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Article
Article Type:
Research Article

Coupled Data Assimilation and Ensemble Initialization with Application to Multiyear ENSO Prediction

Terence J. O’Kane CSIRO Oceans and Atmosphere, Hobart, Tasmania, Australia

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Paul A. Sandery CSIRO Oceans and Atmosphere, Hobart, Tasmania, Australia

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Didier P. Monselesan CSIRO Oceans and Atmosphere, Hobart, Tasmania, Australia

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Pavel Sakov Bureau of Meteorology, Docklands, Melbourne, Victoria, Australia

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Matthew A. Chamberlain CSIRO Oceans and Atmosphere, Hobart, Tasmania, Australia

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Richard J. Matear CSIRO Oceans and Atmosphere, Hobart, Tasmania, Australia

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Mark A. Collier CSIRO Oceans and Atmosphere, Aspendale, Victoria, Australia

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Dougal T. Squire CSIRO Oceans and Atmosphere, Hobart, Tasmania, Australia

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Lauren Stevens CSIRO Oceans and Atmosphere, Aspendale, Victoria, Australia

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Print Publication:
15 Feb 2019
DOI:
https://doi.org/10.1175/JCLI-D-18-0189.1
Page(s):
997–1024
Restricted access

Abstract

We develop and compare variants of coupled data assimilation (DA) systems based on ensemble optimal interpolation (EnOI) and ensemble transform Kalman filter (ETKF) methods. The assimilation system is first tested on a small paradigm model of the coupled tropical–extratropical climate system, then implemented for a coupled general circulation model (GCM). Strongly coupled DA was employed specifically to assess the impact of assimilating ocean observations [sea surface temperature (SST), sea surface height (SSH), and sea surface salinity (SSS), Argo, XBT, CTD, moorings] on the atmospheric state analysis update via the cross-domain error covariances from the coupled-model background ensemble. We examine the relationship between ensemble spread, analysis increments, and forecast skill in multiyear ENSO prediction experiments with a particular focus on the atmospheric response to tropical ocean perturbations. Initial forecast perturbations generated from bred vectors (BVs) project onto disturbances at and below the thermocline with similar structures to ETKF perturbations. BV error growth leads ENSO SST phasing by 6 months whereupon the dominant mechanism communicating tropical ocean variability to the extratropical atmosphere is via tropical convection modulating the Hadley circulation. We find that bred vectors specific to tropical Pacific thermocline variability were the most effective choices for ensemble initialization and ENSO forecasting.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-18-0189.s1.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Terence J. O’Kane, terence.okane@csiro.au

Abstract

We develop and compare variants of coupled data assimilation (DA) systems based on ensemble optimal interpolation (EnOI) and ensemble transform Kalman filter (ETKF) methods. The assimilation system is first tested on a small paradigm model of the coupled tropical–extratropical climate system, then implemented for a coupled general circulation model (GCM). Strongly coupled DA was employed specifically to assess the impact of assimilating ocean observations [sea surface temperature (SST), sea surface height (SSH), and sea surface salinity (SSS), Argo, XBT, CTD, moorings] on the atmospheric state analysis update via the cross-domain error covariances from the coupled-model background ensemble. We examine the relationship between ensemble spread, analysis increments, and forecast skill in multiyear ENSO prediction experiments with a particular focus on the atmospheric response to tropical ocean perturbations. Initial forecast perturbations generated from bred vectors (BVs) project onto disturbances at and below the thermocline with similar structures to ETKF perturbations. BV error growth leads ENSO SST phasing by 6 months whereupon the dominant mechanism communicating tropical ocean variability to the extratropical atmosphere is via tropical convection modulating the Hadley circulation. We find that bred vectors specific to tropical Pacific thermocline variability were the most effective choices for ensemble initialization and ENSO forecasting.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-18-0189.s1.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Terence J. O’Kane, terence.okane@csiro.au

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