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The development of the new GEOS-MITgcm atmosphere-ocean model for coupled data assimilation system

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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: Air sea interactions in the high resolution GEOS-MIT

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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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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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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

    • Sub-seasonal to decadal climate predictions.
    • Observation System Simulation Experiments (OSSEs).

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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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October 2018

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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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Ocean surface current

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Precipitation

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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

GEOS-MITgcm

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October 2018

GEOS

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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

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Conclusions

  • 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.
  • Daily time series suggest a three-four-day cycle induced by air-sea feedbacks.

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October 2018

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Next steps/future work

  • 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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