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Record W3211180976 · doi:10.5194/epsc2021-388

Water-Ice Cloud Thermal Effects at the Phoenix Mission Landing Site

2021· article· en· W3211180976 on OpenAlexaff
Grace Bischof, Brittney A. Cooper, John E. Moores

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsYork University
Fundersnot available
KeywordsAtmosphere (unit)Atmospheric sciencesEnvironmental scienceMars Exploration ProgramAtmosphere of MarsPhoenixLongwaveSolar irradianceSnowMartianMartian surfaceRadiationAstrobiologyMeteorologyGeologyPhysicsGeographyOptics

Abstract

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Introduction: On Mars, near-surface and ground temperatures undergo massive diurnal fluctuations. Amplitudes can surpass 70 K between the daily maximum and minimum temperatures [1]. Because Mars’ thin atmosphere is largely transparent to infrared radiation, the solar radiation from the sun and the outgoing longwave radiation from the surface are the primary drivers of the near-surface temperature [2]. However, the influence from the atmosphere is not entirely negligible. It is well known that dust in the atmosphere has a secondary, but measurable, effect on the temperature by scattering visible-band solar radiation and absorbing longwave radiation [3]. This atmospheric thermal effect is not only caused by dust; Water-ice clouds have a similar influence, where outgoing longwave radiation may be absorbed and reflected back toward the ground, resulting in a warming of the near-surface temperature [4]. This project will investigate the amount of flux reflected by water-ice clouds by calculating the thermal forcing at the Phoenix landing site.Background: The Phoenix mission landed in the Martian northern Arctic, at a latitude of 68.2°N in 2008. Phoenix operated for 151 sols, collecting data up to and through the northern summer solstice. About 60 sols into the mission, water-ice clouds were observed both by images taken by the Stereo Surface Imager (SSI) [5] and by backscatter detected using a light detecting and ranging (LIDAR) instrument onboard the lander [6]. On four occasions, by using the LIDAR and SSI together, surface-fog was detected [7]. In the second half of the mission, surface-based clouds formed nightly around 23:00 Local True Solar Time (LTST). By 01:00 LTST, a second clouds base formed at altitudes near 4 km. The clouds dissipated by the late morning, but were observed to linger as the mission progressed past summer solstice [8].Methods: Data for the near-surface air temperature are acquired from the Planetary Data System. Phoenix carried three thin-wire temperature sensors at heights of 1 m, 0.5 m, and 0.25 m off the deck of the lander, itself located 1 m above the surface. Temperature measurements were recorded every 2 s through the duration of a sol, with an approximately 20-minute break, usually occurring around midday. To determine the thermal impacts due to water-ice clouds, an energy balance at the surface is needed. Adapted from [9], the energy balance is given by where G is the net flux into the ground, S is the solar radiation, α is the surface albedo, LW↓ is the longwave radiation downwelling from the atmosphere, LW↑ is the longwave radiation emitted from the surfaced, H is the sensible heat flux, and L is the latent heat flux of water. R describes the additional longwave radiation downwelling from the atmosphere, which we attribute to water-ice clouds. R is maintained as an independent parameter which may be varied throughout a run of the model in 3 hour-intervals. With this energy balance, a subsurface conduction model is used to find the surface temperature at the Phoenix site. At each timestep, the surface temperature is coupled to the air temperature bywhere the terms are described in [9]. The modelled air temperatures are plotted against the air temperature data collected by Phoenix to evaluate the additional flux reflected by clouds (given by R) that is needed for the model to match the data collected in situ.Results and Discussion: Figure 1(a) shows the modelled temperaure plotted against the Phoenix data for sol 3 of the mission. During the midday, R = 0 W m-2 implying there is no flux reflected from water-ice clouds. Moving into the evening, R = 5 W m-2 starting at 21:00 LTST. This increases to 8 W m-2 by 00:00 LTST. At 03:00 LTST, the reflected flux drops back down to 5 W m-2, and is back to 0 W m-2 by 06:00 LTST. This additional flux is not a dominant energy term, as shown in Figure 1(b), but a resulting temperature increase of 2 K is seen. This analysis suggests that clouds were present at the Phoenix landing site earlier than they were detected in images or LIDAR data products. Moving forward, the amount of flux reflected by water-ice clouds will be determined for each sol of the misson. This will show how the reflected flux evolves diurnally – particularly through the nighttime – and as the mission progressed past summer solstice.References:[1] Martínez G.M. et al (2017) Space Sci Rev, 212, 295–338.[2] Savijärvi H. (2014) Icarus, 242, 105–111.[3] Guzewich S.D. et al (2019) Geophys. Res. Lett., 46, 71–79.[4] Wilson R.J. (2007) Geophys. Res. Lett., 34.[5] Moores J.E. et al (2010) JGR: Planets, 115.[6] Whiteway J.A. et al (2009) Science, 325, 68–70.[7] Moores J.E. et al (2011) Geophys. Res. Lett., 38.[8] Dickinson C. et al (2010) Geophys. Res. Lett., 37.[9] Martínez G.M. et al (2014) JGR: Planets, 119, 1822–1838.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.215
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2021
Admission routes1
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