GFDL Mars General Circulation
Model

R. John Wilson	

NOAA/Geophysical Fluid Dynamics Laboratory	

Princeton, New Jersey	

	

GFDL Mars Modeling GroupJohn Wilson

GFDL Mars Modeling TeamJohn Wilson
GFDL Model Development Group
Collaborators (in funded efforts)
University of Maryland Data Assimilation Group
Eugenia Kalnay Steven Greybush Yongjing Zhao

Ashima ResearchMark Richardson Chris Lee
NASA Ames Research Center
Melinda Kahre
Colleagues
Luca Montabone, Ehouarn Millour, Dave Hinson, Armin Kleinbohl

Topics
Traveling waves climatology. These are associated with pre- and post-solstice dust
activity that is evidently a prominent feature of the Mars dust cycle.

MGS Dust “Assimilation” Experiments
Force a Mars GCM with the evolving column opacity field derived from the TES
observations and MOC imagery. This allows a comparison of simulated temperatures with
TES retrievals to explore the influence of the dust particle size spectrum and the effects ofradiatively active water ice clouds…. and identify model biases.

Interactive dust cycle simulations with a finite dust reservoir. A MGCM
implementation of the idealized modeling described by Pankine and Ingersoll [2002,
2004]. The surface stress lifting threshold varies as a function of surface dust availability,
introducing a negative feedback that allows the surface/atmosphere system to selforganize and support interannual variability in major dust storm activity.
Impact of radiatively active water ice clouds
Some thoughts on Data Assimilation
High Resolution Simulations: A Global Mesoscale ModelGFDL Flexible Modeling System (FMS)• Selection of dynamical cores			Spectral, B-Grid, Finite Volume*		*lat-lon and cubed-sphere geometries 	

	

	

	

• Support for a range of very large multi-processor systems	

NOAA’s Gaea at Oak Ridge (Cray) , Argonne (IBM)	

• Modular physics parameterizations 	• Diagnostics Manager	

Can distribute model output to multiple output files from the run script; specify
instantaneous, time averaged, min/max, diurnal composite with (hourly,
daily….) sampling. 	

• Tracer Manager	

Can add, remove, characterize and initialize tracer fields from the run script 		Finite Volume (FV) Model
Developed by Lin and Rood (1996), Lin and Rood (1997)l 3D version available (Lin 2004), built upon the SW model:l hydrostatic dynamical core used for climate and weather predictionsl Currently part of NCAR’s, NASA’s and GFDL’s General Circulation Modelsll

Numerics: Finite volume approach– conservative and monotonic transport scheme– van Leer second order scheme for time-averaged numerical fluxes– PPM third order scheme (Piecewise Parabolic Method)
for prognostic variables– Staggered grid (Arakawa D-grid), C-grid for mid-time levels– Orthogonal Latitude-Longitude computational grid
The importance of aerosols and tracers in climate
evolution demands strictly conservative transportCubed-Sphere Dynamical Core

• Finite-Volume advection• Provides relatively uniform global
resolution• Scales very efficiently on massivelyparallel computer architectures~ 15 km

C22: 4°×4°

C48: 2°×2°

C90: 1°×1° C180: 0.5°×0.5° C360: 0.25°×0.25°

3 ways to visualize the Cubed Sphere	

Precipitation: C360 HiRam-2	

C48:
2°×2°

C360:
0.25°×0.25° 	Cubed-Sphere Model Developments

2-way nested global/regional dynamical core
Transport of dust and volatiles on/off the polar caps
Dust lifting in selected regions

Non-hydrostatic dynamics
At what scales does this become significant?

Equatorial Zonal Wind Oscillations
C360 0.25x0.25 resolution
Terrestrial Atmosphere; HiRAM physics
Non-Hydrostatic Dynamics

Pressure (hPa)Hydrostatic Dynamics

Years

Years

GFDL MGCM Description• FV dynamical core with cubed-sphere geometry• L28, L36, and L46 with a range of resolutions• Radiation with interactive aerosol: 2 stream with correlated-k gaseous
absorption(NASA/Ames)• Full complement of physics: CO2 & water cycles.
• A finite inventory of dust is maintained, partitioned between surface
dust and aerosol.
• The stress threshold for dust lifting is allowed to vary with surface
dust depth: The lifting threshold increases as dust is depleted froman initial depth that is spatially uniform• Accumulated dust reflects seasonally integrated dust lifting anddeposition; thus providing a memory of past lifting activity

Boundary Conditions• Topography:

MGS MOLA

laser altimeter• Surface Albedo:

MGS TESbroadband bolometer• Surface IR emissivity:

MGS TES• Thermal inertia: Re-derived to allow the MGCM to closely match TES
surface temperature (T7, T20) observations. 3-D field to allow for theeffects of subsurface ice in the polar regions.
• Surface Roughness:(Hebrard et al. 2012, observed rock abundance)

Optional Inputs• Time-evolving column dust opacity:

typically from TES• Time-evolving maps for controlling dust injection:
radiatively active or passive for tracing advection

MGCM Cubed-Sphere Configurations
Typical horizontal resolutions (L28)
Δϕ x ΔλLat x Lon

Wall Clock

C22: 4o x 4o

90x45

700 sols ~ 1.5 hours
24 CPUs

C36: 2.5o x 2.5o

144x72700 sols ~ 1.5 hours
32 CPUs

C48: 2o x 2o

180x90720 sols < 3.5 hours
54 CPUs

C90: 1o x 1.0o

288x180160 sols < 6 hours
54 CPUs

C180: 0.5o x 0.5o

576x360

100 sols ~ 7.5 hours
96 CPUs

C360: 0.25o x 0.25o

1152x720

288 CPUs

Martian Dust Cycle
MY24

MY25

MY26

Mars Global Surveyor
3+ Years•TES temperature andopacity retrievals•Mars Orbiter Camera wide
angle images

Flushing Storms
Pre solstice Ls= 210-240
Post-solstice Ls= 310-350
MY24

MY25

MY26

a) The seasonal variation of zonally-averaged
dust column opacity observed by TES for 3
Mars years (MY24 into MY27). (b) The
variation of tropical opacity. The 2001 global
dust storm occurs at Ls=187 in MY25. The 5
opacity peaks indicated by arrows are associatedwith “flushing” storm events. Data provided by
M.D. SmithTES Dust opacity

Mars Odyssey: THEMIS•Continued pattern of pre- and post-solstice storms in MY27 and MY29
•Solstice storm in MY28 in 2007 (first since MY21 Ls~250)
Data provided by Michael Smith

Flushing Storms

Mars Orbiter Camera (MOC)
AR: Arcadia
AC: Acidalia
UT: Utoptia
MY24: Ls 210 314 336
AC AC AR

MY25: Ls 316 324 324
AC

MY26:

AC UT

Ls 207 214 230
UT AC AC

Wang et al. 2005

MGCM Simulation of Zonal Mean Surface Stress
Winter Westerlies+ baroclinic waves
Thermal Tides
Subtropical Jet
Retreating polar cap
boundary

Polar CO2 caps are shaded

Areocentric Longitude Ls
Units: 10-3 Nm-2

Zonally-averaged eddy rms V
UK Assimilation of TES temperatures from the MGS mission

Areocentric Longitude
2 km agl
Seasonal evolution of the zonally-averaged eddy V variance(bandpass filtered 1.5-20 sols) at ~ 2 km above ground level.
Units are ms-1.

Eddy Meridional Velocity Variance 52.5 N
Reanalysis

Control

Traveling Waves in MY24

UK Reanalysis(MACDA)MY24 Dust Column Opacity Evolution

Ls= 220 - 242°

Flushing
stormsCap Edge
Lifting
due to amplified
Semidiurnal
Tide ?

MY24 Dust Column Opacity Evolution

Ls= 220 - 242°

Flushing
storms3 Regional Episodesof dust lifting

Cap Edge
Lifting
due to amplified
Semidiurnal
Tide ?

Mars Water Cycle and Water Ice Clouds
Important Radiative effects of water ice clouds:
Tropical Clouds: Net Heating due to absorption of
upwelling IR radiation from the relatively hot surface
Intensified Hadley circulation in the upper atmosphere
Stronger forcing of thermal tides

Polar Hood Clouds: Net Cooling due to IR emission
Sharpening of Polar Vortex, modifying the character ofbaroclinic waves; significant for dust liftingTemperature Bias in Mars GCMs

Bias due to the absence of radiatively active water ice clouds
Tropical water ice clouds provide midlevel heating during NH summer
Polar hood clouds yield low level cooling in the equinoctial seasons
Mars Climate Database is shown, results are typical of other models as well.
Originally presented at the Mars Water Cycle Workshop, Paris, 2008

Water Cycle Simulation
Radiatively active water ice clouds

C22L46
Montmessin (2004) ice
cloud scheme (using
simulated dust distribution for dust
cores)

Daytime (top) andNighttime (bottom)
cloudsRadiative Influence of Water Ice Clouds
Improved agreement between simulation and observations with radiatively active
water ice clouds

Pressure

MGCM with cloud effect
MGCM
Simulation

Pressure

MGCM no cloud effect

TES
Reanalysis andretrievals

LatitudeLatitudeRadiative Influence of Water Ice Clouds
Equatorial Temperature
TES – Control
Model is cold in the tropicalupper atmosphere

Addition of water ice
clouds leads to atmosphericwarmingCloud Radiative Effects
Ls ~ 100--- Tropical Water Ice Clouds--- Zonal Mean Temperature
ΔT (shading) Cloud Influence--- Tropical Water Ice Cloud Belt--- ΔT
Net Radiative Heating (shading)-

Dominated by absorption of upwelling LW
radiation in the tropics-

LW cooling by low level clouds in thesouthern (winter) hemisphere.

Zonal Mean Temperature:

MCS

Ls= 160

Significantly deeper coveragethan provided by TES:
~80 km vs 40 km
Prominent polar warming
McCleese et al. 2009

Latitude

MGCM developments:• Parameterized topographic wave dragUnresolved non-orographic waves may also contribute to drag• Radiatively active water ice clouds
Contribute to tropical heating and intensified Hadley circulation

Zonal Mean Temperature:

MCS vs MGCM

Ls= 160

Influence of Water Ice Clouds on Transient Eddy Activity
Eddy Meridional Wind at ~2km
Latitude1.5- 10 sol period

Latitude

Fixed (low) Dust; Passive Clouds

Radiatively Active Clouds

Areocentric Longitude

Note: the simulated water ice clouds
are probably too thick, particularly during
NH spring.

Influence of Water Ice Clouds on Transient Eddy Activity

Latitude

Fixed (low) Dust; Passive Clouds
Higher dust loading yields a
more prominent solstitial
break in eddy activity

Latitude

Radiatively Active Clouds
As with increased dust, clouds
contribute to a stronger
Hadley circulation

Areocentric Longitude

Note: the simulated water ice clouds
are probably too thick, particularly during
NH spring.

Dust “Assimilation”
Goal: A realistic vertical and meridional variation of dust in
simulations with prescribed dust opacity distribution(s)The MGCM predicts the evolution of a 3D dust opacity field subject
to the constraints of the available MGS TES dust column opacity
observations.
Dust is added/removed from the boundary layer as needed to fit theobserved column dust opacity
The dust particle size spectrum plays a significant role in thevertical and meridional extent of the resulting opacity field.
Currently using 5 dust tracer fields.
Dust removal is optional: Sedimentation should yield good
agreement with TES temperatures
Assimilation is being used to “tune” the input dust size spectrum toimprove agreement with temperature observations

Observed (TES) and Simulated Equatorial 50 Pa Temperatures

5 Dust Aerosol Fields + Radiatively Active Water Ice Clouds

Simulated Zonal Mean Temperature and Opacity

TES

MY24/25 Dust Assimilation Experiment
Implied Dust Source Term for 1.2 µm dust

Kahre, Wilson, et al. 2009 Dust Cycle Workshop,

Simulated Dust Evolution for MY24
Dust Opacity

Ls= 223-239

dust injection/sedimentation

Source units in10-9 kgm-2s-1

Simulated U, T, Opacity

MY25 Global
Dust Storm

Pressure

Pressure

Ls= 183

LatitudeSimulated U, T, Opacity

Pressure

Pressure

Ls= 187.8

LatitudeSimulated U, T, Opacity

Pressure

Pressure

Ls= 191

LatitudeSimulated U, T, Opacity

Pressure

Pressure

Ls= 196

Latitude2001 Dust Storm Simulation: Ls= 187°
•Column dust opacity on
bottom plane•Dust mixing ratio (shaded)

Pressure•Temperature (contoured)to 180°Longitude 0

Temperature contoured
at 10 K intervals

Dust, T

LatitudeDust, VV

MOC Image
Dust front at 24° S and 250° E

MGCM Simulation: 1400 LT
Meridional Winds dominated by Tides

V

10 ms-1 intervals

Meridional Wind in the Western (100°E) and Eastern (280°E) Hemisphere
Ls= 187.4
Lon= 100°E
Dominant Tide Circulation
2am (left) vs 2pm (right)
Localized to the western
hemisphere at this time

Lon= 280°E

Low-level daytime
convergence at equator
during daytime

V @ 10 ms-1 intervals

2 pm Winds (top); Diurnal Maximum Surface Stress (bottom)

1400 Local Time Winds (1 km agl)
Wind magnitude (shaded)

Daily maximum stressNm-2 x 103

2 pm Winds (top); Diurnal Maximum Surface Stress (bottom)
wind vectors and amplitude

m/s

stress: 10-3Nm-1

stress: 10-3Nm-1

The evolution of tide wind amplitude
during the expansion phase of the 2001 dust storm

Amplification of tidewinds withincreasing dust
loading:
In turn, theintensified tides leadto further dust lifting

Amplitude m/s~300 m above ground level

Dust Opacity
MY26: Ls=210-220

Longitude

Longitude

LatitudeMY25: Ls=180-190

Interesting differences starting from similar conditions

TES Dust Column Opacity
MY26: Ls=210-220

LatitudeMY25: Ls=180-190

Longitude
Dust OpacityDust Cycle Overview
Current Mars climate modeling is unable to represent theobserved seasonal and interannual variability in theMars dust cycle. This constitutes the major research issue
in Mars atmospheric modeling.
To date, the representation of surface dust reservoirs has
been very simplistic. Yet this seems to be the mostplausible source of long term memory needed forinterannual variability Pankine & Ingersoll[2002,2004]
I will show an example of how adding an element ofpotentially greater realism can yield interannual
variability in the occurrence of global dust storms in aMars general circulation modelMGCM Dust Cycle ModelingInteractive Dust Lifting• A finite inventory of dust is maintained, partitioned between surface dust andaerosol.
• The stress threshold for dust lifting is allowed to vary with surface dust depth:
The lifting threshold increases as dust is depleted from an initial depth that is
spatially uniform• Accumulated dust reflects seasonally integrated dust lifting and deposition;
thus providing a memory of past lifting activity

GCM Modeling of the Mars Dust Cycle
Interactive Dust Lifting Parameterizations
Newman et al. 2002;

Basu et al. 2004, 2006; Kahre et al. 2005, 2006, 2008

Dust lifting by convective activity (parameterized “dust devils”) and byresolved-scale wind stresses (dust injection via saltation process).

F = βƊ(Tsfc,Tair) + αU3(1-R)(1+R2);

with R= Ut/U; U > Ut

Ut is the threshold friction velocity ( surface stress τ= ρU2 )α, β are efficiency factors
Parameters β, α and Ut are adjusted to provide a reasonable
correspondence between simulated global opacity toobservations
	Representative Dust Cycle Simulation
Global Mean Opacity

Opacity (9 µm)

Kahre et al. 2006

Two values of α:More or less dust lifting
during the storm season

Areocentric Longitude Ls
Peak opacity at Ls= 290°; No Interannual Variability

Wind Stress Lifting(logarithmic scaling)

Opacity (9 µm)

Global Mean Opacity

Areocentric Longitude Ls

Dust Devil Lifting

Lifting dominated by Cap Edgeand Subtropical Jet stresses

Areocentric Longitude Ls

Kahre et al. 2006

Convective lifting: relatively weak seasonalvariation; Source amplitude is constrained toallow model to match relatively low opacities
during the NH spring/summer season
Negative feedback: weakens with increased
dust loading
Stress lifting is then required to match theobserved opacity/temperature during the dust
storm season.
positive feedback: stresses increase withincreased radiative forcing

Dust Cycle Modeling with Finite Surface Reservoirs 	

Goal:	  	
  Improved	
  spa8al	
  and	
  temporal	
  variability	
  in	
  dust	  li?ing	
  ac8vity	  	
  Interactive dust lifting with finite surface dust reservoir	

Stress thresholds for dust lifting are allowed to increase as surface dust
is depleted 	Regions of accumulated/depletion reflect seasonally-integrated dust
lifting and deposition; provides a memory of past lifting activity	

C48L28	  (2°x2°)	
  	
  Regions of accumulation/depletion reflect seasonally-integrated
dust lifting and deposition; provides a memory of past lifting
activity. Pattern is equilibrated. 	

Summary• Allowance for finite dust deposits provides a negative
feedback mechanism that can yield interannual
variability• More work remains in analyzing the observations
and improving MGCM parameterizations:• Need to reduce the extent of spatial connectivity, current simulation yielded
only a quiescent/full-on-storm dichotomy• Models do not seem to adequately represent the response to flushing storm
activity. Inclusion of radiatively active polar hood clouds improves thetraveling wave climatology in model simulations. Preliminary results do notshow a big impact on the character of the dust cycle.
• Research and observations. Albedo is currently the best indicator of changes
in surface dust, but is difficult to monitor during dust storm events.
• New and/or improved physical descriptions of dust lifting.

MGCM-LETKF Mars Reanalysis Goals• Innovate ensemble data assimilation methodology for theunique characteristics of the Mars atmosphere and its
observing systems.
• Improve model representation of dust and ice cloud aerosols.
• Provide community with 4-D synoptic states of atmospheric
and aerosol fields to explore science questions: travelingwaves, dust storm evolution, etc.
• Evaluate reanalysis through comparison with other reanalyses
and observation products.
• Assess atmospheric predictability and analyzability.

How sensitive is the temperature reanalysis to the choiceof dust aerosol distribution?

Areocentric Longitude

3 aerosol scenarios:
fixed dust (t= 0.3)
seasonal varying dust
TES dust + ice cloud
Greybush et al. JGR. 2012

How sensitive are temperature reanalyses to the
choice of dust aerosol distribution?

Areocentric Longitude

Synoptic Maps from Reanalysis
Simple	  Reanalysis:	
  Fixed	
  Dust	  No	
  Bias	
  Correc8on	  Do	
  reanalyses	
  with	  diﬀerent	  model	  conﬁgura6ons,	
  ini6al	  condi6ons,	
  and	
  data	  assimila6on	
  techniques	
  converge	
  on	
  the	
  same	  synop6c	
  state	  of	
  traveling	  waves?	
  	
  Advanced	
  Reanalysis:	
  TES	
  Dust	  and	
  Water	  Ice	
  Clouds	  Empirical	
  Bias	
  Correc8on	  Perturbed Assimilation Experiment:
MY25
Reanal, Control andDoubled visible opacity:

Reanal_B, Control_B

Figure 11. (a) Zonally-averaged equatorial T15 for 4 simulations for MY25. (b) The S1(p) (c) DK1(d) S2(p). In all cases, the ReanalB and ControlB cases use the doubled visible dust opacity.

High Resolution Modeling
Global scale mesoscale model
Convective plumes driven by dust aerosol (Rocket dust storms)
Surface/Atmosphere interactions---- slope winds
Frontal Circulations
Vertically propagating gravity waves: interactions with the large scale
circulation
Collaborations with Aymeric Spiga and Takahashi-san

Frontal Storm Simulation
Chyrse Basin 330E, 40N
2°x 2.4° lat-lon
Interactive dust lifting
Dust column opacity(shaded)Winds (1 km agl)
Surface Stress (contours)

Figure 8. High resolution simulation of a frontal system in the Chryse
basin showing column dust opacity (shaded) and low-level (~1 km)
winds. High values of surface stress are indicated by contour lines. The
figure is centered on 330° E, 40° N.

Mars North Polar Cap

C180

0.5° x 0.5°

Winds @ 1 km agl
Subsampled at 1°x1°

80
°

70
°

Uniform resolution: nopolar filtering
Topographic height (km)Longitude-height section of V field at 55° N

FV core: 1x1.2 resolution	

Ls= 185	

Zoom view: 	

Near-surface winds (1 km)
perturbation surfacepressure shaded (Pa) 	

C180 0.5x0.5 Resolution Vertical Velocity 15 Pa

Vertical velocity (m/s) on the 15 Pa (z ≈ 35 km) isobaric surface.

Ls=178.

Gravity Waves

Thermal Tides and Gravity Waves

C360

0.25x0.25 Resolution Tprime (%)