An introduction to the LMD Mars GCM

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

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

3. General Circulation Models/Global Climate modelsGCMs 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...

4. Mars Atmosphere General Circulation models • 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... • The « European » Mars GCM project (since 1995) • Here, now : the LMD Global Climate Model (H2O-CO2-dust cycles + Thermosphere + Photochemistry)

6. 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 :

7. In the Fortran program…

9. 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)

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

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

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

13. 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

14. In the Fortran program…

15. Horizontal gridLMD GCM : standard resolution : 5.625° 3.75°

16. LMD GCM : high resolution : 2° 2°

17. GCM : Zoomed grid to reach resolution: 1.8° 0.6°at reduced cost

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

19. 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

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

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

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

23. 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%

24. 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

25. 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” τvis GCM = 1.65 ×τ1075 cm-1 TES Top of the dust layer (km): (based on Viking and Mariner 9 limb observations)

26. Vertical distribution of the dust(based on models + observation) Analytical formula Defined by one parameter Zmax (km) Altitude (km) Dust mixing ratio (normalized)

27. 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 (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)

32. 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 gcm

33. 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.

34. How accurate are the temperature and wind predictions ?

35. Surface temperatures

36. Mars Pathfinder data:Temperature at 1.2 m MCD, Tsurf Obs (30 sols) MCD T(z=5m)

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

38. Zonal values of surface temperature

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

40. Zonal values of surface temperature

41. Distributions of surface temperature difference between MCDv4.2 and TES • Statistics computed for: • MY24: 102.5 < Ls < 360 • MY25: 0 < Ls < 180 • -50 < latitude < 50 • Bins of 1K Note: MEAN and RMS values are computed from histograms; blue curves are normal distributions of same MEAN and RMS

42. Atmospheric temperatures

43. Mars meteorology: Mars thermal structure and circulation Temperature 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

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

45. Distributions of atmospheric temperature difference, at 106 Pa, between MCDv4.2 (high res.) and TES • Statistics computed for: • Pressure: 106 Pa • MY24: 102.5 < Ls < 360 • MY25: 0 < Ls < 180 • -50 < latitude < 50 • Bins of 1K

46. Zonal values of atmospheric temperature at 106 Pa

47. Zonal values of atmospheric temperature at 106 Pa

48. Zonal values of atmospheric temperature at 106 Pa

49. Zonal values of atmospheric temperature at 106 Pa

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