Planet Four: Derived South Polar Martian Winds Interpreted Using Mesoscale Modeling

Using the P4 catalog, we create histograms of fan directions and fan lengths for each HiRISE image. A series of such histograms for ROI Giza (84.82 °S, 65.7 °E) are shown in Figure 5 (histograms for the other ROIs can be found in Appendix B, and derived P4 fan directions and lengths are in the supplementary data files). The left column shows histograms of fan directions (measured clockwise from north) for six HiRISE images acquired at L_s ranging from 185° to 227°. The right column shows histograms of fan lengths (in meters) for the same HiRISE images that are used for wind speed estimates (see Section 3.2). The bin size of these histograms varies between 1° and 10° for fan direction and between 1 and 3 m for fan length—individually determined for each HiRISE image depending on the number of fans that were detected in the image and the distribution of their directions and lengths. The scale of Y-axis (showing fractional number of fans) changes in accordance to bin width because the histogram is normalized so that its area is equal to 1. This normalization is required to make Weibull and Gaussian curve fitting (see more details on it below) fully automatic and thus procedurally identical for different HiRISE images that have vastly different absolute numbers of fans.

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Inspecting the histograms in Figure 5, we can determine the most probable fan direction. It is equal to the position of the maximum histogram value or—as we aim to reduce the impact of statistical variations within bins—the maximum of a smooth curve fitted to the histogram shape. We use the Weibull distribution to fit the histogram's shape and quantitatively determine the position of its maximum. The Weibull distribution function has two free parameters that are being varied in the fitting procedure: the shape parameter and the scale parameter of the distribution. This allows fits of asymmetrical distributions that are common among P4 histograms of fan directions and lengths. Then, we locate the position of the maximum (or maxima if there are two) of the fitted curve. We do not use the fitting parameters themselves. The position of the maxima serves as the most probable fan direction or fan speed for the subsequent analysis. We keep the fitting procedure simple and fully automatic without any manual alteration of either starting assumptions or fitting quality. This leads to less desirable fit quality for some histograms than could potentially be achieved with manual intervention: for example, the amplitude of the maximum is often fitted poorly. However, the main focus of the fitting is to find the locations of the maxima and maintain uniformity of analysis for all ROIs at all L_s. There are, however, the special cases (e.g., as in ROI Macclesfield in Figure 6) when the histogram has two well-determined modes. In those cases, the Weibull function fails to provide a viable fit. Instead, for bimodal distributions we chose to use a combination of two Gaussian functions. Each Gaussian has two free parameters to vary (mean and standard deviation), while their combination has also to account for the ratio of the two maximums. In either case, Weibull or double Gaussian, the aim is to determine the position of the maximum (or maxima) that determines the most probable fan direction (or length) for a given histogram. We can then plot the most probable fan direction versus L_s, as shown in the bottom panel of Figure 7.

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As evident from Figures 5 and 6, the location of the peak of the fan direction histograms changes. In the case of Giza (Figure 5), it shifts clockwise with season (L_s). This can be interpreted as being due to the shift in wind directions during jet eruptions that happened between these HiRISE observations. It is surprising how marked the shifts are between L_s ≈ 185° and L_s ≈ 200° and between L_s ≈ 203° and L_s ≈ 221°. One would expect that the fan deposits created during the early eruptions at L_s ≈ 185° would still be visible at L_s ≈ 200°. This is partially true: the histogram at L_s ≈ 200° is wider than at L_s ≈ 185°, which means that it includes the fans from previous eruptions. However, the newly created fans are so much more numerous that they dominate the histogram, shifting its maximum toward the north. The shift between L_s ≈ 203° and L_s ≈ 221° is even more pronounced: here the histogram becomes narrower at L_s ≈ 221°, which means that the fans from the previous image are no longer (easily) visible.

We can estimate histogram widths at FWHM either directly from histograms or mathematically from fitted Weibull curves. The histogram widths vary: the histogram at L_s ≈ 185° is the narrowest, and that at L_s ≈ 200° is the widest. The widths of the P4-derived wind direction histograms are not due to inaccuracies in the citizen scientists' measurements. No single correct direction of fans for the HiRISE image can be assigned from P4 data because fan directions differ throughout each image owing to local natural variations in topography and atmospheric conditions at the time of their eruption. In fact, each value entry in the histograms already has its own measurement error. The width of each of these histograms tells us about the physical properties of the distribution of fans, which often (but not always) can be extended to the distribution of winds. For example, a relatively flat distribution with a very wide histogram means that there was no consistent prevailing wind during eruptions of the jets—i.e., the eruption-time winds were constantly changing, the eruption times of day were significantly variable, and/or we are looking at the integrated record of multiple jet eruptions over an extended period of time that all got preserved. Another possibility at some sites is that the wind direction is strongly influenced by local topography and varies considerably in different parts of the area covered by the HiRISE image. As we noted above, this is not the case in Giza, nor is it for the majority of our ROIs, with possible exceptions being ROI Inca City and late spring at ROI Oswego Edge (see Section 6). In contrast, if the histogram shows a narrow well-defined peak, it means that the wind direction during jet eruptions was very stable (and possibly also that the eruption time of day is nearly constant).

In order to visualize the variability of P4 fan directions and lengths, we plotted the FWHM of the histogram at each L_s as an "error bar" (e.g., as in Figure 7). We remind the reader that these are only intended to convey a measure of the variability of P4 markings over each HiRISE image (and, by extension, the variability of the wind and/or eruption time of day) and not to indicate actual error bars (i.e., some kind of measurement quality metric).

The last detail about understanding P4 histograms is a warning about the integrated effect of the seasonal history of surface changes. The very first image in each ROI is acquired when the surface is fresh after the winter deposition of CO₂ ice. Very little dusty jet activity has happened yet, and thus we can treat the values derived from these first images as uncontaminated. The subsequent images tell a different and more complex story because the activity that is observed in the previous images inevitably leaves marks that can be observed later. We often do not have the means to differentiate how much of the previous fan markings are still visible in later images unless the statistics change dramatically (allowing us to neglect this effect). This is what happens in Giza between L_s ≈ 180° and L_s ≈ 230°: the fan directions and sizes constantly change, which means that new fans are being deposited. This is usually the case for most of the ROIs; however, in some it is not. If there is no significant change, it might be because either new fans are exactly the same as the old ones or there are no new fans. These two situations imply two quite different seasonal process scenarios, but they cannot be differentiated using the P4 data alone—in Section 6 we will discuss how the addition of atmospheric modeling may be used to differentiate between them.

Note that all of the above discussions in this section concerning histograms of wind direction can also be applied to histograms of fan lengths.

Using measurements of fan lengths from the P4 catalog, we aim to estimate wind speeds during the jet eruptions. Unlike the wind directions, which can be directly determined from fan directions, wind speed estimates require a more complex approach. We need to consider the state of the Martian atmosphere, physical properties of the materials involved, and the physics behind the jet eruptions. First, we need to determine whether in our problem the flow of the Martian atmosphere around a dust particle can be considered to be a continuum flow. The standard is to use the Knudsen number to evaluate whether the particle is in a continuous flow regime, molecular flow, or a mixture of the two. The Knudsen number indicates how rarefied the flow it describes is. Values of K_n less than 0.01 indicate continuous flow; values between 0.01 and 10, transitional flow; and values greater than 10, molecular flow. The Knudsen number depends on the state of the Martian atmosphere and a representative physical length scale of the problem (in our case, dust particle size):

$$\begin{eqnarray}&&{K}_{n}=\displaystyle \frac{\lambda }{{r}_{p}},\quad \lambda =\displaystyle \frac{2\eta }{{\rho }_{\mathrm{atm}}{V}_{\mathrm{atm}}}.\end{eqnarray} \tag{ 1 }$$

Here λ is the mean free path of CO₂ gas in the atmospheric layer under consideration, r_p is the dust particle radius, η is the dynamic viscosity of CO₂ measured at the atmospheric temperature, ρ_atm is the mass density of the atmospheric gas, and V_atm is the atmospheric gas molecular velocity.

We next consider dust and sand-sized particles gravitationally setting out of the polar spring atmosphere. The particle size distribution of such a mix is discussed in Section 3.2.1. We can estimate the relevant range of K_n during the south polar cold jet eruption season by obtaining selected atmospheric state variables (temperature, pressure, air density) from the Mars Climate Database (MCD, derived from Mars global climate model output; http://www-mars.lmd.jussieu.fr/mars/access.html) for a range of L_s and P4 ROI locations.

We need to consider the atmospheric layers closest to the surface (up to ∼200 m because that is the maximum height jets are able to penetrate upward; Thomas et al. 2010) and their minimum temperatures, because K_n is inversely related to T, and we are interested in the maximum values for K_n . Figure 8 shows that Knudsen number depends weakly on the atmospheric state and much more strongly on particle size. If we consider a commonly used limit for continuous flow (K_n < 0.01), then particles larger than 20 μm can be considered to remain in the continuous flow regime. For smaller particles we must additionally account for Cunningham slip-flow correction, allowing consideration of particle movement in the mixed regime (between molecular and continuous regimes).

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The terminal velocity of particles falling through the atmosphere is determined by the gravity of Mars and the viscosity of the dominantly CO₂ atmosphere. Applying Stokes's law,

$$\begin{eqnarray}&&\displaystyle \frac{4}{3}\,\pi \,{r}_{p}^{3}\,{\rho }_{p}\,g=6\,\pi \,{r}_{p}\,\eta \,{V}_{\mathrm{fall}},\end{eqnarray} \tag{ 2 }$$

where r_p is again the particle radius, ρ_p is its mass density, g is the gravitational acceleration, and V_fall is the terminal velocity of the particle. Thus, after accounting for the Cunningham slip-flow correction (Cunningham 1910; Schmid et al. 2002), terminal velocity is

$$\begin{eqnarray}&&\begin{array}{l}{V}_{\mathrm{fall}}=\displaystyle \frac{2{\rho }_{p}\ g\ {r}_{p}^{2}}{9\,\eta }(1+\alpha {K}_{n}),\\ \alpha =1.246+0.42\ \exp \left(\displaystyle \frac{-0.87}{{K}_{n}}\right).\end{array}\end{eqnarray} \tag{ 3 }$$

The most probable atmospheric gas molecular velocity is

$$\begin{eqnarray}&&{V}_{\mathrm{atm},\mathrm{mp}}=\sqrt{\frac{2\,{k}_{{\rm{B}}}\,{T}_{\mathrm{atm}}}{{M}_{\mathrm{atm}}}},\end{eqnarray} \tag{ 4 }$$

where k_B is the Boltzmann constant, T_atm is the temperature of the atmospheric layer under consideration, and M_atm is the molar mass of the atmospheric gas. We set the upward air advection velocity to zero and neglect the turbulence of the atmosphere—advection and turbulence both decrease sedimentation velocity. Thus, we calculate an upper limit on gravitational sedimentation velocity, which will eventually yield a lower limit on the wind speed.

We can now estimate V_fall—the terminal velocity of the particle—depending on its size. We assume that each mineral particle is entrained by the general atmospheric flow immediately after reaching the maximum height to which particles can be brought up by the jets (i.e., after its vertically oriented momentum becomes insignificant). Due to our assumptions, vertically it is influenced only by gravity and small-scale atmospheric drag, and horizontally it moves with the wind. Therefore, if we know the maximum height to which particles can be brought up by the jet (H_jet) and the length of the resulting surface deposit (L_fan) and ignore horizontal drag effects, the calculation of the inferred wind speed (S_wind) becomes straightforward:

$$\begin{eqnarray}&&{S}_{\mathrm{wind}}=\frac{{L}_{\mathrm{fan}}\,{V}_{\mathrm{fall}}}{{H}_{\mathrm{jet}}}.\end{eqnarray} \tag{ 5 }$$

The length of the fans is obtained from the P4 catalog, the terminal velocity of the particles that were propelled up by the CO₂ jet depends on the size and composition of the particles (see Section 3.2.1), and the assumed height of the jet is discussed in Section 3.2.2.

3.2.1. Particle Size Distribution

3.2.2. Jet Height

The MRAMS simulations discussed here were configured with telescoping south polar stereographic grids (see Figure 9) in order to consistently characterize the atmosphere across the entire circumpolar region poleward of 70 °S at higher resolution, while still being computationally practical. The annulus between ∼70 °S and ∼77 °S was covered by a grid spacing of <20 km (the third telescoping grid), while latitudes poleward of ∼77 °S (poleward of ∼71 °S in some areas, due to the grid's noncircular shape) had a grid spacing of <5.8 km. Note that the grid spacing on each grid decreases radially away from the south pole owing to the map projection used. The vertical grid consisted of 70 layers, ranging in thickness from ∼5 m near the surface to ∼2 km at the top (at a height of ∼60 km). Within the lowest 100 m from the ground, the model configuration had seven vertical layers.

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MRAMS was run at each of seven seasons (L_s = 180°, 190°, 200°, 210°, 220°, 230°, 240°), encompassing most of the time of year that fans were observed to be active. Each seasonal run was centered near the nominal midpoint of each L_s window and was 4.5 sols in duration, with the first 1.5 sols assumed to be spin-up (based on the time primary model output fields took to settle into a more repeatable pattern at large spatial scales). Thus, the final 3 sols of MRAMS output (per seasonal window) were analyzed. The model state was output with a frequency of 10 Mars minutes. For computational expediency, no cloud parameterizations were used, although we recognize that some water-ice clouds are observed to be present in this region and at this season. Atmospheric dust loading for the radiative transfer calculations was set to an MY24 scenario (i.e., including no large dust storms). Dynamic CO₂ ice evolution was permitted.

A time- and spatially dependent effective surface roughness length was used to take into account both the microscale aerodynamic surface roughness (i.e., that due to rocks, sand, and local surface undulations) and the drag on the wind exerted by the magnitude and orientation of sub-grid-scale topography (e.g., dunes, yardangs, gullies, craters; depending on the model grid spacing). Until relatively recently, MRAMS (and many other Mars atmospheric models) would typically use a nearly constant effective surface roughness length of order 1 cm (although often a smaller value over ice) and would explicitly neglect the effect of sub-grid-scale topographic drag. Landed missions and the work of Hébrard et al. (2012) strongly suggest that the microscale aerodynamic surface roughness length is significantly less than ∼1 cm over much of the planet. Thus, the continued use of such a large value amounts to the implicit inclusion of a spatially variable sub-grid-scale topographic drag, but in a way that is not consistent with the actual topography. In order to better include the effects of sub-grid-scale topographic drag, the technique of Georgelin et al. (1994; itself largely based on Mason 1991) was implemented to calculate the effective surface roughness length using 1/128° MOLA topography and the global microscale aerodynamic surface roughness length map of Hébrard et al. (2012). Note that sub-grid-scale topography drag metrics are a function of wind direction, so the effective surface roughness length at each horizontal grid point is recalculated every time step.

Below we describe results for ROI Giza (84.8 °S, 65.7 °E) in detail. We chose Giza for this because it has consistent P4 data coverage for every L_s interval through the study season and the fit between MRAMS model and P4 data is consistently good.

It is often insightful to understand the context of the location under discussion, as well as its topographical setting. The south polar region of Mars is topographically complex, with large craters, plateaus, scarps, and the like. Subsequently, each ROI has unique local and nearby features (along with spatially complex seasonal ice emplacement/sublimation) that modulate its environment and winds. Furthermore, the topography and surface characteristic fields used by any numerical model are constrained by both the model's finite grid spacing and the resolution/accuracy of available observational data sets—it is always important to realize how reality differs from what the model "sees." Even so, at many ROIs the MRAMS configuration used in the work captures much of the relevant topographic detail. To accompany our comparisons of modeled and inferred winds, contextual imagery and topographic context maps for all ROIs are included in Appendix B.

Figure 11 puts one of the HiRISE images measured by P4 (narrowest image in the figure) at ROI Giza into a broader context with a wider CTX image underneath, and MOLA (grayscale) shaded relief in the background. The dark lobes that are evident in the images are fields of myriad dark fan-shaped deposits atop the seasonal CO₂ ice. In these snapshots they are chiefly situated on a modest equator-facing slope. Giza is located near a small outcrop of SPLD, and the local surface is relatively smooth with only a few small craters visible. Figure 12 contains a comparison of the MRAMS topography used for this work (left and middle panels) with the higher-resolution MOLA 1/128° topographic data set (right panel) for ROI Giza—the area shown is a few times larger than that shown in Figure 11. The two topographic fields are generally similar at this ROI, with the narrower valleys/ridges being smoothed out in the model field—note that there is more than a kilometer of vertical relief in the area shown. However, the overall orientations of the valleys, ridges, and plateaus are resolved by MRAMS.

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A relatively simple conceptual model of near-surface winds in the south polar region of Mars during early spring chiefly includes cold/dense katabatic flows heading locally downhill and a more diffuse sublimation flow oriented toward the equator—both of these are subject to the Coriolis effect, which nudges a northward (equatorward) flow to the left in the direction of movement (i.e., toward the northwest and west). Later in the season, the seasonal ice becomes increasingly patchy and more stochastic local and regional thermal and baroclinic circulations become superposed on the former pattern—more day-to-day (and intraday) wind variability would be expected in this period.

Figure 12 also shows the P4-derived wind directions at ROI Giza for MY 29 and MY 30, color-coded by L_s window. Note that although the orientation vectors are displayed as if they were 60 km long (for easy viewing), they are most likely only valid at a much smaller scale (near the circle at the vectors' origin). The P4-inferred flow toward the NW is consistent with downhill katabatic/sublimation flow acted upon by the Coriolis effect. The multiple inferred wind directions at L_s ≈ 220° (although in two different years) are probably due to more stochastic baroclinic circulations in midspring.

Figure 13 shows an example of P4-to-MRAMS comparison for ROI Giza, with columns representing seasonal progression from left (L_s ≈ 180°) to right (L_s ≈ 240°). Rows are the 3 consecutive sols from the MRAMS output (as described in Section 5.1). Vertical dashed lines on the plots show P4-derived wind speeds, and horizontal lines indicate P4-measured fan directions (assumed to be the downwind direction). Dashed lines were chosen intentionally for wind speed to signal that those values must be considered more cautiously owing to multiple assumptions associated with the P4 wind speed estimates. The plotted points represent MRAMS-predicted winds at two different altitudes AGL. Those that are black are from ∼5 m AGL, while those in cyan are from ∼91 m AGL. This type of figure is designed to illustrate the variability of winds within the lowest 100 m of the atmosphere (i.e., encompassing the maximum height assumed for the CO₂ jets) at this ROI. Note that the wind variability within each sol is due to changes in local and regional solar heating throughout each day, interacting with topography and regional-scale circulations. The level of variability between sols in each L_s window is most likely due to transient regional-scale atmospheric eddies (vs. some sort of model spin-up effect).

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The presence of several vertical and horizontal red lines on the same subplot indicates that several HiRISE images were acquired during the L_s interval between this subplot and the previous one. If any of those lines are instead gray (none at this ROI), it indicates that multiple disparate fan directions are present within each HiRISE image. In the case of Giza (Figure 13), there was just one HiRISE image acquired between L_s ≈ 175° and L_s ≈ 185°, three images between L_s ≈ 185° and L_s ≈ 195°, and so on.

At Giza, P4 data fit well to MRAMS model output for both derived wind speeds and fan directions between L_s ≈ 180° and L_s ≈ 210°. There are some differences between sols, with some being more stable (sols 1 and 3), while sol 2 exhibits modeled wind directions that shift through virtually all directions starting at L_s ≈ 200°. Nevertheless, in all 3 sols during this early spring period, P4-derived speeds and directions have corresponding MRAMS values within the tolerances described at the beginning of Section 7. Note that it is common at this ROI for the MRAMS winds to be strongest at or near the P4-inferred wind direction(s).

That is not to say that partial ambiguities do not exist at this ROI. For example, between L_s ≈ 200° and L_s ≈ 210° the directions and lengths of P4 fans do not change much. However, MRAMS winds during these L_s windows vary in direction during the day significantly more than at earlier L_s, and there are many fewer values that match the P4 directions satisfactorily. This could indicate that there are no (or few) new CO₂ jet deposits being created during the L_s ≈ 210° window, and thus the fan orientations do not reflect the more variable wind directions estimated by MRAMS during that same period. Alternatively, the CO₂ jets (and thus fans) may require certain special conditions to occur—perhaps particular times of day and/or strong winds—strongly limiting the range of modeled wind directions that are relevant.

Later, at L_s ≈ 220°, some P4-measured fan orientations show a clear shift from northwest to north or north–northeast, but the fans are not particularly long (e.g., at L_s ≈ 190° the fans are significantly longer). MRAMS also shows a change in behavior with winds now varying widely through the day for all 3 sols, but the P4-inferred wind directions occur when the winds (and vertical wind shear) are strongest. This fan direction shift could thus potentially be due to redistribution of the material already on the ground by strong winds, and not active jet eruptions—perhaps consistent with the above suggestion of jet activity ceasing at this ROI before the L_s ≈ 210° window.

At L_s ≈ 230° and L_s ≈ 240° P4 and MRAMS wind directions match in only 3 out of the 6 sols of MRAMS output analyzed, again when winds are strong. The P4-measured fan lengths are also quite short, which is poorly consistent with CO₂ jet activity occurring during periods of strong winds. Also, note that P4-inferred winds are less robust for this time interval and ROI owing to patches of relatively thin (and thus lower-albedo) surface CO₂ ice. Overall, our results are more consistent with no CO₂ jet activity at Giza during these L_s windows.

Figure 14 presents a different view of the same MRAMS and P4 data for Giza during sol 3 of each L_s window. The first row of plots in the figure is similar to the bottom row of Figure 13, except that only winds from the lowest model are shown and the points are colored by their daily normalized integrated insolation (DNII) values. This DNII quantity is zero at local midnight and is a measure of the time-integrated insolation energy that has reached the ice surface by each model output time step (normalized to a maximum value of unity). It is intended to approximately highlight times of day when solar energy may be actively sublimating CO₂ gas on the underside of the slab ice, as hypothesized by the Kieffer model. Note that DNII is zero until the Sun rises, has a value of 0.5 near local noon, and reaches a value of 1 at sunset (and remains 1 until midnight, crudely representing the potential ability of energy stored in the subsurface earlier in the day to affect the jet process). For L_s ≈ 180° through L_s ≈ 200° at ROI Giza, MRAMS wind directions match P4-inferred values when DNII is generally >0.5. However, for the later L_s windows the DNII is generally very small when the P4 and MRAMS wind directions match. One possible way of interpreting this is that CO₂ jets are active in the afternoon and/or early evening during the L_s ≈ 180° through L_s ≈ 200° windows but cease to be active after that.

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The second row of plots in Figure 14 still shows downwind direction on the y-axis, but the x-axis is now local Mars solar time (LMST) in Mars hours. Points representing MRAMS-simulated downwind directions are colored according to the simulated wind speeds, and black horizontal lines represent P4 fan directions. Shaded areas indicate the times when the Sun is below the horizon (nighttime) for this ROI. At Giza, the Sun does not set at and after about L_s ≈ 200°—when the season progresses into so-called "polar day." The Sun during the polar day goes up and down (low in the sky) with respect to the horizon but never sets below it, while constantly changing its azimuth. This creates special illumination conditions where nearly all inclined surfaces collect solar energy more efficiently than flat ground. In this setting, the local topography might be a defining factor with regard to the level of CO₂ jet activity. This series of plots is intended to more clearly indicate the time(s) of day when MRAMS and P4 wind directions match (and therefore when the jet eruptions may occur). Even so, at ROI Giza no consistent jet eruption time of day is evident.

Note that there are three ROIs with names that start with "Manhattan" that lie within 1° of latitude from each other: Manhattan Classic (86.4 °S, 99 °E), Manhattan Cracks (86.2 °S, 99 °E), and Manhattan Frontinella (87 °S, 99.1 °E). They all share a similar topographic setting and are Category II sites, but Manhattan Classic has P4 fan measurements for more L_s windows than the other two.

From the context information in Figure 15 we can see that ROI Manhattan Classic is situated on complex terrain and includes several differently oriented slopes. Looking at the topography alone, in Figure 16 we see that Manhattan Classic is situated near the head of Chasma Australe. Furthermore, all of the Manhattan ROIs are located on the eastern side of the chasma (see Appendix B), with the topography near Manhattan Classic being the most complex. One might be tempted to assume that cold springtime near-surface airflow so close to such a large valley would be oriented parallel to and down the valley—and the model output indicates that wind deeper within Chasma Australe does just that. However, the air flowing down the chasma has to originate from somewhere, and MRAMS indicates that it flows approximately westward off the SPLD lobe to the east, crosses the Manhattan ROIs, and descends into the chasma. Later in the spring season, differential sublimation of seasonal ices (and heating of patchy ice-free areas) on the various local slopes may also modulate the wind—this is much more challenging for atmospheric modeling to capture. The P4-measured fan directions in Figure 16 are consistent with the schematic flows described above, with fans oriented SW and W–SW during the earliest L_s windows and toward the W–NW later.

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Figure 17 shows a P4–MRAMS comparison for ROI Manhattan Classic. Similar plots for Manhattan Cracks and Manhattan Frontinella can be found in Appendix B. At the earliest observed L_s ≈ 180°, the fit between MRAMS and P4 5 m and 91 m AGL wind directions and speeds for Manhattan Classic is inconsistent for sol 2—only the modeled 91 m AGL winds on sol 2 exhibit a near match to the P4-inferred values. Later during the L_s ≈ 200° window, Manhattan Classic modeled winds generally match with the P4 data for wind directions, but the associated speeds poorly fit for two-thirds of the sols (the 91 m wind speeds get closest to matching). At L_s ≈ 210° and L_s ≈ 220° wind directions do not match between the model and P4 in 2 out of 3 sols (even at the 91 m level). During this period, P4 data indicate significantly higher derived wind speeds than the MRAMS model predicts at ∼5 m AGL (but the wind speeds higher above the ground match). At L_s ≈ 240° the situation changes to a good fit for wind directions and a somewhat poor fit for speeds, with P4-inferred wind speeds being on the smaller side of simulated speeds.

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Similar to at ROI Giza, there is a clear tendency at Manhattan Classic for the wind direction matches to be when the modeled winds are strongest. The 91m-to-5 m vertical wind shear (Figure 17) is often minimal (and even solidly negative) at this ROI, indicating shallow flows of relatively dense air that hug the surface (e.g., katabatic winds). At this ROI there does not appear to be a clear time of day that the CO₂ jet eruptions prefer to occur at (see Appendix B).

In this category there are two ROIs—Buenos Aires and Schenectady. These illustrate two different cases of unusually severe mismatch between MRAMS and P4-derived wind directions and speeds. To determine why, we delve into the rich details that our dual approach (spacecraft imagery + mesoscale atmospheric modeling) provides.

ROI Buenos Aires (81.9 °S, 4.8 °E) is a roughly 25 km long north–south-oriented patch of dark fans situated just west of some hilly and mountainous terrain (Figure 18). Looking at the topography (Figure 19), these fans are located on the western edge of a modest valley, and the hilly terrain seen to the east in Figure 18 is on a slope leading upward to the edge of an isolated mountain range approximately 40 km to the east. It is difficult to predict a priori what the diurnal near-surface airflow might be at this ROI, as, unlike Giza and Manhattan Classic, it is relatively far from the SPLD (and their substantial modulation of airflow). However, katabatic flow toward the west (originating in the mountains to the east) might be expected at "night" (the portion of the polar day when the insolation is weakest)—if no other regional flows eclipse them.

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Table 2 indicates that at ROI Buenos Aires there are no P4 measurements for the L_s ≈ 180°, 190°, and 230° seasonal windows. The modeled and P4-inferred wind directions do not match at all for the 210° and 220° L_s bins and only match for 1 out of 3 sols within the other two L_s windows.

Figure 20 (top row) shows sol 3 of the P4-to-MRAMS comparison for ROI Buenos Aires. It (and the full plot containing all 3 sols in Appendix B) shows that the dominant near-surface airflow in the MRAMS output is toward the south or southwest (roughly parallel to the eastern mountains). This flow appears to be due to a complex combination of regional circulations to the north and south. Meanwhile, the available P4 fan directions are toward the east or E–NE (upslope toward the eastern mountains).

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In HiRISE imagery from L_s ≈ 182°–195° (not measured by P4) at Buenos Aires there appear to be multiple fan orientations at each vent that are more consistent with the MRAMS wind directions at those L_s. The HiRISE data then indicate that starting around L_s ≈ 196° the eastward fan orientations become more common, and they are dominant by L_s ≈ 206°—especially in the north and central parts of the ROI. Furthermore, MRAMS output from only 10 km to the east (81.9 °S, 6.0 °E); Figure 20, bottom row) shows wind directions that match the P4-inferred ones—this is only two model grid points away from the ROI centroid. The 3D model output (not shown) also indicates that as the insolation gets stronger in early spring at this ROI, differential heating of the air over the mountains to the east during midday induces upslope flow. Thus, it appears that the apparent severe mismatch between MRAMS and P4-inferred wind directions at ROI Buenos Aires is probably merely due to insufficient horizontal spatial resolution in the model that does not resolve the full complexity of the hilly slope and mountains to the east. The lack of P4 measurements for the earlier L_s windows also played a role in its current categorization.

ROI Schenectady (85.0 °S, 95.0 °E) is located on the western slope of Chasma Australe, down valley of the Manhattan ROIs. Only one HiRISE image of this area was analyzed by P4 (shown in Figure 21). Even so, for that image we have robust fan statistics with smooth distributions for both derived wind speeds and inferred directions. Figure 21 shows the context of ROI Schenectady, with the dark fans in the HiRISE image (acquired at L_s ≈ 2073) located midslope, but avoiding the valley floor. The associated model topography (Figure 22) indicates that the horizontal resolution of the model configuration used here broadens the lateral slopes of Chasma Australe and poorly resolves the secondary and tertiary SPLD troughs. It also shows that the P4-inferred fan direction during the L_s ≈ 210° window is toward the northeast—consistent with katabatic flow into the chasma.

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Table 2 indicates that at ROI Schenectady there was a match between P4-inferred and MRAMS wind directions on sol 1, but only near matches during the other 2 sols. Modeled wind speeds were also consistently much lower than the P4-derived value, cementing the Category III classification for this ROI. Figure 23 (top row) shows sol 2 of the P4-to-MRAMS comparison for ROI Schenectady. It (and the full plot containing all 3 sols in Appendix B) shows that modeled wind directions at/near the P4-measured fan direction are more common during five out of the seven L_s bins. Having P4 measurements at other seasonal windows would have likely helped this ROI remain in Category II. Furthermore, MRAMS output from only 12 km to the west (85.0 °S, 92.6 °E; Figure 20, bottom row) shows wind directions that match the P4-inferred ones—this is only two model grid points away from the ROI centroid. Thus, it appears that the apparent mismatch between MRAMS and P4-inferred wind directions at ROI Schenectady is probably merely due to insufficient horizontal spatial resolution in the model that unrealistically broadens the lateral slope of Chasma Australe, leading to relevant wind direction shifts occurring farther west than is accurate.

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In the text below we list specific notes for the rest of the ROIs listed in Table 2. We include several of the most relevant associated plots to aid discussions of wind behavior at some ROIs. All other plots (P4–MRAMS comparisons, topographic maps, and context images) for these ROIs can be found in Appendix B.

Albany (81.93 °S, 60.4 °E), Category I: Topographically, Albany is located on a relatively smooth equator-facing slope at the edge of the SLPD. P4 measured fans at this ROI starting at L_s ≈ 221°, and the resulting wind speed and direction estimates fit well to the analogous fields from MRAMS. According to MRAMS, at earlier seasons (L_s ≈ 180°–220°) the wind direction with the strongest winds (and strongest wind shear) remains quite stable, even matching well with the later-season P4-inferred wind directions. The direction of these winds is consistent with katabatic flows descending from the SPLD. Thus, MRAMS does not provide any obvious additional information about when the fans that were observed at ROI Albany by HiRISE were created.

Atka (86.98 °S, 169.7 °E), Category II: Topographic variations near this ROI are extreme: depressions, high hills, valleys, and the ragged edge of the SPLD exhibit, representing over 1 km of relief. Note that there is a deep north-to-south depression just southeast of Atka that the current MRAMS grid configuration does not resolve. It is not clear from the context alone what the primary wind direction(s) might be. For this high-latitude ROI, there are three P4-analyzed HiRISE images: the first was acquired at L_s ≈ 212°, the second at L_s ≈ 243°, and the third at L_s ≈ 250°. The fit between MRAMS and P4 is better for the first image (L_s ≈ 210° window) and barely satisfactory for the second (L_s ≈ 240° window), with 2 out of 3 sols not matching well. Similar to Albany, the modeled wind pattern is relatively stable prior to L_s ≈ 210° and shows a dominant wind direction that is near the measured fan direction at L_s ≈ 210°. Note that both the P4–MRAMS wind direction match and near match at L_s ≈ 240° did not coincide with strong winds. The last image did not provide good P4 fan statistics, which indicates that by L_s ≈ 250° the fan deposits visually blended in with the underlying substrate.

Bilbao (87 °S, 127.3 °E), Category II: This ROI is located at relatively high altitude on a lobe of the SPLD, with the topographic gradient downhill in all cardinal directions except south. Note that there are significant SPLD troughs that MRAMS resolves poorly. In this setting one might expect Bilbao to experience katabatic flow directed toward the north and west. The P4-inferred wind directions shift from toward the west at L_s ≈ 180° to toward the northwest by L_s ≈ 200° and then appear to rotate further clockwise (to N–NE) by L_s ≈ 230°. The fit between P4-inferred and MRAMS wind directions is overall good (but inconsistent between sols) at L_s ≈ 180°–190°, but it worsens in the L_s ≈ 200° window. MRAMS wind speeds near the matching wind directions are generally not consistent with the P4-derived ones throughout this early part of the season—instead, the direction of the strongest winds seen in the MRAMS results is about 180° from the P4-inferred ones. This indicates that the model prefers directing flow down the opposite side of the SPLD lobe, but perhaps this preference would be reversed if the model had better resolved the nearby SPLD troughs. Later at L_s ≈ 230°–240°, there are MRAMS wind directions (coincident with strong winds and wind shear) that match P4 results, but they occur near local midnight when insolation is at a minimum (note that the Sun never sets at ROI Bilbao at these L_s). This is unexpected because the Kieffer model of jet/fan occurrence is driven by insolation. However, perhaps residual pent-up gas pressure under the ice slab (produced at times of day with greater insolation) is only able to be released when the ice is more brittle.

Binghamton (73.53 °S, 339.5 °E), Category I: This is our most equatorward ROI and is located in the heavily cratered southern highlands, in a basin near higher terrain in the north (Figure 24). Binghamton has a P4-analyzed HiRISE image at L_s ≈ 223° that indicates a wind direction (E–SE) and speed that is a good match to the MRAMS results in the L_s ≈ 220° window. The MRAMS results indicate that from L_s ≈ 180° to 210° the primary wind direction is between east and southeast, consistent with the later P4 results. However, during the L_s ≈ 220° window as the seasonal cap edge draws nearer, a second flow regime is established (blowing toward the west and southwest), and it becomes more dominant as the season wears on. Late in the spring, at L_s ≈ 254°, P4 data show that there are no longer any visible fans.

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Buffalo (82.5 °S, 80 °E), no category: This ROI is located near the edge of the SPLD, where, again, the current MRAMS resolution cannot resolve the troughs well. Despite having two HIRISE images for this ROI—at L_s ≈ 224° and 241°—the P4 fan statistics are poor here and prohibit us from estimating any wind properties. There are two possible explanations for the citizen scientists not marking many fans at those L_s: either CO₂ jet activity in this location is infrequent then, or the wind is calm at the time of jet eruptions (leading to spots/blotches, not fans). The MRAMS results indicate that the strongest early spring winds at Buffalo generally flow toward the northwest and west, but by the L_s ≈ 210° window, the strongest winds flow more toward the southwest. MRAMS indicates that the near-surface wind direction with the strongest wind speeds becomes much more variable at and after the L_s ≈ 220° window but does not indicate a slowdown of the winds as the poor P4 statistics might suggest—instead, the winds significantly increase in magnitude. This suggests that the jets at ROI Buffalo may be largely inactive by L_s ≈ 224°.

Caterpillar (74.22 °S, 168.5 °E), Category I: This is the second most equatorward ROI and is situated on a plateau within complex terrain, north of a regional ridge. The current MRAMS configuration resolves many of the salient topographic features, but many nearby smaller craters and valleys are poorly resolved. P4 analyzed one HiRISE image at L_s ≈ 196°, and those data show a main wind direction peak, as well as a little secondary peak. MRAMS simulations also show a two-peaked distribution of wind directions on some sols in the early spring. Despite the complex topographic setting, MRAMS results match the P4-inferred wind directions well during the L_s ≈ 200° window—once again, MRAMS wind speeds (and vertical wind shear) are strongest at the matching wind directions.

Cortland (82.69 °S, 273.1 °E), no category: Situated within the jagged northern edge of an SPLD lobe, much of the important topography near this ROI is not resolved well by the current MRAMS configuration. P4 fan statistics are too poor here to calculate the dominant wind direction. MRAMS results indicate moderately strong winds at Cortland, with two main wind directions in early spring—one toward the northwest and west one, and another toward the northeast and east.

Geneseo (85.18 °S, 92 °E), no category: This ROI is located high upon the western slope of Chasma Australe, southwest of ROI Schenectady. It lies near an SPLD trough that is not well resolved by the current MRAMS configuration. P4 fan statistics are too poor here to calculate the dominant wind direction. In early spring, MRAMS indicates moderate winds toward the northeast, north, and northwest at Geneseo. Later in the spring, the wind directions become more variable, but the strongest modeled winds blow toward the E–NE.

Halifax (87 °S, 72.3 °E), Category I: This ROI is on a broad slope at the eastern edge of an SPLD lobe, with some SPLD troughs relatively nearby. The head of Chasma Australe is about 100 km downhill toward the northeast, and it might be expected that katabatic flows would generally follow this path. P4-inferred winds are available at L_s ≈ 214° and match well with MRAMS output in the L_s ≈ 210° window, although this match occurs near local midnight. Note that the modeled 91 m/5 m wind shear peaks near the matching wind direction.

Inca City (81.38 °S, 295.8 °E), Category II: This is probably the most challenging location among our ROIs for both P4 analysis and atmospheric modeling because of its local topography that includes 300 m high ridges arranged in square patterns (that the current MRAMS configuration cannot resolve). More generally, Inca City is located on the northeast side of a sizable ridge, with higher-elevation regional topography to the west and south. The near-surface winds here would appear to be affected by both the influences of mesoscale atmospheric dynamics and very localized insolation-driven phenomena related to the slopes of the local ridges mentioned above. Thus, it is surprising to see a good fit between P4-inferred and MRAMS wind directions (Figure 25) in the early spring. P4 fan directions predominantly point downhill on the sides of the local ridges. Even so, the first fans (i.e., within the L_s ≈ 180° window) appear on the equator-facing slopes of those ridges because these slopes are the first to be illuminated by the early spring Sun. Later fans appear on the other slopes, resulting in a secondary peak that appears in the P4 fan direction histograms. One can follow the development of the second peak (as well as the third, fourth, up to the seventh peak) in the wind direction histograms in Figure 26. It may or may not be a coincidence that the two dominant P4-inferred wind directions generally coincide with the two dominant flow directions in the MRAMS results (toward the NW/W–NW and E–NE/NE). In the context of the larger-scale topography, winds toward the northwest would be flowing roughly parallel to and around the sizable ridge that Inca City is located upon, and winds toward the northeast would be flowing downhill perpendicular to the slope (like katabatic flow). The deflection of (and drag on) the cold airflow by the local steep ridges at this ROI may either modulate this larger-scale flow or mimic it. During the L_s ≈ 230° and 240° windows, the MRAMS wind directions only inconsistently match the P4-inferred ones—perhaps the unresolved effects of the local ridges are more important at these times.

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Ithaca (85.13 °S, 180.7 °E), Category II: Located just north of a weakness in the SPLD, this ROI has a broad regional plateau to its north and channels, depressions, and mesas to the south. One might expect katabatic flows to generally be directed southward here and be turned toward the left (east in this case) by the Coriolis effect. Indeed, in the early spring P4-inferred wind directions are generally toward the E–SE at Ithaca, matching the MRAMS results well through the L_s ≈ 200° window. During the L_s ≈ 210° window, MRAMS wind directions have shifted further toward the east and nearly match the P4 results. The strongest winds and vertical wind shear occur at/near the matching wind directions during the early spring period. MRAMS results indicate that the wind directions become more variable during the L_s ≈ 230° and 240° windows, and where the wind directions match between P4 and MRAMS in the L_s ≈ 240° bin, wind speeds are not consistently strong. At ROI Ithaca the P4-inferred wind direction does not strongly change throughout the spring. This is an unusual case among our ROIs and means that from P4 data alone we cannot determine whether the fans seen in the second image of the seasonal sequence are new fans or they are the same ones that appeared already in the first image of that sequence.

Macclesfield (85.4 °S, 103.9 °E), Category II: This ROI is located on a relatively narrow ridge of the SPLD east of and overlooking Chasma Australe. This ridge is resolved partially, but not well, by the current MRAMS configuration. As with the other ROIs near Chasma Australe, one might expect overall flow to be directed toward the chasma. At Macclesfield, P4 fan distributions show a peculiar feature—one wind direction dominates the distribution in the early spring, but after L_s ≈ 190° a secondary peak appears. It indicates flow in almost the opposite direction of the primary peak, and at L_s ≈ 214° it has almost as many fans as the dominant direction (see the histogram in Figure 6). MRAMS results only show hints of a strongly bimodal wind direction distribution at this location, but they do strongly reproduce the main P4-inferred direction. Overall, the MRAMS and P4-inferred wind directions match well, except for MRAMS not exhibiting the secondary peak during the L_s ≈ 200° window. The dominant P4-inferred wind direction keeps shifting slowly until the end of the spring interval studied here, which suggests that new fan deposits are being created even later in spring. We propose an explanation for this behavior: Macclesfield has two distinct areas, one of which (the western portion of the ROI) has a slope more favorable for early CO₂ activity. CO₂ jets start erupting in this area and are directed by the winds dominant at early L_s. Later in spring, the other area (the eastern portion of the ROI) becomes active. By this time not only have the atmospheric dynamics shifted and the dominant winds changed, but the time of jet eruptions might have shifted as well. We do not know how big this shift is, but it is reasonable to expect a shift, especially at the beginning of the polar day season. At the latitude of Macclesfield polar day begins at around L_s ≈ 200°, coinciding with the first appearance of the secondary peak in P4-inferred wind directions.

Oswego Edge (87 °S, 86.4 °E), Category II: This ROI is perched above and just southwest of the head of Chasma Australe, and again one might generally expect katabatic flows into and down the chasma. A large SPLD trough lies just to the north, but it is not well resolved by the current MRAMS configuration. P4 wind directions consistently match well with MRAMS model results, but early in the spring the associated MRAMS wind speeds are often too weak to match the P4-derived ones well. However, later in the season (during and after the L_s ≈ 230° window) the P4-derived and MRAMS wind speeds (near the matching wind direction) are consistent with each other. The less-than-ideal resolution of the MRAMS topography may be driving the early spring wind speed inconsistencies.

Pisaq (86.8 °S, 178.0 °E), Category I: This ROI is situated within a deep depression/channel, surrounded by irregular pieces of SPLD highlands. Despite the complex topography, the current MRAMS configuration captures most nearby important topographic features well. At Pisaq, P4 data exhibit a bimodal distribution for wind directions that is only weakly pronounced at L_s ≈ 215° but becomes stronger by L_s ≈ 235°. Some of the fans that point to different directions have a common source, proving that the bimodal distribution here is due to the wind changing with time between different jet eruptions and not because of spatial variability of the wind. The P4-inferred wind directions are consistent with katabatic flow from the higher terrain immediately to its southwest. MRAMS results do not show a similar bimodal distribution during the L_s ≈ 210° window. However, by the L_s ≈ 220° bin the model has a similar bimodal wind direction distribution for the strongest winds. Overall, the MRAMS and P4 winds match well at Pisaq, but the fit is less robust near L_s ≈ 210°.

Portsmouth (87.3 °S, 167.8 °E), Category I: Located about 40 km southwest of Pisaq, this ROI is located in a higher-elevation depression/channel at the foot of the SPLD lobe that crosses the south pole. The current MRAMS configuration cannot resolve many of the nearby smaller troughs and ridges. Portsmouth has P4-inferred wind directions that are nearly constant from about L_s ≈ 200° to 240°. MRAMS simulations show that while the modeled wind directions vary widely throughout the day, the wind speeds at most directions are weak. In early spring (before about L_s ≈ 205°), the stronger MRAMS winds are generally oriented toward the east (within ± 30° or so). For the rest of the spring season, the strongest modeled winds generally blow northward (within ± 30°). These stronger winds are consistent with katabatic flow off the SPLD lobe to the south and occur at local times when the insolation is low. MRAMS winds match those derived from P4 measurements well at this ROI.

Potsdam (81.68 °S, 66.3 °E), Category I: This ROI is located on a broad equator-facing slope at the edge of the SPLD. The nearby topography is generally well resolved by the current MRAMS configuration. Here one might expect katabatic flows from the higher reaches of the SPLD to be directed downslope and turned left (further toward the west) by the Coriolis effect. The P4-inferred wind directions are consistent with this—directed toward the west early in the season, and gradually becoming toward the northwest later in the season—and match well with the strongest MRAMS winds.

Rochester (83.2 °S, 158.4 °E), no category: Situated on a high part of the SPLD between two deep chasmas (Figure 27), the topography near this ROI is generally well represented in the current MRAMS configuration. P4 analyzed three HiRISE images acquired from L_s ≈ 220°–240°. All of these images have too few fans for reasonable statistics, but they curiously have a large number of small dark blotches. This is an unusual situation among our ROIs, as it superficially implies nearly calm winds during the jet eruptions. However, the MRAMS results indicate the presence of moderate to strong winds at Rochester throughout the spring season considered, at many times of day. There may be a few explanations for this: (1) The location is somehow protected by local topography, and there is effectively no wind that could deflect 100 m tall CO₂ jets to create fan-shaped deposits. This seems unlikely based on the available imagery and topography data. (2) The blotches observed by P4 may be the result of very short (in height) CO₂ jets that do not "feel" the winds much. (3) The blotches might be sublimation spots on the ground, and not formed by CO₂ jets at all. We prefer the second hypothesis. All three HiRISE images for this ROI were acquired relatively late in spring, when the seasonal CO₂ layer is thinning, and when we would expect jet eruptions to become weak or stop entirely. Even so, the surface albedo signature of a weak eruption and direct surface sublimation would be effectively indistinguishable from each other.

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Starburst (81.8 °S, 76.1 °E), Category I: Located on a broad equator-facing slope at the edge of the SPLD, this ROI lies to the east of Potsdam. The nearby topography is generally well resolved by the current MRAMS configuration but misses some of the nearby SPLD troughs. The winds here would be expected to be similar to that at Potsdam. The P4 data for Starburst have good fan statistics until L_s ≈ 213°, after which the number of blotches significantly overtakes the number of fans, suggesting that the CO₂ jets may be much weaker later in midspring. MRAMS fits P4 wind directions well, generally when modeled wind speeds are greatest (even when blotches are common in midspring). This suggests a situation similar to ROI Rochester, where the CO₂ jets are weak and thus short in height.

Taichung (82.3 °S, 306 °E), Category II: This ROI is located immediately to the north of a ragged SPLD edge. There is complex smaller-scale topography nearby, particularly toward the west, that MRAMS does not resolve well with its current configuration. P4 analyzed a single HiRISE image acquired at L_s ≈ 228° that shows a bimodal wind direction distribution with the two peaks at 320° and 41° (clockwise from north; toward the northwest and northeast, respectively). The main fan direction (toward the northwest) coincides well with the dominant winds in the MRAMS results, and the model indicates that that flow persists through the spring season studied. The secondary wind direction (toward the northeast) inferred by P4 at L_s ≈ 228° is robustly present in the MRAMS results at and before the L_s ≈ 220° window. This may indicate that the northeast-oriented fan deposits at Taichung were created before L_s ≈ 228°.

Troy (85.02 °S, 259 °E), Category I: This ROI is situated within a broad (but rough) valley/channel near the edge of the SPLD. The current MRAMS configuration does not resolve the small-scale complex topography nearby. The earliest HiRISE image in Troy was acquired at L_s ≈ 181° and has 195 blotches and just four fans. Thus, we are unable to draw any direct conclusions about the wind distributions at this early L_s using P4. This is similar to the situation at ROI Rochester, with the difference that at Troy the image with almost no fans was taken quite early in spring. It is unreasonable to assume that the jet activity has stopped by that L_s or that sublimation could expose patches of the surface substrate. If there were no fans and no blotches, we could assume that CO₂ jet activity has not started yet, but the presence of a significant number of blotches contradicts this. Thus, two possibilities remain: that this ROI generally does not have strong winds at the beginning of spring at the time of day that the jets are erupting, or that CO₂ jets at and just before this L_s are very weak and therefore short in height. The MRAMS results show a variety of wind speeds at Troy during the L_s ≈ 180° window, with stronger winds blowing toward the E–NE and W–SW (both consistent with flow through the valley that it is located within) and much weaker winds at other directions. The next HiRISE image here was acquired at L_s ≈ 244°. It shows a bimodal distribution of fan directions with peaks at 99° and 147° (clockwise from north; toward the east and southeast, respectively). MRAMS fits these directions well in 2 out of 3 sols during the L_s ≈ 240° window, although the strongest winds predicted by MRAMS at that L_s are toward the west.

Wellington (82.2 °S, 225.2 °E), Category II: Located on the slope of a smaller SPLD trough, winds at this ROI might be expected to be oriented generally toward the northeast (i.e., downhill). However, the current configuration of MRAMS does not resolve the associated SPLD trough well. Two HiRISE images at Wellington were analyzed by P4: the first at L_s ≈ 178° and the second at L_s ≈ 211°. The first image exhibits a distribution of fan directions with one maximum (at 99°; toward the east), while the second one shows a bimodal distribution with the two dominant wind directions at 36° and 76° (toward the northeast and E–NE, respectively). MRAMS modeling indicates a low wind direction variance at this ROI in early spring, increasing greatly in midspring. Note that the MRAMS wind directions during the L_s ≈ 200° window would match the P4-measured fan directions at L_s ≈ 211° very well, so perhaps the CO₂ jets had already ceased to be active by the time that image was acquired.

Hansen et al. (2019) have shown that there are indications that seasonal polar activity varies between the years with and without extended (regional or global) dust storms. However, the extent of this study and the data used were limited. Since this work has shown that MRAMS can successfully fit wind directions derived from P4, questions about the influence of global dust storms on the seasonal processes in the polar regions can be studied in more detail and utilize data from more locations around the south pole. For this work, MRAMS was run with a standard dust scenario (MY24; i.e., including no large dust storms), and the P4 data used were also for the low-dust years MY29 and MY30. However, it is possible to use P4-derived winds from more dusty years and compare them to MRAMS runs using relevant atmospheric dust loading scenarios that include a regional or global dust storm.

CO₂ jet eruptions have not been directly observed so far. One of the goals of the P4–MRAMS comparison project is to try to estimate the timing of CO₂ jet eruptions to help plan remote sensing observations of these jets in action. We have not yet reached the goal of determining the time(s) of day that jet eruptions occur because of the rich detail about the winds (especially their time variability) that the mesoscale climate modeling has provided for the many ROIs considered. However, since MRAMS was successful at predicting winds consistent with P4-analyzed fans for most ROIs, this goal is not out of reach. At many ROIs the MRAMS wind direction that corresponds to the P4-derived wind occurs during a time when the Sun is well above the horizon. However, in some cases the modeled wind direction occurs in the early local evening, or even near midnight. A more detailed evaluation of this timing is outside the scope of the current paper, but we have high expectations that for ROIs with good fits (Category I) further analysis will bring additional insights into the timing of the jets.