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Development of the GEOS-MITgcm atmosphere-ocean model for coupled data assimilation

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Ehud STROBACH1,2, Andrea MOLOD2, Atanas TRAYANOV2,3, William PUTMAN2, Gael FORGET4, Jean-Michel CAMPIN4, Chris HILL4, Dimitris MENEMENLIS5, Patrick HEIMBACH6

�1University of Maryland, United States, 2NASA / GMAO, United States, 3Science Systems and Applications, Inc., 4Massachusetts Institute of Technology, United States, 5Jet Propulsion Laboratory, California Institute of Technology, United States, 6University of Texas at Austin, United States

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October 2018

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Overview

  • Part I: Earth System models and data assimilation
  • Part II: Towards a closed budget planetary assimilation system GEOS- MIT model
  • Part III: Air sea interactions in the high resolution GEOS-MIT

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October 2018

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Part I:� Earth System models and data assimilation.

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October 2018

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Earth System Models

  • Numerical models representing physical processes in the atmosphere, ocean, cryosphere and land surface.

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    • The planet is divided into a 3-dimensional grid.
    • A set of differential equations describing the circulation is defined.
    • Variables such as temperature, wind and pressure are predicted for each of the grid cells at different times.

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October 2018

Source: https://en.wikipedia.org/wiki/General_circulation_model

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Modeling Timeline

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October 2018

Source: https://science2017.globalchange.gov/chapter/4/

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Earth System Models

  • Domain – the location where predictions are made.
  • Dynamical core - equations describing the thermodynamics and fluid dynamics of the model.
  • Parameterizations (physics) - approximations for processes that can not be represented directly in a model because they are either too small scale, too complicated or not well understood.

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Atmospheric General Circulation Models (AGCM)

Dynamical core:

  • Newton’s Second Law of Motion
  • First Law of Thermodynamics
  • Conservation of Mass
  • Equation of State

Physics:

  • Radiation
  • Clouds and convection
  • Turbulence
  • Planetary Boundary Layer (PBL)
  • Surface layer
  • …

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October 2018

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Land Surfce Models (LSMs)

Simulate the exchange of surface water and energy (temperature) fluxes at the soil–atmosphere interface.

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October 2018

From: https://www.jsg.utexas.edu/noah-mp

Noah-MP

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Oceanic General Circulation Models (OGCM)

Dynamical core:

  • Newton’s Second Law of Motion
  • First Law of Thermodynamics
  • Conservation of Mass
  • Equation of State

Physics:

  • Mixed layer
  • Sub Grid Scale ocean eddies

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October 2018

Source: Griffies and Treguier (2013)

He et al (2018)

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Sea-ice Models

  • Multiple ice thickness categories.
  • Multiple thermodynamic layers within the sea ice.
  • Sea ice rheology (how the ice moves/deforms under stress)
  • Ridging (Ice being pushed around into piles)
  • Snow accumulation

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October 2018

Los Alamos Sea Ice model (CICE4)

Source: Climate Lab Book

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Other Sub-models

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  • Biosphere - Equations to Predict evolution of vegetation.
  • Cryosphere - Growth/Ablation of Glaciers and ice sheets.
  • Chemistry (Atmosphere & Ocean) - Reactions between gases to predict concentrations.
  • Biology (Ocean) - Growth of algae, biota which are relevant for carbon cycle.

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October 2018

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Data Assimilation

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October 2018

  • Mathematical discipline that seeks to optimally combine theory (usually in the form of a numerical model) with observation. (Wikipedia)

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  • Data Assimilation Systems components:
    • Observation system
    • Model
    • Data assimilation algorithm
  • Uses:
    • Initialization of GCMs.
    • Investigate past patterns of variability )Reanlysis).

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Source: https://blogs.surrey.ac.uk/mathsresearch/2017/03/27/epsrc-awards-grant-to-naratip-santitissadeekorn-for-data-assimilation/

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Example – CFSR reanalysis

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October 2018

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Net heat flux from various reanalysis datasets

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October 2018

Valdivieso et al (2017)

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Overall motivation of the research program

    • Couple the models underlying the MERRA-2 atmospheric reanalysis (GEOS) and the ECCO-v4 ocean state estimate (MITgcm).
    • Develop a prototype ocean-ice-atmosphere coupled data assimilation system.
    • Work toward closed budget global data assimilation system.

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October 2018

Applications

    • Recent sea ice and ice sheet changes.
    • Sub-seasonal to decadal climate predictions.
    • Observation System Simulation Experiments (OSSEs).

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Part II: �Towards a closed budget planetary assimilation system�  GEOS-MIT model�

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October 2018

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GEOS GCM main relevant features

  • Dynamical core: finite-volume (Lin, 2004)
  • Physics:
    • Moist processes: Based on the Relaxed Arakawa–Schubert (RAS) scheme (Moorthi and Suarez,1992).
    • Turbulent mixing: Non-local scheme (Lock et al., 2000).
    • Surface layer: Monin–Obukhov similarity theory (Helfand and Schubert,1995).
    • Radiation: long wave (Chou and Suarez, 1994), short wave (Chou and Suarez, 1999).
    • Gravity wave drag: Orographic (McFarlane, 1987) and non-orographic (Garcia and Boville, 1994).
    • Land surface model: Koster et al. (2000).
    • Chemistry: Goddard Chemistry, Aerosol, Radiation, and Transport (GOCART, Chin et al. 2002).
    • Glacial thermodynamic: Cullather et al. (2014).

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October 2018

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MITgcm main relevant features

  • Dynamical core: finite-volume (Adcroft et al., 1997).
  • Nonlinear free-surface & real freshwater flux.
  • Physics:
    • Sub-grid scale eddy parameterization (Gent and Mcwilliams, 1990; Redi,1982).
    • Ocean vertical mixing:
      • KPP - The nonlocal K-profile parameterization scheme (Large et al., 1994).
      • GGL90 - TKE vertical mixing scheme (Gaspar et al., 1990).

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October 2018

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GEOS-MIT air-sea coupling

Exchange grid

  • The exchange grid is a new grid composed of all cells enclosed by the two grids intersections.
  • Exchange of properties between the ocean and the atmosphere is done on the exchange grid.
  • Conservative exchange of water heat and momentum.

Sea-ice

  • Thermodynamics – Los Alamos Sea Ice model (CICE4) (Hunke and Lipscomb 2010).
  • Advection – viscous-plastic (VP) model (Hilber, 1979; Hilber, 1980; Losch et al. ,2010).

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October 2018

Atmospheric

grid

Oceanic

grid

Exchange grid

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Experimental setup

  • 8 year run (2000/04-2008/04)
  • Atmosphere – GEOS:
    • Atmospheric initial conditions – MERRA-2
    • Horizontal grid type – Cubed sphere, 1◦ X1◦ .
    • Vertical grid type – hybrid sigma-pressure, 72 levels
  • Ocean – MITgcm
    • Oceanic initial conditions – ECCO-v4
    • Horizontal grid type – Lat-Lon-Cap, 1◦ X1◦
    • Vertical grid type – z* rescaled height vertical coordinate, 50 levels

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October 2018

Cubed sphere grid (left) and Lat-Lon-Cap (right)

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Net heat flux [W/m2]

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October 2018

GEOS-MIT

ECCO-v4

GEOS-MIT - ECCO-v4

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Global ocean temperature drift

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October 2018

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Net fresh water flux [mm/day]

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October 2018

GEOS-MIT

ECCO-v4

GEOS-MIT - ECCO-v4

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Global sea level and salt

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October 2018

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Sea-ice Area – North Pole (fraction of grid cell)

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October 2018

March

September

GEOS-MIT

ECCO

GEOS-MIT - ECCO

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Sea-ice Area – North Pole (fraction of grid cell)

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October 2018

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Sea-ice Area - South Pole (fraction of grid cell)

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October 2018

March

September

ECCO

GEOS-MIT

GEOS-MIT - ECCO

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Sea-ice Area – North Pole (fraction of grid cell)

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Conclusions (part I)

  • GEOS-MIT model is now operational.
  • Too much net heat flux to the ocean.
  • “The double ITCZ (intertropical convergence zone) problem”
  • Sea-ice area is too large in summer but overall realistic.
  • Tuning is about to commence using Green’s function method (Menemenlis et al., 2005).

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October 2018

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Part III:�Air sea interactions in the high resolution GEOS-MIT

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October 2018

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Current objectives of this study

    • Develop a high resolution coupled ocean-atmosphere run for studying air sea interactions and simulating an observation system.
    • Investigate the ability of the coupled model to capture the strong observed positive correlations between SST and wind stress/speed.
    • Compare near-surface diagnostics of the fully coupled ocean-atmosphere set-up to equivalent atmosphere-only simulations.

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Background: observed SST/wind speed anomaly correlations

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October 2018

“Most often negative correlations between SST and surface wind speed variability are observed in the extra-tropics for seasonal means and on the basin scale”

Xie et al (2004)

SST-wind relation in the North Pacific and Atlantic Oceans, (left) COADS SST (color shade), surface wind vectors, and SLP regressed upon the Pacific decadal oscillation index (Mantua et al. 1997). (right) COADS SST (color in ◦C ) and NCEP surface wind (m s-1) composites in Jan-Mar based on a cross-equatorial SST gradient index (Okumura et al. 2001).

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Background: observed SST/wind stress anomaly correlations

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October 2018

Two-month averages (January–February 2008) of spatially high-pass-filtered sea surface temperature (SST) overlaid as contours on spatially high-pass-filtered wind stress.

Agulhas Return Current

Gulf Stream

“Satellite observations have revealed a remarkably strong positive correlation between sea surface temperature (SST) and surface winds on oceanic mesoscales of 10–1000 km.”

Chelton et al., Oceanography (2010)

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1 DegC contours

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Background: modeled SST/wind speed correlation

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October 2018

Temporal correlation of high-pass filtered surface wind speed with SST. (a) 1.0° ocean and 0.5° atmosphere (b) 0.1° ocean and 0.5° atmosphere (c) 0.1° ocean and 0.25° atmosphere. (d) Satellite observations.

“… the output of a suite of Community Climate System Model (CCSM) experiments indicates that … correlation between SST and surface wind stress, is realistically captured only when the ocean component is eddy resolving.”

Bryan et al., J. Clim. (2010)

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Methods - models

  • Atmosphere – GEOS:
    • Horizontal grid type – Cubed sphere, 1/8⚬ X1/8⚬
    • Vertical grid type – hybrid sigma-pressure, 72 levels
  • Ocean – MITgcm
    • Horizontal grid type – Lat-Lon-Cap, 1/12 ◦ X1/12 ◦
    • Vertical grid type – z* rescaled height vertical coordinate, 90 levels

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October 2018

Cubed sphere grid (left) and Lat-Lon-Cap (right)

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Methods - experimental setup

  1. Ocean only – MITgcm (OGCM):
    • Jan, 1 – Jun 15, 2012
    • Forcing: 0.14◦, 6 hourly ECMWF
  2. Atmosphere Only – GEOS (AGCM)
    • Feb, 9 – Apr 9, 2012
    • Forcing: SST and ice fraction from run 1
    • Initial conditions: MERRA-2
  3. Coupled – GEOS-MITgcm (AOGCM)
    • Feb, 9 – Apr 9, 2012
    • Ocean initial conditions: from run 1
    • Atmospheric initial conditions: MERRA-2 (same as the run 2)

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October 2018

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Ocean surface current

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October 2018

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Precipitation

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October 2018

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Wind stress (shading) and SST (contours)

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October 2018

Wind Stress [N m-2]

GEOS-MITgcm: Agulhas Return Current

GEOS : Agulhas Return Current

GEOS-MITgcm: Gulf Stream

GEOS: Gulf Stream

Solid Black – positive anomaly

White – zero

Dashed black – negative anomaly

Both GEOS and GEOS-MITgcm show positive correlation between wind stress and SST consistent with previous results

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October 2018

The linear relation between the stress and the SST in our coupled model is closer to the observed values compared to the previous modeling study.

Linear relation between wind stress and SST

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October 2018

Agulhas Return Current

Wind speed is lagging the SST by ~1day

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October 2018

Gulf Stream

Wind speed is lagging the SST by ~1day

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October 2018

GEOS-MITgcm

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October 2018

GEOS

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October 2018

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Possible mechanism

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October 2018

Positive SST anomaly

Positive wind speed anomaly

Negative SST anomaly

Negative wind speed anomaly

Increase instability

and draw

horizontal

momentum

from upper

levels

Increase

upward latent and sensible heat fluxes

Increase stability

Reduce upward latent and sensible

heat flux

Days

Days

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October 2018

MERRA-2

(2006-2015)

An observational based product (MERRA-2) demonstrates cycles of several days both in surface wind and SST

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October 2018

GEOS-MIT

MERRA-2

The GEOS-MIT model is able to reproduce the MERRA-2 spectral density but with higher SST amplitudes. May indicate strong air-sea interactions.

GEOS

2 months

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Conclusions (part II)

  • First analysis of the ~10km coupled GEOS-MITgcm model reproduces realistic synoptic and mesoscale patterns.
  • The coupled model shows positive correlations between SST and wind speed/stress, and the relation is slightly closer to observational estimates compared to previous simulations.
  • The fact that the atmosphere-only experiment can reproduce the positive correlation suggests that the atmosphere responds to the ocean and not the opposite.
  • Daily time series suggest a three-four-day cycle induced by air-sea feedbacks.

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Next steps/future work

  • Model tuning using green’s function method.
  • Increasing horizontal resolution (~1km).
  • Initialized sub-seasonal to decadal prediction system.
    • Observation System Simulation Experiments (OSSE).

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October 2018

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