The accuracy of nearshore hydrodynamic model predictions depends on the quality of the forcing applied at the offshore boundary. Waves are represented statistically in phase-averaged models, while they are represented individually in phase-resolving models. Since XBeach Surf-Beat fully resolves infragravity waves, the frequency distribution of the forcing wave spectra affects the computation of the forced infragravity waves at the offshore boundary. We ran XBeach Surf-Beat simulations with two types of forcing: parametric forcing, where a JONSWAP spectrum spectra is reconstructed internally, and spectral forcing, which utilises the observed full directional spectra. For unimodal wave conditions, both the JONSWAP reconstruction and spectral forcing produced results that closely matched infragravity wave observations. However, under bimodal wave conditions, the parametric forcing (JONSWAP) was less accurate compared to spectral forcing. In regions influenced by multiple wave climates, spectral forcing provides a more accurate representation of nearshore hydrodynamic processes.

Tropical cyclones (TCs) are a leading cause of extreme coastal sea levels and inundation, with impacts on communities, infrastructure and ecosystems projected to grow under sea-level rise and possible shifts in TC activity. Robust estimates of TC-driven extreme water levels are essential for coastal engineering design and adaptation planning, yet observational records along most coastlines span only a few decades and contain too few intense storms to constrain the upper tail of the distribution. Coastlines that have not yet been impacted by a severe TC are particularly vulnerable to underestimation from local records, even where the true hazard is substantial. Here we present a physics-based Monte Carlo emulator that generates synthetic time series of annual maximum total water levels by probabilistically combining storm surge from coupled hydrodynamic-wave simulations of thousands of synthetic TC events with tides and background sea level variability. Non-linear tide-surge interactions, which can modulate surge by more than 1.5 m, are explicitly resolved through a radial basis functions interpolation scheme. Return period estimates are derived empirically without assuming parametric extreme value distributions. We find that conventional extreme value analysis of observational records substantially underestimates TC risk. TC-induced storm surge contributes significantly to extreme sea levels in northern Queensland even at sub-centennial return periods, while southern regions experience TC dominance only at rarer events. Importantly, projected changes in TC activity and sea level rise can be ingested directly by modifying the input distributions, without re-running hydrodynamic simulations, providing a transferable and decision-relevant tool for assessing TC-driven coastal hazards under a changing climate.

Accurate representation of tropical cyclone (TC) surface wind and atmospheric pressure fields is crucial for numerical modelling of storm surges and extreme ocean (wind-)waves to adequately predict and inform related impacts to coastal communities, infrastructure, and ecosystems, yet it remains a challenge. This study evaluates three parametric TC wind models (the Kepert, McConochie, and Chavas-Lin-Emanuel (CLE15) models) alongside surface wind fields from two dynamical regional atmospheric reanalyses (one convection permitting). While most comparable studies primarily evaluate surface wind models against point wind observations, we instead assess how model choice and parameterisation propagate through coupled surge-wave simulations to influence the prediction of storm surges and wind-waves. To do so, we leverage an unstructured mesh coupled hydrodynamic-wave model previously developed to support improved coastal hazard characterisation for Australia. We dynamically simulate storm surges, tide and wind-wave fields for eight historical TCs in the Queensland/Great Barrier Reef region and validate the results against storm surge (tide gauge) and storm wave (buoy) observations. The findings highlight how storm surge and especially wind-wave fields allow for a more integrated, albeit indirect, evaluation of the wind fields compared to direct in-site wind measurements. The findings also illustrate the utility of parametric TC wind models, since even relatively high-resolution, convection permitting regional dynamical reanalyses are limited in their ability to sufficiently resolve the inner-core winds (particularly of intense TCs) for coastal hazard applications such as predicting coastal extreme sea levels. All three parametric models markedly improve the peak surge and wave estimates compared to the regional atmospheric reanalyses. However, there are notable differences among them. The McConochie (double vortex) model exhibits the largest biases, producing an overly broad wind field that overestimates the spatial extent, timing, and duration of storm surge and wave events. The CLE15 and Kepert models demonstrate comparable performance and strong suitability for TC-induced surge and wave modelling in Queensland, with CLE15 model yielding appreciably more accurate and consistent predictions across the cases examined. These results provide guidance for model selection in TC hazard assessments, indicating that physics-based radial wind profiles (like CLE15) can offer improved predictive skill over regionally derived empirical models and reanalysis wind fields.

We present the implementation and validation of the Coupled circulation-wave Coastal Hazards Prediction System (CCHaPS), a national-scale modelling framework for simulating tides, storm surge, waves, and sea levels around Australia. CCHaPS employs a 2D barotropic circulation model with a spectral wind-wave model on unstructured mesh. Forcing is provided by TPXO tides and high-resolution atmospheric, ocean and wave reanalyses and projections, developed primarily through the Australian Climate Service. A 40-year hindcast (1981-2020) was produced, capturing major climate variability and extreme events. This paper provides an overview of the CCHaPS framework and validation of the hindcast. Validation was conducted against a comprehensive suite of observations, including tide gauges, wave buoys, current profilers and satellite altimetry. The CCHaPS hindcast consistently reproduces total water levels with high skill, achieving Pearson's correlation coefficients >= 0.95 and normalized RMSE <= 5% at 76 of 100 national tide gauges evaluated. Tidal harmonic analysis shows strong agreement for primary constituents (M2, S2, K1, O1), with average complex errors <8 cm. Non-tidal residuals are also well represented, with nRMSE <= 4% at 97 sites. Depth-averaged current speeds and directions show close agreement with measurements from 63 locations. Wave validation against 94 buoys and satellite altimetry confirms robust performance in simulating significant wave height and peak period, including extremes. The CCHaPS hindcast thus provides a robust, nationally consistent dataset that is publicly available to support coastal hazard assessment, extreme value analysis, and climate adaptation planning. Associated work includes CCHaPS climate projections and probabilistic analysis.

Addressing global challenges such as coastal hazards and climate change requires innovative tools capable of analyzing complex environmental drivers, including waves, storm surges, and cyclones, across varying scales. These tools are vital for predicting floods, assessing risks, and planning adaptive responses. BlueMath-Hub has been developed as a global collaborative initiative to provide accessible, customizable solutions for both researchers and practitioners. It aims to simplify the use of advanced statistical and numerical models, fostering creative and scalable approaches in coastal science and engineering. BlueMath, the core of this platform, is an open-source repository of Python tools accessible via a cloud-based Jupyter Hub environment. It integrates statistical methods and numerical model wrappers within a modular framework. The system includes: (a) BlueMath-Toolkit, providing tools for data mining, interpolation, and model integration; (b) BlueMath-Statistical Downscaling, focusing on extreme events and generalized models; (c) BlueMath-Hybrid Downscaling, combining statistical and numerical approaches for optimized solutions; and (d) BlueMath-Climate Services, supporting integrated applications such as compound flooding assessments. BlueMath is continuously evolving, with its tools already applied in research, publications, and training. By lowering barriers to entry and enabling collaborative workflows, BlueMath-Hub supports the development of innovative solutions to mitigate the impacts of a changing climate.

A multi-decadal global wind-wave hindcast dataset—WHACS: the Wave Hindcast for ACS—spanning 1979 to near present was developed to offer insight into historical wave conditions both directly and as boundary forcing to localised simulations. Applications for WHACS include coastal management, climate research, and renewable energy projects, ultimately helping communities and industries make informed decisions to improve safety, efficiency, and resilience regarding wave conditions. This dataset features a near-global spherical multi-cell (SMC) grid that aligns with the Bureau operational wave forecast model and has been calibrated to better represent extreme wave conditions by improving the representation of extreme winds. Spanning from 1979 to near present, WHACS available output consists of multiple hourly bulk and spectral partition wave parameters for the native SMC grid, as well as regular global and regional regridded bulk wave parameters. For the Indo-Pacific, a gridded output of full spectral data is available across exclusive economic zones.

The historical TC-related cost-intensity relationship is examined using documented costs of TCs that passed within 50 km of Vanuatu over the period 1972–2023. These are used to estimate the cost of TCs (i.e., for 72

Small Island Developing States (SIDS) are highly exposed to extreme sea level (ESL) events, which are expected to intensify under climate change. Understanding the exposure and economic implications of ESL-driven coastal inundation is essential for informing risk reduction and adaptation planning. This study presents a scalable methodological framework for assessing exposure and economic impacts of ESL-driven coastal inundation on critical assets. The framework integrates high-resolution geospatial data, probabilistic return interval analysis, and climate scenario projections to quantify present and future coastal risk. Applied to Vanuatu as a case study, the framework evaluates exposure and replacement costs for buildings and road networks under 50- and 100-year ESL events, across baseline (1981-2020) and future climate conditions (2041-2060 and 2081-2100), assuming low (RCP2.6) and high (RCP8.5) greenhouse gas emission scenarios. Results indicate a substantial increase in both exposed assets and associated economic losses under future scenarios. For instance, the total replacement cost of buildings exposed to 50-year ESL events rises from USD 59 million (5 % of GDP) under baseline conditions to USD 97 million (9 % of GDP) by mid-century under high emissions, and up to USD 163 million (15 % of GDP) by late century. Comparable increases are evident for road assets, particularly in low-lying local government areas adjacent to major waterways. Beyond quantifying local impacts, this case study demonstrates how the framework can be adapted for regional and national coastal risk assessments to inform climate adaptation planning, infrastructure investment, and disaster risk management in other SIDS in the Pacific.

Wind waves play a crucial role in coastal dynamics and can significantly impact coastal sea levels, especially during extreme events. Ocean winds are changing as the Earth is warming, and hence the waves. The Australian Climate Service (https://www.acs.gov.au/), recognised wind waves as a crucial element to support future coastal climate mitigation and adaptation strategies. Wind wave climate future projections are, however, plagued by uncertainties. One of the primary sources of uncertainty originates from the resolution of the Coupled Model Intercomparison Project (CMIP) General Circulation Model (GCM) surface wind speed products. We hereby assess different approaches to regional wind wave climate modelling, to understand the impact of the CMIP6 GCM wind speed resolution. We evaluate the Southeast Australia wave climate results from an unstructured grid regional wave model nested in a global wave model. We compare 30 years (1985-2014) of historical wave climate simulations using wind vectors from the CMIP6 Meteorological Research Institute (MRI) CMIP, AMIP, and HighResMIP experiments (nominal resolutions of ~150 km for CMIP and AMIP, and 25 km for HighResMIP). We then compare these results with the wave model forced by the MRI CMIP surface winds dynamically downscaled with the Conformal Cubic Atmospheric Model (CCAM) (~12.5 km resolution). The findings indicate that the wind wave climate models yield divergent results, particularly at the extremes where the most interest lies for future coastal sea level projections. We discuss the reasons for the differences and propose the best way forward for developing regional wind wave climate projections.

Waves produced by tropical cyclones (TCs) can be estimated using non-stationary wave models forced with time-varying wind fields. However, dynamical simulations are time and computationally demanding at regional-scale domains since high temporal and spatial resolutions are required to correctly simulate TC-induced wave propagation processes. Applications such as early warning systems, coastal risk assessments and future climate projections benefit from fast and accurate estimates of wave fields induced by close-to-real storm tracks geometry. The proposed SHyTCWaves methodology constitutes a novel tool capable of estimating the spatio-temporal variability of directional wave spectra produced by TCs in deep waters, using a hybrid approach and statistical techniques to reduce CPU time effort. This work demonstrates that TC-induced waves can be reconstructed using a stop-motion approach based on the addition of successive 6 h periods of time-varying storm conditions. The developed hybrid model reduces a TC track to a number of segments that are parameterized in terms of 10 representative TC features, and generates a library of cases dynamically pre-computed which allow to ensemble consecutive 6 h analog segments representing the original TC track. The metamodel has been compared and corrected with available satellite data, and its applicability is exemplified for TC Ofa in the South Pacific.

For Williamstown tide gauge, at the northern-most point of Port Phillip Bay (PPB), Melbourne, Victoria, tide registers from 1872 to 1966 and marigrams from 1950 to 1966, were digitized to extend sea-level records back almost 100 years. Despite some vertical datum issues in the early part of the record, the data set is suitable for extreme sea-level trend analysis after removal of the annual mean sea level. The newly digitized data was combined with the digital record to produce a combined record from 1872 to 2020. Analysis of this record revealed known problems of siltation of the tide gauge stilling well and associated reduction in tidal range at times during 1880-1895 and 1910-1940. A positive trend in tidal amplitude of 0.41 +/- 0.01 mm yr-1 was found over 1966-2020, likely due to reduced hydraulic friction at the narrow entrance to PPB. Extreme sea-level trends were examined over 1872-2020 for storm tides (the combination of storm surge and tide) after removal of the annual mean, and residuals after subtraction of the predicted tides. A non-stationary Gumbel distribution with a time-varying location parameter revealed statistically significant declining trends in the residuals of -0.73 +/- 0.02 mm yr-1, consistent with the observed poleward movement of storm surge-producing mid-latitude weather systems. For storm tides a smaller declining trend of -0.40 +/- 0.01 mm yr-1 was found. These trends are approximately an order of magnitude smaller than the current positive rates of mean sea level rise, meaning that storm tide hazard will continue to increase in the future. This information is relevant for future adaptation planning. Sea levels recorded by tide gauges can be studied to understand how sea levels are changing due to mean sea-level rise (SLR) from ocean warming and melting of land-based ice as well as due to changes in extreme sea levels (storm tides) due to the combination of astronomical tides and severe-weather-induced storm surges. Digitizing historical paper tide gauge records lengthens the digital records and enables more reliable trend analysis. In this study, tide records from 1872 to 1966 were digitized for the Williamstown tide gauge, within the semi-enclosed Port Phillip Bay, in southeastern Australia, lengthening the existing digital record by almost 100 years. After removing the trend due to SLR, a small negative trend in extreme sea levels was found. Looking at the components separately, a small negative trend in storm surges was found, consistent with the observed poleward movement of the weather systems that cause them. A positive trend in tidal range was found, possibly due to more tidal in and outflow through the narrow entrance to the bay under SLR. The net effect of SLR and storm-tide trends is an increasing trend in extreme sea-levels. This information is relevant for coastal managers considering coastal adaptation options. Sub-daily data for the Williamstown, Australia tide gauge has been extended back to 1872 through digitization of paper records Trends in extreme sea level events show a small declining trend over the entire record although trends in tidal range are positive over recent decades

Accurate and timely early warning systems are a vital component in mitigating the risks faced by coastal communities worldwide. Unlike aggregated wave parameters, information extracted from the complete directional wave spectra is often indispensable in the development of such systems in multi-modal environments, such as remote islands, where concurrent waves from various directions are common. Dynamically simulating the wave propagation, although accurate, can be computationally demanding and time-consuming, particularly for resource-constrained communities. In this study, we introduce as an alternative, a novel additive hybrid model known as BinWaves. This model relies on the propagation of a reduced number of monochromatic wave systems and linear wave theory, facilitating the efficient reconstruction of the full directional wave spectra in nearshore areas. To showcase the capabilities of BinWaves, we have implemented the system in the Pacific Islands of Samoa and American Samoa and validated it against full spectral numerical simulations and available buoy data. Given its similar accuracy and higher computational efficiency when compared with dynamic wave models, BinWaves has proven to be a great alternative for reconstructing historical time series, or, more importantly analysing climate change scenarios, tasks that go beyond the capacities of small islands developing states.

Wind waves play a crucial role in coastal dynamics and can significantly impact coastal sea levels, especially during extreme events. Ocean winds are changing as the Earth is warming, and hence the waves. The Australian Climate Service (https://www.acs.gov.au/), recognised wind waves as a crucial element to support future coastal climate mitigation and adaptation strategies. Wind wave climate future projections are, however, plagued by uncertainties. One of the primary sources of uncertainty originates from the resolution of the Coupled Model Intercomparison Project (CMIP) General Circulation Model (GCM) surface wind speed products. We hereby assess different approaches to regional wind wave climate modelling, to understand the impact of the CMIP6 GCM wind speed resolution. We evaluate the Southeast Australia wave climate results from an unstructured grid regional wave model nested in a global wave model. We compare 30 years (1985-2014) of historical wave climate simulations using wind vectors from the CMIP6 Meteorological Research Institute (MRI) CMIP, AMIP, and HighResMIP experiments (nominal resolutions of ~150 km for CMIP and AMIP, and 25 km for HighResMIP). We then compare these results with the wave model forced by the MRI CMIP surface winds dynamically downscaled with the Conformal Cubic Atmospheric Model (CCAM) (~12.5 km resolution). The findings indicate that the wind wave climate models yield divergent results, particularly at the extremes where the most interest lies for future coastal sea level projections. We discuss the reasons for the differences and propose the best way forward for developing regional wind wave climate projections.

Pacific Island countries are vulnerable to climate variability and change. Developing strategies for adaptation and planning processes in the Pacific requires new knowledge and updated information on climate science. In this paper, we review key climatic processes and drivers that operate in the Pacific, how they may change in the future and what the impact of these changes might be. In particular, our emphasis is on the two major atmospheric circulation patterns, namely the Hadley and Walker circulations. We also examine climatic features such as the South Pacific Convergence Zone and Intertropical Convergence Zone, as well as factors that modulate natural climate variability on different timescales. It is anticipated that our review of the main climate processes and drivers that operate in the Pacific, as well as how these processes and drivers are likely to change in the future under anthropogenic global warming, can help relevant national agencies (such as Meteorological Services and National Disaster Management Offices) clearly communicate new information to sector-specific stakeholders and the wider community through awareness raising.

Long-term and accurate wave hindcast databases are often required in different coastal engineering projects. The assessment of the nearshore wave climate is often accomplished by using downscaling techniques to translate offshore waves to coastal areas. However, dynamical downscaling approaches may incur huge computational cost. Additionally, the common use of bulk parameterizations are often not accurate for multidimensional waves. To overcome these limitations, we present a hybrid downscaling approach that combines mathematical algorithms (statistical downscaling) and numerical modeling (dynamical downscaling) over the individual spectral partitions. Every wave partition is downscaled and aggregated afterward by using principles of wave linear theory. By assuming linearity in the propagation of the wave celerity, the application of the method is limited from offshore to intermediate water depths. In addition, the method proposed uses a technique to simplify the spectral boundary conditions in complex domains. The methodology has been applied and validated in the island states of Samoa, American Samoa, Majuro, and Kwajalein, showing good skill at reproducing the spectral hourly time series of significant wave height, peak period, and peak direction. Moreover, an accurate representation of the observed energy spectrum was achieved. This study provides insight into the numerical approximation of the combined sea-swell states while improving the quality of fast spectral forecasting and early warning systems.

Numerical prediction of coastal inundation can be complex due to the multiple physical processes involved and typically requires two-dimensional numerical model extents, particularly in areas with complex along-shore morphology. Such model domains often incur relatively high computational expense. Recent extreme inundation studies for Wellington, New Zealand, were executed using the numerical tool, XBeach. Here, the two-dimensional physical dynamics associated with multiple small embayments and both reef and sandy beach substrates, require large, high spatial resolution numerical model extents, informed by a multi-source elevation surface. XBGPU, is a translation of key XBeach features into code that permits GPU-based acceleration. The present study presents a comparison between XBeach and XBGPU for the same numerical model configuration and extents. Three model resolutions were employed, ranging from a typical desktop CPU based XBeach model resolution, to the highest resolution model that will require High Performance Computing (HPC) scale resources. Two CPU HPC facilities were used and five GPUs to investigate the scalability of both XBeach and XBGPU. The latter ranged from desktop grade units to GPUs associated with professional computing facilities. XBeach scalability is investigated by mean of the speed-up ratio, the time saving ratio and the computational efficiency. These are in reference to the computational speed of a model running on one CPU core. The XBGPU speed-up ratio is presented as a function of the slowest GPU. Direct comparisons between XBeach and XBGPU were achieved by using computational capacity as a metric. The results indicate that even a desktop grade GPU can compete with the computational efficiency of HPC-scale CPU facilities. Small model resolutions presented inefficient scaling on both high-performance CPUs and GPUs while the high-resolution model presented near linear scalability for the high-performance GPUs. Nevertheless, HPCs can be efficient to solve large computational problems if enough CPUs are employed.

Tropical cyclone (TC) Pam formed in the central south Pacific in early March 2015. It reached a category 5 severity and made landfall or otherwise directly impacted several islands in Vanuatu, causing widespread damage and loss of life. It then moved along a southerly track between Fiji and New Caledonia, generating wind-waves of up to approximately 15 m, before exiting the region around March 15th. The resulting swell propagated throughout the central Pacific, causing flooding and damage to communities in Tuvalu, Kiribati and Wallis and Futuna, all over 1,000 km from TC Pam’s track. The severity of these remote impacts was not anticipated and poorly forecasted. In this study, we use a total water level (TWL) approach to estimate the climatological conditions and factors contributing to recorded impacts at islands in Tuvalu and Kiribati. At many of the islands, the estimated TWL associated with Pam was the largest within the ∼40-year period of available data, although not necessarily the largest in terms of estimated wave setup and runup; elevated regional sea-level also contributed to the TWL. The westerly wave direction likely contributed to the severity, as did the locally exceptional storm-swell event’s long duration; the overall timing and duration of the event was modulated by astronomical tides. The findings of this study give impetus to the development, implementation and/or improvement of early warning systems capable of predicting such reef-island flooding. They also have direct implications for more accurate regional flood hazard analyses in the context of a changing climate, which is crucial for informing adaptation policies for the atolls of the central Pacific.

The Australian marine research, industry, and stakeholder community has recently undertaken an extensive collaborative process to identify the highest national priorities for wind-waves research. This was undertaken under the auspices of the Forum for Operational Oceanography Surface Waves Working Group. The main steps in the process were first, soliciting possible research questions from the community via an online survey; second, reviewing the questions at a face-to-face workshop; and third, online ranking of the research questions by individuals. This process resulted in 15 identified priorities, covering research activities and the development of infrastructure. The top five priorities are 1) enhanced and updated nearshore and coastal bathymetry; 2) improved understanding of extreme sea states; 3) maintain and enhance the in situ buoy network; 4) improved data access and sharing; and 5) ensemble and probabilistic wave modeling and forecasting. In this paper, each of the 15 priorities is discussed in detail, providing insight into why each priority is important, and the current state of the art, both nationally and internationally, where relevant. While this process has been driven by Australian needs, it is likely that the results will be relevant to other marine-focused nations.

The operational Australian Bluelink ocean forecast system is used to transform physical oceanographic observations into coherent analyses and predictions. These analyses and predictions form the basis for information services about the marine environment and its ecosystem, and can provide boundary data for weather predictions. Bluelink information services are available to marine industries (e.g. commercial fishing, aquaculture, shipping, oil and gas, renewable energy), government agencies (e.g. search and rescue, defence, coastal management, environmental protection), and other stakeholders (e.g. recreation, water sports, artisanal and sport fishing) who depend on timely and accurate information about the marine environment. This review highlights the last 15 years of Bluelink achievements delivering mesoscale (eddy-resolving) to sub-mesoscale and short- to medium-range (days to weeks) ocean forecasts and reanalyses. Key achievements include the development of a global ocean forecasting and reanalysis system, a relocatable ocean-atmosphere model and a littoral zone analysis and forecasting capability. Beyond the traditional short-term forecasting of physical ocean properties (temperature, salinity, surface height, currents, waves), marine activities such as water quality and habitat management as well as climate monitoring increasingly rely on operational oceanographic data and products. These are areas of active research of the Bluelink team in collaboration with national and international partners.