ORCID Profile
0000-0003-0853-8190
Current Organisation
Australian Bureau of Meteorology
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Ecological impacts of climate change and ecological adaptation | Marine and estuarine ecology (incl. marine ichthyology) | Evolutionary biology | Biological adaptation |
Publisher: Frontiers Media SA
Date: 30-06-2021
DOI: 10.3389/FMARS.2021.687833
Abstract: Seasonal forecasts of sea surface temperature (SST) have become increasingly important tools in recent years for reef managers to help inform and coordinate management responses to mass coral bleaching events. This manuscript presents new operational thermal stress forecast products for prediction of coral bleaching risk, based on the seasonal ensemble prediction system ACCESS-S1 (Australian Community Climate and Earth System Simulator–Seasonal Version 1). These accumulated thermal stress products form critical tools for reef management, providing advance warning of high thermal stress, and increased risk of coral bleaching in the coming season. Degree Heating Months (DHM) consider both the magnitude and duration of thermal stress, both of which are important in determining reef impacts. Both hindcast and operational realtime DHM forecasts are assessed for past bleaching events across Australia, and the impacts of different drivers and local forcings between regions compared. Generally, the model has the highest skill when forecasting events driven by large scale climate drivers such as the El Niño Southern Oscillation (ENSO) which impacts coral reefs on all sides of Australia. ACCESS-S1 hindcasts indicate higher skill on the west Australian coast than the Great Barrier Reef for summer months, except for the North West Shelf. Realtime forecasts of the 2020 Great Barrier Reef coral bleaching event, used operationally by reef managers throughout this event, are also presented. This work advances our understanding of the 2020 event, provides skill assessments for the new DHM products, and discusses the use of a stationary baseline in a changing climate. High DHM values can indicate an increased risk of marine heatwaves, which are likely to have increasing impacts on Australia’s reef systems in the future under a warming climate.
Publisher: Elsevier BV
Date: 2014
Publisher: Informa UK Limited
Date: 31-10-2018
Publisher: American Geophysical Union (AGU)
Date: 22-08-2015
DOI: 10.1002/2015GL065091
Publisher: Elsevier BV
Date: 10-2015
Publisher: Springer Science and Business Media LLC
Date: 12-05-2009
DOI: 10.1007/S00267-009-9295-7
Abstract: The frequency and severity of mass coral bleaching events are predicted to increase as sea temperatures continue to warm under a global regime of rising ocean temperatures. Bleaching events can be disastrous for coral reef ecosystems and, given the number of other stressors to reefs that result from human activities, there is widespread concern about their future. This article provides a strategic framework from the Great Barrier Reef to prepare for and respond to mass bleaching events. The framework presented has two main inter-related components: an early warning system and assessment and monitoring. Both include the need to proactively and consistently communicate information on environmental conditions and the level of bleaching severity to senior decision-makers, stakeholders, and the public. Managers, being the most timely and credible source of information on bleaching events, can facilitate the implementation of strategies that can give reefs the best chance to recover from bleaching and to withstand future disturbances. The proposed framework is readily transferable to other coral reef regions, and can easily be adapted by managers to local financial, technical, and human resources.
Publisher: American Meteorological Society
Date: 02-2011
Abstract: Mass coral bleaching, associated with anomalously warm ocean temperatures over large regions, poses a serious threat to the future health of the world coral reef systems. Seasonal forecasts from coupled ocean–atmosphere models can be a valuable resource for reef management, providing early warning of potential bleaching conditions, allowing for a proactive management response. Here, the ability of a dynamical seasonal forecast model (Predictive Ocean Atmosphere Model for Australia, POAMA) to forecast degree heating months (DHMs) in the tropical oceans is assessed, with particular focus on the 1997/98 El Niño–Southern Oscillation (ENSO) and associated global bleaching events. The model exhibits useful skill in forecasting sea surface temperatures (SSTs) across the tropical oceans for 1982–2006 and reproduced both the magnitude and distribution of DHM values observed during the 1997/98 ENSO event. In general, observed teleconnections between ENSO indices and tropical SST at various lags are well captured by the model. In particular, strong observed correlations between peak ENSO indices and SST in the Caribbean in the following summer were reproduced. The model also shows skill in predicting ocean conditions conducive to bleaching in non-ENSO years, capturing the anomalously warm conditions in the Caribbean region in 2005. Probabilistic forecasts of DHM values above certain thresholds for the Caribbean show useful skill and could be valuable in the assessment of the likelihood of bleaching for the region.
Publisher: Wiley
Date: 04-2016
DOI: 10.1111/FOG.12083
Publisher: Elsevier BV
Date: 04-2016
Publisher: Canadian Science Publishing
Date: 05-2011
DOI: 10.1139/F2011-031
Abstract: Capture of the target, bycatch, and protected species in fisheries is often regulated through spatial measures that partition fishing effort, including areal closures. In eastern Australian waters, southern bluefin tuna (SBT, Thunnus maccoyii ) are a quota-limited species in a multispecies longline fishery minimizing capture by nonquota holders is an important management concern. A habitat preference model (conditioned with electronic tag data) coupled with ocean reanalysis data has been used since 2003 to generate real-time predicted maps of SBT distribution (nowcasts). These maps are used by fishery managers to restrict fisher access to areas with high predicted SBT distribution. Here we use the coupled ocean–atmosphere model, POAMA (predictive ocean atmosphere model for Australia), and a habitat model to forecast SBT distribution at lead times of up to 4 months. These forecasts are comparable with nowcasts derived from the operational system, and show skill in predicting SBT habitat boundaries out to lead-times of 3–4 months. For this fishery, seasonal forecasts can provide managers and fishers with valuable insights into future habitat distributions for the upcoming months, to better inform operational decisions.
Publisher: Frontiers Media SA
Date: 27-06-2022
DOI: 10.3389/FCLIM.2022.907919
Abstract: With climate heating, Aotearoa New Zealand is expected to experience more marine heatwaves (MHW) in the coming decades. These extreme events are already impacting the island nation's marine and coastal environments and marine industries at a variety of scales. There will potentially be substantial benefits in developing an early warning system–specifically ocean seasonal forecast tools. This near-term 2,030 horizon scan reviews studies supporting the development of this capability and notes work needed to enable stakeholders to benefit from this knowledge. Review findings congregate around six themes (1) MHW impacts, (2) mechanistic understanding, (3) observational basis, (4) seasonal forecast tools, (5) supporting Te Tiriti (The Treaty of Waitangi) and Māori aspirations, and (6) end-user engagement. The primary recommendation is a cross-institutional, cross-sector MHW Taskforce that would address, in a coordinated and effective fashion, the real, multi-faceted challenges associated with the committed pathway of warming. A range of sub-recommendations follow that connect with the United Nations Ocean Decade initiative.
Publisher: Elsevier BV
Date: 08-2008
Publisher: American Geophysical Union (AGU)
Date: 06-2021
DOI: 10.1029/2020JC017060
Abstract: Coastal high water level events are increasing in frequency and severity as global sea‐levels rise, and are exposing coastlines to risks of flooding. Yet, operational seasonal forecasts of sea‐level anomalies are not made for most coastal regions. Advancements in forecasting climate variability using coupled ocean‐atmosphere global models provide the opportunity to predict the likelihood of future high water events several months in advance. However, the skill of these models to forecast seasonal sea‐level anomalies has not been fully assessed, especially in a multi‐model framework. Here, we construct a 10‐model ensemble of retrospective forecasts with future lead times of up to 11 months. We compare predicted sea levels from bias‐corrected forecasts with 20 years of observations from satellite‐based altimetry and shore‐based tide gauges. Forecast skill, as measured by anomaly correlation, tends to be highest in the tropical and subtropical open oceans, whereas the skill is lower in the higher latitudes and along some continental coasts. For most locations, multi‐model averaging produces forecast skill that is comparable to or better than the best performing in idual model. We find that the most skillful predictions typically come from forecast systems with more accurate initializations of sea level, which is generally achieved by assimilating altimetry data. Having relatively higher horizontal resolution in the ocean is also beneficial, as such models seem to better capture dynamical processes necessary for successful forecasts. The multi‐model assessment suggests that skillful seasonal sea‐level forecasts are possible in many, though not all, parts of the global ocean.
Publisher: Elsevier BV
Date: 03-2017
Publisher: CSIRO Publishing
Date: 09-03-2022
DOI: 10.1071/ES21012
Abstract: The Tasman Sea has been identified as a climate hotspot and has experienced several marine heatwaves (MHWs) in recent years. These events have impacted coastal regions of New Zealand (NZ), which has had a follow-on effect on local marine and aquaculture industries. Advance warning of extreme marine heat events would enable these industries to mitigate potential losses. Here we present an assessment of the forecast skill of the Australian Bureau of Meteorology’s seasonal prediction system, Australian Community Climate and Earth-System Simulator-Seasonal v1.0 (ACCESS-S1), for three key aquaculture regions around NZ: Hauraki Gulf, Western Cook Strait and Foveaux Strait. We investigate the skill of monthly sea surface temperature anomaly (SSTA) forecasts, and forecasts for SSTA exceeding the 90th percentile, which is an accepted MHW threshold. We find that the model has skill for predicting extreme heat events in all three regions at 0–2 month lead times. We then demonstrate that ACCESS-S1 was able to capture observed monthly SSTA exceeding the 90th percentile around coastal NZ during the 2019 Tasman Sea MHW at a lead time of 1 month. Finally, we discuss the relationship between SSTA in the Tasman Sea and SSTA in coastal regions of NZ, and thus the Tasman Sea as a source of model SSTA skill in the three key coastal regions. Results from this study show that skilful forecasts of ocean heat extremes in regional areas have the potential to enable marine operators in the aquaclture industry to mitigate losses due to MHWs, especially in a warming climate.
Publisher: Frontiers Media SA
Date: 24-12-2021
DOI: 10.3389/FCLIM.2021.801217
Abstract: Changing ocean conditions due to anthropogenic climate change, particularly the increasing severity and frequency of extreme events, are a growing concern for a range of marine sectors. Here we explore the global trends in marine heatwaves (MHWs), specifically onset and decline rates, two metrics which describe how quickly a MHW will emerge or disappear from a location. These rates determine the reaction window —the start of a MHW event to peak MHW temperatures—and the coping window —time from peak temperatures to the end of an event—two important time periods relevant to a marine decision-maker. We show that MHW onset and decline rates are fastest in dynamic ocean regions and that overall, the global trend in onset rate is greater than the global trend in decline rate. We map ocean regions where these rates are changing together with forecast skill from a seasonal dynamical model (ACCESS-S). This analysis highlights areas where the length of the preparation window for impending MHWs is increased by using forecasts, and areas where marine decision-makers should be prepared for rapid responses based on realtime observations as MHWs evolve. In regions such as south Africa and Kerguelen, northwest Atlantic, northwest Pacific, southwest South Atlantic and off Australian east coast where rapid median onset and decline rates are observed, there is also a positive trend in onset and decline rates i.e., MHWs are developing and declining more rapidly. This will be a concern for many decision-makers operating in these regions.
Publisher: Wiley
Date: 04-04-2013
DOI: 10.1002/JOC.3486
Publisher: Frontiers Media SA
Date: 23-04-2018
Publisher: Elsevier BV
Date: 04-2017
Publisher: Springer Science and Business Media LLC
Date: 22-06-2019
Publisher: Elsevier BV
Date: 2009
Publisher: Springer Science and Business Media LLC
Date: 12-02-2014
Publisher: Springer Science and Business Media LLC
Date: 21-10-2009
Publisher: American Meteorological Society
Date: 12-2011
Publisher: American Meteorological Society
Date: 12-2011
Publisher: American Geophysical Union (AGU)
Date: 28-04-2023
DOI: 10.1029/2022JC019342
Abstract: Satellite altimetry measurements of sea surface height provide near‐global ocean state observations on sub‐monthly time scales, which are not always utilized by seasonal climate forecasting systems. As early as the mid‐1990s, attempts were made to assimilate altimetry observations to initialize climate models. These experiments demonstrated improved ocean forecasting skill, especially compared to experiments that did not assimilate subsurface ocean temperature information. Nowadays, some operational climate forecasting models utilize altimetry in their assimilation systems, whereas others do not. Here, we assess the impact of altimetry assimilation on seasonal prediction skill of ocean variables in two climate forecasting systems that are from the European Centre for Medium‐Range Weather Forecasts (SEAS5) and the Australian Bureau of Meteorology (ACCESS‐S). We show that assimilating altimetry improves the initialization of subsurface ocean temperatures, as well as seasonal forecasts of monthly variability in upper‐ocean heat content and sea level. Skill improvements are largest in the subtropics, where there are typically less subsurface ocean observations available to initialize the forecasts. In the tropics, there are no noticeable improvements in forecast skill. The positive impact of altimetry assimilation on forecast skill related to the subsurface ocean does not seem to affect predictions of sea surface temperature. Whether this is because current forecasting systems are close to the potential predictability limit for the ocean surface, or perhaps altimetry observations are not fully exploited, remains a question. In summary, we find that utilizing altimetry observations improves the overall global ocean forecasting skill, at least for upper‐ocean heat content and sea level.
Publisher: Elsevier BV
Date: 10-2015
Publisher: American Meteorological Society
Date: 07-2016
Abstract: Over the last 30 years, coral reefs around the world have been under considerable stress because of increasing anthropogenic pressures, overfishing, pollution, and climate change. A primary stress factor is anomalously warm water events, which can cause mass coral bleaching and widespread reef damage. Forecasts of sea surface temperature (SST) and the associated risk of coral bleaching can assist managers, researchers, and other stakeholders in monitoring and managing coral reef resources. At the Australian Bureau of Meteorology, monthly forecasts of SST and thermal stress metrics have been developed that are based on a dynamical seasonal prediction system known as the Predictive Ocean Atmosphere Model for Australia (POAMA). To support the effective use of these forecasts in risk-based decision-making frameworks in the western and central tropical Pacific Ocean, the skill of these forecast tools in this region was assessed using several categorical forecast skill scores. It was found that the model provides SST forecasts with statistically significant skill up to 8 months in advance (correlation coefficient 0.4 p = 0.05) across the region. The highest skill ( r 0.9) was achieved over the central equatorial Pacific Ocean, likely as a result of this region’s strong relationship with the El Niño–Southern Oscillation (ENSO). Potential forecast value was assessed using a simplified cost–loss ratio decision model, which indicated that POAMA’s seasonal hot-spot thermal stress forecasts can provide valuable information to reef management and policy makers in the western Pacific region.
Publisher: IOP Publishing
Date: 12-2021
Abstract: The 2020 marine heatwave (MHW) in the Great Barrier Reef (GBR) and Coral Sea led to mass coral bleaching. Sea surface temperature anomalies reached +1.7 °C for the whole of the GBR and Coral Sea and exceeded +2 °C across broad regions (referenced to 1990–2012). The MHW reached Category 2 (Strong) and warm anomalies peaked between mid-February and mid-March 2020. The MHW’s peak intensity aligned with regions of reduced cloud cover and weak wind speeds. We used a MHW framework to assess the ability of an operational coupled ocean-atmosphere prediction system (Australian Community Climate and Earth System Simulator Seasonal version 1) to capture the MHW’s severity, duration, and spatial extent. For initial week predictions, the predicted MHW severity generally agreed with the magnitude and spatial extent of the observed severity for that week. The model ensemble mean did not capture the MHW’s development phase at lead times beyond the first week. The model underestimated the MHW’s spatial extent, which reached up to 95% of the study area with at least Moderate severity and up to 43% with at least Strong severity. However, most forecast ensemble members correctly predicted the period of Strong severity in the first week of the model forecast. The model correctly predicted MHW conditions to persist from mid-February to mid-March but did not capture the end of the MHW. The inability to predict the end of the event and other periods of less skilful prediction were related to subseasonal variability owing to weather systems, including the passage of tropical cyclones not simulated in the model. On subseasonal time scale, evaluating daily to weekly forecasts of ocean temperature extremes is an important step toward implementing methods for developing operational forecast extremes products for use in early warning systems.
Publisher: Springer Science and Business Media LLC
Date: 28-08-2013
Publisher: American Meteorological Society
Date: 04-2017
Abstract: Sea level anomaly extremes impact tropical Pacific Ocean islands, often with too little warning to mitigate risks. With El Niño, such as the strong 2015/16 event, comes weaker trade winds and mean sea level drops exceeding 30 cm in the western Pacific that expose shallow-water ecosystems at low tides. Nearly opposite climate conditions accompany La Niña events, which cause sea level high stands (10–20 cm) and result in more frequent tide- and storm-related inundations that threaten coastlines. In the past, these effects have been exacerbated by decadal sea level variability, as well as continuing global sea level rise. Climate models, which are increasingly better able to simulate past and future evolutions of phenomena responsible for these extremes (i.e., El Niño–Southern Oscillation, Pacific decadal oscillation, and greenhouse warming), are also able to describe, or even directly simulate, associated sea level fluctuations. By compiling monthly sea level anomaly predictions from multiple statistical and dynamical (coupled ocean–atmosphere) models, which are typically skillful out to at least six months in the tropical Pacific, improved future outlooks are achieved. From this multimodel ensemble comes forecasts that are less prone to in idual model errors and also uncertainty measurements achieved by comparing retrospective forecasts with the observed sea level. This framework delivers online a new real-time forecasting product of monthly mean sea level anomalies and will provide to the Pacific island community information that can be used to reduce impacts associated with sea level extremes.
Publisher: Elsevier BV
Date: 06-2017
Start Date: 2023
End Date: 12-2025
Amount: $660,607.00
Funder: Australian Research Council
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