ORCID Profile
0000-0002-4227-3221
Current Organisation
Australian Bureau of Meteorology
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Publisher: American Meteorological Society
Date: 04-2019
Abstract: An approach to reduce forecast data to coastal waveguide coordinates is described and demonstrated, informed by the literature on coastally trapped waves (CTWs). All discussion is limited to the Australian mainland but the approach is generally relevant to regions where CTWs influence sea level, including the Americas and Africa. The approach does not produce new forecasts, but aims to focus forecaster attention on aspects of sea level forecasts prominent on the long Australian coast. The approach also explicitly addresses spatial issues associated with measuring coastal paths. Coastal paths are scale dependent and forecast models discretize the coastal boundary differently. A well-defined coastal path is required for the quantitative application of CTW concepts such as propagation distance and offshore direction. The relevance of coastally trapped signals and remote forcing is documented in the oceanographic literature, but is effectively unknown to the general public and rarely mentioned in press reports of sea level events such as nuisance flooding. Routine presentation of forecast guidance in waveguide coordinates could contribute to the transfer of oceanographic research understanding into forecast narratives. In addition, the approach can facilitate quantitative forecast evaluations that target CTW properties. Two ocean forecast systems are contrasted in this framework for the Australian mainland. One year of daily forecasts are compared, with indications that model baroclinicity is of practical relevance.
Publisher: Frontiers Media SA
Date: 30-08-2019
Publisher: Informa UK Limited
Date: 02-01-2015
Publisher: Elsevier BV
Date: 2008
Publisher: American Meteorological Society
Date: 04-2010
Abstract: A method is described that estimates the time evolution of surface ergence and other secondary circulation properties of an ocean eddy. The method is novel because it is applied to the observations of a single surface drifting buoy. Surface drifting buoys located on ocean eddies provide Lagrangian trajectories that orbit the local extremum in geopotential. At each instance, the position of the buoy lies on the boundary of a closed material surface over the eddy. Assuming, on an ocean eddy, that the material boundary points vary smoothly in time, a method is developed to estimate all points along this boundary. An ellipse is fitted to the approximated material boundary to provide a continuum of properties, including centroid, aspect ratio, orientation, and area. The time rate of change of these properties provides approximations to the eddy velocity, moment of inertia, secondary rotation, and surface ergence. Two surface drifting buoys deployed on an anticyclonic eddy in the East Australian Current are used to demonstrate the analysis method. The estimated surface ergence is compared and interpreted using the observed separation of the two drifting buoys and other independent observations.
Publisher: Copernicus GmbH
Date: 07-12-2021
DOI: 10.5194/ESSD-13-5663-2021
Abstract: Abstract. BRAN2020 (2020 version of the Bluelink ReANalysis) is an ocean reanalysis that combines observations with an eddy-resolving, near-global ocean general circulation model to produce a four-dimensional estimate of the ocean state. The data assimilation system employed is ensemble optimal interpolation, implemented with a new multiscale approach that constrains the broad-scale ocean properties and the mesoscale circulation in two steps. There is a separation in the scales that are corrected in the two steps: the high-resolution step corrects the mesoscale dynamics in the same way as previous versions of BRAN, while the extra coarse step is effective at correcting biases that develop at large scales. The reanalysis currently spans January 1993 to December 2019 and assimilates observations of in situ temperature and salinity, as well as of satellite sea-level anomaly and sea surface temperature. BRAN2020 is planned to be updated to within months of real time after this initial release, until an updated version of BRAN is available. Reanalysed fields from BRAN2020 generally show much closer agreement to observations than all previous versions with misfits between reanalysed and observed fields reduced by over 30 % for some variables, for subsurface temperature and salinity in particular. The BRAN2020 dataset is comprised of daily averaged fields of temperature, salinity, velocity, mixed-layer depth and sea level. Reanalysed fields realistically represent all of the major current systems within 75∘ S and 75∘ N, excluding processes relating to sea ice but including boundary currents, equatorial circulation, Southern Ocean variability and mesoscale eddies. BRAN2020 is publicly available at 0.25914/6009627c7af03 (Chamberlain et al., 2021b) and is intended for use by the research community.
Publisher: Informa UK Limited
Date: 03-07-2015
Publisher: Elsevier BV
Date: 03-2011
Publisher: Springer Science and Business Media LLC
Date: 08-05-2015
Publisher: Informa UK Limited
Date: 18-08-2015
Publisher: Elsevier BV
Date: 12-2019
Publisher: Elsevier BV
Date: 03-2011
Publisher: Springer Science and Business Media LLC
Date: 05-04-2014
Publisher: The Oceanography Society
Date: 09-2009
Publisher: Elsevier BV
Date: 2020
Publisher: Informa UK Limited
Date: 17-04-2015
Publisher: Informa UK Limited
Date: 08-2012
Publisher: American Meteorological Society
Date: 06-2010
Abstract: This study investigates the impact of atmosphere–ocean coupling on predicted tropical cyclone (TC) intensity change and the ocean response in the Australian region. The coupled model comprises the Australian Bureau of Meteorology’s Tropical Cyclone Limited-Area Prediction System (TC-LAPS) and a regional version of the BLUElink ocean forecasting system. A series of case study forecasts are presented and the differences between coupled and uncoupled forecasts, operational forecasts, and posterior objective analyses are compared. A coupled model ensemble is also developed that uses different first-order approximations of the effects of surface waves on surface stress in an inertial coupling method. In each of the cases, the use of reanalyzed sea surface temperatures significantly improves the prediction of TC intensity change in the intensification phase. The results show that dynamic air–sea coupling has a modest impact on intensity in cases where SST cooling is significant and is likely to be important for predicting the rate of TC intensification, peak intensity, and deintensification. Results also show that there is a definite coupled signal and suggest inherent biases in the atmospheric model that could potentially be removed. With different parameterizations of surface wave effects, results show modest sensitivity in TC intensity of up to 10 hPa in minimum surface pressure however, in some cases there was significant sensitivity in the predicted ocean response. The results also highlight the relative increased complexity of tropical cyclone prediction in the Australian region compared to other regions. In cases where the forecast TC track was reasonably skillful, there were improvements in the predicted ocean response with respect to observations compared to an ocean reanalysis.
Publisher: Elsevier BV
Date: 10-2021
Publisher: Informa UK Limited
Date: 12-1998
Publisher: Bureau of Meteorology, Australia
Date: 03-2011
DOI: 10.22499/2.6101.001
Publisher: Informa UK Limited
Date: 07-11-2020
Publisher: Frontiers Media SA
Date: 03-09-2019
Publisher: Informa UK Limited
Date: 18-08-2015
Publisher: American Geophysical Union (AGU)
Date: 11-2014
DOI: 10.1002/2013JC009678
Publisher: Informa UK Limited
Date: 12-1999
Publisher: American Geophysical Union (AGU)
Date: 09-2013
DOI: 10.1002/JGRC.20317
Publisher: Journal of Marine Research/Yale
Date: 05-2017
Publisher: Elsevier BV
Date: 03-2008
Publisher: The Oceanography Society
Date: 09-2009
Publisher: American Geophysical Union (AGU)
Date: 29-01-2011
DOI: 10.1029/2010JC006260
Publisher: Copernicus GmbH
Date: 05-02-2020
Abstract: Abstract. We introduce ACCESS-OM2, a new version of the ocean–sea ice model of the Australian Community Climate and Earth System Simulator. ACCESS-OM2 is driven by a prescribed atmosphere (JRA55-do) but has been designed to form the ocean–sea ice component of the fully coupled (atmosphere–land–ocean–sea ice) ACCESS-CM2 model. Importantly, the model is available at three different horizontal resolutions: a coarse resolution (nominally 1∘ horizontal grid spacing), an eddy-permitting resolution (nominally 0.25∘), and an eddy-rich resolution (0.1∘ with 75 vertical levels) the eddy-rich model is designed to be incorporated into the Bluelink operational ocean prediction and reanalysis system. The different resolutions have been developed simultaneously, both to allow for testing at lower resolutions and to permit comparison across resolutions. In this paper, the model is introduced and the in idual components are documented. The model performance is evaluated across the three different resolutions, highlighting the relative advantages and disadvantages of running ocean–sea ice models at higher resolution. We find that higher resolution is an advantage in resolving flow through small straits, the structure of western boundary currents, and the abyssal overturning cell but that there is scope for improvements in sub-grid-scale parameterizations at the highest resolution.
Publisher: The Oceanography Society
Date: 09-2009
Publisher: The Oceanography Society
Date: 09-2009
Publisher: Frontiers Media SA
Date: 17-06-2021
DOI: 10.3389/FEART.2021.696985
Abstract: Blue Maps aims to exploit the versatility of an ensemble data assimilation system to deliver gridded estimates of ocean temperature, salinity, and sea-level with the accuracy of an observation-based product. Weekly maps of ocean properties are produced on a 1/10°, near-global grid by combining Argo profiles and satellite observations using ensemble optimal interpolation (EnOI). EnOI is traditionally applied to ocean models for ocean forecasting or reanalysis, and usually uses an ensemble comprised of anomalies for only one spatiotemporal scale (e.g., mesoscale). Here, we implement EnOI using an ensemble that includes anomalies for multiple space- and time-scales: mesoscale, intraseasonal, seasonal, and interannual. The system produces high-quality analyses that produce mis-fits to observations that compare well to other observation-based products and ocean reanalyses. The accuracy of Blue Maps analyses is assessed by comparing background fields and analyses to observations, before and after each analysis is calculated. Blue Maps produces analyses of sea-level with accuracy of about 4 cm and analyses of upper-ocean (deep) temperature and salinity with accuracy of about 0.45 (0.15) degrees and 0.1 (0.015) practical salinity units, respectively. We show that the system benefits from a ersity of ensemble members with multiple scales, with different types of ensemble members weighted accordingly in different dynamical regions.
Publisher: Informa UK Limited
Date: 18-08-2015
Publisher: American Geophysical Union (AGU)
Date: 05-01-2011
DOI: 10.1029/2010GL045574
Publisher: Copernicus GmbH
Date: 24-09-2018
Abstract: Abstract. The ocean mixed layer depth is an important parameter describing the exchange of fluxes between the atmosphere and ocean. In ocean modelling a key factor in the accurate representation of the mixed layer is the parameterization of vertical mixing. An ideal opportunity to investigate the impact of different mixing schemes was provided when the Australian Bureau of Meteorology upgraded its operational ocean forecasting model, OceanMAPS to version 3.0. In terms of the mixed layer, the main difference between the old and new model versions was a change of vertical mixing scheme from that of Chen et al. (1994) to the General Ocean Turbulence Model. The model estimates of the mixed layer depth were compared with those derived from Argo observations. Both versions of the model exhibited a deep bias in tropical latitudes and a shallow bias in the Southern Ocean, consistent with previous studies. The bias, however, was greatly reduced in version 3.0, and variance between model runs decreased. Additionally, model skill against climatology also improved significantly. Further analysis discounted changes to model resolution outside of the Australian region having a significant impact on these results, leaving the change in vertical mixing scheme as the main factor in the assessed improvements to mixed layer depth representation.
Publisher: Springer Netherlands
Date: 2011
Publisher: Informa UK Limited
Date: 05-2004
Publisher: Wiley
Date: 28-07-2015
DOI: 10.1002/QJ.2579
Publisher: World Scientific Publishing Co. Pte. Ltd.
Date: 2006
Publisher: American Geophysical Union (AGU)
Date: 04-2019
DOI: 10.1029/2018JC014482
Publisher: American Geophysical Union (AGU)
Date: 09-09-2010
DOI: 10.1029/2009JC005876
Publisher: American Geophysical Union (AGU)
Date: 10-12-2013
DOI: 10.1002/2013GL057752
Abstract: A novel extension to time‐lagged ensemble forecasting called multicycle ensemble forecasting improves the independent s ling of forecast model errors. Multicycle is defined such that each forecast cycle is independent of the previous forecast cycle. For an M cycle system the background field for each cycle is from a model hindcast M cycles earlier. The model errors have a factor M longer period to grow compared with a sequential system however, the increased independence in the forecast model errors provide weighted ensemble averages with greater skill and reliability over the 0 lag forecast and a good spread‐error relationship. This cost‐efficient technique is relevant to global ocean forecasting where an ensemble method is computationally prohibitive.
Publisher: American Meteorological Society
Date: 09-2002
Publisher: American Meteorological Society
Date: 05-1997
Publisher: Springer Netherlands
Date: 2011
Publisher: Wiley
Date: 10-2005
DOI: 10.1256/QJ.05.95
Publisher: Informa UK Limited
Date: 17-04-2015
Publisher: Elsevier BV
Date: 2009
Publisher: Informa UK Limited
Date: 18-08-2015
Publisher: Informa UK Limited
Date: 17-04-2015
Publisher: GODAE OceanView
Date: 11-08-2018
Publisher: Elsevier BV
Date: 08-2017
Publisher: Wiley
Date: 28-07-2022
DOI: 10.1002/ECY.3795
No related grants have been discovered for Gary Brassington.