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 »

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... General Circulation Models/ Global Climate models GCMs

NASA Ames Research Center (USA, since 1969 !) LMD, (France, since 1990) Oxford university (UK, ~ 1993) NOAA GFDL (USA, since 1992) Recently : New projects: –Caltech « Planetary WRF » –Tohoku University (Japan) –York University, Toronto (Canada) : GLOBAL MARS MULTISCALE MODEL (GM3) –Germany, GISS etc... Mars Atmosphere General Circulation models  The « European » Mars GCM project (since 1995)  Here, now : the LMD Global Climate Model (H2O-CO2-dust cycles + Thermosphere + Photochemistry)

The minimum General Circulation Model for Mars 1) Hydrodynamical code  to compute large scale atmospheric motions LMD : grid point model : Typical resolution 64x48, 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 CO2 and dust Surface thermal balance Subgrid scale atmospheric motions :  Turbulence in the boundary layer  Convection  Relief drag  Gravity wave drag CO2 condensation :

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 (~ 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

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

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

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 Exemple : 32 layers

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 3D 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: Thermal Inertia (SI) TES data : Mellon et al.(2000)

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

DUST : so important for Atmospheric dynamics and thermal structure Problem : below 50 km : the 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 TES data assimilation (Montabone and Lewis) : “Martian Year 24” τ vis GCM = 1.65 × τ 1075 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) Altitude (km) Dust mixing ratio (normalized) Analytical formula Defined by one parameter Zmax (km)

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 NetCDF library available on the web ( ) 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 : You can performed “chained simulations using various scripts (run0, run_mcd)…  To be detailed in practical work (see also the user manual) You get output files containing 4D data (3D+time) or 3D data (2D +time) gcm

OUTPUT FILES NetCDF file diagfi.nc –NetCDF file diagfi.nc stores the instantaneous physical variables throughout the sim­ulation 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 NetCDF 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 MCD T(z=5m) Obs (30 sols)

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

Zonal values of surface temperature

TES Observation GCM Predictions (retrieved through Mars Climate Database ( “ MCD ”

Zonal values of surface temperature

Statistics computed for:  MY24: < Ls < 360  MY25: 0 < Ls < 180  -50 < latitude < 50  Bins of 1K Distributions of surface temperature difference between MCDv4.2 and TES 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 and circulation 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 Temperature Profile

Comparison with MGS TES temperature observations Zonal mean temperature (K) GCM (« MCD V4.1 »)TES observations

Statistics computed for:  Pressure: 106 Pa  MY24: < Ls < 360  MY25: 0 < Ls < 180  -50 < latitude < 50  Bins of 1K Distributions of atmospheric temperature difference, at 106 Pa, between MCDv4.2 (high res.) and TES

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 ? GCM Observations

Statistics computed for:  MY24 to MY27 (except MY25 storm)  -50 < latitude < 50  Bins of 1K  Altitude bands of 10 km Distributions of atmospheric temperature differences between MCDv4.2 (high res.) and Radio Occultations

Statistics computed for:  MY24 to MY27 (except MY25 storm)  -50 < latitude < 50  Bins of 1K  Altitude bands of 10 km Distributions of atmospheric temperature differences between MCDv4.2 (high res.) and Radio Occultations

Statistics computed for:  MY24 to MY27 (except MY25 storm)  -50 < latitude < 50  Bins of 1K  Altitude bands of 10 km Distributions of atmospheric temperature differences between MCDv4.2 (high res.) and Radio Occultations

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 H2O + ice = water cycle Dust particles = dust cycle Chemical species = photochemistry and thermosphere

Modelling the CO2 cycle: Modelling the dust cycle : Modelling the water cycle: Toward a “complete” model of the Martian climate system Forget et al… Newman et al. Forget et al… 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...)

Mars GCM Dynamic with L. Montabone M. Angelats (LMD) Thermosphere Avec F. Gonzalez-G. (LMD), M. Lopez V. (IAA) M. Angelats, Photo- Chimistry F. Lefevre (SA) S. Lebonnois (LMD) Water Cycle F. Montmessin (SA-LMD) H. Bottger (UO) Dust Cycle With Eric Hebrard (LISA) C. Newman, (UO) CO2 cycle With E. Millour K. Dassas,G. Tobie, (LMD) Synthesis : application of the LMD GCM Strong Collaboration with SA (IPSL, France), University of Oxford (UK), IAA (Grenade, Espagne) Paléoclimate & Geology with J.B. Madeleine (LMD) B. Levrard, IMCCE) B; Haberle (NASA) J. Head, YOU ! Paléoclimate & Geology with J.B. Madeleine (LMD) B. Levrard, IMCCE) B; Haberle (NASA) J. Head, YOU ! Early Mars (with B. Haberle) Early Mars (with B. Haberle) HDO cycle F. Montmessin (SA) T. Fouchet (LESIA) HDO cycle F. Montmessin (SA) T. Fouchet (LESIA) « 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 Radon cycle P.Y. Meslin (IRSN) Radon cycle P.Y. Meslin (IRSN)