An introduction to the LMD Mars GCM The

An introduction to the LMD Mars GCM The LMD Mars GCM team 15/10/2007

General Circulation Models/ Global Climate models GCMs Numerical simulators of the Earth or Mars environment: designed to simulate the « entire reality »

General Circulation Models/ Global Climate models GCMs Used on Earth for: • Understanding the climate system: coupling with oceans, clouds, etc… • Weather forecasts & Meteorological data assimilation • Climate changes, paleoclimates simulations • Chemistry, hydrology, biosphere studies, etc. . .

Mars Atmosphere General Circulation models • NASA Ames Research Center (USA, since 1969 !) • LMD, (France, since 1990) • Oxford university (UK, ~ 1993) The « European » Mars GCM project (since 1995) Here, now : the LMD Global Climate Model (H 2 O-CO 2 -dust cycles + Thermosphere + Photochemistry) • NOAA GFDL (USA, since 1992) • Recently : New projects: – Caltech « Planetary WRF » – Tohoku University (Japan) – York University, Toronto (Canada) : GLOBAL MARS MULTISCALE MODEL (GM 3) – Germany, GISS etc. . .

The minimum General Circulation Model for Mars 1) Hydrodynamical code to compute large scale atmospheric motions LMD : grid point model : Typical resolution 64 x 48 , possibility of zoom 2) Physical parameterizations to force the dynamic to compute the details of the local climate • • • Radiative heating & cooling of the atmosphere (solar and thermal IR) by CO 2 and dust Surface thermal balance Subgrid scale atmospheric motions : Turbulence in the boundary layer • CO 2 condensation : Convection Relief drag Gravity wave drag

In the Fortran program…

The dynamical core • Solve the Navier Stokes equations simplified by the following assumption: – The atmosphere is thin compared to the planet radius – Hydrostatic approximation (the vertical wind is much smaller than the horizontal one. . . ) – Limited resolution : requires some “numerical dissipation” to absorb the energy cascad toward small scale and stabilize the model create model dependent behavior. . . • Horizontal discretization (~100 300 km): – Grid point models at LMD (and also at NASA Ames, GFDL, WRF) – Exist also : Spectral models (in the Fourrier space) (Oxford, Tokohu)

Vertical discretization : Evolution from Terrain following “sigma” (σ= p/ps) coordinates to σ p «hybrid» coordinates 25 layers σ coordinates

Vertical discretization : Evolution from Terrain following “sigma” (σ= p/ps) coordinates to σ p «hybrid» coordinates 32 layers hybrid coordinates

Vertical discretization : Evolution from Terrain following “sigma” (σ= p/ps) coordinates to σ p «hybrid» coordinates 50 layers hybrid coordinates

Exemple : 32 layers vertical grid Numbers of layers depends of project : • 50 layers : full model with Thermosphere (top above 300 km) • 32 layers : top above 120 km (no thermosphere) • 25 layer : reference for low atmosphere studies • 18 layers : for long paleoclimate studies

In the Fortran program…

Horizontal grid LMD GCM : standard resolution : 5. 625° 3. 75°

LMD GCM : high resolution : 2° 2°

GCM : Zoomed grid to reach resolution: 1. 8° 0. 6° at reduced cost

An example of discretized horizontal grid, in the Fortran program…

The input maps and climatology • In the GCM, everything is deduced from physical equations and physical constants except : – Topography map – Albedo map – Thermal inertia map – Dust 3 D climatology

GCM surface fields • MOLA topography (of course) • ALBEDO (old map…):

GCM surface fields • MOLA topography (of course) • Home made Therma inertia map: Thermal Inertia (SI)

GCM surface fields • MOLA topography (of course) • Home made Therma inertia map: TES data : Mellon et al. (2000) Thermal Inertia (SI)

GCM surface fields • MOLA topography (of course) • Home made Therma inertia map: Paige et al. (1994) Decrease : 25% TES data : Mellon et al. (2000) Palluconni and Kieffer (1981) Decrease : 8% Paige and Keegan (1994) Decrease : 28% Thermal Inertia (SI)

DUST : so important for Atmospheric dynamics and thermal structure • Problem : below 50 km : thermal structure is sensitive to the dust distribution Require to prescribe a “dust distribution” problem analogous to Sea Surface Temperature forcing in Earth climate

Prescribed reference “Martian year 24” dust scenario” Prescribed dust opacity at 700 Pa : Varies as a function of Latitude, Longitude and time Based on 1999 -2001 TES data assimilation (Montabone and Lewis) : “Martian Year 24” τ vis GCM = 1. 65 ×τ1075 cm-1 TES Top of the dust layer (km): (based on Viking and Mariner 9 limb observations)

Vertical distribution of the dust (based on models + observation) Alt itu de (k m) Analytical formula Defined by one parameter Zmax (km) Dust mixing ratio (normalized)

Some practical stuff • The code is in Fortran (mostly Fortran 77, with some Fortran 90), compile on Unix/Linux platform (PC, SUN, DEC, etc…) • It uses Net. CDF library available on the web (http: //www. unidata. ucar. edu/packages/netcdf/faq. html#howtoget) • You put the source code somewhere, and run somewhere else • For this purpose, one must initialize UNIX environment variable LMDGCM, LIBOGCM as well as NCDFINC and NCDFLIB (see User Manual)

Running the GCM: • You need an initial state → • You need some “definition” files : gcm • You can performed “chained simulations using various scripts (run 0, run_mcd)… To be detailed in practical work (see also the user manual) • You get output files containing 4 D data (3 D+time) or 3 D data (2 D +time) gcm

OUTPUT FILES • Net. CDF file diagfi. nc – Net. CDF file diagfi. nc stores the instantaneous physical variables throughout the sim ulation at regular intervals (set by the value of parameter ecritphy in parameter file “run. def”). – Any variable from any sub-routine of the physical part can be stored by calling subroutine writediagfi • Net. CDF file stats. nc – Store a “mean” diurnal day, with timestep typically 2 martian hours.

How accurate are the temperature and wind predictions ?

Surface temperatures

Mars Pathfinder data: Temperature at 1. 2 m MCD, Tsurf Obs (30 sols) MCD T(z=5 m)

Zonal values of surface temperature TES Observation GCM Predictions (retrieved through Mars Climate Database (“MCD”

Zonal values of surface temperature

Zonal values of surface temperature TES Observation GCM Predictions (retrieved through Mars Climate Database (“MCD”

Zonal values of surface temperature

Distributions of surface temperature difference between MCDv 4. 2 and TES • Statistics computed for: § MY 24: 102. 5 < Ls < 360 § MY 25: 0 < Ls < 180 § 50 < latitude < 50 § Bins of 1 K Note: MEAN and RMS values are computed from histograms; blue curves are normal distributions of same MEAN and RMS

Atmospheric temperatures

Mars meteorology: Mars thermal structure Temperature and circulation Profile Lower atmosphere (z < 50 km): : Global thermal structure: mostly well understood If the dust is known : variability, properties not understood Role of clouds : We can now study the details of meteorology (comparative meteorology) Still not well constrained, but of key importance, : small scale Phenomena (waves, convection, etc…) Almost no data on winds Big problem : the polar regions

Comparison with MGS TES temperature observations Zonal mean temperature (K) GCM ( « MCD V 4. 1 » ) observations TES

Distributions of atmospheric temperature difference, at 106 Pa, between MCDv 4. 2 (high res. ) and TES • Statistics computed for: § Pressure: 106 Pa § MY 24: 102. 5 < Ls < 360 § MY 25: 0 < Ls < 180 § 50 < latitude < 50 § Bins of 1 K

Zonal values of atmospheric temperature at 106 Pa

Zonal values of atmospheric temperature at 106 Pa

Zonal values of atmospheric temperature at 106 Pa

Zonal values of atmospheric temperature at 106 Pa

Comparison with MGS radio-occultation (In many cases, very good agreement) At various seasons : GCM Observations

Comparison with MGS radio-occultation (In many cases, very good agreement) At various local time GCM Observations

Comparison with MGS radio-occultation Northern summer tropical inversions (1) CLOUDS !! (radiatively active clouds will be included soon in the GCM) GCM Observations

Comparison with MGS radio-occultation Morning at high northern latitude in summer radiative cooling to ice hazes ? Observations GCM

Distributions of atmospheric temperature differences between MCDv 4. 2 (high res. ) and Radio Occultations • Statistics computed for: § MY 24 to MY 27 (except MY 25 storm) § 50 < latitude < 50 § Bins of 1 K § Altitude bands of 10 km

Distributions of atmospheric temperature differences between MCDv 4. 2 (high res. ) and Radio Occultations • Statistics computed for: § MY 24 to MY 27 (except MY 25 storm) § 50 < latitude < 50 § Bins of 1 K § Altitude bands of 10 km

Distributions of atmospheric temperature differences between MCDv 4. 2 (high res. ) and Radio Occultations • Statistics computed for: § MY 24 to MY 27 (except MY 25 storm) § 50 < latitude < 50 § Bins of 1 K § Altitude bands of 10 km

Mars meteorology: Mars thermal structure and circulation Lower atmosphere (z < 50 km): Global thermal structure well understood Only If the dust is known : variability, properties not understood Role of clouds Still not well constrained, but of key importance, : small scale phenomena (waves, convection, etc…) Almost no data on winds Problem : the polar regions

Comparison with MGS radio-occultation Southern Winter polar night

Mars thermal structure (and circulation) ? Upper atmosphere (z> 50 km): « ignorance-sphere » Key issue for : • comparative meteorology • Preparation of future missions • So far : significant disagreement with the available observations from Mars Express Spicam

SPICAM vs GCM Temperature Mean profile (S. winter)

Using the GCM with tracers • H 2 O + ice = water cycle • Dust particles = dust cycle • Chemical species = photochemistry and thermosphere

Toward a “complete” model of the Martian climate system Modelling the CO 2 cycle: Forget et al… Modelling the dust cycle : Newman et al. Forget et al… Modelling the water cycle: Montmessin et al. Bottger et al.

Modelling the water cycle • Success : Simulation of the bulk seasonal water cycle as observed by TES and MAWD and water clouds seasonal variations (TES) • Issue : – – – Particle size, cloud thickness, vertical distribution (need more data). Radiative effect of water clouds Role of regolith Surface frost Etc. . .

Montmessin et al. 2004

Modelling the dust cycle • Success : Simulation of local and regional dust storms. Initiate global dust storms. Vertical and size distribution (need more data) • Issue : Subgrid scale phenomenon • Problem : The interranual variability (Global dust storms. . . )

Synthesis : application of the LMD GCM Strong Collaboration with SA (IPSL, France), University of Oxford (UK) , IAA (Grenade, Espagne) Dynamic with L. Montabone M. Angelats (LMD) CO 2 cycle Thermosphere Avec F. Gonzalez-G. (LMD), M. Lopez V. (IAA) M. Angelats, With E. Millour K. Dassas, G. Tobie, (LMD) Mars Dust Cycle GCM Photo. Chimistry F. Lefevre (SA) S. Lebonnois (LMD) With Eric Hebrard (LISA) C. Newman, (UO) « Mars Climate database » • Reference tool for the scientific and space community • Version « pro » on DVD ROM requested by ~100 teams in 16 countries • Interactive web site Water Cycle Radon cycle P. Y. Meslin (IRSN) F. Montmessin (SA-LMD) H. Bottger (UO) Paléoclimate & Geology HDO cycle F. Montmessin (SA) T. Fouchet (LESIA) with J. B. Madeleine (LMD) B. Levrard, IMCCE) B; Haberle (NASA) J. Head, YOU ! Early Mars (with B. Haberle)