The Diverse Meteorology of Jezero Crater over the First 250 Sols of Perseverance on Mars

Nature Geoscience | Volume 16 | January 2023 | 19–28 19 nature geoscience https://doi.org/10.1038/s41561-022-01084-0Article The diverse meteorology of Jezero crater over the first 250 sols of Perseverance on Mars NASA’s Perseverance rover’s Mars Environmental Dynamics Analyzer is collecting data at Jezero crater, characterizing the physical processes in the lowest layer of the Martian atmosphere. Here we present measurements from the instrument’s first 250 sols of operation, revealing a spatially and temporally variable meteorology at Jezero. We find that temperature measurements at four heights capture the response of the atmospheric surface layer to multiple phenomena. We observe the transition from a stable night-time thermal inversion to a daytime, highly turbulent convective regime, with large vertical thermal gradients. Measurement of multiple daily optical depths suggests aerosol concentrations are higher in the morning than in the afternoon. Measured wind patterns are driven mainly by local topography, with a small contribution from regional winds. Daily and seasonal variability of relative humidity shows a complex hydrologic cycle. These observations suggest that changes in some local surface properties, such as surface albedo and thermal inertia, play an influential role. On a larger scale, surface pressure measurements show typical signatures of gravity waves and baroclinic eddies in a part of the seasonal cycle previously characterized as low wave activity. These observations, both combined and simultaneous, unveil the diversity of processes driving change on today’s Martian surface at Jezero crater. The Perseverance rover landed on 18 February 2021 at 18.44° N 77.45° E, near the northwest rim of Jezero crater, on the inner northwest slopes of Isidis Planitia1. On board the rover is the most complete environmental station sent to date to another planet: the Mars Environmental Dynamics Analyzer (MEDA) instrument2. It includes new capabilities, compared with previous missions 3–8 , that enable better characterization of the diversity of physical processes driving near-surface environmental changes in Jezero. MEDA acquires data autonomously, on a regular and configurable basis, in sessions that typically cover more than 50% of a sol. Sampling sessions, typically one hour long, alternate every sol between even and odd hours, allowing for a complete characteriza - tion of daily and seasonal cycles every other sol (Extended Data Fig. 1 shows the temporal coverage of the measurements made). MEDA also provides context for the investigations that other rover instruments and systems are conducting and supports the planning of Ingenuity flights, as well as landing of a possible future mission to return samples collected by Perseverance. In this Article, we present results for the first 250 sols of the mission (solar longitudes L s = 6°–121°), northern hemisphere spring to early summer. The active atmospheric surface layer The atmospheric surface layer (ASL) is the lower part of the atmosphere in direct interaction with the surface, having a depth that varies on Mars from a few metres during daytime to tens of metres at night9,10. In the ASL, energy and mass exchanges between surface and atmosphere Received: 8 May 2022 Accepted: 21 October 2022 Published online: 9 January 2023 Check for updates e-mail: [email protected]; manuel.delatorrejuar[email protected]; [email protected] A list of authors and their affiliations appears at the end of the paper Nature Geoscience | Volume 16 | January 2023 | 19–28 20 Article https://doi.org/10.1038/s41561-022-01084-0 verified by careful analysis of the ground temperature in consideration of the winds, which indicate that the effects are <0.5 K. Near-surface thermal profile Another new feature enabled by MEDA is the simultaneous tracking of temperatures at four heights—surface, 0.85 m, 1.45 m and about 40 m—around the clock (Fig. 2a). The variation of these temperatures along a sol reflects the four main regimes of the ASL: (1) daytime convection, (2) an evening transition where the convective boundary layer collapses, (3) a night-time steady regime and (4) a morning transition where the inversion fades and a convective boundary layer grows. Figure 2b–d shows an example of the daily evolution of the thermal gradient. Daytime convection peaks at noon with the maximum of the derivative of the temperature (T, in Kelvin) with the height (z, in meters) (dT/dz) max ≈ –35 K m –1 while night-time stable stratification peaks at 20:00 with (dT/dz)max ≈ +8 K m–1 in the first metre from the surface, reaching values well above the adiabatic gradient g/Cp = 0.0045 K m–1. Figure 2e shows the seasonal evolution of mean temperatures, where the daily average thermal gradient is dominated by the daytime convective period. In most sols, night-time thermal stability weakens as the night progresses, and unstable conditions often develop from 2:00 onward. A similar observation was reported for InSight 15 and attributed to the radiative influence of the hardware. In addition, Curiosity’s Rover Environmental Monitoring Station instrument, measuring on the deeper Gale crater, has seen this inversion broken only during the global dust storm 16 . With MEDA, we observe that the night-time inversion depends on the local terrain properties. Measurements over high TI terrain result in the break-up of the night-time thermal inversion due to warm night-time surfaces (Extended Data Fig. 3). However, air temperatures at different levels are not sensitive to the TI of the specific terrain, and the influence of the terrain on air temperatures occur, and its hydrological cycle provides constraints on the photochemistry of surface and near-surface air. Most of the atmospheric dynamics in this layer are driven by radiative processes10. Albedo, net radiative flux and thermal inertia (TI) are key elements of that forcing and result in the radiative surface energy budget (SEB). MEDA’s thermal infrared sensor (TIRS) and radiation and dust sensor (RDS) enable quantification of all SEB terms (see Methods for a detailed description of each term) on the surface of Mars (Fig. 1a), an important step in improving the predictive capabilities of numerical models. When Perseverance is parked every sol, TI is obtained by minimizing the difference between measured and numerically simulated values of the diurnal amplitude of ground temperature (Fig. 1b). The MEDA-derived TI values range from 180 to 605 J m–2 K–1 s–½, as in Gale, ref. 11. The surface albedo (Fig. 1d) is obtained around the clock, using downwelling (0.2−1.2 μm) and reflected (0.3−3.0 μm) solar flux measurements (Fig. 1c,e,f). A radiative transfer model, COMIMART12, is used to convert both fluxes to 0.2−5.0 μm. The SEB is then measured and used as an upper boundary condition to solve the heat conduction equation for homogeneous terrains in models 13,14 . Figure 1a,b shows the diurnal cycle of retrieved fluxes and surface temperature, respectively. Figure 1d shows the diurnal evolution of measured albedo on the sols with the highest (sol 125) and lowest (sol 209) values of the studied period. On both sols, the minimum value is reached near noon and increases as solar zenith angle (SZA) approaches 90°. The relative maximum at ~8:00 and ~17:00 occurs when SZA = ~55° and the specular reflection is within TIRS field of view (FoV). This behaviour points to non-Lambertian albedo at the surface, not observable from surface satellites in nadir pointing. Importantly, modelling of the thermal and radiative environment shows that the effect of the radioisotope thermoelectric generator (RTG) on TIRS FoV ground heating is negligible. This fact has been –400 –300 –200 –100 0 100 200 300 400 500 0 3 6 9 12 15 18 21 24 Surface forcing (W m–2) LMST (h) LMST (h) SWd SWu LWd LWu Tf G 180 190 200 210 220 230 240 250 260 270 280 290 0 3 6 9 12 15 18 21 24 Ground temperature (K) LMST (h) TIRS TI = 230 Sol 30 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 4 6 8 10 12 14 16 18 Albedo Sol 125 Sol 209 TI = 230 cba fed Sol 30 Sol 125 TI = 605 Sol 209 TI = 290 Fig. 1 | SEB components measured in situ in Jezero. a, Diurnal variation of the SEB on sol 30 obtained from MEDA (symbols) and simulated with single-column models (solid lines; for further details, see Methods and Extended Data Fig. 2). b, Ground temperature measured by MEDA (black symbols) and obtained by solving the heat conduction equation (solid red line) using MEDA’s net heat flux, G. The best fit is obtained for TI = 230 J m–2 K–1 s–1/2. c, Footprint (green shade) of the ground temperature sensor on sol 30. For horizontal terrains, this footprint covers an area of a few square metres. d, Diurnal evolution of broadband albedo on sols 125 (blue) and 209 (red). The albedo shows a minimum close to noon, with increasing values as the solar zenith angle increases. This non-Lambertian behaviour is similar in other sols, regardless of the type of terrain and geometry of the rover. e,f, As in c, for sols 125 (e) and 209 (f), with MEDA-derived TI values of 605 and 290 J m–2 K–1 s–1/2, respectively. Shown data result from the mean of 300 samples at the beginning and half of each hour, plus or minus the standard deviation. lmst, Local Mean Solar Time; SWd, downwelling solar flux; SWu, solar flux reflected by the surface; LWd, downwelling long-wave atmospheric flux; LWu, upwelling long-wave flux emitted by the surface; Tf, turbulent heat flux. Credit: panels c,e,f from NASA/JPL-Caltech. Nature Geoscience | Volume 16 | January 2023 | 19–28 21 Article https://doi.org/10.1038/s41561-022-01084-0 decreases progressively from 0.85 to 40.00 m (Fig. 2d and Extended Data Fig. 3). Winds driven by horizontal gradients of surface thermal properties 17 may be the origin of the discrepancies we observe from measurements with respect to the predictions from one-dimensional radiative equilibrium models. Temperature fluctuations are common throughout the sol; these rise after sunrise, peaking near noon (amplitudes ΔT max ≈ 10 K), and are convective in nature. These subside before sunset and increase again during the break-up of the night-time inversion, suggesting strong nocturnal fluctuations (Extended Data Fig. 4). The oscillations are created by turbulent processes in the atmosphere whose characteristics can be investigated by analysing the spectral power density of temperatures, pressures and winds 18–20 . Figure 2e shows the power spectral densities of temperatures during the convective period, averaged over 250 sols. The results show typical slopes of turbulence in daytime hours, with changes at other times of the sol; these will be analysed in later works. MEDA enables the identification of different dynamical regimes, forced, inertial and dissipative, more clearly than with previous instruments19,20 and at different altitudes. Pressure fluctuations Turbulence is also present in pressure and horizontal wind measurements. Figure 3a shows examples of the daily pressure cycle on different sols; this cycle has the contribution of different dynamic phenomena, as discussed in the following. Analysis of the rapid pressure fluctuations shows a power spectrum similar to that of temperature (Extended Data Fig. 5), different from that expected from Kolmogorov turbulence but similar to that measured by InSight15. During the stable night-time period, the pressure fluctuations are at the detector noise level. Convection generates transient events detectable through several MEDA sensors, especially in temperature and pressure data (Extended Data Fig. 6). Some events are dust devils (DDs), also detected as slight drops (∼0.4–26.0%) in radiation sensor readings or imaged with Perseverance cameras 22 . Jezero exhibits the highest abundance of DDs so far detected by a mission on the surface of Mars 23,24 . The pressure drops detected in this period range from ∼0.3 to 6.5 Pa in intensity and last from 1 to 200 s. DDs where simultaneous MEDA wind data are available have estimated diameters from 5 to 135 m (ref. 23), with vortices having rotational speeds of ~4–24 m s–1. A small number of these produced measurable albedo changes on the surface as they removed dust, observed through variations in the upward/downward radiation ratio measured with TIRS and RDS sensors (Extended Data Fig. 7)23. Wind speeds show a daily cycle, with maximum values of 7 m s –1 in the afternoon and near null between 4:00 and 6:00 (Fig. 3b). Strong gusts are detected with maximum speeds of 25 m s –1 at midday. Turbu - lence creates fluctuating winds of about 2–4 m s–1 at night, where they a eb c d Sols 75–83, Ls = 43° Sunset Sunrise 285 235 230 225 220 215 210 205 1 0.1 0.01 0.001 0.0001 350 345 340 335 Maximum daily irradiance (W m–2) 330 325 320 315 270 255 240 <T> (K) Temperature (K) ∆T/dz (K m–1) Spectral density power (K2 Hz–1) ∆T/dz (K m–1) 225 210 195 180 0 0 2 4 6 8 10 10 –10 12 LTST (h) 14 16 18 20 20 –20 –30 –40 –50 –0.5 0 0.5 –1 –1.5 22 24 25 50 75 100 125 Sol –1.24 –1.33 –2.62 –1.80 –2.52 –1.37 –0.75 –1.11 –0.64 150 175 200 225 250 15 30 45 60 Ls (°) 75 90 105 120 0 2 4 6 8 10 12 LTST (h) 14 16 18 20 22 24 0 2 4 6 8 10 12 f (Hz) LTST (h) 0.01 0.10.001 14 16 18 20 22 24 TI = 330 J m–2 K–1 s–1/2 (Tsurf - Tz = 40 m)/∆z (Tsurf - Tz = 1.45 m)/∆z (Tsurf - Tz = 0.85 m)/∆z (Tsurf – Tz = 40 m)/∆z Fig. 2 | Daily cycle of temperatures under typical values of TI. a, Temperature at the surface (Tsurf; red), at z = 0.85 m (blue), z = 1.45 m (green) and z ≈ 40 m (green yellow). b, Thermal gradients from the surface to the near surface at 0.85 m (blue) and 1.45 m (green). c, Thermal gradient from the surface to 40 m (green yellow) and from 1.45 m to 40 m (grey). The adiabatic thermal gradient, dT/dz = –0.0047 K m–1, is shown in b and c with a horizontal light-blue line (a horizontal line that is partially covered by the curve (Tsurf − Tz=40 m)/Δz, which can be seen quite well in the center of the figure (LTST from 10 to 15 h)). Shown data in a–c result from the mean on 12 min intervals (740 samples), plus or minus the standard deviation. d, Seasonal evolution of mean daily air temperatures. TIRS and atmospheric temperature sensor temperatures (solid lines) at the surface (red), z = 0.85 m (blue), z = 1.45 m (green) and z = 40 m (green yellow) are compared with predictions from the Mars Climate Database21 (dashed lines). Yellow line shows the maximum irradiance measured by RDS. e, Statistical analysis of power spectra of temperature fluctuations at 0.85 m (blue), 1.45 m (green) and 40 m (yellow) between 10:00 and 15:00 h. Lines show the fit to the data in each frequency range. Figures reflect the estimated exponential indices. TI = 330 J m– 2 K–1 s–1/2. Dots are averages over a 12 min window. ltst, local true solar time. Nature Geoscience | Volume 16 | January 2023 | 19–28 22 Article https://doi.org/10.1038/s41561-022-01084-0 are probably responses to horizontal shear flows 25 , and 5–7 m s –1 during the convective hours (Fig. 3c). Pre-landing atmospheric modelling predicted that daytime upflows and night-time downflows on the Isidis basin slopes would dominate the overall wind pattern, with Jezero local topography causing a relatively small but measurable effect13,14. Wind data support the dominance of daytime upslope currents from roughly southeast, with a reversal of winds at night14 (Fig. 3c,d). These diurnal wind patterns drive aeolian erosion at Jezero23 and show a variety of behaviours resulting from a complex interaction among regional circulation, slope winds and interaction with the general circulation and the large-scale Hadley cell flow. Atmospheric dust properties SkyCam is tracking the regular morning–evening opacity cycle in the 600–800 nm wavelength range as a function of time (Fig. 4a) 2 . During the clear season covered by this study, persistently higher opacity is observed in the morning (optical depth (OD) ∼0.5) than in the afternoon (OD ∼0.4) (refs. 26,27). Analysis of RDS data at different wavelengths and observation geometries allows determination of the optical properties of the dust, and derivation of the OD variation at high temporal resolution, similar to the more sparsely sampled Viking record 28 (Fig. 4b). The dust optical properties and OD are estimated by comparing the temporal variation of the measured sky spectral intensity with radiative transfer simulations (Fig. 4c shows an example). Most of the particle-size information is obtained when the Sun trajectory is near one of the RDS lateral sensor’s FoV. From these observations, we found particle sizes ranging from ∼1.2 to 1.4 μm, consistent with previous studies29–31, with corrections suggested by refs. 32–34. To estimate the non-sphericity of those particles, the T-matrix approach was used to compute the phase function, single-scattering albedo and extension cross section. These first sols of the mission fell within the aphelion cloud belt season and near the peak latitude for water-ice clouds 35 . It is therefore likely that some of the afternoon opacity in Fig. 4a, including the a b Wind direction Early morning 360° (N0) 315° (NW) 270° (W) 225° (SW) 180° (S) 135° (SE) 90° (E) 45° (NE) 0° (N) Night-time LMST (h) N 330° 300° 270° 90° 240° 210° 180° 150° 120° 0.5 1.0 60° 30° 0° S W E 330° 300° 270° 90° 240° 210° 180° 150° 120° 60° 30° 0° S W E N 330° 300° 270° 90° 240° 210° 180° 150° 120° 60° 30° 0° S W E 0.5 1.0 N 0.5 1.0 Pressure (Pa) Daytime LTST (h) LTST (h) Wind speed (m s–1)Wind speed (m s–1)Wind speed (m s–1) 151050 151050 15 2420161284 755 Sol 101 Sol 102 Sol 104 Sol 105 Sol 103 760 765 <µ> (m s–1) 770 775 9 7 6 5 4 3 2 1 0 0 4 8 12 16 20 24 0 4 8 12 16 20 24 8 750 0 5 100 0.7 0.4 0.3 f(v) 0.2 0.1 0 0.5 0.6 0.40 0.25 0.20 f(v) 0.15 0.05 0.10 0 0.30 0.35 0.25 0.10 f(v) 0.05 0 0.15 0.20 c d Fig. 3 | Fluctuations in the daily pressure cycle and wind patterns. a, Daily pressure cycles for sols 101–105, showing deviations from the mean values (in 10 s intervals) caused by different dynamic phenomena. b, Mean values of wind intensity, averaged in intervals of 5 min. Error bars show the standard deviations. c, Wind azimuths in different time slots (dotted lines, ~120° azimuth, show the regional slope direction in the Isidis basin). We observe that upward winds predominate during the day and downward winds during the night, a trend attributable to local flows acting in Jezero and the interaction with other scales (top). Histogram of wind speed at those hours, that is, the frequency distribution of the occurrence of each speed, f(v) (bottom). d, Mean directions from which the winds blow, averaged in intervals of 5 min. Error bars show the standard deviations. Nature Geoscience | Volume 16 | January 2023 | 19–28 23 Article https://doi.org/10.1038/s41561-022-01084-0 increase around sol 70 and most of the morning–afternoon difference, is due to water-ice hazes36,37. Clouds were observed around sols 70 and 180, but discrete clouds were not typically present during daytime22. During this season, a low dust OD (0.3–0.6), with low variability, is typical of other sites38,39. We have also found cloud signatures during daytime and twilight. In the latter cases (Fig. 4d) 40 , we could constrain the cloud altitudes using radiative transfer simulations41. In most cases, we found cloud altitudes around or above 40 km and particle sizes larger than 1 μm (indicative of water-ice particles). The daily OD evolution was also retrieved from TIRS infrared measurements, strengthening the case for night-time clouds. Figure 4e shows the diurnal variation of thermal infrared aerosol OD, contributed a cd b e 0.7 0.6 0.5 0.3 0.4 Optical deptn Solar irradiance (W m–2) Aerosol optical depth 0.2 0.1 0 0.380 0.375 0.365 0.370 Opacity at 650 nm CI (I450nm/I950nm) 0.360 0.355 3.5 3 2.5 2 1 1.5 4 0 50 Morning (open) Afternoon (filled) 100 150 200 250 90 0 3 6 9 12 15 18 21 24 6.69516.6956.695 Time (×108) Local time (LTST) χ2 = 8.42 AOD = 0.507 reff = 1.4 µm 6.6956.695 10–1 10–2 0.45 0.40 0.35 0.30 0.25 0.20 92 94 96 98 100 15:1115:10 15:12 Time (LST/LTST) Sol 21 Sol 30 (Ls = 20°) Sol 200 (Ls = 98°) Sol 296 (sunrise) SZA 15:13 15:14 Sol Fig. 4 | Daily monitoring of atmospheric opacity and characterization of dust particles and clouds. a, OD derived from SkyCam images to follow the day– night cycle in the visual range (550 nm). Values are observed to be higher in the morning than in the afternoon, consistent with measurements made occasionally with MastCam-Z22. b, RDS observations also allow the derivation of the OD at a temporal resolution of 1 s, enabling the study of short-duration events, such as DD. An example of a temporal increase in opacity due to a nearby DD occurred on sol 21 around 15:11. c, An example of dust particle radius and OD estimation using RDS observations at different wavelengths and radiative transfer simulations. The best fit is obtained for an effective radius reff = 1.4 μm. d, Variation of the colour index (CI), defined as the ratio between RDS observations at zeniths at 450 and 950 nm (ref. 41) to the SZA measured on sol 296. The SZA of maximum CI indicates that this cloud layer is above 45 km. e, Aerosol OD (AOD) at 9 μm retrieved from TIRS observations for sols 30 and 200. TIRS observations enable AOD to be retrieved at all local times. The large diurnal variation for sol 200 is probably caused by water-ice clouds. Nature Geoscience | Volume 16 | January 2023 | 19–28 24 Article https://doi.org/10.1038/s41561-022-01084-0 by both dust and water-ice clouds, as a function of local time for two representative sols. Because TIRS observes thermal infrared radiation, this retrieval is possible for all local times, including during the night, a capability not available on previous rovers except by Mini-TES on board Mars Exploration Rover, which could make only limited and occasional night-time observations42. The OD observed by TIRS during sol 30 (Ls = 20°) shows a moderate variation with greater opacity at night than during the day. By sol 200, the aphelion season cloud belt 43 was near its peak annual amplitude, and data reveal a notable diurnal variation in OD with maximum clouds shortly after dawn. This ability to track OD throughout each sol is a powerful tool that leads to new insights about how dust and water-ice clouds interact with the surface and the rest of the atmosphere. A complex humidity cycle Measuring the relative humidity (RH) in the ASL on diurnal and seasonal timescales is a key element in understanding hydrological processes in the Martian atmosphere44. MEDA’s humidity sensor (HS) often finds a nocturnal hydrological cycle more complex than anticipated in numerical predictions13. Figure 5a shows the daily and seasonal behaviour of RH, while Fig. 5b shows the daily maxima recorded in the period studied. Likewise, Fig. 5c shows the seasonal variations of night-time water-vapour volume mixing ratio (VMR). Within the diurnal cycle, the maximum, typically in the range 15–30% in RH (referring to HS temperature), occurs in the early morning, with maximum VMR being reached around midnight. Nocturnal water-vapour amounts at Jezero during the seasons are lower than those at Gale and model predictions13. MEDA measured a seasonal minimum in night-time VMR near Ls = 70°, with higher abundance and greater variability at the end of the season (Fig. 5c). In addition, a large increase in VMR was observed on the evening of sol 104 (see Extended Data Fig. 8 for details of the evolution of VMR and RH on those sols), accompanied by cooling atmospheric temperatures. The increase in RH slowly returned to typical values, while the temperature continued dropping. This behaviour may be due to a single dry air mass advected over the rover bringing cold, dry air, which then remains in the area. An alternative explanation is that it is related to the local surface and possible exchange processes. In the early morning of sol 105, frost conditions were possible as seen by comparing the calculated frost point (at 1.45 m) with the TIRS ground temperature. A resulting hypothesis is that, if subsurface exchange is occurring, the actual frost point at the atmosphere–surface interface may be lower owing to less vapour present in the atmosphere. Non-local dynamical phenomena The daily pressure cycles at Jezero showed a rich variability, reflecting the action of different dynamical mechanisms in the atmosphere under a variety of spatial and temporal scales (Fig. 3a). On the seasonal scale, as the northern polar cap sublimated, the daily mean pressure increased from 735 Pa on sols 15–20 (L s = 13°–16°) to a maximum of 761 Pa on sols 99–110 (Ls = 52°–57°). It then gradually decreased to 650 Pa in sol 250 (Ls = 125°) as the southern polar cap grew (Fig. 6a). Extended Data Fig. 9a shows the deviations of the daily pressure averages of Fig. 6a from a polynomial fit of degree 5, observing oscillations with periods in the range of 3–5 sols and amplitudes varying between 1 and 3 Pa, a signature of what could be high-frequency b 30 44 80 Ls (°) Time (sol) 12 15 18 41.6 75 100 125 150 175 225 250200 53.0 64.2 75.5 86.7 98.1 109.7 121.6 21 0 03 06 09 12 100 120 140 160 Time (sol) Time (LTST) 180 200 220 240 53 62 71 80 89 98 107 117 Max %RH 25 20 15 10 5 0 30 25 20 15 10 5 0 50 40 30 20 10 Ls (°) Ls (°) RH in the sensor (%) RH in the sensor (%) VMR (ppm) 120 110 100 90 80 70 60 50 40 a c Fig. 5 | Daily and seasonal cycles of RH and VMR observed at Jezero. a, Daily and seasonal evolution of RH for sols 80–250, Ls 44–122°. The uncertainty in the estimation of this magnitude, which is temperature dependent, is <3.5% in the temperature range recorded by HS. b, Seasonal evolution of the maximum RH values observed, reaching an absolute maximum of 29% RH, referring to the temperature recorded by HS itself, for sols 80–250, Ls 44–122°. c, Seasonal behaviour of the night-time VRM derived from RH for sols 64–250, averaging seconds 2–5 from the beginning of each acquisition session. The uncertainty in the estimation of this magnitude, for the temperatures and the RH range shown in a, is <20%. Maintenance regeneration heatings of the sensor heads are marked as blue bars (these data should not be taken into consideration). Nature Geoscience | Volume 16 | January 2023 | 19–28 25 Article https://doi.org/10.1038/s41561-022-01084-0 travelling waves arising from baroclinic instabilities also reported elsewhere 13 . In addition, Extended Data Fig. 9b shows the regular oscillations of the residuals resulting from a fit to the approximately 1 h measurement series that, on average, have a peak-to-peak amplitude of 0.2–0.4 Pa and periods between 12 and 20 min. The properties of these oscillations are similar to those reported in Gale, observed by the Rover Environmental Monitoring Station instrument on the Curiosity rover 45 and in Elysium Planitia by the InSight mission 15 , which have been interpreted as produced by the passage of gravity waves. Figure 6b shows the seasonal and diurnal pressure variability where the daily patterns of pressure changes are observed. Thermal tides cause the large modulation of the daily pressure (P) cycle 46 . A Fourier analysis of that cycle shows that up to six components are present, with maximum amplitudes ranging from 0.2 to 10.0 Pa. Figure 6c shows the wide variability of the normalized amplitude of diurnal and semidiurnal tides. The maximum relative change observed in the studied period is (δP/〈P〉)max ≈ 0.013. Smaller changes also occurred in tidal components 3 and 4. The semidiurnal component showed a very strong drop between sols 20 and 50; although still under investigation, it may suggest a relation to the dust loading present on those sols or the development of disturbances at the polar cap edge46. Tides are also detected in the Fourier analysis of the temperature data with half amplitudes of 26 K (diurnal), 2–6 K (semidiurnal) and 2–4 K (terdiurnal), as shown in Fig. 6d. The tidal variability is related mainly to changes in atmospheric opacity produced by clouds and dust loading at different altitudes22. The period studied is the non-dusty season on Mars, which makes the variability relatively low. A rich and dynamic near-surface environment Mars 2020 mission includes a payload to monitor an environment that exhibits a rich diversity of behaviours. Many of the measurements made by MEDA so far are the first time they have been obtained on Mars, revealing interesting surprises in Jezero’s atmosphere. The SEB is measured for the first time in situ. The design of future engineering systems, the understanding and modelling of photochemical reactions at the surface and the interpretation of satellite measurements benefit from these results. An example is the characterization of the non-Lambertian reflection of the surface that must be considered in the interpretation of orbital observations of variations in albedo when trying to understand changes in the physical properties of the surface. Globally, the measured daily temperature cycle agrees with model predictions (although with some deviations in the vertical temperature gradient), expected magnitude of thermal oscillations and the seasonal evolution. In addition, the observed vortex convective activity matches the predictions of large eddy simulation using the MarsWRF model23. However, when analysing vertical temperature profiles, we find a diversity of nocturnal responses that raise intriguing questions about what is happening at the different locations traversed by the rover. 0 3 6 9 12 15 18 21 LTST (h) 50 100 150 200 250 Sol on Jezero 0 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 5 min pressure (s.d.) Pa 0 25 50 75 100 125 150 175 200 225 250 Sol 0 4 8 12 16 20 24 28 Amplitude (K) Diurnal Semidiurnal Terdiurnal 660 640 1.6 1.2 0.8 0.4 0 0 50 14 36 57 77 98 120 100 150 200 2500 36 Diurnal Semidiuranal Terdiurnal 57 77 98 12014 c a d b 50 100 150 200 250 740 720 700 680 760 780 Pressure (Pa) Normalized amplitude δP/P (%) Time (Perseverance sol) Time (sol) Ls (°) Ls (°) Fig. 6 | Atmospheric pressure variability as a consequence of various dynamic mechanisms at various scales. a, Seasonal evolution of daily mean pressure values and their daily ranges. b, Detrended standard deviation of pressure, as a function of sol and LTST, after subtracting least squares fit of the observed pressure. Removing the tidal components shows that the dominant contribution to pressure variability occurs during the hours with strong convection and after a calm period. c, The first three components of the Fourier analysis of the pressure cycle: diurnal (24 h period; black), semidiurnal (12 h period; blue) and terdiurnal (6 h period; red) components. d, Equivalent Fourier analysis of the temperature data at z = 1.45 m: diurnal (blue) and semidiurnal (red) components. Nature Geoscience | Volume 16 | January 2023 | 19–28 26 Article https://doi.org/10.1038/s41561-022-01084-0 Several independent radiation sensors and methods have measured the occurrence and even development of night-time clouds long before the peak of the cloud season. The ability to track OD throughout each sol is a powerful tool that has shown the prevalence of clouds near dawn and will lead to new insights about how dust and water-ice clouds interact with the surface and the rest of the atmosphere. The observed nocturnal hydrologic cycle is more complex than anticipated by the models and is also observed in Gale. This unpredicted behaviour can be due to a variety of causes yet to be explored in detail. Thermal tides show smaller pressure amplitudes at Jezero when compared with Curiosity’s observations47 or Viking48. While the general behaviour at Jezero was predicted by the models13,14, there are differences in its amplitude and timing probably due to the interaction of local topography with the air mass exchanges between the interior and exterior of the basin. Another interesting result is the existence of multisol waves at a time of the season when baroclinic waves have been observed to have very limited activity46. Overall, MEDA observations show a dynamic environment rich in atmospheric phenomena that is different from other locations on Mars studied by previous missions. The characterization of Jezero’s atmosphere plays an important role in the development of the Mars Sample Return mission and in the exposure of the samples being collected by Perseverance. Online content Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi.org/10.1038/s41561-022-01084-0. References 1. Farley, K. A. et al. Mars 2020 mission overview. Space Sci. Rev. 216, 142 (2020). 2. Rodriguez-Manfredi, J. A. et al. The Mars Environmental Dynamics Analyzer, MEDA. A suite of environmental sensors for the Mars 2020 mission. Space Sci. Rev. 217, 48 (2021). 3. Hess, S. L., Henry, R. M., Leovy, C. B., Ryan, J. A. & Tillman, J. E. Meteorological results from the surface of Mars: Viking 1 and 2. J. Geophys. Res. 82, 4559–4574 (1997). 4. 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To view a copy of this license, visit http://creativecommons. org/licenses/by/4.0/. © The Author(s) 2023 1Centro de Astrobiología (INTA-CSIC), Madrid, Spain. 2Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA. 3Dept. Fí sica Aplicada, Universidad del País Vasco (UPV/EHU), Bilbaoí, Spain. 4Lunar and Planetary Institute, Houston, TX, USA. 5Space Science Institute, Boulder, CO, USA. 6Aeolis Corporation, Sierra Madre, CA, USA. 7Finnish Meteorological Institute, Helsinki, Finland. 8Instituto Nacional de Técnica Aeroespacial (INTA), Madrid, Spain. 9NASA Goddard Space Flight Center, Greenbelt, MD, USA. 10Dept. Física y Matemáticas, Universidad de Alcalá, Alcalá de Henares, Spain. 11Institute of Physical Chemistry Rocasolano, CSIC, Madrid, Spain. 12Cornell University, Ithaca, NY, USA. 13Carnegie Institution, Washington, DC, USA. 14Institut Supérieur de l’Aéronautique et de l’Espace (ISAE-SUPAERO), Université de Toulouse, Toulouse, France. 15NASA Ames Research Center, Mountain View, CA, USA. 16Plancius Research, Severna Park, MD, USA. 17Instituto de Microelectrónica de Sevilla (US-CSIC), Seville, Spain. 18Dept. de Ingeniería Electrónica, Universidad Politécnica de Cataluña, Barcelona, Spain. 19Dept. of Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, MI, USA. 20Dept. de Mecánica de Fluidos y Propulsión Aeroespacial, Universidad Politécnica de Madrid, Madrid, Spain. 21Southwest Research Institute, Boulder, CO, USA. *A full list of members and their affiliations appears at the end of the paper. **A full list of members and their affiliations appears in the Supplementary Information. e-mail: manfr[email protected].es; manuel.delatorr[email protected]v; [email protected]; the MEDA team J. A. Rodriguez-Manfredi1, M. de la Torre Juarez2, A. Sanchez-Lavega3, R. Hueso3, G. Martinez4, M. T. Lemmon5, C. E. Newman6, A. Munguira3, M. Hieta7, L. K. Tamppari2, J. Polkko7, D. Toledo8, E. Sebastian1, M. D. Smith9, I. Jaakonaho7, M. Genzer7, A. De Vicente-Retortillo1, D. Viudez-Moreiras1, M. Ramos10, A. Saiz-Lopez11, A. Lepinette1, R. J. Sullivan12, J. Gomez-Elvira1, J. A. Rodriguez-Manfredi 1 , M. de la Torre Juarez 2 , A. Sanchez-Lavega 3 , R. Hueso 3, G. Martinez 4, M. T. Lemmon 5, C. E. Newman 6, A. Munguira 3, M. Hieta7, L. K. Tamppari2, J. Polkko7, D. Toledo8, E. Sebastian1, M. D. Smith9, I. Jaakonaho 7, M. Genzer7, A. De Vicente-Retortillo1, D. Viudez-Moreiras 1, M. Ramos 10, A. Saiz-Lopez 11, A. Lepinette 1, M. Wolff5, R. J. Sullivan 12, J. Gomez-Elvira1, V. Apestigue 8, P. G. Conrad13, T. Del Rio-Gaztelurrutia 3, N. Murdoch 14, I. Arruego 8, D. Banfield 15, J. Boland2, A. J. Brown 16, J. Ceballos 17, M. Dominguez-Pumar 18, S. Espejo 17, A. G. Fairén1,12, R. Ferrandiz1, E. Fischer 19, M. Garcia-Villadangos1, S. Gimenez1, F. Gomez-Gomez1, S. D. Guzewich 9, A.-M. Harri7, J. J. Jimenez8, V. Jimenez18, T. Makinen 7, M. Marin1, C. Martin 1, J. Martin-Soler1, A. Molina 1, L. Mora-Sotomayor 1, S. Navarro 1, V. Peinado1, I. Perez-Grande 20, J. Pla-Garcia1, M. Postigo1, O. Prieto-Ballesteros1, S. C. R. Rafkin21, M. I. Richardson6, J. Romeral1, C. Romero 1, H. Savijärvi7, J. T. Schofield2, J. Torres8, R. Urqui1, S. Zurita1 & the MEDA team* Nature Geoscience Article https://doi.org/10.1038/s41561-022-01084-0 Extended Data Fig. 2 | Seasonal evolution of the SEB terms over the first 250 sols on Mars, as recorded by MEDA. Seasonal evolution for the first 250 sols of the M2020 mission of (a) The atmospheric opacity at 880 nm retrieved from MastCam-Z between 06:00 and 18:00 LMST. (b) Daily maximum, mean and minimum values of the net heat flux into the soil (G). (c) Daily maximum downwelling solar flux in the 0.2–5 μm range (SWd) at the surface (red) and at the top of the atmosphere (gray). The seasonal evolution of SWd at the surface is governed by that the top of the atmosphere during the aphelion season, when the aerosol opacity is low and relatively stable. (d) Daily maximum solar flux in the 0.2–5 μm range reflected by the surface (SWu). (e) Daily maximum upwelling longwave flux in the 6–80 um range emitted by the surface (LWu). (f) daily maximum downwelling atmospheric longwave flux in the 6–80 um range (LWd). All these values were calculated as described in the Methods section. Nature Geoscience Article https://doi.org/10.1038/s41561-022-01084-0 Extended Data Fig. 3 | Daily cycle temperatures under typical values of thermal inertia and atmosphere’s thermal gradients. Daily Cycle of temperatures at Jezero under low and high values of TI of the local terrain, and thermal gradients of the atmosphere. (a-c) Low thermal inertia with TI = 260 J·m- 2·K-1·s-1/2, as in sols 20-22. (d-f) High thermal inertia terrain with TI = 545 J·m-2·K- 1·s-1/2, as in sols 140-150. Colors legend is as in Fig. 2. Low surface thermal inertia (left panels) result in more stable conditions with respect to vertical motions during nighttime than high values of the surface thermal inertia. This is due to the strong locality of surface temperatures, which change strongly in different terrains, compared to a more regular behavior of air temperatures (a and d). The data shown in these figures are the result of averaging the values over 12-minute time intervals, with the standard deviation as the error bar. Nature Geoscience Article https://doi.org/10.1038/s41561-022-01084-0 Extended Data Fig. 4 | See next page for caption. Nature Geoscience Article https://doi.org/10.1038/s41561-022-01084-0 Extended Data Fig. 4 | Analysis of thermal oscillations along the sols. Analysis of thermal oscillations along the sol, for the periods (a) sols 20-22, (b) sols 75-76, (c) sols 140-150, corresponding to different TI values. Blue dots correspond to ATS measurements at z = 0.84 m, green dots to ATS measurements at z = 1.45 m, and yellow dots to TIRS measurements at z ~40 m. The large amplitudes around 20:00 h correspond to thermal contamination by the RTG at that time, as confirmed by simultaneous wind and rover yaw. The oscillations were calculated by detrending the temperature data in each halfhour period using a 2nd order polynomial. Nature Geoscience Article https://doi.org/10.1038/s41561-022-01084-0 Extended Data Fig. 5 | Analysis of the rapid pressure fluctuations. Power spectrum of pressure fluctuations from sols 231 to 241. The straight line determines the power law fit in the frequency range 0.001 to 0.1 Hz. Nature Geoscience Article https://doi.org/10.1038/s41561-022-01084-0 Extended Data Fig. 6 | Pressure drops as observed by MEDA. Pressure drops on MEDA data. (a) Daily distribution of pressure drops identified using the algorithm described in14. (b) Selection of events with at least a pressure drop of 0.5 Pa and a pressure curve compatible with a vortex after visualization of each individual event. (c) Selection of events in (b) that also have a simultaneous drop in the light measured by the RDS Top7 photodiode with a drop of at least 0.5%. Histograms have been corrected from sampling effects. The error bars represent the standard deviation obtained in Monte Carlo simulations that reproduce the number of events detected per hour, using a fixed probability equal to the total number of events divided by the total number of hours of observations. The central value represents the mean number of events detected per hour. (d-h) Examples of a variety of pressure drops including: (d) weak vortices typical of the early morning; (e) night-time pressure drops coincident with increases in temperature and driven by thermal pulses from the RTG when the wind flows from the back of the rover to the Remote Sensing Mast (wind data not shown); (f) the most dusty event captured in the first 250 sols, also one of the most intense and longest; (g) long and noisy pressure drops typically found at noon and suggestive of the passage of the boundaries of convective cells; (h) long vortex just after sunset. Further information on events like (f) and (g) is given in23. Nature Geoscience Article https://doi.org/10.1038/s41561-022-01084-0 Extended Data Fig. 7 | Example of a Dust Devil record observed by MEDA, on sol 166. Dust Devil record observed on sol 166; the pink line reflects the sudden pressure drop caused by the passage of the convective vortex, while the blue line shows the jump caused in the radiation ratio measured by TIRS and RDS, versus the same magnitude on preceding sols (cyan line). The data shown in the plot are the result of the mean over 10-sec intervals, plus-minus the standard deviation. Nature Geoscience Article https://doi.org/10.1038/s41561-022-01084-0 Extended Data Fig. 8 | Evolution of VMR and RH on sols of maximum variability. MEDA instrument has recorded a more complex hydrological cycle than expected. (a) Detail of the daily evolution of RH in characteristic sols (sols 104 to 110), where the complex and temporally variable structure of the nocturnal hydrological cycle can be observed, sometimes not foreseen by the simulation models. (b) Daily evolution of the VMR for the sols shown in (a). A large increase in the VMR was observed on the evening of sol 104. Nature Geoscience Article https://doi.org/10.1038/s41561-022-01084-0 Extended Data Fig. 9 | Analysis of pressure deviations recorded by MEDA. Deviations from the recorded pressure values suggest the possibility of high-frequency traveling waves. (a) Oscillations obtained in the differences between the daily mean values in Fig. 6a and the polynomial fit of degree 5 of those values (coefficient of determination, R-squared = 0.985), where 2 intervals are observed: before and after sol ~80. (b) Oscillations obtained in the infra-daily differences. Due to their properties, these oscillations are compatible with the propagation of gravity waves. Nature Geoscience Article https://doi.org/10.1038/s41561-022-01084-0 Extended Data Fig. 10 | Estimation of the optical properties of atmospheric dust from RDS data. Simulated signals for RDS Top5 channel for different opacities, and effective radius. (a) RDS Top5 channel signals simulated for different dust opacities and, a constant dust effective radius of reff = 1.2 μm. (b) Best fit between the simulations and the observations obtained.