An Ensemble Ocean Data Assimilation System for Seasonal Prediction

Yin, Yan; Alves, Oscar; Oke, Peter R.

doi:10.1175/2010mwr3419.1

Cited by 126 publications

(77 citation statements)

References 74 publications

(84 reference statements)

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“…Ocean initial conditions are generated by the POAMA Ensemble Ocean Data Assimilation System (PEODAS; Yin et al 2011). PEODAS uses an approximate ensemble Kalman filter system which utilises covariances from a time evolving model ensemble (Oke et al 2005).…”

Section: Data Assimilationmentioning

confidence: 99%

Seasonal prediction of global sea level anomalies using an ocean–atmosphere dynamical model

et al. 2014

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Advanced warning of extreme sea level events is an invaluable tool for coastal communities, allowing the implementation of management policies and strategies to minimise loss of life and infrastructure damage. This study is an initial attempt to apply a dynamical coupled oceanatmosphere model to the prediction of seasonal sea level anomalies (SLA) globally for up to 7 months in advance. We assess the ability of the Australian Bureau of Meteorology's operational seasonal dynamical forecast system, the Predictive Ocean Atmosphere Model for Australia (POAMA), to predict seasonal SLA, using gridded satellite altimeter observation-based analyses over the period 1993-2010 and model reanalysis over 1981-2010. Hindcasts from POAMA are based on a 33-member ensemble of seasonal forecasts that are initialised once per month for the period 1981-2010. Our results show POAMA demonstrates high skill in the equatorial Pacific basin and consistently exhibits more skill globally than a forecast based on persistence. Model predictability estimates indicate there is scope for improvement in the higher latitudes and in the Atlantic and Southern Oceans. Most characteristics of the asymmetric SLA fields generated by ElNino/La Nina events are well represented by POAMA, although the forecast amplitude weakens with increasing lead-time.

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Section: Data Assimilationmentioning

confidence: 99%

Seasonal prediction of global sea level anomalies using an ocean–atmosphere dynamical model

et al. 2014

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“…The ERA-Interim reanalysis dataset (Dee et al 2011) was used to assess the ENSO teleconnection in the pressure and geopotential height fields. The thermocline depth is estimated by the depth of 20 °C isotherm, which was computed from two ocean reanalyses, Simple Ocean Data Assimilation (SODA; version 2.2.4) reanalysis (Carton et al 2000) and the Australian Bureau of Meteorology's newly developed POAMA Ensemble Ocean Data Assimilation System (PEODAS; Yin et al 2011). …”

Section: Models Experiments and Validation Datasetsmentioning

confidence: 99%

Evaluation of El Niño-Southern Oscillation in the ACCESS coupled model simulations for CMIP5

Rashid¹,

Sullivan²,

Hirst³

et al. 2013

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One of the key performance measures for Coupled General Circulation Models (CGCMs) is their ability to realistically simulate the prominent modes of climate variability. Here, we investigate the realism of El Niño-Southern Oscillation (ENSO), the dominant mode of observed interannual climate variability, as simulated by the Australian Community Climate and Earth System Simulator (AC-CESS). We examine the key ENSO properties calculated from observations and from the pre-industrial control and historical simulations performed by versions 1.0 and 1.3 of the ACCESS coupled model (ACCESS-CM), as part of the Coupled Model Intercomparison Project phase 5 (CMIP5). The ENSO properties examined are the spatial structure and timescales, phase locking to the annual cycle, growth and decay of Niño3 SST anomalies, and the coupled ENSO evolution, as well as the ENSO induced teleconnection patterns (as diagnosed by linear regressions of the three-dimensional atmospheric circulation field, rainfall, sea-level pressure, and the upper-tropospheric geopotential heights onto the Niño3 index). The coupled evolutions of SST, zonal wind stress, and the thermocline depth during a typical ENSO event are used to illustrate the essential features of ENSO dynamics. Our results show that both versions of the ACCESS-CM perform reasonably well in simulating the ENSO properties and teleconnections. In particular, the spatial structure, timescales, phase locking, and the growth and decay rates of ENSO are well simulated. The evolution of SST, zonal wind stress, and the thermocline depth during ENSO events is also reasonably well simulated, although the SST (zonal wind stress) response is found to be stronger (weaker) than observed. The simulated ENSO teleconnection patterns are also mostly realistic, despite some adverse impact of the equatorial Pacific cold SST bias on the local precipitation response. The present study shows that the ACCESS-CMs simulate the key ENSO properties (e.g. amplitude and spatial structure, timescales, and atmospheric teleconnections) better than most of the previous generations of CGCMs.

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“…POAMA2 forecasts are initialized with realistic atmosphere, land, and ocean conditions that are generated from separate atmosphere/land surface (Hudson et al 2011) and ocean (Yin et al 2011) data assimilation systems. Sea-ice and ozone are set to their respective climatological annual cycles.…”

Section: S134mentioning

confidence: 99%

The Effect of Increasing CO2 on the Extreme September 2016 Rainfall Across Southeastern Australia

Hope

Lim

Hendon

et al. 2018

Bulletin of the American Meteorological Society

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An Ensemble Ocean Data Assimilation System for Seasonal Prediction

Cited by 126 publications

References 74 publications

Seasonal prediction of global sea level anomalies using an ocean–atmosphere dynamical model

Seasonal prediction of global sea level anomalies using an ocean–atmosphere dynamical model

Evaluation of El Niño-Southern Oscillation in the ACCESS coupled model simulations for CMIP5

The Effect of Increasing CO2 on the Extreme September 2016 Rainfall Across Southeastern Australia

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