Lewis, S. R.

Montabone, L.

Read, P. L.

Ruan, T.

Young, R. M. B.

English

Open Research Online (The Open University)

Open Research OnlineThe Open University’s repository of research publicationsand other research outputsRegional and global dust storms on Mars investigatedusing data assimilationConference or Workshop ItemHow to cite:Ruan, T.; Young, R. M. B.; Read, P. L.; Lewis, S. R. and Montabone, L. (2017). Regional and global duststorms on Mars investigated using data assimilation. In: Sixth International Workshop on the Mars Atmosphere:Modelling and Observations, 17-20 Jan 2017, Granada, Spain, CNES.For guidance on citations see FAQs.c© 2017 The AuthorsVersion: Version of RecordLink(s) to article on publisher’s website:http://www-mars.lmd.jussieu.fr/granada2017/abstracts/ruan granada2017.pdfCopyright and Moral Rights for the articles on this site are retained by the individual authors and/or other copyrightowners. For more information on Open Research Online’s data policy on reuse of materials please consult the policiespage.oro.open.ac.ukREGIONAL AND GLOBAL DUST STORMS ON MARS INVESTIGATED US-ING DATA ASSIMILATION.T. Ruan, R. M. B. Young, P. L. Read, Atmospheric, Oceanic and Planetary Physics, Department of Physics,University of Oxford, Clarendon Laboratory, Parks Road, Oxford, OX1 3PU, UK (roland.young@physics.ox.ac.uk),S. R. Lewis, School of Physical Sciences, The Open University, Walton Hall, Milton Keynes, MK7 6AA, UK,L.Montabone, Atmospheric, Oceanic and Planetary Physics, Department of Physics, University of Oxford, ClarendonLaboratory, Parks Road, Oxford, OX1 3PU, UK, Space Science Institute, Boulder, CO 80301, USA.IntroductionThe dust cycle is a key component of the Martian cli-mate, and its full understanding is extremely importantfor understanding the evolution of the Martian environ-ment. Dust particles injected into the atmosphere greatlyaffect the dynamical and thermal state of the Martian at-mosphere via its general circulation.Intensive measurements of atmospheric temperatureand dust extending over nearly ten Mars Years (MY) nowexist with unprecedented spatial coverage, thanks to var-ious orbital spacecraft including Mars Global Surveyor(MGS, 1997-2006), Mars Odyssey (MO, 2001-present),and Mars Reconnaissance Orbiter (MRO, 2006-present).Such observations have already helped to improve ourunderstanding of Mars’ weather and climate, but theirincomplete coverage of the planet restrains our ability tostudy the general circulation in full detail. On the otherhand, numerical models provide four-dimensional simu-lated data with high temporal and spatial resolution, butoften fail to produce the dust cycle’s realistic variability.To aid this approach, data assimilation provides a so-lution that is consistent with both observations and mod-elled physical constraints. It corrects model-predictedvariables towards observations such that the resulting so-lution can represent the full observed variability of theclimate. This approach has been widely used as an effec-tive tool in operational weather forecasting systems orclimate models for analyzing meteorological variablesfor the Earth 1,2.Lewis & Read 3 implemented the analysis correction(AC) scheme 1 in a simple version of a Mars GeneralCirculation Model (MGCM) in order to assimilate tem-perature profiles from the Pressure Modulator InfraredRadiometer (PMIRR) instrument on-board the Mars Ob-server spacecraft (1993). Their results showed that as-similation of such observations was feasible and that itimproved the agreement between model and observa-tions. Lewis et al. 4 extended this approach to includedust tracer assimilation, which was combined with afull MGCM to assimilate thermal profiles and Column-Integrated Dust Opacity (CIDO) rescaled to a referencelevel (τref ) using retrievals from the Thermal EmissionSpectrometer (TES) on-board MGS 5. More recently,a complementary approach has been developed 6,7 us-ing the ensemble Kalman filter 8, which was used to as-similate TES temperature retrievals; MCS temperatureobservations were later assimilated using this scheme 9.Lewis et al. 4 and later studies 10–12 assimilated TEStemperature profiles and τref without explicitly advect-ing the dust tracer field, using an empirical relation 13 toprescribe the vertical distribution of dust. A more recentdataset from MCS on board MRO 14 provides verticallyresolved, global measurements of the atmospheric dustdistribution. With this in hand, here we use an updateddust data assimilation system that advects the dust andassimilates both CIDO and Layer-Integrated Dust Opac-ity (LIDO) observations to better represent the Martiandust cycle. We apply it to a southward-moving regionaldust storm during MY29 and to the MY28 global duststorm (GDS).Model and data assimilationWe use the UK version of a three-dimensional Mar-tian Global Climate Model (UK-LMD MGCM) 15. Themodel combines a spectral dynamical solver at triangu-lar truncation T31, corresponding to a 96×48 longitude-latitude grid in real space, a tracer transport scheme anddust lifting routines, along with a full range of physicalparameterizations.The equations for a hydrostatic, adiabatic and in-viscid gas surrounding a rotating spherical planet arecast in vorticity-divergence form. In the vertical, levelsare defined in terms of the terrain-following σ coordi-nate system using a standard finite difference approach.There are 25 levels with the first three at 4, 19, and 44mabove the surface, to resolve detailed surface processesrepresented in the model. The model top varies overtime but is typically around 100 km, with a sponge layer(linear drag on eddy vorticity and divergence) in theuppermost three level to reduce spurious reflection ofvertically propagating waves. There are 480 dynamicaland 96 physics timesteps per sol.The radiative transfer scheme calculates atmosphericabsorption and emission due to carbon dioxide and air-borne dust; water’s radiative effects are not included.The balance between incoming radiative flux and ther-mal conduction in the soil contributes to changes in sur-face temperature, using a surface thermal inertia field de-rived from TES and Viking measurements 15 and topog-REGIONAL AND GLOBAL DUST STORMS ON MARS INVESTIGATED USING DATA ASSIMILATIONraphy from the Mars Orbiter Laser Altimeter (MOLA)on MGS 16. The surface roughness length z0 is based ona global map compiled by He´brard et al. 17 , and imple-mented in the UK-MGCM by Mulholland 18 .The dust transport scheme includes a dust liftingscheme, tracer advection, gravitational sedimentationand deposition. We use a 1.5 µm particle size basedon Mars Exploration Rover observations 19. The twomost important distinct mechanisms responsible for theinjection of dust into atmosphere are thought to be dustlifting by near-surface wind stress, and dust lifting bydust devils 20.Figure 1: The route followed by the centre of the regional duststorm. Sols are relative to the beginning of the storm period.The data assimilation scheme is based on the Anal-ysis Correction sequential estimation (AC) scheme 1 butwith modifications specific to Mars 4. The assimilationstep is computationally inexpensive compared with therest of the model, and so is performed at each dynami-cal timestep. Lewis et al. 4 describe the scheme in fulldetail. Temperature assimilations are the same, but useTES data. We have extended the dust assimilation touse active dust transport, and assimilate both CIDO andLIDO, simultaneously where possible.Observational datasetsMeasurements of atmospheric temperature and dust nowexist covering the Martian atmosphere over nearly eightMYs. The instruments for measuring temperature anddust on board these spacecraft are TES on MGS 21,THEMIS on MO 22 and MCS on MRO 14.TES and THEMIS dust retrievals contain CIDO data,while MCS contains the most up-to-date satellite ob-servations with vertically resolved, simultaneous globalmeasurements of atmospheric temperature, LIDO, andwater 23. This contains information on the day-to-dayvariability of Martian weather from the near surface tothe top of the middle atmosphere around 80 km altitude 14.The spacecraft have different operational periods and or-bits, so their retrievals have different temporal and spatialcoverage. Only THEMIS has overlapping observationalperiods with the other two datasets. Further details ofthe THEMIS dataset, including the retrieval algorithm,can be found in Smith et al. 5,24 and Smith 22 .Upward-looking surface observations provide a viewof CIDO that is independent of satellite-based datasets,although only over a limited area. The MER missionsSpirit (14.57 ◦S, 175.48 ◦E) and Opportunity (1.95 ◦S,5.53 ◦W) provide almost continuous data coverage dur-ing MY28 and MY29. Both rovers carried a Pancamcamera, which included solar filters at 440 nm and 880 nmwavelengths.Figure 2: Peak dust opacity τref at the storm center.MY29 southward-moving regional dust stormA dust storm was reported for more than 20 sols inweekly MRO MARCI (Mars Color Imager) WeatherReports (MMWR) 25. The storm formed in the northernhemisphere over Utopia and western Elysium on MY29,Ls ≈ 305.2◦, moved southward along the Utopia stormtrack through Isidis and crossed the equator into Hel-las, strengthened with additional dust lifting betweenHellas and Noachis, moved westward through Noachis,halted but continued to spread dust over Noachis, Hellas,Promethei and part of Cimmeria, and decayed aroundMY29, Ls ≈ 313.9◦.This event is interesting because the dust cloud as-sociated with this storm appeared to travel through oneof the important channels connecting the Northern andSouthern hemisphere (Utopia-Isidis), the duration of thisstorm duration was quite long, and the THEMIS andMCS datasets provide excellent coverage in this areaduring this period.CIDO and LIDO retrievals from THEMIS and MCSrespectively were assimilated into the model over thisperiod. Every 10th THEMIS data point and every 10thMCS vertical profile was not assimilated, but retainedfor out-of-sample verification.One advantage of using data assimilation is that it ispossible to track the route of a migrating dust storm ina way that is not possible using observations alone. Weassumed the the dust storm to be rectangular, initially11.25◦×11.25◦ during formation over Elysium, increas-ing to 33.75◦×33.75◦ as it moved south. The rectanglewith the highest average τref defined the location of thestorm, and the grid cell with the highest τref within thatREGIONAL AND GLOBAL DUST STORMS ON MARS INVESTIGATED USING DATA ASSIMILATIONrectangle defined its centre. Figure 1 shows the locationof the storm centre tracked in this way. It is possiblethat the changing location of the dust storm is due to asequence of discrete lifting events rather than the physi-cal movement of the storm as a coherent system, but thisremains an open question about Mars’ dust cycle and theevolution of individual events.Figure 3: Reanalysis showing daily-averaged τref during theprecursor event leading up to the MY28 GDS. Sol 0 corre-sponds toLs ≈ 260.7◦, when the dust storm was first reportedin the MMWR. Arrows show the 4m wind velocity.The peak opacity at the storm centre can be seenin Fig. 2. In sols 0–5, the centre remained betweenArabia and Hellas until the dust cloud formed a clearshape in sol 5. From sols 8–11, two separate centrescombined and moved westward, with the storm reachingits maximum opacity during sol 10 (see Fig. 2). Onceit arrived at its farthest westward point, the storm centreslowly decayed.Data assimilation was also able to reveal how highthe dust cloud penetrated during the evolution of thisstorm. Before this storm, dust layer tops were normallyseen to reach to heights of ∼12 km, but the reanaly-sis showed that this storm transported dust as high as∼30 km.Figure 4: τref averaged over the MY28 GDS period (Ls ≈269.1◦ ∼ 309.5◦, top left), averaged over the same Ls rangeduring MY29 (top right), and their absolute (bottom left) andpercentage (bottom right) differences.MY28 global dust stormDuring MY28 a precursory dust storm travelled south-wards from Chryse, originating onLs ≈ 261◦. After fivesols, dust clouds were observed near Noachis, where thestorm grew significantly in scale and intensity, devel-oping into a planet-encircling storm soon afterwards 26.However, Wang & Richardson 26 were unable to un-ambiguously establish a clear relationship between theprecursor dust storm from Chryse Planitia, dust eventsin Noachis, and what actually led to the initiation of theMY28 GDS. We have used assimilation to investigatethe evolution of the MY28 GDS and its effect on thewider circulation.During this period THEMIS CIDO retrievals werequite sparse, mainly covering the Southern Hemisphere.MCS provided more extratropical profiles, and a rela-tively even distribution globally, but almost no data tothe south of ∼45◦ within Ls ≈ 270◦ ∼ 295◦, coveringmost of the GDS period. This was compensated for by ahigh THEMIS data density in the Southern Hemisphereduring that period. Both datasets were assimilated intothe MGCM over the period of the GDS.The assimilation showed that during the precursorevent (Fig. 3), dust devil lifting appeared to providethe background dust level, mostly in the southern hemi-sphere. Dust lifted by wind stress was found to be morestrongly correlated with various centres of dust activityduring the same period. Wind stress dust lifting wasimportant in Alba Patera, the Acidalia-Chryse channel,Xanthe Terra, Elysium Planitia, and Noachis Terra.To gauge the effect of the MY28 GDS on the widercirculation, we compared the analysis with an analysisREGIONAL AND GLOBAL DUST STORMS ON MARS INVESTIGATED USING DATA ASSIMILATIONFigure 5: Top: Zonal-time mean temperature (K, top); Bot-tom: zonal-time mean mass stream function (1010 kg s−1,contours) and adiabatic heating rate (K sol−1, colour). Bothare averaged overLs ≈ 269.1◦ ∼ 309.5◦ for MY28 (left) andMY29 (right). The black line shows the approximate zonalmean topography. Stream function contour intervals are loga-rithmic with negative values indicating clockwise circulation.covering Ls ≈ 269.1◦ ∼ 309.5◦ in the following MY29,which did not have a GDS. Figure 4 shows maps ofτref averaged over the MY28 GDS, compared with thesame period in MY29, together with the differences be-tween these two averages. The percentage differencesuggests that the largest difference compared to MY29was found between 30◦–60◦ in the Northern Hemisphere,which corresponds to the boundary of the polar vortex.Although during the GDS the dust seemed was mostlyconfined to latitudes 60◦S and 45◦N, the percentage dif-ference compared to MY29 was largest in the NorthPolar region. In contrast, the South Polar region had thelowest percentage differences compared to MY29.Because of the thermal effects of suspended dustparticles, temperature variations on Mars always showa strong correlation with the distribution of dust. Fig-ure 5 (top) shows the zonal-time mean temperature dur-ing MY28 and MY29. The atmosphere during the MY28GDS was warmer than during MY29, and the the verticaltemperature gradient below 100 Pa over the South Polewas larger during MY29. During MY28 the polar warm-ing in the Northern Hemisphere was very strong, with adeep vertical extension (∼ 150 Pa — above 0.1 Pa). Themore intensive heating is near the top of the dust storm,however, which reduces the incoming solar radiation in-tensity in the lower atmosphere, so the atmosphere nearthe ground was cooler during MY28.Figure 5 (bottom) shows the zonally averaged streamfunction and adiabatic heating rate during the GDS timewindow. In the GDS year the meridional circulation wassignificantly strengthened, both vertically and poleward.The adiabatic heating rate in the North Polar region alsoincreased significantly with a maximum value more thantriple that during MY29.SummaryWe have updated the UK-MGCM dust data assimilationsystem to actively transport dust and simultaneously as-similate CIDO and LIDO observations. This was usedto study a regional dust storm during MY29 and theMY28 global dust storm. The reanalyses reveal severalfeatures of the atmospheric circulation that cannot bededuced using the observations or model by themselves.AcknowledgmentsTES, THEMIS, MCS, and MOLA data were obtainedfrom the NASA Planetary Data System. LM acknowl-edges support from NASA’s Mars Data Analysis Pro-gram under grant no. NNX13AK02G. RMBY, PLR, andSRL are funded by ESA (4000114138/15/NL/PA). SRLalso thanks STFC (ST/L000776/1) and the EU (Horizon2020, Ref: 633127) for support in related areas.References1 Lorenc, AC et al. 1991, QJRMS, 117, 592 Kalnay, E 2003, Atmospheric Modeling, Data As-similation and Predictability (CUP)3 Lewis, SR, & Read, PL 1995, ASR, 16, 94 Lewis, SR et al. 2007, Icarus, 192, 3275 Smith, MD et al. 2003, JGR, 108, 51156 Hoffman, MJ et al. 2010, Icarus, 209, 4707 Greybush, SJ et al. 2012, JGR, 117, E110088 Evensen, G 2003, Ocean Dyn., 53, 3439 Navarro, T et al. 2014, GRL, 41, 662010 Wilson, RJ et al. 2008, GRL, 35, L0720211 Rogberg, P et al. 2010, QJRMS, 136, 161412 Montabone, L et al. 2014, Geosci. Data J., 1, 12913 Conrath, BJ 1975, Icarus, 24, 3614 Kleinbo¨hl, A et al. 2009, JGR, 114, E1000615 Forget, F et al. 1999, JGR, 104, 2415516 Smith, DE et al. 2001, JGR, 106, 2368917 He´brard, E et al. 2012, JGR, 117, E0400818 Mulholland, DP 2012, DPhil thesis, University ofOxford19 Lemmon, MT et al. 2004, Science, 306, 175320 Newman, CE et al. 2002, JGR, 107, 512321 Smith, MD 2004, Icarus, 167, 14822 Smith, MD 2009, Icarus, 202, 44423 McCleese, DJ et al. 2010, JGR, 115, E1201624 Smith, MD et al. 2000, JGR, 105, 953925 Malin, MC et al. 2009, MRO MARCI Weather Re-ports for 13/7/2009–2/8/2009, Malin Space ScienceSystems Captioned Image Releases, MSSS-92,-93,-94, http://www.msss.com/msss images/26 Wang, H & Richardson, MI 2013, Icarus, 251, 112REGIONALANDGLOBALDUST STORMSONMARS INVESTIGATED USING DATA ASSIMI-LATION

Regional and global dust storms on Mars investigated using data assimilation

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Regional and global dust storms on Mars investigated using data assimilation

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