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The region where the East Australian Current (EAC) separates from the coast is dynamic and the shelf circulation is impacted by the interplay of the western boundary current and its eddy field with the coastal ocean. This interaction can drive upwelling, retention or export. Hence understanding the connection between offshore waters and the inner shelf is needed as it influences the productivity potential of valuable coastal rocky reefs. Near urban centres, artificial reefs enhance fishing opportunities in coastal waters, however these reefs are located without consideration of the productivity potential of adjacent waters. Here we identify three dominant modes of mesoscale circulation in the EAC separation region (~31.5−34.5°S); the ‘EAC mode’ which dominates the flow in the poleward direction, and two eddy modes, the ‘EAC eddy mode’ and the ‘Eddy dipole mode’, which are determined by the configuration of a cyclonic and anticyclonic eddy and the relationship with the separated EAC jet. We use a Lagrangian approach to reveal the transport pathways across the shelf to understand the impact of the mesoscale circulation modes and to explore the productivity potential of the coastal waters. We investigate the origin (position and depth) of the water that arrives at the inner-mid shelf over a 21-day period (the plankton productivity timescale). We show that the proportion of water that is upwelled from below the euphotic zone varies spatially, and with each mesoscale circulation mode. Additionally, shelf transport timescales and pathways are also impacted by the mesoscale circulation. The highest proportion of upwelling (70%) occurs upstream of 32.5°S, associated with the EAC jet separation, with vertical displacements of 70–120 m. From 33 to 33.5°S, water comes from offshore above the euphotic layer, and shelf transport timescales are longest. The region of highest retention over the inner shelf is immediately downstream of the EAC separation region. The position of the

Western boundary current (WBC) extensions such as the East Australian Current (EAC) southern extension are warming 2-3 times faster than the global average. However, there are nuances in the spatial and temporal variability of the warming that are not well resolved in climate models. In addition, the physical drivers of ocean heat content (OHC) extremes are not well understood. Here, using a high-resolution ocean model run for multiple decades, we show nonuniform warming trends in OHC in the EAC, with strong positive trends in the southern extension region (∼368-388S) but negative OHC trends equatorward of 338S. The OHC variability in the EAC is associated with the formation of anticyclonic eddies, which is modulated by transport ∼880 km upstream (EAC mode) and the westward propagation of Rossby waves (eddy mode). Diagnosing the drivers of temperature extremes has implications for predictability both in the EAC and inWBCs more broadly, where ocean warming is already having considerable ecological impacts.

The red gurnard (Chelidonichthys kumu) is a coastal benthic fish supporting substantial fisheries in the Indo-West Pacific. Despite this, biological knowledge of the species is limited to New Zealand, placing other fished populations at risk of overexploitation if they are less productive. We determined the age, growth, reproductive biology and commercial catch composition of C. kumu from the trawl fishery in New South Wales (NSW). We also tested the hypothesis that growth rates and body size in NSW would be smaller than New Zealand, given the less productive marine environment in NSW and relative food limitation. Similar to New Zealand, individuals from NSW ranged in length from 13 to 56 cm FL and 1 to 13 years of age, with the largest and oldest fish being predominantly female. Females grew faster and to a greater maximum size than males. Growth was broadly similar between populations in NSW and New Zealand. Spawning period in NSW was protracted (11 months) and peaked in winter, contrasting with a peak in spring/summer in New Zealand. Size-at-maturity was similar between the two regions. The current study provides fundamental biological information to inform stock assessment and management of C. kumu in NSW.

Small Island States (SIDS) are among the nations most exposed to climate change (CC) and are characterised by a high degree of vulnerability. Their unique nature means there is a need for more studies focused on the limits to CC adaptation on such fragile nations, particularly regarding their problems and constraints. This paper addressed a perceived need for research into the limitations of adaptation on SIDS, focusing on the many unique restrictions. To this end, the study identified and described the adaptation limits they have by using a review of the literature and an analysis of case studies from a sample of five SIDS in the Caribbean and Pacific regions (Barbados, Trinidad and Tobago, Cook Islands, Fiji, Solomon Islands and Tonga). This research’s findings showed that an adaptable SIDS is characterised by awareness of various values, appreciation and understanding of a diversity of impacts and vulnerabilities, and acceptance of certain losses through change. The implications of this paper are two-fold. It explains why island nations continue to suffer from the impacts of CC and suggest some of the means via which adequate policies may support SIDS in their efforts to cope with the threats associated with a changing climate. This study concluded that, despite the technological and ecological limits (hard limits) affecting natural systems, adaptation to CC is limited by such complex forces and societal factors (soft limits) that more adequate adaptation strategies could overcome.

Coastal winds transport water masses and larval fish onshore or offshore which may influence estuarine recruitment, yet our understanding of the mechanism underlying this relationship is limited. Here, we combine datasets from a historical database of larval fish off southeast Australia with a high-resolution atmospheric reanalysis model to show that normalised abundance of coastally spawned larvae increased with weak to moderate upwelling favourable winds 14 days prior to sampling. The increase in abundance may reflect increased nutrient and plankton availability for larval fish. Normalised larval abundance decreased following strong upwelling favourable winds but increased after onshore (downwelling favourable) winds, due to wind-driven transport. By combining a commercial estuarine fisheries catch-rate dataset (4 species, 8 estuaries, 10 years) and the high-resolution atmospheric reanalysis model, we show that negative effects of upwelling favourable winds during the spawning period can be detected in lagged estuarine commercial fisheries catch rates (lagged by 2–8 years depending on species' growth rates), potentially representing the same mechanism proposed for larval fish. Upwelling favourable winds in the southeast Australian region have increased since 1850 while onshore winds have decreased, which may have reduced larval recruitment to estuaries. Coastal winds are likely an important factor for estuarine recruitment in the southeast Australian region and future research on the estuarine recruitment of fish should incorporate coastal winds.

We develop novel locally defined diagnostics for the efficiency of the ocean's biological pump by tracing carbon throughout its lifetime in the ocean from gas injection to outgassing and counting the number of passages through the soft-tissue and carbonate pumps. These diagnostics reveal that the biological pump's key controls on atmospheric pCO2 are the mean number of lifetime pump passages per dissolved inorganic carbon (DIC) molecule at the surface and the mean aphotic sequestration time of regenerated DIC. We apply our diagnostics to an observationally constrained carbon-cycle model that features spatially varying stoichiometric ratios and is embedded in a data-assimilated global ocean circulation. We find that for the present-day ocean an average of 44 ± 4% of DIC in a given water parcel makes at least one lifetime passage through the soft tissue pump, and about 4% makes at least one passage through the carbonate pump. The global mean number of lifetime pump passages per molecule, including the fraction with zero passages, is (Formula presented.) and (Formula presented.) for the soft-tissue and carbonate pumps. Using idealized perturbations to sweep out a sequence of states ranging from zero biological activity ((Formula presented.) ppmv) to complete surface nutrient depletion ((Formula presented.) ppmv), we find that fractional changes in (Formula presented.) are dominated by fractional changes in the number of soft-tissue pump passages. At complete surface nutrient depletion, the mean fraction of DIC that has at least one lifetime passage through the soft-tissue pump increases to 69 ± 5% with (Formula presented.).

Over 90% of the buildup of additional heat in the Earth system over recent decades is contained in the ocean. Since 2006, new observational programs have revealed heterogeneous patterns of ocean heat content change. It is unclear how much of this heterogeneity is due to heat being added to and mixed within the ocean leading to material changes in water mass properties or is due to changes in circulation that redistribute existing water masses. Here we present a novel diagnosis of the ''material'' and ''redistributed'' contributions to regional heat content change between 2006 and 2017 that is based on a new ''minimum transformation method'' informed by both water mass transformation and optimal transportation theory. We show that material warming has large spatial coherence. The material change tends to be smaller than the redistributed change at any geographical location; however, it sums globally to the net warming of the ocean, whereas the redistributed component sums, by design, to zero. Material warming is robust over the time period of this analysis, whereas the redistributed signal only emerges from the variability in a few regions. In the North Atlantic Ocean, water mass changes indicate substantial material warming while redistribution cools the subpolar region as a result of a slowdown in the meridional overturning circulation. Warming in the Southern Ocean is explained by material warming and by anomalous southward heat transport of 118 6 50 TW through redistribution. Our results suggest that near-term projections of ocean heat content change and therefore sea level change will hinge on understanding and predicting changes in ocean redistribution.

Ocean data timeseries are vital for a diverse range of stakeholders (ranging from government, to industry, to academia) to underpin research, support decision making, and identify environmental change. However, continuous monitoring and observation of ocean variables is difficult and expensive. Moreover, since oceans are vast, observations are typically sparse in spatial and temporal resolution. In addition, the hostile ocean environment creates challenges for collecting and maintaining data sets, such as instrument malfunctions and servicing, often resulting in temporal gaps of varying lengths. Neural networks (NN) have proven effective in many diverse big data applications, but few oceanographic applications have been tested using modern frameworks and architectures. Therefore, here we demonstrate a “proof of concept” neural network application using a popular “off-the-shelf” framework called “TensorFlow” to predict subsurface ocean variables including dissolved oxygen and nutrient (nitrate, phosphate, and silicate) concentrations, and temperature timeseries and show how these models can be used successfully for gap filling data products. We achieved a final prediction accuracy of over 96% for oxygen and temperature, and mean squared errors (MSE) of 2.63, 0.0099, and 0.78, for nitrates, phosphates, and silicates, respectively. The temperature gap-filling was done with an innovative contextual Long Short-Term Memory (LSTM) NN that uses data before and after the gap as separate feature variables. We also demonstrate the application of a novel dropout based approach to approximate the Bayesian uncertainty of these temperature predictions. This Bayesian uncertainty is represented in the form of 100 monte carlo dropout estimates of the two longest gaps in the temperature timeseries from a model with 25% dropout in the input and recurrent LSTM connections. Throughout the study, we present the NN training process including the tuning of the large number of NN hyperparame

Low elevation coastal zones (LECZ) are extensive throughout the southeastern United States. LECZ communities are threatened by inundation from sea level rise, storm surge, wetland degradation, land subsidence, and hydrological flooding. Communication among scientists, stakeholders, policy makers and minority and poor residents must improve. We must predict processes spanning the ecological, physical, social, and health sciences. Communities need to address linkages of (1) human and socioeconomic vulnerabilities; (2) public health and safety; (3) economic concerns; (4) land loss; (5) wetland threats; and (6) coastal inundation. Essential capabilities must include a network to assemble and distribute data and model code to assess risk and its causes, support adaptive management, and improve the resiliency of communities. Better communication of information and understanding among residents and officials is essential. Here we review recent background literature on these matters and offer recommendations for integrating natural and social sciences. We advocate for a cyber-network of scientists, modelers, engineers, educators, and stakeholders from academia, federal state and local agencies, non-governmental organizations, residents, and the private sector. Our vision is to enhance future resilience of LECZ communities by offering approaches to mitigate hazards to human health, safety and welfare and reduce impacts to coastal residents and industries.

Rapid and accurate prediction of peak storm surges across an extensive coastal region is necessary to inform assessments used to design the systems that protect coastal communities’ life and property. Significant advances in high-fidelity, physics-based numerical models have been made in recent years, but use of these models for probabilistic forecasting and probabilistic hazard assessment is computationally intensive. Several surrogate modeling approaches based on existing databases of high-fidelity synthetic storm surge simulations have been recently suggested to reduce computational burden without substantial loss of accuracy. In these previous studies, however, the surrogate modeling approaches relied on a tropical cyclone condition at one moment (usually at or near landfall), which is not always most correlated with the peak storm surge. In this study, a new one-dimensional convolutional neural network model combined with principal component analysis and a k-means clustering (C1PKNet) is presented that can rapidly predict peak storm surge across an extensive coastal region from time-series of tropical cyclone conditions, namely the storm track. The C1PKNet model was trained and cross-validated for the Chesapeake Bay area of the United States using existing database of 1031 high-fidelity storm surge simulations, including both landfalling and bypassing storms. Moreover, the performance of the C1PKNet model was evaluated based on observations from three historical hurricanes (Hurricane Isabel in 2003, Hurricane Irene in 2011, and Hurricane Sandy in 2012). The results indicate that the C1PKNet model is computationally efficient and can predict peak storm surges from realistic tropical cyclone track time-series. We believe that this new surrogate model can enhance coastal resilience by providing rapid storm surge predictions.

Low elevation coastal zones (LECZ) are extensive throughout the southeastern United States. LECZ communities are threatened by inundation from sea level rise, storm surge, wetland degradation, land subsidence, and hydrological flooding. Communication among scientists, stakeholders, policy makers and minority and poor residents must improve. We must predict processes spanning the ecological, physical, social, and health sciences. Communities need to address linkages of (1) human and socioeconomic vulnerabilities; (2) public health and safety; (3) economic concerns; (4) land loss; (5) wetland threats; and (6) coastal inundation. Essential capabilities must include a network to assemble and distribute data and model code to assess risk and its causes, support adaptive management, and improve the resiliency of communities. Better communication of information and understanding among residents and officials is essential. Here we review recent background literature on these matters and offer recommendations for integrating natural and social sciences. We advocate for a cyber-network of scientists, modelers, engineers, educators, and stakeholders from academia, federal state and local agencies, non-governmental organizations, residents, and the private sector. Our vision is to enhance future resilience of LECZ communities by offering approaches to mitigate hazards to human health, safety and welfare and reduce impacts to coastal residents and industries.

Low elevation coastal zones (LECZ) are extensive throughout the southeastern United States. LECZ communities are threatened by inundation from sea level rise, storm surge, wetland degradation, land subsidence, and hydrological flooding. Communication among scientists, stakeholders, policy makers and minority and poor residents must improve. We must predict processes spanning the ecological, physical, social, and health sciences. Communities need to address linkages of (1) human and socioeconomic vulnerabilities; (2) public health and safety; (3) economic concerns; (4) land loss; (5) wetland threats; and (6) coastal inundation. Essential capabilities must include a network to assemble and distribute data and model code to assess risk and its causes, support adaptive management, and improve the resiliency of communities. Better communication of information and understanding among residents and officials is essential. Here we review recent background literature on these matters and offer recommendations for integrating natural and social sciences. We advocate for a cyber-network of scientists, modelers, engineers, educators, and stakeholders from academia, federal state and local agencies, non-governmental organizations, residents, and the private sector. Our vision is to enhance future resilience of LECZ communities by offering approaches to mitigate hazards to human health, safety and welfare and reduce impacts to coastal residents and industries.

Rapid and accurate prediction of peak storm surges across an extensive coastal region is necessary to inform assessments used to design the systems that protect coastal communities’ life and property. Significant advances in high-fidelity, physics-based numerical models have been made in recent years, but use of these models for probabilistic forecasting and probabilistic hazard assessment is computationally intensive. Several surrogate modeling approaches based on existing databases of high-fidelity synthetic storm surge simulations have been recently suggested to reduce computational burden without substantial loss of accuracy. In these previous studies, however, the surrogate modeling approaches relied on a tropical cyclone condition at one moment (usually at or near landfall), which is not always most correlated with the peak storm surge. In this study, a new one-dimensional convolutional neural network model combined with principal component analysis and a k-means clustering (C1PKNet) is presented that can rapidly predict peak storm surge across an extensive coastal region from time-series of tropical cyclone conditions, namely the storm track. The C1PKNet model was trained and cross-validated for the Chesapeake Bay area of the United States using existing database of 1031 high-fidelity storm surge simulations, including both landfalling and bypassing storms. Moreover, the performance of the C1PKNet model was evaluated based on observations from three historical hurricanes (Hurricane Isabel in 2003, Hurricane Irene in 2011, and Hurricane Sandy in 2012). The results indicate that the C1PKNet model is computationally efficient and can predict peak storm surges from realistic tropical cyclone track time-series. We believe that this new surrogate model can enhance coastal resilience by providing rapid storm surge predictions.

Rapid and accurate prediction of peak storm surges across an extensive coastal region is necessary to inform assessments used to design the systems that protect coastal communities’ life and property. Significant advances in high-fidelity, physics-based numerical models have been made in recent years, but use of these models for probabilistic forecasting and probabilistic hazard assessment is computationally intensive. Several surrogate modeling approaches based on existing databases of high-fidelity synthetic storm surge simulations have been recently suggested to reduce computational burden without substantial loss of accuracy. In these previous studies, however, the surrogate modeling approaches relied on a tropical cyclone condition at one moment (usually at or near landfall), which is not always most correlated with the peak storm surge. In this study, a new one-dimensional convolutional neural network model combined with principal component analysis and a k-means clustering (C1PKNet) is presented that can rapidly predict peak storm surge across an extensive coastal region from time-series of tropical cyclone conditions, namely the storm track. The C1PKNet model was trained and cross-validated for the Chesapeake Bay area of the United States using existing database of 1031 high-fidelity storm surge simulations, including both landfalling and bypassing storms. Moreover, the performance of the C1PKNet model was evaluated based on observations from three historical hurricanes (Hurricane Isabel in 2003, Hurricane Irene in 2011, and Hurricane Sandy in 2012). The results indicate that the C1PKNet model is computationally efficient and can predict peak storm surges from realistic tropical cyclone track time-series. We believe that this new surrogate model can enhance coastal resilience by providing rapid storm surge predictions.

Low elevation coastal zones (LECZ) are extensive throughout the southeastern United States. LECZ communities are threatened by inundation from sea level rise, storm surge, wetland degradation, land subsidence, and hydrological flooding. Communication among scientists, stakeholders, policy makers and minority and poor residents must improve. We must predict processes spanning the ecological, physical, social, and health sciences. Communities need to address linkages of (1) human and socioeconomic vulnerabilities; (2) public health and safety; (3) economic concerns; (4) land loss; (5) wetland threats; and (6) coastal inundation. Essential capabilities must include a network to assemble and distribute data and model code to assess risk and its causes, support adaptive management, and improve the resiliency of communities. Better communication of information and understanding among residents and officials is essential. Here we review recent background literature on these matters and offer recommendations for integrating natural and social sciences. We advocate for a cyber-network of scientists, modelers, engineers, educators, and stakeholders from academia, federal state and local agencies, non-governmental organizations, residents, and the private sector. Our vision is to enhance future resilience of LECZ communities by offering approaches to mitigate hazards to human health, safety and welfare and reduce impacts to coastal residents and industries.

Rapid and accurate prediction of peak storm surges across an extensive coastal region is necessary to inform assessments used to design the systems that protect coastal communities’ life and property. Significant advances in high-fidelity, physics-based numerical models have been made in recent years, but use of these models for probabilistic forecasting and probabilistic hazard assessment is computationally intensive. Several surrogate modeling approaches based on existing databases of high-fidelity synthetic storm surge simulations have been recently suggested to reduce computational burden without substantial loss of accuracy. In these previous studies, however, the surrogate modeling approaches relied on a tropical cyclone condition at one moment (usually at or near landfall), which is not always most correlated with the peak storm surge. In this study, a new one-dimensional convolutional neural network model combined with principal component analysis and a k-means clustering (C1PKNet) is presented that can rapidly predict peak storm surge across an extensive coastal region from time-series of tropical cyclone conditions, namely the storm track. The C1PKNet model was trained and cross-validated for the Chesapeake Bay area of the United States using existing database of 1031 high-fidelity storm surge simulations, including both landfalling and bypassing storms. Moreover, the performance of the C1PKNet model was evaluated based on observations from three historical hurricanes (Hurricane Isabel in 2003, Hurricane Irene in 2011, and Hurricane Sandy in 2012). The results indicate that the C1PKNet model is computationally efficient and can predict peak storm surges from realistic tropical cyclone track time-series. We believe that this new surrogate model can enhance coastal resilience by providing rapid storm surge predictions.

Low elevation coastal zones (LECZ) are extensive throughout the southeastern United States. LECZ communities are threatened by inundation from sea level rise, storm surge, wetland degradation, land subsidence, and hydrological flooding. Communication among scientists, stakeholders, policy makers and minority and poor residents must improve. We must predict processes spanning the ecological, physical, social, and health sciences. Communities need to address linkages of (1) human and socioeconomic vulnerabilities; (2) public health and safety; (3) economic concerns; (4) land loss; (5) wetland threats; and (6) coastal inundation. Essential capabilities must include a network to assemble and distribute data and model code to assess risk and its causes, support adaptive management, and improve the resiliency of communities. Better communication of information and understanding among residents and officials is essential. Here we review recent background literature on these matters and offer recommendations for integrating natural and social sciences. We advocate for a cyber-network of scientists, modelers, engineers, educators, and stakeholders from academia, federal state and local agencies, non-governmental organizations, residents, and the private sector. Our vision is to enhance future resilience of LECZ communities by offering approaches to mitigate hazards to human health, safety and welfare and reduce impacts to coastal residents and industries.

Low elevation coastal zones (LECZ) are extensive throughout the southeastern United States. LECZ communities are threatened by inundation from sea level rise, storm surge, wetland degradation, land subsidence, and hydrological flooding. Communication among scientists, stakeholders, policy makers and minority and poor residents must improve. We must predict processes spanning the ecological, physical, social, and health sciences. Communities need to address linkages of (1) human and socioeconomic vulnerabilities; (2) public health and safety; (3) economic concerns; (4) land loss; (5) wetland threats; and (6) coastal inundation. Essential capabilities must include a network to assemble and distribute data and model code to assess risk and its causes, support adaptive management, and improve the resiliency of communities. Better communication of information and understanding among residents and officials is essential. Here we review recent background literature on these matters and offer recommendations for integrating natural and social sciences. We advocate for a cyber-network of scientists, modelers, engineers, educators, and stakeholders from academia, federal state and local agencies, non-governmental organizations, residents, and the private sector. Our vision is to enhance future resilience of LECZ communities by offering approaches to mitigate hazards to human health, safety and welfare and reduce impacts to coastal residents and industries.

Low elevation coastal zones (LECZ) are extensive throughout the southeastern United States. LECZ communities are threatened by inundation from sea level rise, storm surge, wetland degradation, land subsidence, and hydrological flooding. Communication among scientists, stakeholders, policy makers and minority and poor residents must improve. We must predict processes spanning the ecological, physical, social, and health sciences. Communities need to address linkages of (1) human and socioeconomic vulnerabilities; (2) public health and safety; (3) economic concerns; (4) land loss; (5) wetland threats; and (6) coastal inundation. Essential capabilities must include a network to assemble and distribute data and model code to assess risk and its causes, support adaptive management, and improve the resiliency of communities. Better communication of information and understanding among residents and officials is essential. Here we review recent background literature on these matters and offer recommendations for integrating natural and social sciences. We advocate for a cyber-network of scientists, modelers, engineers, educators, and stakeholders from academia, federal state and local agencies, non-governmental organizations, residents, and the private sector. Our vision is to enhance future resilience of LECZ communities by offering approaches to mitigate hazards to human health, safety and welfare and reduce impacts to coastal residents and industries.

Low elevation coastal zones (LECZ) are extensive throughout the southeastern United States. LECZ communities are threatened by inundation from sea level rise, storm surge, wetland degradation, land subsidence, and hydrological flooding. Communication among scientists, stakeholders, policy makers and minority and poor residents must improve. We must predict processes spanning the ecological, physical, social, and health sciences. Communities need to address linkages of (1) human and socioeconomic vulnerabilities; (2) public health and safety; (3) economic concerns; (4) land loss; (5) wetland threats; and (6) coastal inundation. Essential capabilities must include a network to assemble and distribute data and model code to assess risk and its causes, support adaptive management, and improve the resiliency of communities. Better communication of information and understanding among residents and officials is essential. Here we review recent background literature on these matters and offer recommendations for integrating natural and social sciences. We advocate for a cyber-network of scientists, modelers, engineers, educators, and stakeholders from academia, federal state and local agencies, non-governmental organizations, residents, and the private sector. Our vision is to enhance future resilience of LECZ communities by offering approaches to mitigate hazards to human health, safety and welfare and reduce impacts to coastal residents and industries.

Low elevation coastal zones (LECZ) are extensive throughout the southeastern United States. LECZ communities are threatened by inundation from sea level rise, storm surge, wetland degradation, land subsidence, and hydrological flooding. Communication among scientists, stakeholders, policy makers and minority and poor residents must improve. We must predict processes spanning the ecological, physical, social, and health sciences. Communities need to address linkages of (1) human and socioeconomic vulnerabilities; (2) public health and safety; (3) economic concerns; (4) land loss; (5) wetland threats; and (6) coastal inundation. Essential capabilities must include a network to assemble and distribute data and model code to assess risk and its causes, support adaptive management, and improve the resiliency of communities. Better communication of information and understanding among residents and officials is essential. Here we review recent background literature on these matters and offer recommendations for integrating natural and social sciences. We advocate for a cyber-network of scientists, modelers, engineers, educators, and stakeholders from academia, federal state and local agencies, non-governmental organizations, residents, and the private sector. Our vision is to enhance future resilience of LECZ communities by offering approaches to mitigate hazards to human health, safety and welfare and reduce impacts to coastal residents and industries.

Accurate forecasting of ocean currents in dynamic regions remains a critical challenge due to the sparsity of observations in global ocean observing networks and the limited resolution of present-day regional ocean models. Lately, traditional observing platforms have been complemented by newly available data streams capable of sampling at higher spatial and/or temporal resolutions in dynamically significant regions in near-real time. However, the relative merits and trade-offs of incorporating these “nontraditional” observations into ocean state estimates have not been thoroughly investigated. Here, we perform a detailed statistical and dynamical comparison of two high-resolution reanalysis products assimilating different combinations of traditional and nontraditional observations in the East Australian Current (EAC) system, a vigorous western boundary current. We show that sea-surface height and temperature are well-constrained by satellite measurements; however, below the surface, a reanalysis incorporating fully available observations better represents the ocean state. The core of the EAC jet is effectively constrained by subsurface observations from deep water moorings upstream of jet separation, while radar-derived nearshore surface velocities in the separation zone are found to resolve the submesoscale cyclonic band inshore of the EAC. Cost function sensitivity analysis of both products reveals excessive model adjustment at depth causing the reanalyzes to overestimate alongshore transport relative to a 22-year freely evolving simulation. Overall, the assimilation of nontraditional observations delivers marked improvement in representing dynamical features of the EAC. However, this improvement is not as pronounced in the model forecast due to the introduction of nonphysical dynamics or forcing, suggesting that other improvements such as increased model resolution are required.

The strength of the meridional overturning circulation (MOC) in the North Atlantic is dependent upon the formation of dense waters that occurs at high northern latitudes. Wintertime deep convection in the Labrador and Irminger Seas forms the intermediate water mass known as Labrador Sea Water (LSW). Changes in the rate of formation and subsequent export of LSW are thought to play a role in MOC variability, but formation rates are uncertain and the link between formation and export is complex. We present the first observation-based application of a recently developed regional thermohaline inverse method (RTHIM) to a region encompassing the Arctic and part of the North Atlantic subpolar gyre for the years 2013, 2014, and 2015. RTHIM is a novel method that can diagnose the formation and export rates of water masses such as the LSW identified by their temperature and salinity, apportioning the formation rates into contributions from surface fluxes and interior mixing. We find LSW formation rates of up to 12 Sv (1 Sv ≡ 106 m3 s21) during 2014–15, a period of strong wintertime convection, and around half that value during 2013 when convection was weak. We also show that the newly convected water is not exported directly, but instead is mixed isopycnally with warm, salty waters that have been advected into the region, before the products are then exported. RTHIM solutions for 2015 volume, heat, and freshwater transports are compared with observations from a mooring array deployed for the Overturning in the Subpolar North Atlantic Program (OSNAP) and show good agreement, lending validity to our results.

Like many western boundary currents, the East Australian Current (EAC) extension is projected to get stronger and warmer in the future. The CMIP5 multimodel mean (MMM) projection suggests up to 5°C of warming under an RCP85 scenario by 2100. Previous studies employed Sverdrup balance to associate a trend in basin wide zonally integrated wind stress curl (resulting from the multidecadal poleward intensification in the westerly winds over the Southern Ocean) with enhanced transport in the EAC extension. Possible regional drivers are yet to be considered. Here we introduce the NEMO-OASIS-WRF coupled regional climate model as a framework to improve our understanding of CMIP5 projections. We analyze a hierarchy of simulations in which the regional atmosphere and ocean circulations are allowed to freely evolve subject to boundary conditions that represent present-day and CMIP5 RCP8.5 climate change anomalies. Evaluation of the historical simulation shows an EAC extension that is stronger than similar ocean-only models and observations. This bias is not explained by a linear response to differences in wind stress. The climate change simulations show that regional atmospheric CMIP5 MMM anomalies drive 73% of the projected 12 Sv increase in EAC extension transport whereas the remote ocean boundary conditions and regional radiative forcing (greenhouse gases within the domain) play a smaller role. The importance of regional changes in wind stress curl in driving the enhanced EAC extension is consistent with linear theory where the NEMO-OASIS-WRF response is closer to linear transport estimates compared to the CMIP5 MMM.

As the dominant driver of interannual climate variability globally, any changes in the remote impacts of the El Niño-Southern Oscillation (ENSO) due to climate change are of considerable importance. Here we assess whether climate models from Phase 5 of the Coupled Model Intercomparison Project (CMIP5) project robust changes in ENSO’s regional temperature and precipitation teleconnections in the late 21st century, comparing the historical simulations (between 1950 and 1999) and high-emission future simulations (between 2040 and 2089). In order to quantify the importance of internal variability in these projected changes, we examine an ensemble of coupled model simulations from the Max-Planck-Institute Grand Ensemble (MPI-GE). Except for a few regions, the changes in ENSO’s temperature and precipitation teleconnections for most regions are not significant across the majority of models. Exceptions include consistent projected changes to temperature teleconnections over equatorial South America and East Africa, which are robust during La Niña events. Despite this, by assessing all regions together, a significant amplification of the temperature teleconnections is identified for La Niña events. Additionally, we find an overall projected weakening relative to the historical precipitation teleconnection when analysis is limited to regions that correctly reproduce the observed precipitation teleconnections. It remains unclear to what extent a change in regional ENSO teleconnections will be apparent, as it is clear that the changes in ENSO’s teleconnections are relatively small compared to the regional variability during the historical period.

© 2020 Royal Meteorological Society Convective initiation and growth are sensitive to atmospheric conditions, especially humidity, in ways that need to be understood and quantified. It is not clear, however, how well current observations and modelling systems can serve to test this understanding. We simulate multiple cases of convergent cloud lines observed during January 2016 over northeastern Australia using the Weather Research and Forecasting (WRF) model (version 3.7.1), initialized by and nudged to reanalysis data. Overall, WRF appears to skilfully simulate the convergence lines. However, the associated convective clouds as seen by satellite were not properly captured by the model. The model humidity was tested against the current version (V3-00) of the Megha-Tropiques satellite, which although up to 30% too moist in the lowest retrieved layer over the dry continental interior compared to radiosondes, is usually accurate to within ±10% near the cloud lines. Unperturbed WRF simulations were found to be 10–20% too dry in this region. Compensating for this humidity bias via the model initialization produced more realistic simulations of clouds and convection, and this had a comparable or larger impact than changing model physics. This result highlights the importance of observations of water vapour that are accurate and have good spatial coverage, especially in the lower troposphere, for properly constraining simulations of convective environments and for model evaluation of moist processes.

High-frequency interactions between the ocean and atmosphere have the potential to affect lower frequency or mean state climate in various regions. Here we examine the importance of sub-daily air-sea interactions over the Maritime Continent region to the rectification of longer timescale variation. In order to determine the importance of these high-frequency interactions, we conducted two regional ocean–atmosphere coupled simulations over the Maritime Continent where exchanges between the oceanic and atmospheric components are performed either every hour (i.e. resolving diurnal changes) or every day. We find that coupling frequency has a significant influence on mean sea surface temperature (SST) and the mean state and diurnal cycle of rainfall over certain regions of the western Maritime Continent where air-sea interactions are strong during the Asian monsoon season, with little effect in other regions or seasons. Without sub-daily air-sea interactions, the mean SST along the southwest off Sumatra is ~ 2 °C warmer during the period from June to October as a result of a deepening of thermocline along the coast. This deepening is linked to anomalous downwelling equatorial eastward propagating Kelvin waves triggered by westerly anomalies in the eastern equatorial Indian Ocean. In addition, the mean rainfall in the vicinity of ocean warming increases, thereby producing an enhanced barrier layer that also provides a positive warming feedback. Although the coupling frequency has little impact on the timing of the rainfall diurnal cycle, suppression of sub-daily coupling significantly changes the diurnal rainfall amplitude causing a relative decrease (increase) in amplitude over the coast of Northwestern (Southwestern) Sumatra during the South Asian monsoon season.

Submesoscale lenses of water with anomalous hydrographic properties have previously been observed in the East Australian Current (EAC) system, embedded within the thermocline of mesoscale anticyclonic eddies. The waters within these lenses have high oxygen content and temperature–salinity properties that signify a surface origin. However, it is not known how these lenses form. This study presents field observations that provide insight into a possible generation mechanism via subduction at upper-ocean fronts. High-resolution hydrographic and velocity measurements of submesoscale activity were taken across a front between a mesoscale eddy dipole downstream of the EAC separation point. The front had O(1) Rossby number, strong vertical shear, and flow conducive to symmetric instability. Frontogenesis was measured in conjunction with subduction of an anticyclonic water parcel, indicative of intrathermocline eddy formation. Twenty-five years of satellite imagery reveals the existence of strong mesoscale strain coupled with strong temperature fronts in this region and indicates the conditions that led to frontal subduction observed here are a persistent feature. These processes impact the vertical export of tracers from the surface and dissipation of mesoscale kinetic energy, implicating their importance for understanding regional ocean circulation and biological productivity.

Global habitat deterioration of marine ecosystems has led to a need for active interventions to halt or reverse the loss of ecological function. Restoration has historically been a key tool to reverse habitat loss and restore functions, but the extent to which this will be sufficient under future climates is uncertain. Emerging genetic technologies now provide the ability for restoration to proactively match adaptability of target species to predicted future environmental conditions, which opens up the possibility of boosting resistance to future stress in degraded and threatened habitats. As such, the choice of whether to restore to historical baselines or anticipate the future remains a key decision that will influence restoration success in the face of environmental and climate change. Here, we present an overview of the different motives for restoration – to recover or revive lost or degraded habitats to extant or historical states, or to reinforce or redefine for future conditions. We focus on the genetic and adaptive choices that underpin each option and subsequent consequences for restoration success. These options span a range of possible trajectories, technological advances and societal acceptability, and represent a framework for progressing restoration of marine habitat forming species into the future.

Since 1979, three extreme El Niño events occurred, in 1982/83, 1997/98, and 2015/16, with pronounced impacts that disrupted global weather patterns, agriculture, fisheries, and ecosystems. Although all three episodes are referred to as strong equatorial eastern Pacific (EP) El Niño events, the 2015/16 event is considered a mixed regime of both EP and central Pacific (CP) El Niño. During such extreme events, sea surface temperature (SST) anomalies peak over the EP region, hereafter referred to as an extreme warm El Niño (ExtWarmEN) event. Simultaneously, the intertropical convergence zone (ITCZ) moves southward to the usually dry and cold Niño-3 region, resulting in dramatic rainfall increases to more than 5 mm day21 averaged over boreal winter, referred to as an extreme convective El Niño (ExtConEN) event. However, in climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) that are able to simulate both types of events, ExtConEN events are found not to always coincide with ExtWarmEN events and the disassociation becomes more distinct under greenhouse warming when the increased frequency of ExtConEN events is notably larger than that of ExtWarmEN events. The disassociation highlights the role of eastward migration of western Pacific convection and equatorward shift of the South Pacific convergence zone associated with the faster warming over the EP region as a result of greenhouse warming.

We diagnose changes in the decadal-mean ocean transport over the 21st century as simulated by the ACCESS-1.3 model under RCP8.5 forcing. A matrix formulation of advection-diffusion is used to quantify transport into the ocean interior and from the interior back to the surface. We compute the ocean volumes last ventilated at the surface, as well as the volumes next reexposed to the atmosphere, per unit area, and the distributions of water in transit between key ventilation regions. We find that for the 2090s circulation, Antarctic Bottom Water formation and Antarctic ventilation have collapsed but are compensated by increased mode water formation and ventilation at the poleward edge of the southern subtropical gyres. Reexposure of deep water back to the atmosphere in the Antarctic Southern Ocean is also reduced but compensated by enhanced reexposure in the subpolar Southern Ocean. Labrador Sea ventilation is suppressed, while there is increased ventilation in the eastern subpolar Atlantic. The 2090s surface-to-surface interbasin transport is much more sluggish than that of the 1990s, with 30–40% reduced volume flow rates. For transport from last ventilation in the subpolar North Atlantic to next ventilation in the tropical Eastern Pacific, the 2090s residence-time distribution has a ∼40% longer mode with much more slowly decaying tails and correspondingly lagged path densities. The 2090s mean times since last ventilation, as well as the mean times to reexposure are older in the Antarctic Southern Ocean and elsewhere progressively with depth, by up to ∼1,000 years in the abyss.

The Indian Ocean has warmed rapidly and notably at a faster rate than the other tropical ocean basins in the latter half of the twentieth century. We conduct sensitivity experiments using an atmospheric general circulation model to determine the impact of Indian Ocean surface warming on large-scale global atmospheric circulation trends and rainfall distribution, in terms of its pattern and magnitude. Indian Ocean warming drives changes in the local Indian Ocean Walker cell that leads to anomalous easterlies over the Pacific Ocean and strengthens the Pacific Walker Circulation. The anomalous Indian Ocean Walker cell results in anomalous subsidence over Central Africa and the tropical Atlantic, where it is associated with a precipitation decrease over the equator. During austral summer, Indian Ocean warming is associated with the intensification of the northern hemisphere Hadley cell and strengthening of the extratropical atmospheric circulation resembling a positive North Atlantic Oscillation. During austral winter, it is associated with weakening of the southern hemisphere Hadley cell and strengthening of a positive Southern Annular Mode pattern. More intensive warming in the western region of the Indian Ocean basin compared to the east has a significant impact on rainfall trends in the basin, easterly wind trend in the western Pacific and intensity of Hadley circulation changes. It is, however, the Indian Ocean warming across the entire basin that dominates the drying of the tropical Atlantic and the trends in extratropical modes of variability. This study suggests the Indian Ocean warming could have potentially influenced global atmospheric circulation trends observed in the recent decades.

The tropospheric response to midwinter sudden stratospheric warmings (SSWs) is examined using an idealized model. SSW events are triggered by imposing high-latitude stratospheric heating perturbations of varying magnitude for only a few days, spun off from a free-running control integration (CTRL). The evolution of the thermally triggered SSWs is then compared with naturally occurring SSWs identified in CTRL. By applying a heating perturbation, with no modification to the momentum budget, it is possible to isolate the tropospheric response directly attributable to a change in the stratospheric polar vortex, independent of any planetary wave momentum torques involved in the initiation of an SSW. Zonal-wind anomalies associated with the thermally triggered SSWs first propagate downward to the high-latitude troposphere after ;2 weeks, before migrating equatorward and stalling atmidlatitudes,where they straddle the near-surface jet. After;3weeks, the circulation and eddy fluxes associated with thermally triggered SSWs evolve very similarly to SSWs in CTRL, despite the lack of initial planetary wave driving. This suggests that at longer lags, the tropospheric response to SSWs is generic and it is found to be linearly governed by the strength of the lower-stratospheric warming, whereas at shorter lags, the initial formation of the SSWpotentially plays a large role in the downward coupling. In agreement with previous studies, synoptic waves are found to play a key role in the persistent tropospheric jet shift at long lags. Synoptic waves appear to respond to the enhanced midlatitude baroclinicity associated with the tropospheric jet shift, and preferentially propagate poleward in an apparent positive feedback with changes in the high-latitude refractive index.

Transport of larvae by ocean currents is an important dispersal mechanism for many species. The timing and location of spawning can have a large influence on settlement location. Shifts in the known spawning habitat of fish, whether due to climate or the discovery of new spawning stock, can influence the distribution of juveniles and our understanding of connectivity. The globally distributed species; Pomatomus saltatrix, is one such example where a previously unrecognised summer spawning event and a more southern latitudinal extent was recently reported for the southwest Pacific population. Although restrictions are in place to protect the traditional spawning event, the importance of the newly recognised summer spawning event is uncertain. Here, we investigate larval dispersal of P. saltatrix using particle tracking simulations to identify the contributions of the different spawning events to settlement. By modelling dispersal of larvae released in northern and mid-latitude regions over the Austral spring and summer, we show that the newly recognised mid-latitude summer spawning event contributes over 50% of the larvae reaching southern latitudes. This is due to a reduced (1–2 days) pelagic larval duration (associated with temperature), resulting in reduced larval mortality, and the seasonal (summer) strengthening of the East Australian Current (EAC) transporting particles ~50 km further south. These findings demonstrate that in dynamic boundary current systems such as the EAC, the final settlement location of larvae that are transported by ocean currents can vary considerably depending on the timing and location of spawning and that multiple spawning events are important for maximum dispersal.

© 2020 Elsevier B.V. Remotely-sensed ocean colour is the main tool for estimating chlorophyll a (Chl-a) concentration and primary productivity on the global scale. In order to investigate the source of errors in remotely-sensed Chl-a concentration we obtained in situ bio-optical properties, in situ reflectances, satellite-derived reflectances and the Chl-a concentration satellite products of the Ocean and Land Colour Instrument (OLCI) Instrument on board Sentinel-3 A in waters off eastern Australia. The mesoscale eddies of these oligotrophic waters provide contrasting phytoplankton communities that allowed us to focus on the effect of phytoplankton size as a source of errors. In these waters, cold-core cyclonic eddies (CE) are dominated by large phytoplankton cells, while small cells dominate warm-core anticyclonic eddies (ACE). The chlorophyll-specific absorption and backscattering from contrasting sites show significant difference due to the differing package effect of phytoplankton size distributions. After normalising the absorption and backscattering spectra to Chl-a associated with just small phytoplankton, the spectra of optical properties become much more similar, showing that small-sized phytoplankton dominate IOPs even when large cells contain the greater fraction of Chl-a concentration of the phytoplankton community. Measured in situ reflectances agreed with reflectances calculated using a simple optical model based on measured IOPs. Furthermore, the in situ measured reflectances agreed well with the OLCI reflectance (mean normalised bias (MNB) of 7% for wavelengths <600 nm). However, a systematic underestimation of Chl-a concentrations by the OLCI algorithms was found in the region of cyclonic eddies characterised by increased Chl-a concentration and dominance of large-sized phytoplankton. A similar underprediction was found in Chl-a concentration calculated with the band-ratio OC4Me algorithm using in situ and IOP-calculated reflectance. Excluding Chl-a

© 2020 Elsevier B.V. Remotely-sensed ocean colour is the main tool for estimating chlorophyll a (Chl-a) concentration and primary productivity on the global scale. In order to investigate the source of errors in remotely-sensed Chl-a concentration we obtained in situ bio-optical properties, in situ reflectances, satellite-derived reflectances and the Chl-a concentration satellite products of the Ocean and Land Colour Instrument (OLCI) Instrument on board Sentinel-3 A in waters off eastern Australia. The mesoscale eddies of these oligotrophic waters provide contrasting phytoplankton communities that allowed us to focus on the effect of phytoplankton size as a source of errors. In these waters, cold-core cyclonic eddies (CE) are dominated by large phytoplankton cells, while small cells dominate warm-core anticyclonic eddies (ACE). The chlorophyll-specific absorption and backscattering from contrasting sites show significant difference due to the differing package effect of phytoplankton size distributions. After normalising the absorption and backscattering spectra to Chl-a associated with just small phytoplankton, the spectra of optical properties become much more similar, showing that small-sized phytoplankton dominate IOPs even when large cells contain the greater fraction of Chl-a concentration of the phytoplankton community. Measured in situ reflectances agreed with reflectances calculated using a simple optical model based on measured IOPs. Furthermore, the in situ measured reflectances agreed well with the OLCI reflectance (mean normalised bias (MNB) of 7% for wavelengths <600 nm). However, a systematic underestimation of Chl-a concentrations by the OLCI algorithms was found in the region of cyclonic eddies characterised by increased Chl-a concentration and dominance of large-sized phytoplankton. A similar underprediction was found in Chl-a concentration calculated with the band-ratio OC4Me algorithm using in situ and IOP-calculated reflectance. Excluding Chl-a

Future climate is typically projected using multi-model ensembles, but the ensemble mean is unlikely to be optimal if models’ skill at reproducing historical climate is not considered. Moreover, individual climate models are not independent. Here, we examine the interplay between the benefits of optimising an ensemble for the performance of its mean and the the effect this has on ensemble spread as an uncertainty estimate. Using future Australian precipitation change as a case study, we perform optimal subset selection based on present-day precipitation, sea surface temperature and/or 500 hPa eastward wind climatologies. We use either one, two, or all three variables as predictors. Out-of-sample projection skill is assessed using a model-as-truth approach (rather than observations). For multiple variables, multi-objective optimisation is used to obtain Pareto-optimal subsets (an ensemble of model subsets), to gauge the uncertainty in optimisation arising from the multiple constraints. We find that the spread of climate model subset averages typically under-represents the true projection uncertainty (overconfidence), but that the situation can be significantly improved using mixture distributions for uncertainty estimation. The single best predictor, present-day precipitation, gives the most accurate results but is still overconfident—a consequence of calibrating too specifically. It is only when all three constraints are used that projection skill is improved and overconfidence is eliminated, but at the cost of a poorer best estimate relative to one predictor. We thus identify an important trade-off between accuracy and precision, depending on the number of predictors, which is likely relevant for any subset selection or weighting strategy.

Horizontal fluxes of heat and other scalar quantities in the ocean are due to correlations between the horizontal velocity and tracer fields. However, the limited spatial resolution of ocean models means that these correlations are not fully resolved using the velocity and temperature evaluated on the model grid, due to the limited spatial resolution and the boxcar-averaged nature of the velocity and the scalar field. In this article, a method of estimating the horizontal flux due to unresolved spatial correlations is proposed, based on the depth-integrated horizontal transport from the seafloor to the density surface whose spatially averaged height is the height of the calculation. This depth-integrated horizontal transport takes into account the subgrid velocity and density variations to compensate the standard estimate of horizontal transport based on staircase-like velocity and density. It is not a parameterization of unresolved eddies, since it utilizes data available in ocean models without relying on any presumed parameter such as diffusivity. The method is termed the horizontal residual mean (HRM). The method is capable of estimating the spatial-correlation-induced water transport in a 1/48 global ocean model, using model data smoothed to 3/48. The HRM extra overturning has a peak in the Southern Ocean of about 1.5 Sv (1 Sv = 106 m3 s-1 ). This indicates an extra heat transport of 0.015 PW on average in the same area. It is expected that implementing the scheme in a coarse-resolution ocean model will improve its representation of lateral heat fluxes.

Mesoscale eddies are ubiquitous in the ocean, transporting semi-isolated water masses as well as advecting tracers and biota. The extent to which eddies impact the environment depends on the time they retain water parcels. Here we quantify retention times of mesoscale eddies in a (1/10)° model of the East Australian Current and its extension along the southeast coast of Australia. We find that retention times vary widely, between 3 and 357 days, but peak around 24 and 27 days for anticyclones and cyclones, respectively. Changes in eddy shape, though not in eddy size, relate to water exchange between the eddy and the background flow. An increase in eccentricity (eddy elongation) often leads to water leakage, while a decrease is associated with water retention. Thus, the change in eddy eccentricity can be used as a diagnostic of the eddy's likelihood to exchange water with its surrounding. We find that water within a region of the eddy that is close to uniform rotation and rotating faster than uniform vorticity is more likely to be retained. Typical retention times are long enough for eddies to transport water across regions of contrasting hydrographic properties, develop a biogeochemical response, and influence connectivity patterns.

The rate at which the ocean moves heat from the tropics toward the poles, and from the surface into the interior, depends on diabatic surface forcing and diffusive mixing. These diabatic processes can be isolated by analyzing heat transport in a temperature coordinate (the diathermal heat transport). This framework is applied to a global ocean sea ice model at two horizontal resolutions (1/48 and 1/108) to evaluate the partioning of the diathermal heat transport between different mixing processes and their spatial and seasonal structure. The diathermal heat transport peaks around 228C at 1.6 PW, similar to the peak meridional heat transport. Diffusive mixing transfers this heat from waters above 228C, where surface forcing warms the tropical ocean, to temperatures below 228C where midlatitude waters are cooled. In the control 1/48 simulation, half of the parameterized vertical mixing is achieved by background diffusion, to which sensitivity is explored. The remainder is associated with parameterizations for surface boundary layer, shear instability, and tidal mixing. Nearly half of the seasonal cycle in the peak vertical mixing heat flux is associated with shear instability in the tropical Pacific cold tongue, highlighting this region's global importance. The framework presented also allows for quantification of numerical mixing associated with the model's advection scheme. Numerical mixing has a substantial seasonal cycle and increases to compensate for reduced explicit vertical mixing. Finally, applied to Argo observations the diathermal framework reveals a heat content seasonal cycle consistent with the simulations. These results highlight the utility of the diathermal framework for understanding the role of diabatic processes in ocean circulation and climate.

Ocean boundary current systems are key components of the climate system, are home to highly productive ecosystems, and have numerous societal impacts. Establishment of a global network of boundary current observing systems is a critical part of ongoing development of the Global Ocean Observing System. The characteristics of boundary current systems are reviewed, focusing on scientific and societal motivations for sustained observing. Techniques currently used to observe boundary current systems are reviewed, followed by a census of the current state of boundary current observing systems globally. Next steps in the development of boundary current observing systems are considered, leading to several specific recommendations.

Marine heatwaves (MHWs) are extreme climatic events in oceanic systems that can have devastating impacts on ecosystems, causing abrupt ecological changes and socioeconomic consequences. Several prominent MHWs have attracted scientific and public interest, and recent assessments have documented global and regional increases in their frequency. However, for proactive marine management, it is critical to understand how patterns might change in the future. Here, we estimate future changes in MHWs to the end of the 21st century, as simulated by the CMIP5 global climate model projections. Significant increases in MHW intensity and count of annual MHW days are projected to accelerate, with many parts of the ocean reaching a near-permanent MHW state by the late 21st century. The two greenhouse gas (GHG) emission scenarios considered (Representative Concentration Pathway 4.5 and 8.5) strongly affect the projected intensity of MHW events, the proportion of the globe exposed to permanent MHW states, and the occurrence of the most extreme MHW events. Comparison with simulations of a natural world, without anthropogenic forcing, indicate that these trends have emerged from the expected range of natural variability within the first half of the 21st century. This discrepancy implies a degree of “anthropogenic emergence,” with a departure from the natural MHW conditions that have previously shaped marine ecosystems for centuries or even millennia. Based on these projections we expect impacts on marine ecosystems to be widespread, significant and persistent through the 21st century.

Instrumented moorings (hereafter referred to as moorings), which are anchored buoys or an anchored configuration of instruments suspended in the water column, are highly valued for their ability to host a variety of interchangeable oceanographic and meteorological sensors. This flexibility makes them a useful technology for meeting end user and science-driven requirements. Overall, societal needs related to human health, safety, national security, and economic prosperity in coastal areas are met through the availability of continuous data from coastal moorings and other complementary observing platforms within the Earth-observing system. These data streams strengthen the quality and accuracy of data products that inform the marine transportation industry, the tourism industry, fisheries, the military, public health officials, coastal and emergency managers, educators, and research scientists, among many others. Therefore, it is critical to sustain existing observing system networks, especially during this time of extreme environmental variability and change. Existing fiscal and operational challenges affecting the sustainability of observing networks will likely continue into the next decade, threatening the quality of downstream data and information products - especially those used for long-term monitoring, planning, and decision-making. This paper describes the utility of coastal moorings as part of an integrated coastal observing system, with an emphasis on stakeholder engagement to inform observing requirements and to ensure data products are tailored to user needs. We provide 10 recommendations for optimizing moorings networks, and thus downstream data products, to guide regional planners, and network operators: 1. Develop strategies to increase investment in coastal mooring networks 2. Collect stakeholder priorities through targeted and continuous stakeholder engagements 3. Include complementary systems and emerging technologies in implementation planning acti

The OceanGliders program started in 2016 to support active coordination and enhancement of global glider activity. OceanGliders contributes to the international efforts of the Global Ocean Observation System (GOOS) for Climate, Ocean Health and Operational Services. It brings together marine scientists and engineers operating gliders around the world: (1) to observe the long-term physical, biogeochemical, and biological ocean processes and phenomena that are relevant for societal applications; and, (2) to contribute to the GOOS through real-time and delayed mode data dissemination. The OceanGliders program is distributed across national and regional observing systems and significantly contributes to integrated, multi-scale and multi-platform sampling strategies. OceanGliders shares best practices, requirements, and scientific knowledge needed for glider operations, data collection and analysis. It also monitors global glider activity and supports the dissemination of glider data through regional and global databases, in real-time and delayed modes, facilitating data access to the wider community. OceanGliders currently supports national, regional and global initiatives to maintian and expand the capabilities and application of gliders to meet key global challenges such as improved measurement of ocean boundary currents, water transformation and storm forecast.