed box indicates bounds of Figure 16. (b) The same image overlain with thermal inertia derived from THEMIS image I18262008. The dashed line shows the MSL landing ellipse, the star indicates the actual landing location. Thin black outlines show the Low and High Thermal Inertia Fan units. The blue boxes show the regions sampled for the histograms in Figure 19.

The peaks visible in the individual-image histograms are not apparent in the total histogram, since each individual histogram appears shifted along the thermal inertia axis with respect to one another. There is no correlation between the size of the shift and the season when the image was taken; indeed images taken at

◦ solar longitudes within 10 Ls often display a large oﬀset with respect to one another. Similar issues were observed in thermal inertia derived from THEMIS images by Putzig et al. (2013) and Jakosky et al. (2006).

The cause is not fully understood, but may be due in part to a combination of diﬀerences in the times of day when the images were taken and sub-pixel lateral heterogeneity or layering. Another possible cause of the oﬀsets could be the methods used to assign albedo, dust opacity and elevation values to each pixel, which might not be accurately representing the true values. This could be caused by interpolation artifacts from the 20-ppd maps, or from the scatter in the dust-opacity history, or could be caused by actual changes in the surface conditions in the case of albedo or dust opacity. However, Figure 3 indicates that an error in dust opacity, albedo or slope is unlikely to lead to nighttime thermal inertia derivation errors of greater that

∼10 tiu.

33 Figure 16: Detail of MRO Context Camera Image B07 012340 1750 XN 05S222W showing Peace Vallis. El- evation contours from Mars Express HRSC derived DTM H5273 0000 DA4 (Jaumann et al. 2007). Contours are spaced at 10 m intervals. Interpretation is diﬃcult because the contours are derived from a data set with lower resolution compared to the Context Camera image, but the valley appears to be tens of meters deep.

Geological Interpretation Anderson & Bell (2010) describe the low thermal inertia fan as showing a subdued, mantled texture, while the high thermal inertia fan is composed of a layered, fractured material.

The transition from the low to the high thermal inertia fan units is associated with the surface becoming less mantled and more rocky. They state that the transition is also marked by a sharp ∼10 m drop, but this elevation change is not visible in the HiRISE Digital Terrain Model (DTM) of the area. They interpret the fan as a lithified alluvial fan, and suggest that flow-aligned ridges in the western part of the low thermal inertia fan may be inverted channels or the remains of debris flow lobes. They also suggest that the principal difference between the two fan units is that the low thermal inertia region has a thin (less than a few meters) layer of unconsolidated material overlaying the higher thermal inertia unit beneath. This

34 Figure 17: Histogram of apparent thermal inertia derived from THEMIS images sampling the Peace Vallis and Fan area shown in Figure 15. Colored lines represent histograms from individual images. The black dashed line is the histogram from all images. Individual histograms display similar shapes, but peaks appear to be shifted in thermal inertia with respect to one another. 60% of pixels lie within the gray region which straddles a modal value of 270 tiu.

hypothetical formation sequence for the region has the mantling layer originally covering the entire fan, and

being subsequently eroded away from the high thermal inertia area (Anderson & Bell 2010). Fergason et al.

(2012) provide a similar interpretation for the region, noting that the constant albedo over both fan units suggests that a very thin layer of dust lies over the whole area.

Figure 18 shows the Peace Vallis’ alluvial fan. The MRO Context Camera image (Figure 18a) shows that the high thermal inertia fan appears less smooth than the low thermal inertia fan. The HiRISE Digital

Terrain Model (DTM) shows neither a change in elevation nor a change in slope over the boundary. Figure 19 shows the histograms for thermal inertia for all pixels that fall within the blue boxes in Figure 18. The low thermal inertia fan histograms have peaks spread over a range of ∼200 tiu about a mean peak location of

∼300 tiu. The high thermal has modal thermal inertia values typically ∼200 tiu greater compared to the

low thermal inertia fan.

Using HiRISE imagery, it is possible to take a detailed look at some of the features in the area. Figure 20a

shows evidence of layering along the edge of an escarpment. In some areas (Figure 20b) an upper layer of crust

or rock appears to have fractured and slumped into depressions, while in some places blocks of crust or rock

layers appear to have slid down slopes (Figure 20c). One block of mantle material had slid into a conﬁguration

suitable for its thickness to be estimated from the HiRISE DTM (Figure 20d); it is approximately 1 m thick.

MSL has observed outcrops of lithiﬁed ﬂuvial conglomerates that have been interpreted as forming parts of

horizontal strata throughout the region (Williams et al. 2013).

35 (a)

(c)

Figure 18: The Peace Vallis Alluvial Fan. (a) From MRO Context Camera image B03 010639 1752 XI 04S222W. (b) With thermal inertia derived from THEMIS image I01350002. The dashed box shows the boundaries of Figure 20. The blue boxes indicates the area sampled for the histograms shown in Figures 19.

36 Figure 19: Histograms from THEMIS pixels within the low thermal inertia fan (top pane) and the high thermal inertia fan (bottom pane). Colored lines represent histograms from individual images, the black dashed line is the histogram from all images, histograms from individual THEMIS images are plotted in the same color in each plot. 60% of pixels have values which fall within the gray area. The thick blue lines are the histograms for THEMIS image I01350002, shown in Figure 18c.

Figure 21 shows the annual median TES-derived thermal inertia derived for the Peace Vallis fan. The annual median maps were not suitable for finding a representative value for the thermal inertia of the two fan units because measurements within the region were not distributed uniformly during the season, so that measurements taken at different times of year dominated in different pixels; this is particularly evident in the low thermal inertia region of Figure 21b, where the region does not appear homogeneous in its thermal properties but we know from THEMIS images, and from the work of Anderson & Bell (2010) and Fergason et al. (2012), that it is. A better way to find representative values for the regions’ thermal inertia is to use the 0th order term in a harmonic fit to seasonal measurements of apparent thermal inertia within the regions.

The thermal inertia value found in this way should not be considered an estimate of the true thermal inertia of the heterogeneous or layered surface; the surface is, after all, composed of a combination of materials with

37 (a) (b)

Figure 20: Subset of HiRISE image ESP 018854 1755 of the High thermal inertia fan. Contours in (a), (b), and (d) from Digital Terrain Model (DTM) generated from ESP 018854 1755 and ESP 018920 1755 (Kirk et al. 2008). Contour spacing is 0.5 m, with thicker contours every 5 m. Contour locations should be treated with caution; DTM and HiRISE image were georeferenced with an oﬀset of ∼240 m and realigned by eye. (a) The edge of the escarpment between rougher northern terrain and smoother southern terrain. The escarpment is approximately 2.5 m high, and the northern terrain appears layered. (b) A fractured region within the northern terrain. The upper layer appears to have slumped into a depression, forming polygonal blocks up to 20 m across. (c) A ∼150 m diameter crater on the northern terrain. A block indicated with an arrow, appears to have fallen down from the northern crater wall. (d) The block has a thickness of ∼1 m.

38 diﬀerent thermal inertias. However, estimating an appropriate “averaged” value allows for a comparison

between this work and the work of other researchers who typically only quote one value and do not address

the sub-pixel heterogeneity of the surface. The 0th order term method allows for the estimation of an average value when the seasonal distribution of measurements is not uniform enough for a simple mean or median value to be relevant.

(a) (b)

Figure 21: Annual-median thermal inertia in the Peace Vallis fan region derived from TES (a) daytime and (b) nighttime measurements. The dashed boxes indicate the four pixels used to ﬁnd the seasonal trends for thermal inertia shown in Figure 22.

◦ The ∆10 Ls TES-derived thermal inertia maps have sparse coverage, so to ﬁnd a seasonal trend in apparent thermal inertia, the mean value from any measurements within each set of four pixels shown in

Figure 21 was used. If no measurements existed within those pixels for a particular ∆10◦ map, then that

◦ point is not recorded. While the majority of ∆10 Ls maps contained no measurements, around one third contained measurements in more than one out of the four pixels. The measurements are assumed to have

been taken at the midpoint of the ∆10◦ period. Figure 22 shows the apparent thermal inertia for all TES

measurements in the fan area, plotted against season. The best-ﬁtting second-order harmonic was found by

minimizing the least-squares diﬀerence between the measured values and the function given by:

I = C0 + C1 cos(Ls + D1) + C2 cos(2Ls + D2) (9)

The value of C0 is taken as an appropriate representative value for the thermal inertia within the observed region. Table 4 shows the bulk thermal inertia properties derived for the high and low thermal inertia fan units, together with values derived from THEMIS measurements and the values given by Anderson & Bell

39 (2010) and Fergason et al. (2012). THEMIS-derived nighttime values are lower than TES-derived values for

the low thermal inertia fan, but the two methods agree for the high thermal inertia fan. All values found in

this work are lower than those of Anderson & Bell (2010), who used thermal inertia derived from THEMIS

nighttime images with the techniques of Fergason et al. (2006). In their own study of the thermal properties

within the proposed MSL landing ellipse, Fergason et al. (2012) also used nighttime THEMIS images and

the same techniques. They provide ranges for the thermal inertia of the high and low thermal inertia fan

units which are in agreement with my own TES and THEMIS-derived values.

Table 4: Apparent Thermal Inertia of Peace Vallis Fan. TES values are taken as the best fitting value of the 0th order term to a harmonic fit to the data, uncertainty taken from the square root of the covariance matrix for the fit. THEMIS values are taken as the central value of the range within which 60% of pixels fall, with uncertainty the half-width of that range. Units are tiu.

Source Low Thermal Inertia Fan High Thermal Inertia Fan TES-derived Daytime 321 ± 12 387 ± 18 TES-derived Nighttime 384 ± 19 447 ± 11 THEMIS-derived Nighttime 310 ± 50 450 ± 100 Anderson & Bell (2010) (Nighttime) 460 620 Fergason et al. (2012) (Nighttime) 275–380 395–555

The dashed lines in Figure 22 show modeled curves for apparent thermal inertia assuming two idealized

two component structures, dust over rock and sand over rock. The seasonal derived thermal inertia values do

not follow any of the idealized curves well, but a clear trend characterized by higher nighttime-derived and

◦ lower daytime-derived thermal inertia from Ls 0–180 (Southern hemisphere winter) and vice versa from Ls

◦ 180–360 is evident. The best-ﬁtting value of C1 from Eq. 9 gives a value for the amplitude of the seasonal change, which is 80–150 tiu (lower for daytime values) for both the high and low thermal inertia units.

This large change in apparent thermal inertia over the course of the year is only possible with a layered structure with a large thermal inertia contrast between the two materials. In particular the two-component models that best replicate the trends in these measurements for both fan units are those that have either a sub-millimeter layer of dust or a 1–2 cm layer of sand over rock, with the dust/sand layer being up to twice as thick on the low thermal inertia fan as on the high thermal inertia fan. The model curves are those created by the MARSTHERM website for a project centered on Gale Crater, using the components detailed in Table 3. It is unlikely that the diﬀerence in thermal properties between the two regions is really caused by a thicker coating of dust or sand on the low thermal inertia fan; even if a realistic scenario could explain why atmospheric conditions would deposit more dust on one area compared to the other, it would be very diﬃcult to explain why the boundary between the two regions is so sharp. No three-component modeling was conducted, but it would be interesting to test the structure proposed by Anderson & Bell (2010) and

40 41

(a) (b)

Figure 22: TES and model-derived apparent thermal inertia as a function of solar longitude. Blue and red dots represent mean TES-derived ◦ thermal inertia from ∆10 Ls seasonal maps within the pixels shown in Figure 21 for (a) the low thermal inertia fan, and (b) the high thermal inertia fan. Dashed lines are model curves for layers of dust (top pane) or sand (bottom pane) over rock; δ signiﬁes the seasonal skin depth, 20 cm in the case of dust, 70 cm for sand. Dotted line shows the best ﬁtting second order harmonic curve for the data. Fergason et al. (2012) for the low thermal inertia unit, with a sub-millimeter layer of dust on a ∼1 m layer

of indurated crust over a half space of rock.

The high thermal inertia fan is the location of the MSL Curiosity rover. The ﬂuvial origin of the fan

has been conﬁrmed by observations of sedimentary conglomerates within outcrops at its landing site and in

areas along its traverse (Williams et al. 2013). The MSL descent engines removed a layer of unconsolidated regolith (dust or sand) from the landing site, and revealed a horizontal rock layer composed of pebbles and an unresolved ﬁner component (Williams et al. 2013), supporting the interpretation of the seasonal trend described above. Figure 23 shows the region around Curiosity’s landing site and subsequent traverse across part of the high thermal inertia fan to the feature nicknamed Yellowknife Bay.

Figure 23: A section of HiRISE image ESP 032436 1755 COLOR showing the traverse route of Curiosity from Bradbury landing in the west, darkened due to dust removal by the landing thrusters, to Yellowknife bay in the east. Blue markers indicate the position of Curiosity on sol 30 and sol 100. Grid spacing is 100 m, so the GTS ﬁeld of view is approximately 1% the size of each square. The reﬂective object 40 m to the south east of the sol 100 position is Curiosity on sol 316.

4.3 Diurnal Temperature Cycles

The Ground Temperature Sensor (GTS) onboard Curiosity measures the brightness temperature of an

∼100 m2 area slightly to the side of the rover. The GTS measurement routine typically consists of measuring the temperature continually for six minutes at the beginning of each hour, with occasional hour-long

“extended” measurements. The routine hourly measurements provide an opportunity to compare diurnal

42 temperature variations to idealized two-component model curves. I analyzed the GTS temperature measurements for MSL Sol 30 and Sol 100. Sol 30 was chosen because the rover remained stationary that day and the navigation images show a region that appears largely homogeneous, promising a reasonably straightforward analysis. Sol 100 was chosen because an analysis of the data from that day was conducted by Hamilton et al. (2013) and is available in the public domain. Figure 23 shows the position of Curiosity on those dates.

Table 5 shows details from each location, including the date and the Martian Ls of MSL sol 30 and 100.

Table 5: MSL Diurnal Temperature Cycles

MSL sol Ls Date Albedo Assumed Best Single Best 2-comp IR τD component model 30 167 2012-09-05 0.21 0.14 350 tiu Dust Rock 60:40 100 208 2012-11-16 0.21 0.5 110 tiu 1.4 cm Sand over Crust

GTS data for MSL Sols 30 and 100 were downloaded from the Planetary Data System geoscience node at http://pds-geosciences.wustl.edu/missions/msl/. Typically the first minute or so of every 6 minute measurement block was assigned a poor quality factor within the data files, so I ignored those data. The remaining data from the first 6 minutes of the hour was used to compute the mean and standard deviation of temperature for each hour.

For each of the sol 30 and sol 100 locations, the elevation and albedo from the TES project maps, and the pressure and dust opacity estimated using the same forecasting technique as for the THEMIS image processing were used. Both locations have a TES albedo of 0.21. The MARSTHERM model was run under the appropriate conditions for the two sites using a range of thermal inertia values to generate diurnal temperature curves for a number of single-component models. The model was also run to generate diurnal temperature curves for a range of two component layered and horizontally mixed surfaces; these models used the four components described in Table 3, plus one other component called “dusty sand”, which has the same properties as sand except an albedo of 0.26, to represent sand grains with a very thin coating of dust, as was observed by Curiosity at the sol 100 site (Kocurek et al. 2013).

Next, I compared the measured data with each model. The chi-squared statistic was used to measure the goodness of ﬁt between each model and the measurements. Models with low chi-squared values were compared by eye with the measurements and with known information from each site.

4.3.1 MSL sol 30

By sol 30, Curiosity had driven ∼100m southeast from the Bradbury landing site. Figure 23 shows that it had left the area disturbed by the ﬁnal stages of its landing and entered a somewhat homogeneous and

43 ﬂat area. This interpretation is borne out by imagery taken by Curiosity that day; Figure 24 shows a fairly

uniform mixture of rocks smaller than ∼10 cm and some smoother ﬁne-grained material. Dirt on the rover’s

wheel appears ﬁne enough to be dust, but the rover’s tracks do not appear to have broken the surface

continuously; this might be indicative of coarser material than dust, or of a thin crust atop the dust that

can bear the weight of the rover in some places, or it might be caused by the small rocks protruding above

the surface and supporting the rover over the ﬁner material.

Figure 24: MSL mosaic N L000 0029 EDR004CYLTSB0000 DRIVEM1 showing the nature of the area around Curiosity on sol 30. The image shows a fairly uniform mixture of rocks smaller than ∼10 cm and some ﬁner smooth material. The tracks left by Curiosity appear to be <1 cm deep, and are not continuous. For scale, Curiosity’s wheels base is 2.8 m wide (Grotzinger et al. 2012), and the wheel treads are ∼40 cm wide.

Figure 25 shows the GTS temperature values together with some model curves. A horizontal mixture of

60% dust with 40% rock fits the measurements well. The best-fitting single-component model has a thermal inertia of 350 tiu, which is lower than THEMIS-derived values for the high thermal inertia fan. However, the single-component model warms up too late in the morning and cools down too fast in the evening, compared to the measurements. A layered structure with 1.4 cm of dust-coated sand over rock also fits the measured temperatures well, though it lags the warming period in the morning and does not reach the same temperature peak. The decrease in the cooling rate in the early evening was not predicted by any of the models explored, a problem also encountered by Hamilton et al. (2013) . This feature is seen to some extent

in many of the diurnal temperature cycles (c.f. Figure 27) and has not yet been explained; consequently, I

44 did not consider the 18:00 and 19:00 data points when calculating the goodness of ﬁt for this location.

The dust-rock mixture and the dusty sand-rock layer both fit the measured temperature reasonably well. The dust-rock mixture is better supported by the imagery at the location, but a subsurface rock layer was exhumed during Curiosity’s landing, and would be more consistent with seasonal trends in TES- derived thermal inertia. Nothing in the images negates the possibility of a rock layer beneath the dust and rock mixture seen at the surface. Ultimately, the near-surface properties that could produce the measured temperature curve are non-unique, so it would be possible to better fit the data by complicating the model with a third or fourth component, but if the additional complexity is not required to explain the MSL imagery or the measured data, then the temptation to include it should be avoided. Having said that, it seems likely that a dusty sand-rock mixture overlying a crust might retain enough heat in the early evening to fit the data while also agreeing with the imagery and observations at the Bradbury landing site.

Figure 25: REMS GTS temperature for MSL sol 30 (error bars), together with model temperature curves. The best ﬁtting single component model (dotted line) represents a thermal inertia of 350 tiu. It matches the amplitude reasonably well, but is warmer during the night and cooler during the morning compared to the data. The best layered model has 1.4cm of dusty sand over crust, but it is too cool during the evening. The two best ﬁtting models are horizontal mixtures of dust and rock; a 50:50 mix slightly underestimates the full temperature range, while a 60:40 mix slightly overestimates it.

45 4.3.2 MSL sol 100

Hamilton et al. (2013) showed a single day of ground temperature measurements taken by the GTS on

MSL sol 100. The measurements were taken at the Rocknest sand ﬁeld; Figure 26 shows an image of the

area taken by Curiosity. Compared to the site for sol 30, this region is much more complicated; a sloped

ridge of sand is in the foreground, with a mixture of rocks and sand beyond. Analysis of this site is further

complicated because the rover reoriented itself in the afternoon of sol 100, not only changing the GTS ﬁeld

of view, but also causing the rover to radiate more heat as it powered up for the move (Hamilton 2013,

Pers. Comm.). Rocknest was the site of the ﬁrst scooping activity by Curiosity, where the rover scooped

material from a wind sculpted ridge of sand (Kocurek et al. 2013). The ridge surface was characterised by coarse ∼1 mm sand grains coated in dust, while the interior was composed of sand ﬁner than 0.15 mm. The act of scooping up the sand caused the formation of millimetre-scale ﬁssures on the surface, indicating the existence of a 1–2 grain thickness of indurated crust.

Hamilton et al. (2013) attempted to match the GTS temperatures for MSL sol 100 with model temperatures from the KRC thermal model (Kieﬀer 2013). They used observations by TES and the MRO

Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) instrument to find the albedo, and they used opacity extracted from a THEMIS image taken on the same day. They found that they could match daytime temperatures if they modelled a surface with a thermal inertia of 550 tiu, or nighttime temperatures if they modelled a surface with a thermal inertia of 250 tiu, but were unable to find a single thermal inertia value to fit the entire diurnal temperature variation. They suggest uncertainties in atmospheric conditions, albedo, surface slopes, and the possibilities of surface heterogeneity as possible explanations.

The temperature measurements are shown in Figure 27, together with a range of diﬀerent model results.

The decrease in the evening cooling rate described for sol 30 is longer lived and of larger amplitude in the sol 100 data. It is plausible that the heating caused by the rover’s movement that day, and the presence of rocks in the ﬁeld of view, could provide some explanation for this anomaly. I omitted the data from 18:00,

19:00, 20:00 and 21:00 when ﬁtting curves to the data.

Sol 100 corresponds to Ls 208, which is right in the middle of the annual dust-storm period (see Figure 6. Rather than using the forecasted IR dust opacity value of 1.3, I was advised by Victoria Hamilton (2013,

Pers. Comm.) to use a value of 0.5. However, modeling of a single-component model returned very different results to those shown in Hamilton et al. (2013). Figure 27 shows that the best-fitting model, in terms of the minimum chi-squared statistic, had a thermal inertia of 110 tiu, much lower than either of the values that Hamilton et al. (2013) use to fit parts of the curve. I found that the daytime curve was best fit by

46 Figure 26: MSL Image NRA 406377709EDR F0050178NCAM00338M, taken by the left navigation camera on sol 100 in Rocknest. The image shows a sloped crest of sand in the foreground, with a mixture of rocks and sand behind. The tread print was left by Curiosity when it re-orientated itself on the afternoon of sol 100. a thermal inertia of 140 tiu, and the nighttime by 270 tiu. None of the ﬁts match the morning warming period. It is not surprising that a single-component model fails to match temperature measurements from a complicated region like Rocknest, but why the models do not follow those of Hamilton et al. (2013) is not clear. A previous study (Fergason et al. 2006) found close correspondence between results from the KRC model and those from Mellon et al. (2000).

The results of two-component modeling do a better job of fitting the amplitude, but still struggle to fit both the morning warming period and the afternoon cooling period. Models with 1–2cm of sand over rock or crust fit the data the best. One possible explanation for the models’ inability to fit the morning warming

47 Figure 27: REMS GTS temperature for MSL sol 100 (error bars), together with model temperature curves assuming a single component structure with infra red dust opacity of 0.5. In terms of the chi-squared statistic a model with I = 110 tiu fits the data better, but over predicts the amplitude of the variation and does not come close to modeling the morning warming period. Values of 140 tiu and 270 tiu fit the observations in the daytime and nighttime better, respectively, but no model successfully matches the morning warming period. period is that the sand ridge in the foreground of Figure 26 is sloped. It is difficult to tell exactly which parts of the surrounding area the GTS field of view sampled, but if the sand ridge occupied a significant segment of the field of view during the morning, then the slope could have produced a discernible effect (c.f. the bottom panes of Figure 3). The image in Figure 26 was taken during the afternoon, and the orientation of shadows in the image suggest that the right side of the slope is eastward facing. In this case, the scene would have warmed up earlier in the morning compared to a similar level surface. A sand-over-crust layer with an eastward aspect might show a similar amplitude to the measured data, while warming up early enough in the morning to better fit the data from that period.

Rocknest appears to be too complicated an area to be modelled with a two component horizontal structure. This is not particularly surprising given the complex nature of the area observed in images of the site.

A more complicated structure which includes sloped surfaces could be modelled and would likely provide a better ﬁt to the measured temperatures, but other concerns, particularly the movement of the rover during

48 the day, call into question the value of pursuing a better ﬁt at this site.

Figure 28: REMS GTS temperature for MSL sol 100 (error bars), together with model temperature curves assuming infra-red dust opacity of 0.5. Two-component models ﬁt the amplitude better than the best ﬁtting single-component model, but still fail to match the data in the periods after dawn and before sunset.

49 5 Conclusions

Thermal inertia derived from THEMIS nighttime images allows researchers to observe relative differences in thermal inertia for surfaces on the scale of 100 meters per pixel, enough to allow comparison between distinct geomorphic units observed in higher resolution visible images. The values of thermal inertia derived from THEMIS images show offsets of hundreds of tiu from one image to another, an effect whose cause is not fully understood and which makes useful comparisons between images difficult. TES-derived thermal inertia is of a lower spatial resolution, but seasonal variations in apparent thermal inertia provide a means of investigating sub-pixel structure and sub-surface layering.

The THEMIS derived thermal inertia on Mt Sharp decreases with elevation, supporting a hypothesis by

Thomson et al. (2011) which was based on nighttime THEMIS temperature measurements. The values and the seasonal variation in TES-derived thermal inertia on the summit area are consistent with a layer of up to 1 cm of dust over a rock half-space.

TES-derived thermal inertia in the vicinity of the MSL landing site are consistent with those calculated by Fergason et al. (2012), but generally lower than those quoted by Anderson & Bell (2010). Curiosity landed on one of two distinct thermophysical units of an alluvial fan in the northern crater floor. Each unit was investigated using thermal inertia derived from both TES and THEMIS. THEMIS results clearly show the relative difference in thermal inertia between the two units, but unexplained offsets between individual images means that comparisons between images and with other data sets is difficult. Seasonal variations in the TES-derived thermal inertia for the two units suggest that a layered structure with a dust or sand layer on top of a higher thermal inertia base layer is plausible for both units, though the modeling is inherently non-unique; for the high thermal inertia unit, the modelled structure would be consistent with the structure proposed by Anderson & Bell (2010), that of a thin layer of dust overlying a bedrock, but they suggested a more complex structure for the low thermal inertia unit that involves a cemented mantling material over the bedrock. Two-component modeling could not test such a structure, and the non-uniform seasonal distribution of TES-derived thermal inertia would likely make it difficult to show an improved fit with such a model compared to a reasonable two-component model.

In situ observations by the MSL Curiosity rover of outcrops of bedrock beneath a layer of dust on the high thermal inertia unit further support the dust-over-rock structure hypothesis. Temperature measurements by the REMS instrument provided diurnal temperature variations to be compared with modelled temperatures for simple models. For Curiosity’s location on MSL sol 30, I found that the measured diurnal temperature variation is consistent with a simple two-component model consisting of a mixture of dust and rock, or a

50 layer of sand over rock. The dust-rock mixture is consistent with images from Curiosity for that location, while the layered structure is consistent with the seasonal variations in apparent thermal inertia described above. It is likely that a three-component model would be closer to the truth, with a dust-pebble mixture on the surface overlying bedrock. Diurnal variations at the MSL sol 100 location prove more diﬃcult to match with simple two-component models, but images of this site suggest that the area is more complex, and complications also may arise due to the movement of the rover on that day.

With the exception of the two component diurnal temperature models used to match MSL measured temperatures, all of the analysis conducted on Gale Crater was achieved using models that will be accessible from the MARSTHERM website when it is made public on December 12th 2013. The website will provide easy access to thermophysical analysis tools for researchers studying other regions of Mars, without the need for them to develop or maintain a thermal model of their own.

51 6 References

Agle, D. C. 2012. NASA: ‘Mount Sharp’ On Mars Links Geology’s Past and Future [Press release]. Retrieved

from http://www.nasa.gov/mission pages/msl/news/msl20120328.html.

Albertz, J., Gehrke, S., Whlisch, M., Lehmann, H., & Schumacher, T. 2004. Digital cartography with HRSC

on Mars Express. The International Archives of Photogrammetry, Remote Sensing and Spatial Information

Sciences, 869874.

Anderson, R., & Bell, J. 2010. Geologic mapping and characterization of Gale Crater and implications for

its potential as a Mars Science Laboratory landing site. The Mars Journal, 5(Sept.), 76–128.

Blunck, J. 1982. Mars and its Satellites, A Detailed Commentary on its Nomenclature. 2 edn. New York:

Exposition Press.

Christensen, P. 2003. 2001 Mars Odyssey Thermal Emission Imaging System (THEMIS) Data Processing

User’s Guide.

Christensen, P. R., Bandfield, J. L., Hamilton, V. E., Ruff, S. W., Kieffer, H. H., Titus, T. N., Malin, M. C.,

Morris, R. V., Lane, M. D., Clark, R. L., Jakosky, B. M., Mellon, M. T., Pearl, J. C., Conrath, B. J.,

Smith, M. D., Clancy, R. T., Kuzmin, R. O., Roush, T., Mehall, G. L., Gorelick, N., Bender, K., Murray,

K., Dason, S., Greene, E., Silverman, S., & Greenﬁeld, M. 2001. Mars Global Surveyor Thermal Emission

Spectrometer experiment: Investigation description and surface science results. Journal of Geophysical

Research: Planets, 106, 23823–23871.

Christensen, P. R., Jakosky, B. M., Kieﬀer, H. H., Malin, M. C., McSween, H. Y., Nealson, K., Mehall, G. L.,

Silverman, S. H., Ferry, S., Caplinger, M., & Ravine, M. 2004. The Thermal Emission Imaging System

(THEMIS) for the Mars 2001 Odyssey Mission. Space Science Reviews, 110(Jan.), 85–130.

Christensen, P.R., Gorelick, N.S., Mehall, G.L., & Murray, K.C. THEMIS Public Data Releases, Planetary

Data System node, Arizona State University, http://themis-data.asu.edu.

Clancy, R. T., Sandor, B. J., Wolﬀ, M. J., Christensen, P. R., Smith, M. D., Pearl, J. C., Conrath, B. J., &

Wilson, R. J. 2000. An intercomparison of ground-based millimeter, MGS TES, and Viking atmospheric

temperature measurements: Seasonal and interannual variability of temperatures and dust loading in the

global Mars atmosphere. Journal of Geophysical Research: Planets, 105, 9553–9571.

52 Di Achille, G., & Hynek, B. M. 2010. Ancient ocean on Mars supported by global distribution of deltas and

valleys. Nature Geoscience, 3(July), 459–463.

Fergason, R. L., Christensen, P. R., & Kieﬀer, H. H. 2006. High-resolution thermal inertia derived from the

Thermal Emission Imaging System (THEMIS). Journal of Geophysical Research: Planets.

Fergason, R. L., Christensen, P. R., Golombek, M. P., & Parker, T. J. 2012. Surface Properties of the Mars

Science Laboratory Candidate Landing Sites: Characterization from Orbit and Predictions - Springer.

Space Science Reviews, 170(1-4), 739–773.

Gomez-Elvira, J., Armiens, C., Castaner, L., Dominguez, M., Genzer, M., Gomez, F., Haberle, R., Harri,

A.-M., Jiminez, V., Kahanpaa, H., Kowalski, L., Lepinette, A., Martin, J., Martinez-Frias, J., McEwan,

I., Mora, L., Moreno, J., Navarro, S., Pablo, M. A., Peinado, V., Pena, A., Polkko, J., Ramos, M.,

Renno, N. O., Ricart, J., Richardson, M., Rodriguez-Manfredi, J., Romeral, J., Sebastian, E., Serrano,

J., Torre Juarez, M., Torres, J., Torrero, F., Urqui, R., Vazquez, L., Velasco, T., Verdasca, J., Zorzano,

M.-P., & Martin-Torres, J. 2012. REMS: The Environmental Sensor Suite for the Mars Science Laboratory

Rover. Space Science Reviews, 170(Aug.), 583–640.

Grotzinger, J. P., Crisp, J., Vasavada, A. R., Anderson, R. C., Baker, C. J., Barry, R., Blake, D. F., Conrad,

P., Edgett, K. S., Ferdowski, B., Gellert, R., Gilbert, J. B., Golombek, M., Gomez-Elvira, J., Hassler,

D. M., Jandura, L., Litvak, M., Mahaﬀy, P., Maki, J., Meyer, M., Malin, M. C., Mitrofanov, I., Simmonds,

J. J., Vaniman, D., Welch, R. V., & Wiens, R. C. 2012. Mars Science Laboratory Mission and Science

Investigation. Space Science Reviews, 170(July), 5–56.

Hamilton, V. E., Vasavada, A., Haberle, R. M., de la Torre Juarez, M., Zorzano-Mier, M. P., Martin-Torres,

J., Armiens, C., Sebastian-Martinez, E., Rodriguez-Manfredi, J. A.., Martinez-Frias, J., de Pablo Hernan-

dez, M. A., Ramos, M., Richardson, M. I., Gomez-Elvira, J., & Team, MSL Science. 2013. Prelimanary

results From The Mars Science Laboratory REMS Ground Temperature Sensor At Rocknest. Presented

at the 43rd Lunar and Planetary Science Conference, The Woodlands, Texas.

Hayashi, J. N., Jakosky, B. M., & Haberle, R. M. 1995. Atmospheric eﬀects on the mapping of Martian

thermal inertia and thermally derived albedo. Journal of Geophysical Research: Planets, 100, 5277–5284.

Heavens, N. G., Richardson, M. I., Lawson, W. G., Lee, C., McCleese, D. J., Kass, D. M., Kleinbohl,

A., Schoﬁeld, J. T., Abdou, W. A., & Shirley, J. H. 2010. Convective instability in the martian middle

atmosphere. Icarus, 208(Aug.), 574–589.

53 Hynek, B. M., Beach, M., & Hoke, M. R. T. 2010. Updated global map of Martian valley networks and

implications for climate and hydrologic processes. Journal of Geophysical Research: Planets, 115.

Jakosky, B. M., Hynek, B. M., Pelkey, S. M., Mellon, M. T., Martinez-Alonso, S., Putzig, N. E., Murphy,

N., & Christensen, P. R. 2006. Thermophysical properties of the MER and Beagle II landing site regions

on Mars. Journal of Geophysical Research, 111.

Jaumann, R., Neukum, G., Behnke, T., Duxburry, T.C., Eichentopf, K., van Gasselt, S., Giese, B., Gwinner,

K., Hauber, E., Hoﬀmann, H., Hoﬀmeister, A., Kohler, U., Matz, K.D., McCord, T.B., Mertens, V.,

Oberst, J., Pischel, R., ReiB, D., Ress, E., Roatsch, T., Saiger, P., Scholten, F., Schwarz, G., Stephan,

K., Whlisch, M., & the HRSC Co-Investigator Team. 2007. The High Resolution Stereo Camera (HRSC)

Experiment on Mars Express: Instrument Aspects and Experiment Conduct from Interplanetary Cruise

through Nominal Mission. Planetary and Space Science, 55, 928–952.

Kieﬀer, H. H. 1976. Soil and Surface Temperatures at the Viking Landing Sites. Science, 194(4271),

1344–1346.

Kieﬀer, H. H. 2013. Thermal model for analysis of Mars infrared mapping. Journal of Geophysical Research:

Planets, 118(3), 451–470.

Kirk, R. L., Howington-Kraus, E., Rosiek, M. R., Anderson, J. A., Archinal, B. A., Becker, K. J., Cook,

D. A., Galuszka, D. M., Geissler, P. E., Hare, T. M., Holmberg, I. M., Keszthelyi, L. P., Redding, B. L.,

Delamere, W. A., Gallagher, D., Chapel, J. D., Eliason, E. M., King, R., & McEwen, A. S. 2008. Ultrahigh

resolution topographic mapping of Mars with MRO HiRISE stereo images: Meter-scale slopes of candidate

Phoenix landing sites. Journal of Geophysical Research: Planets, 113.

Kocurek, G., Bridges, N, Edgett, K.S., Goetz, W, Lewis, K. W., Madsen, M. B., Rubin, D. M., Sullivan,

R. J., & Team, MSL Science. 2013. Rocknest Sand Shadow At The Curiosity Field Site: Morphology,

Origin and Stabilization. Presented at the 43rd Lunar and Planetary Science Conference, The Woodlands,

Texas.

Malin, M. C., & Edgett, K. S. 2000. Sedimentary Rocks of Early Mars. Science, 290, 1927–1937.

Malin, Michael C., Bell, J. F., Cantor, B. A., Caplinger, M. A., Calvin, W. M., Clancy, R. T., Edgett, K. S.,

Edwards, L., Haberle, R. M., James, P. B., Lee, S. W., Ravine, M. A., Thomas, P. C., & Wolﬀ, M. J.

2007. Context Camera Investigation on board the Mars Reconnaissance Orbiter. Journal of Geophysical

Research: Planets, 112.

54 Masursky, H., Boyce, J. M., Dial, A. L., Schaber, G. G., & Strobell, M. E. 1977. Classiﬁcation and time of

formation of Martian channels based on Viking data. Journal of Geophysical Research, 82, 4016–4038.

McEwen, A. S., Eliason, E. M., Bergstrom, J. W., Bridges, N. T., Hansen, C. J., Delamere, W. A., Grant,

J. A., Gulick, V. C., Herkenhoﬀ, K. E., Keszthelyi, L., Kirk, R. L., Mellon, M. T., Squyres, S. W., Thomas,

N., & Weitz, C. M. 2007. Mars Reconnaissance Orbiter’s High Resolution Imaging Science Experiment

(HiRISE). Journal of Geophysical Research: Planets, 112.

Mellon, M. T, Jakosky, B. M, Kieﬀer, H. H, & Christensen, P. R. 2000. High-Resolution Thermal Inertia

Mapping from the Mars Global Surveyor Thermal Emission Spectrometer. Icarus, 148(2), 437–455.

Mellon, M. T., Fergason, R. L., & Putzig, N. E. 2008. The thermal inertia of the surface of Mars. Pages 399–

427 of: The Martian Surface - Composition, Mineralogy, and Physical Properties. Cambridge University

Press.

Michalski, J. R., Kraft, M. D., Sharp, T. G., Williams, L. B., & Christensen, P. R. 2005. Mineralogical

constraints on the high-silica martian surface component observed by TES. Icarus, 174(Mar.), 161–177.

Milliken, R. E., Grotzinger, J. P., & Thomson, B. J. 2010. Paleoclimate of Mars as captured by the

stratigraphic record in Gale Crater. Geophysical Research Letters, 37(Feb.).

Moore, H. J., Bickler, D. B., Crisp, J. A., Eisen, H. J., Gensler, J. A., Haldemann, A. F. C., Matijevic, J. R.,

Reid, L. K., & Pavlics, F. 1999. Soil-like deposits observed by Sojourner, the Pathﬁnder rover. Journal of

Geophysical Research: Planets, 104, 8729–8746.

Neukum, G., & Jaumann, R. 2004. HRSC: The High Resolution Stereo Camera of Mars Express. Pages

17–35 of: Mars Express: The scientiﬁc payload. ESA, Noordwijk.

Palluconi, F. D., & Kieﬀer, H. H. 1981. Thermal inertia mapping of Mars from 60◦ S to 60◦ N. Icarus,

45(2), 415–426.

Pelkey, S. M., & Jakosky, B. M. 2002. Surﬁcial Geologic Surveys of Gale Crater and Melas Chasma, Mars:

Integration of Remote-Sensing Data. Icarus, 160(2), 228–257.

Pollack, J. B., Haberle, R. M., Schaeﬀer, J., & Lee, H. 1990. Simulations of the general circulation of the

Martian atmosphere: 1. Polar processes. Journal of Geophysical Research: Solid Earth, 95, 1447–1473.

55 Presley, M. A., & Christensen, P. R. 1997. Thermal conductivity measurements of particulate materials 2.

Results. Journal of Geophysical Research: Planets, 102, 6551–6566.

Putzig, N., & Mellon, M. 2007. Apparent thermal inertia and the surface heterogeneity of Mars. Icarus,

191(Nov.), 68–94.

Putzig, N., Mellon, M., Kretke, K., & Arvidson, R. 2005. Global thermal inertia and surface properties of

Mars from the MGS mapping mission. Icarus, 173(Feb.), 325–341.

Putzig, N. E., Mellon, M. T., Herkenhoﬀ, K. E., Phillips, R. J., Davis, B. J., Ewer, K. J., & Bowers, L. M.

2013. Thermal behavior and ice-table depth within the north polar erg of Mars. Icarus, July.

Rogers, A. D., & Bandﬁeld, J. L. 2009. Mineralogical characterization of Mars Science Laboratory candidate

landing sites from THEMIS and TES data. Icarus, 203, 437–453.

Shorthill, R. W., Moore, H. J., Scott, R. F., Hutton, R. E., Liebes, S., & Spitzer, C. R. 1976. The ”Soil” of

Mars (Viking 1). Science, 194(Jan.), 91–97.

Smith, D. E., Zuber, M. T., Frey, H. V., Garvin, J. B., Head, J. W., Muhleman, D. O., Pettengill, G. H.,

Phillips, R. J., Solomon, S. C., Zwally, H. J., Banerdt, W. B., Duxbury, T. C., Golombek, M. P., Lemoine,

F. G., Neumann, G. A., Rowlands, D. D., Aharonson, O., Ford, P. G., Ivanov, A. B., Johnson, C. L.,

McGovern, P. J., Abshire, J. B., Afzal, R. S., & Sun, X. 2001a. Mars Orbiter Laser Altimeter: Experiment

summary after the ﬁrst year of global mapping of Mars. Journal of Geophysical Research: Planets, 106,

23689–23722.

Smith, M. D., Pearl, J. C., Conrath, B. J., & Christensen, P. R. 2001b. Thermal Emission Spectrometer

results: Mars atmospheric thermal structure and aerosol distribution. Journal of Geophysical Research:

Planets, 106, 23929–23945.

Smith, Michael D. 2004. Interannual variability in TES atmospheric observations of Mars during 1999-2003.

Icarus, 167(Jan.), 148–165.

Thomson, B. J., Bridges, N. T., Milliken, R., Baldridge, A., Hook, S. J., Crowley, J. K., Marion, G. M.,

de Souza Filho, C. R., Brown, A. J., & Weitz, C. M. 2011. Constraints on the origin and evolution of

the layered mound in Gale Crater, Mars using Mars Reconnaissance Orbiter data. Icarus, 214(Aug.),

413–432.

56 Tillman, J. E., Johnson, N. C., Guttorp, P., & Percival, D. B. 1993. The Martian annual atmospheric

pressure cycle: Years without great dust storms. Journal of Geophysical Research: Planets (19912012),

98, 1096310971.

Williams, R. M. E., Grotzinger, J. P., Dietrich, W. E., Gupta, S., Sumner, D. Y., Wiens, R. C., Mangold,

N., Malin, M. C., Edgett, K. S., Maurice, S., Forni, O., Gasnault, O., Ollila, A., Newsom, H. E., Dromart,

G., Palucis, M. C., Yingst, R. A., Anderson, R. B., Herkenhoﬀ, K. E., Le Mouelic, S., Goetz, W., Madsen,

M. B., Koefoed, A., Jensen, J. K., Bridges, J. C., Schwenzer, S. P., Lewis, K. W., Stack, K. M., Rubin,

D., Kah, L. C., Bell, J. F., Farmer, J. D., Sullivan, R., Van Beek, T., Blaney, D. L., Pariser, O., Deen,

R. G., & Team, MSL Science. 2013. Martian Fluvial Conglomerates at Gale Crater. Science, 340(May),

1068–1072.