ORCID Profile
0000-0002-3163-5610
Current Organisation
CSIRO Oceans and Atmosphere
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Publisher: European Space Agency
Date: 31-12-2010
Publisher: Elsevier BV
Date: 07-2013
Publisher: Copernicus GmbH
Date: 03-05-2013
Abstract: Abstract. Analysis of the variability of the last 18 yr (1993–2012) of a 32 yr run of a new near-global, eddy-resolving ocean general circulation model coupled with biogeochemistry is presented. Comparisons between modelled and observed mean sea level (MSL), mixed layer depth (MLD), sea level anomaly (SLA), sea surface temperature (SST), and {\\\\chla} indicate that the model variability is realistic. We find some systematic errors in the modelled MLD, with the model generally deeper than observations, which results in errors in the {\\\\chla}, owing to the strong biophysical coupling. We evaluate several other metrics in the model, including the zonally averaged seasonal cycle of SST, meridional overturning, volume transports through key straits and passages, zonally averaged temperature and salinity, and El Niño-related SST indices. We find that the modelled seasonal cycle in SST is 0.5–1.5 °C weaker than observed volume transports of the Antarctic Circumpolar Current, the East Australian Current, and Indonesian Throughflow are in good agreement with observational estimates and the correlation between the modelled and observed NINO SST indices exceeds 0.91. Most aspects of the model circulation are realistic. We conclude that the model output is suitable for broader analysis to better understand upper ocean dynamics and ocean variability at mid- and low latitudes. The new model is intended to underpin a future version of Australia's operational short-range ocean forecasting system.
Publisher: American Meteorological Society
Date: 03-2008
Publisher: American Meteorological Society
Date: 05-2008
Publisher: Elsevier BV
Date: 03-2008
Publisher: American Meteorological Society
Date: 06-2008
Publisher: Elsevier BV
Date: 2008
Publisher: American Meteorological Society
Date: 03-2000
Publisher: Copernicus GmbH
Date: 15-08-2012
Publisher: Stockholm University Press
Date: 2008
Publisher: American Geophysical Union (AGU)
Date: 04-2003
DOI: 10.1029/2002JC001370
Publisher: Elsevier BV
Date: 03-2011
Publisher: Wiley
Date: 14-02-2014
DOI: 10.1002/QJ.2281
Publisher: Elsevier BV
Date: 08-2010
Publisher: Wiley
Date: 28-07-2015
DOI: 10.1002/QJ.2579
Publisher: Journal of Marine Research/Yale
Date: 05-2010
Publisher: American Geophysical Union (AGU)
Date: 09-2002
DOI: 10.1029/2000JC000511
Publisher: World Scientific Publishing Co. Pte. Ltd.
Date: 2006
Publisher: American Geophysical Union (AGU)
Date: 10-2007
DOI: 10.1029/2007GL031549
Publisher: American Geophysical Union (AGU)
Date: 07-2022
DOI: 10.1029/2022EA002312
Abstract: Since the Argo program began, 568 floats returned almost 31,000 profiles, at high‐southern latitudes, with no measured position. These data are either disseminated with positions linearly interpolated between known positions, or with no geographic positions. Here, we present a simple method for estimating unknown Argo float trajectories. We try to identify trajectories that approximately follow contours along three different properties: potential vorticity ( f / H ), sea‐level, and density at 1,000 m. No single property‐constraint can be used to estimate trajectories for all position‐gaps. Each constraint fails for 9%–18% of gaps, where no continuous contour between the end‐points exists. But all constraints fail for the same position‐gap, for fewer than 1% of cases. For a given position‐gap, when a trajectory is identified using two or three different constraints, we select the shortest trajectory to be used to “fill the gap”. This selection process could be performed better by an Expert Operator, inspecting each estimated trajectory, and selecting the trajectory that is most consistent with a priori knowledge of the circulation in the vicinity of the position‐gap. Nonetheless, using the objective metric for selection, we find that 41.2% of position‐gaps use the f / H ‐constraint, 32.1% use density, and 25.8% use sea‐level. We assess the estimated trajectories for consistency, by comparing bottom depths beneath trajectories to the deepest measurements in each profile. We find inconsistencies for 11.6% of position‐gaps using our method, compared to 28.0% using linearly‐interpolated trajectories. Adoption of the estimated trajectories for measurements under ice may yield benefits to many applications.
Publisher: Elsevier BV
Date: 04-2001
Publisher: Elsevier BV
Date: 03-2011
Publisher: Elsevier BV
Date: 15-04-2008
Publisher: Elsevier BV
Date: 10-2004
Publisher: American Geophysical Union (AGU)
Date: 03-2009
DOI: 10.1029/2008GL036997
Publisher: American Geophysical Union (AGU)
Date: 08-2022
DOI: 10.1029/2022JC018679
Publisher: Wiley
Date: 10-2005
DOI: 10.1256/QJ.05.95
Publisher: American Geophysical Union (AGU)
Date: 06-2015
DOI: 10.1002/2014JC010538
Publisher: American Meteorological Society
Date: 07-2007
DOI: 10.1175/JCLI4170.1
Publisher: Elsevier BV
Date: 10-2009
Publisher: Elsevier BV
Date: 08-2014
Publisher: Elsevier BV
Date: 12-2012
Publisher: Elsevier BV
Date: 2011
Publisher: American Meteorological Society
Date: 03-2011
Abstract: A new ensemble ocean data assimilation system, developed for the Predictive Ocean Atmosphere Model for Australia (POAMA), is described. The new system is called PEODAS, the POAMA Ensemble Ocean Data Assimilation System. PEODAS is an approximate form of an ensemble Kalman filter system. For a given assimilation cycle, a central forecast is integrated, along with a small ensemble of forecasts that are forced with perturbed surface fluxes. The small ensemble is augmented with multiple small ensembles from previous assimilation cycles, yielding a larger ensemble that consists of perturbed forecasts from the last month. This larger ensemble is used to represent the system’s time-dependent background error covariance. At each assimilation cycle, a central analysis is computed utilizing the ensemble-based covariance. Each of the perturbed ensemble members are nudged toward the central analysis to control the ensemble spread and mean. The ensemble-based covariances generated by PEODAS potentially yield dynamically balanced analysis increments. The time dependence of the ensemble-based covariance yields spatial structures that change for different dynamical regimes, for ex le during El Niño and La Niña conditions. These differences are explored in terms of the dominant dynamics and the system’s errors. The performance of PEODAS during a 27-yr reanalysis is evaluated through a series of comparisons with assimilated and independent observations. When compared to its predecessor, POAMA version 1, and a simulation with no assimilation of subsurface observations, PEODAS demonstrates a quantitative improvement in skill. PEODAS will form the basis of Australia’s next operational seasonal prediction system.
Publisher: American Geophysical Union (AGU)
Date: 28-08-2012
DOI: 10.1029/2012GL053091
Publisher: Springer Science and Business Media LLC
Date: 12-09-2006
Publisher: American Geophysical Union (AGU)
Date: 20-01-2011
DOI: 10.1029/2009JC005926
No related grants have been discovered for Peter Oke.