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Marine heatwaves (MHWs) can severely impact marine biodiversity, fisheries, and aquaculture. Consequently, there is an increasing desire to understand the drivers of these events to inform their predictability so that proactive decisions may be made to reduce potential impacts. In the Tasman Sea (TS), several relatively intense and broad-scale MHWs have caused significant damage to marine fisheries and aquaculture industries. To assess the potential predictability of these events, we first determined the main driver of each MHW event in the TS from 1993 to 2021. We found that those MHWs driven by ocean advection – approximately 45% of all events – are generally longer in duration, less intense, and affected a smaller area compared with the remaining 55%, which are driven by air-sea heat fluxes, are shorter in duration, and more surface intense. As ocean advection-driven events in the TS have been closely studied and reported previously, we focus here on atmospherically driven MHWs. The predictability of these events is assessed by identifying the patterns of atmospheric pressure, winds, and air-sea heat fluxes in the southern hemisphere that coincide with MHWs in the Tasman Sea. We found that atmospherically driven MHWs in this region are more likely to occur during the positive phase of the asymmetric Southern Annular Mode (A-SAM) – which presents as an atmospheric zonal wave-3 pattern and is more likely to occur during La Niña years. These A-SAM events are linked to low wind speeds and increased downward solar radiation in the TS, which lead to increased surface ocean temperatures through the reduction of mixing.

ACCESS-S2 is a major upgrade to the Australian Bureau of Meteorology’s multi-week to seasonal prediction system. It was made operational in October 2021, replacing ACCESS-S1. The focus of the upgrade is the addition of a new weakly coupled data assimilation system to provide initial conditions for atmosphere, ocean, land and ice fields. The model is based on the UK Met Office GloSea5-GC2 seasonal prediction system and is unchanged from ACCESS-S1, aside from minor corrections and enhancements. The performance of the assimilation system and the skill of the seasonal and multi-week forecasts have been assessed and compared to ACCESS-S1. There are improvements in the ACCESS-S2 initial conditions compared to ACCESS-S1, particularly for soil moisture and aspects of the ocean, notably the ocean currents. More realistic soil moisture initialisation has led to increased skill for forecasts over Australia, especially those of maximum temperature. The ACCESS-S2 system is shown to have increased skill of El Nino–Southern Oscillation forecasts over ACCESS-S1 during the challenging autumn forecast period. Analysis suggests that ACCESS-S2 will deliver improved operational forecast accuracy in comparison to ACCESS-S1. Assessments of the operational forecasts are underway. ACCESS-S2 represents another step forward in the development of seasonal forecast systems at the Bureau of Meteorology. However, key rainfall and sea surface temperature biases in ACCESS-S1 remain in ACCESS-S2, indicating where future efforts should be focused.

Great Barrier Reef (GBR) marine park managers rely on seasonal forecasts of sea surface temperature (SST) to better inform and coordinate their management responses to mass coral bleaching events. The Bureau of Meteorology’s new seasonal forecast model ACCESS-S1 is well placed for integration in marine park managers’ risk management systems, with model benefits including high ocean resolution and probabilistic forecasts from a 99 member ensemble. The SST forecast skill was assessed for the GBR region against satellite SST observations over the model hindcast period 1990–2012. ACCESS-S1 was most successful in forecasting larger warm anomalies in the GBR associated with climate drivers that persisted over many months (e.g. ENSO events). The model consistently performed better than persistence reference forecasts over the critical summer period. The model was less successful in forecasting short-term events driven by regional weather patterns, with a reduction in skill between pre-monsoon and post-monsoon onset. Forecasts in the northern GBR often exhibited the highest skill. The model was successfully able to predict SST anomalies associated with the peak of the East Australian Current. The ability of the model to discriminate between two dichotomous events (whether or not a threshold is exceeded) ranged from excellent at lead time 0 (first month forecast) to reasonable at lead times 1 and 2. Increasing the ensemble size using time-lagged ensemble members showed improvement in probabilistic skill for warm anomaly events. Model reliability showed good ability in matching the observed frequency for warm anomaly events, although slightly overconfident. The results demonstrate that ACCESS-S1 can provide skilful SST forecasts in support of coral reef management activities on sub-seasonal to seasonal timescales. Seasonal SST forecasts from ACCESS-S1 are currently available at the Bureau of Meteorology’s website for the GBR and greater Coral Sea region.

We present a first assessment of the Sea Surface Temperature (SST) and 300m Heat Content (HC) ensemble mean skill of the Australian Community Climate and Earth-System Simulator-Seasonal v1.0 (ACCESS-S1) around New Zealand on seasonal timescales, using a set of retrospective ensemble forecasts for 1990-2012. This was verified against Reynolds AVHRR analysis and Bluelink ReANalysis 3.5 (BRAN3.5). For inshore areas with depth

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.

Seasonal forecasting of environmental conditions and marine species distribution has been used as a decision support tool in commercial and aquaculture fisheries. These tools may also be applicable to species targeted by the recreational fisheries sector, a sector that is increasing its use of marine resources, and making important economic and social contributions to coastal communities around the world. Here, a seasonal forecast of the habitat and density of dolphinfish (Coryphaena hippurus), based on sea surface temperatures, was developed for the east coast of New South Wales (NSW), Australia. Two prototype forecast products were created; geographic spatial forecasts of dolphinfish habitat and a latitudinal summary identifying the location of fish density peaks. The less detailed latitudinal summary was created to limit the resolution of habitat information to prevent potential resource over-exploitation by fishers in the absence of total catch controls. The forecast dolphinfish habitat model was accurate at the start of the annual dolphinfish migration in NSW (December) but other months (January - May) showed poor performance due to spatial and temporal variability in the catch data used in model validation. Habitat forecasts for December were useful up to five months ahead, with performance decreasing as forecast were made further into the future. The continued development and sound application of seasonal forecasts will help fishery industries cope with future uncertainty and promote dynamic and sustainable marine resource management.

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 individual 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.

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.

Marine weather and climate forecasts are essential in planning strategies and activities on a range of temporal and spatial scales. However, seasonal dynamical forecast models, that provide forecasts in monthly scale, often have low offshore resolution and limited information for inshore coastal areas. Hence, there is increasing demand for methods capable of fine scale seasonal forecasts covering coastal waters. Here, we have developed a method to combine observational data with dynamical forecasts from POAMA (Predictive Ocean Atmosphere Model for Australia; Australian Bureau of Meteorology) in order to produce seasonal downscaled, corrected forecasts, extrapolated to include inshore regions that POAMA does not cover. We demonstrate the method in forecasting the monthly sea surface temperature anomalies in the Great Australian Bight (GAB) region. The resolution of POAMA in the GAB is approximately 2° × 1° (lon. × lat.) and the resolution of our downscaled forecast is approximately 1° × 0.25°. We use data and model hindcasts for the period 1994–2010 for forecast validation. The predictive performance of our statistical downscaling model improves on the original POAMA forecast. Additionally, this statistical downscaling model extrapolates forecasts to coastal regions not covered by POAMA and its forecasts are probabilistic which allows straightforward assessment of uncertainty in downscaling and prediction. A range of marine users will benefit from access to downscaled and nearshore forecasts at seasonal timescales.

A changing climate, in particular a warming ocean, is likely to impact marine industries in a variety of ways. For example, aquaculture businesses may not be able to maintain production in their current location into the future, or area-restricted fisheries may need to follow the fish as they change distribution. Preparation for these potential climate impacts can be improved with information about the future. Such information can support a risk-based management strategy for industries exposed to both short-term environmental variability and long-term change. In southern Australia, adverse climate impacts on valuable seafood industries have occurred, and they are now seeking advice about future environmental conditions. We introduce a decision tree to explain the potential use of long-term climate projections and seasonal forecasts by these industries. Climate projections provide insight into the likely time in the future when current locations will no longer be suitable for growing or catching particular species. Until this time, seasonal forecasting is beneficial in helping industries plan ahead to reduce impacts in poor years and maximize opportunities in good years. Use of seasonal forecasting can extend the period of time in which industries can cope in a location as environmental suitability declines due to climate change. While a range of short-term forecasting approaches exist, including persistence and climatological forecasts, only dynamic model forecasts provide a viable option for managing environmental risk for marine industries in regions where climate change is reducing environmental suitability and creating novel conditions.

Commercial fishing involves locating fish in a variable environment, and a fisher's historical experience with environmental conditions and the influence on fish distribution underpins their economic efficiency. However, in many regions, changing environments are reducing the utility of this experience. In the Great Australian Bight, recent environmental changes have modified the summer distribution of southern bluefin tuna (SBT, Thunnus maccoyii). This has affected the timing and location of fishing activity and contributed to economic impacts, at the same time as international competition is lowering value of the catch. The SBT purse-seine fishery is managed under a strict quota, so catching more fish is not an option to reduce fixed costs; instead fish must be caught more efficiently in a changing environment. Following discussion with industry stakeholders, we developed a seasonal forecast system based on a three stage process. We first assessed needs through discussions with industry. We then developed a SBT habitat forecast system based on a seasonal environmental forecasting model (POAMA: the Predictive Ocean Atmosphere Model for Australia) coupled with a habitat preference model for SBT (developed using data from tagged fish). Based on a historical evaluation of the environmental forecasting model and the habitat model, we expect temperature-based habitat forecasts to have useful skill up to two months into the future during the months of interest. The final stage involved forecast delivery via an industry-specific website and engagement with stakeholders, which led to improved presentation and contextualization of the forecasts. The forecasts, which are updated daily, are now being used by SBT fishers and have proven a useful aid in their decision-making.

A wide range of aquaculture industries are exposed to environmental conditions and variability which can impact on production. Information about future climate conditions on a range of time scales can improve risk management, buffer production against unfavourable environmental conditions, and allow maximised production during opportune times. Seasonal forecasting, providing information beyond weather forecasting up to several months into the future, can aid such decision making. In north-east Australian coastal pond-based prawn farms environmental stresses influence timing of farming periods, animal growth and survival. Here we describe the development, packaging and provision of regional and local forecast products, derived from the Predictive Ocean Atmosphere Model for Australia (POAMA; version 2), to the Queensland prawn industry. Product development followed a three-stage process; (i) assessment of management needs and critical timescales, (ii) development and evaluation of the forecast products, and (iii) forecast implementation and refinement of the forecast products. Prawn farm managers identified the critical forecast variables as minimum and maximum air temperatures and rainfall at lead times of up to 2–3 months. The POAMA forecast skill for all three variables was evaluated, with three spatially averaged regional environmental indexes derived from POAMA showing a strong relationship with large-scale observed conditions. Forecast accuracy was assessed using model hindcast data together with historical observations, and was similar in all three regions, higher for temperature than rainfall, and declined with lead time in all cases. Forecast indices were then scaled using local weather station information for a subset of prawn farms in the study. Discussion with prawn farm managers helped refine the format and visualisation of the forecasts. Tailored forecast packages were then delivered through a web-based system and directly by email. These forecasts could aid a range of management decisions, and user feedback led to further refinement. This approach has great potential to be extended to other coastal aquaculture industries using these and other environmental variables. Information about future conditions, such as provided by seasonal forecasting, can assist aquaculture managers in development of production plans that will be more robust to short-term environmental variability and represents the first adaptation step on a pathway to coping with longer term climate change.

Sea level varies on a range of time scales from tidal to decadal and centennial change. To date, little attention has been focussed on the prediction of interannual sea level anomalies. Here we demonstrate that forecasts of coastal sea level anomalies from the dynamical Predictive Ocean Atmosphere Model for Australia (POAMA) have significant skill throughout the equatorial Pacific and along the eastern boundaries of the Pacific and Indian Oceans at lead times out to 8 months. POAMA forecasts for the western Pacific generally have greater skill than persistence, particularly at longer lead times. POAMA also has comparable or greater skill than previously published statistical forecasts from both a Markov model and canonical correlation analysis. Our results indicate the capability of physically based models to address the challenge of providing skillful forecasts of seasonal sea level fluctuations for coastal communities over a broad area and at a range of lead times.

Recent developments in global dynamical climate prediction systems have allowed for skillful predictions of climate variables relevant to living marine resources (LMRs) at a scale useful to understanding and managing LMRs. Such predictions present opportunities for improved LMR management and industry operations, as well as new research avenues in fisheries science. LMRs respond to climate variability via changes in physiology and behavior. For species and systems where climate-fisheries links are well established, forecasted LMR responses can lead to anticipatory and more effective decisions, benefitting both managers and stakeholders. Here, we provide an overview of climate prediction systems and advances in seasonal to decadal prediction of marine-resource relevant environmental variables. We then describe a range of climate-sensitive LMR decisions that can be taken at lead-times of months to decades, before highlighting a range of pioneering case studies using climate predictions to inform LMR decisions. The success of these case studies suggests that many additional applications are possible. Progress, however, is limited by observational and modeling challenges. Priority developments include strengthening of the mechanistic linkages between climate and marine resource responses, development of LMR models able to explicitly represent such responses, integration of climate driven LMR dynamics in the multi-driver context within which marine resources exist, and improved prediction of ecosystem-relevant variables at the fine regional scales at which most marine resource decisions are made. While there are fundamental limits to predictability, continued advances in these areas have considerable potential to make LMR managers and industry decision more resilient to climate variability and help sustain valuable resources. Concerted dialog between scientists, LMR managers and industry is essential to realizing this potential.

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 ocean–atmosphere 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 El-Nino/La Nina events are well represented by POAMA, although the forecast amplitude weakens with increasing lead-time.

The expected increase in the frequency of mass coral bleaching under climate change underlines the importance of thermal stress monitoring systems for coral reef management. ReefTemp Next Generation (RTNG) is a sophisticated remote sensing application designed to operationally monitor the ocean temperatures that can lead to coral bleaching across the Great Barrier Reef. Products are derived from state-of-the-art satellite data; and newly calculated climatologies and management thresholds for bleaching are presented. RTNG is a key component of the Great Barrier Reef Marine Park Authority’s Early Warning System, which informs management action and response strategies.

Marine aquaculture businesses are subject to a range of environmental conditions that can impact on day to day operations, the health of the farmed species, and overall production. An understanding of future environmental conditions can assist marine resource users plan their activities, minimise risks due to adverse conditions, and maximise opportunities. Short-term farm management is assisted by weather forecasts, but longer term planning may be hampered by an absence of useful climate information at relevant spatial and temporal scales. Here we use dynamical seasonal forecasts to predict water temperatures for south-east Tasmanian Atlantic salmon farm sites several months into the future. High summer temperatures pose a significant risk to production systems of these farms. Based on twenty years of historical validation, the model shows useful skill (i.e., predictive ability) for all months of the year at lead-times of 0–1 months. Model skill is highest when forecasting for winter months, and lowest for December and January predictions. The poorer performance in summer may be due to increased variability due to the convergence of several ocean currents offshore from the salmon farming region. Accuracy of probabilistic forecasts exceeds 80% for all months at lead-time 0 months for the upper tercile (warmest 33% of values) and exceeds 50% at a lead-time of 3 months. This analysis shows that useful information on future ocean conditions up to several months into the future can be provided for the salmon aquaculture industry in this region. Similar forecasting techniques can be applied to other marine industries such as wild fisheries and pond aquaculture in other regions. This future knowledge will enhance environment-related decision making of marine managers and increase industry resilience to climate variability.

Dreissenid mussels have been hypothesized to cause selective decreases of phytoplankton in nearshore areas (nearshore shunt hypothesis) as well as the near-complete loss of the offshore phytoplankton spring bloom in some Laurentian Great Lakes. To evaluate whether mussels can reasonably be expected to mediate such changes, we extended the three-dimensional hydrodynamic-ecological model (ELCOM-CAEDYM) to include mussels as a state variable and applied it to Lake Erie (USA-Canada). Mussel-mediated decreases in mean phytoplankton biomass were highly sensitive to the assigned mussel population size in each basin. In the relatively deep east basin, mussels were predicted to decrease phytoplankton in both nearshore and offshore zones, even during periods of thermal stratification but especially during the spring phytoplankton maximum. Spatially, impacts were associated with mussel distributions but could be strong even in areas without high mussel biomass, consistent with advection from areas of higher mussel biomass. The results supported the nearshore shunt hypothesis that mussel impacts on phytoplankton should be greater in nearshore than offshore waters and also supported suggestions about the emerging importance of deep water offshore mussels. The results of this study provide an important insight into ecological role of mussels in lowering plankton productivity in some world’s largest lakes.

Coral bleaching, triggered by elevated water temperatures, is a serious threat to the future health of the world coral reef systems and represents a considerable challenge for reef management. Seasonal forecast products from coupled ocean–atmosphere models can be used to predict anomalously warm conditions several months in advance and provide early warning of bleaching, allowing for a proactive management response to bleaching. Predictions on a seasonal timescale are the most practical for reef managers, as strategies can be implemented at the start of summer prior to the onset of bleaching. To more accurately assess the potential for bleaching, both duration and magnitude of the thermal stress must be considered, in addition to the probability of an event occurring. In this study, we assess the ability of the Australian Bureau of Meteorology seasonal forecast model (POAMA) to forecast monthly HotSpots and Degree Heating Months in the Great Barrier Reef, Australia, with particular focus on the major 1997/1998 and 2001/2002 bleaching events. Probabilistic forecasts for exceeding certain thresholds are also assessed. This work is part of an ongoing research effort to apply a dynamical seasonal model to the problem of coral bleaching and to provide valuable forecast tools for reef managers at useful time scales. Copyright © 2012 Royal Meteorological Society

[Extract] The preservation of coral reefs under a changing climate requires a coordinated approach that integrates observational, experimental, and modeling efforts with practical management and sound government policy. Coral reefs are among the most species-rich habitats in the world, but also among the most vulnerable to our current high-emission path. Observations of the climate system have shown an increase in global average surface temperature during the twentieth century, with an increased rate of warming since 1950.

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.

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.

Coral bleaching, triggered by high ocean temperatures, is a growing challenge for reef management. This paper assesses the ability of an operational seasonal prediction system (POAMA) to forecast summer SST anomalies in real-time across the Great Barrier Reef. Real-time POAMA SST forecasts compare well to observed conditions, particularly during ENSO active years. Prediction skill, however, can be limited by model resolution, ensemble system design and the inherent predictability of events such as monsoon onset. Advanced warning of anomalously warm conditions, and hence bleaching risk, allows for a proactive management approach and is an invaluable tool for reef management under a changing climate.

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.

Barbamarco Lagoon is a small lagoon adjoining the Northern Adriatic Sea and is the site of a commercially valuable clam (Tapes philippinarum) fishery. A three-dimensional (3D) coupled hydrodynamic–ecological model was applied to the lagoon with the objective of assessing impacts on clam food supply, commercial harvests and water quality of different clam rearing strategies, lagoon morphologies and flow regimes. Harvest and net growth to seeding ratios, total harvest value, clearance efficiencies and clam satiety were used to quantify the commercial success of different management strategies, while bottom dissolved oxygen concentrations were used as an indicator of ecosystem health. Increasing exchange with the Northern Adriatic Sea or increasing freshwater inputs into the lagoon improved clam food supply and increased both harvest production and ecosystem health in model simulations of the system. Results indicated that the high spatial and temporal variability of clam production and water quality responses must be considered for a holistic assessment of the outcomes of strategies in the context of ecological and production carrying capacity.

Coral bleaching is a serious problem threatening the world coral reef systems, triggered by high sea surface temperatures (SST) which are becoming more prevalent as a result of global warming. Seasonal forecasts from coupled ocean–atmosphere models can be used to predict anomalous SST months in advance. In this study, we assess the ability of the Australian Bureau of Meteorology seasonal forecast model (POAMA) to forecast SST anomalies in the Great Barrier Reef, Australia, with particular focus on the major 1998 and 2002 bleaching events. Advance warning of potential bleaching events allows for the implementation of management strategies to minimise reef damage. This study represents the first attempt to apply a dynamical seasonal model to the problem of coral bleaching and predict SST over a reef system for up to 6 months lead-time, a potentially invaluable tool for reef managers.

Barbamarco Lagoon (area = 7 km2) is in the Po River Delta, adjoining the Northern Adriatic Sea, and supports a commercially valuable clam (Tapes philippinarum) fishery. This study investigated interactions of the lagoon with adjacent coastal waters and inland riverine inputs by modelling both the lagoon and the Northern Adriatic Sea, using a coupled three-dimensional (3D) hydrodynamic-ecological model (ELCOM-CAEDYM) adapted to include the clam population. The clam model accounted for carbon (C), nitrogen (N) and phosphorus (P) biomass in the benthos through parameterisations for filtration, excretion, egestion, respiration, mortality, and harvesting. Multiple clam size classes were included in a new population dynamics sub-model. Output from the coupled model was validated against hydrodynamic and water quality data from intensive field sampling and routine monitoring. Time scales of tidal flushing, primary production and clam grazing were investigated with the model to demonstrate that food supply to clam populations is dominated by phytoplankton inputs from the Northern Adriatic Sea. Effects of clam cultivation on nutrient concentrations and phytoplankton biomass in Barbamarco Lagoon were primarily localised, with strong tidal flushing minimising impacts of clam filtration on lagoon-wide nutrient concentrations at current clam stocking levels. Clam populations were found to alter the cycling of nutrients in the system, causing the lagoon to become a net sink for particulate organic matter and to export dissolved organic matter to the adjacent sea via tidal flushing. Ecosystem health and sensitivity of nutrient cycles to clam cultivation are important considerations for the long term sustainable management and potential expansion of the fishery.

This study investigates the coupling of physical and biological processes in the Northern Adriatic Sea through use of a dynamic mass balance approach derived from an interdisciplinary model of hydrodynamics and water quality. Emphasis is placed on modelling the role of nutrient inputs from the Po River and the response of phytoplankton concentrations and distributions, and basin-wide nutrient fluxes. The effects of riverine inflows, seasonal stratification and meteorological forcing were assessed with a dynamically coupled 3D hydrodynamic (ELCOM) and ecological (CAEDYM) model after validation with in situ field and remote sensing data. Model simulation results and mass balance calculations indicate a close coupling of physical and biological processes over a range of space and time scales. Comparison of fluxes from advection and particulate organic matter sedimentation indicate that nutrient and phytoplankton levels of the Northern Adriatic Sea are sensitive to variations in riverine nutrient loadings. A net total phosphorus accumulation of 4.61 × 10 6 kg was determined for the basin in 2002, with different climate scenarios and riverine nutrient loadings resulting in varied system responses reflected in this net TP accumulation. Changes in net accumulation of nutrients are considerably lower than variations in nutrient loadings, which suggests that the Northern Adriatic has substantial buffering capacity.

(2002). Effects of atmospheric nutrient inputs and climate change on the trophic status of Crystal Lake, Wisconsin. SIL Proceedings, 1922-2010: Vol. 28, No. 1, pp. 467-470.