Regional Wave Model Research Articles

Free Infragravity Waves on the Inner Shelf: Observations and Parameterizations at Two Southern California Beaches

AbstractNumerical predictions of nearshore waves and shoreline runup are usually initialized on the inner shelf, seaward of the surfzone, with sea‐swell (SS) waves from local wave buoys or regional wave models. Lower frequency infragravity (IG) waves are not reliably measured by buoys or included in regional models. Here, co‐located pressure and velocity observations are used to characterize IG waves in 10–15 m depth in southern California. Shoreward propagating IG waves are often dominated by free waves, with the boundwave energy fraction <30% for moderate and low energy incident SS waves. Only 5% of records, with energetic long swell, show primarily bound waves. The shoreline slope of concave beaches increases by ∼3 between spring high and low tides, and free seaward and shoreward IG energy in 10–15 m vary tidally. The observed linear dependency of free IG energy on SS energy and period is consistent with Ardhuin et al. (2014, https://doi.org/10.1016/j.ocemod.2014.02.006)'s parameterization (R2 = 0.71). Including the tide level as a proxy for beach slope and modifying the SS frequency dependency increases R2 to 0.91. The ratio of free seaward to shoreward propagating IG energy suggests between 50 and 100% of the energy radiated seaward in depths of 10–15 m is trapped offshore and redirected shoreward. Free (random phase) and bound (phase‐coupled) IG waves are combined to initialize the SWASH numerical model. SWASH predicted runup is only weakly influenced by waves at the offshore boundary. Nonlinear IG generation and dissipation in the shoaling and surfzone overwhelm the effects of shoreward propagating waves observed at the offshore boundary.

Cross-Shore Modeling Features: Calibration and Impacts of Wave Climate Uncertainties

Numerical models can be powerful tools for evaluating the best scenarios for the construction of artificial nourishments to mitigate coastal erosion. Until recent decades, when looking at medium- to long-term simulations, cross-shore and alongshore processes have been studied separately. Accounting for both processes in a shoreline evolution numerical model would improve the understanding and predictive capacity of future changes in coastline evolution. The AX-COAST project aims to develop new capacities in modeling cross-shore sediment transport processes by adding the CS-Model, a cross-shore numerical model, into the existing LTC (Long-Term Configuration) model. The LTC model is a shoreline evolution numerical model which is a module of the cost–benefit assessment tool COAST. This work presents the first steps of the CS-Model implementation, which involve evaluating its performance by calibrating the model with extensive measured datasets of wave climate, beach profiles, tide levels, etc., from coastal areas in IJmuiden and Sand Motor in the Netherlands. The results show good agreement between modeled and observed values. Additionally, wave climate datasets derived from global and regional wave models were considered to evaluate modeling performance at IJmuiden. Using derived timeseries from the wave models did not significantly lead to different results compared to using measured data. The obtained mean absolute and relative errors for each profile were low for both types of datasets. Calibration processes with consistent data are important in modeling simulations to accurately represent the study area and ensure the credibility of future simulations.

Impact of sea level changes on future wave conditions along the coasts of western Europe

Abstract. Wind waves and swells are major drivers of coastal environment changes and coastal hazards such as coastal flooding and erosion. Wave characteristics are sensitive to changes in water depth in shallow and intermediate waters. However, wave models used for historical simulations and projections typically do not account for sea level changes whether from tides, storm surges, or long-term sea level rise. In this study, the sensitivity of projected changes in wave characteristics to the sea level changes is investigated along the Atlantic European coastline. For this purpose, a global wave model is dynamically downscaled over the northeastern Atlantic for the 1970–2100 period under the SSP5–8.5 climate change scenario. Twin experiments are performed with or without the inclusion of hourly sea level variations from regional 3D ocean simulations in the regional wave model. The largest impact of sea level changes on waves is located on the wide continental shelf where shallow-water dynamics prevail, especially in macro-tidal areas. For instance, in the Bay of Mont-Saint-Michel in France, due to an average tidal range of 10 m, extreme historical wave heights were found to be up to 1 m higher (+30 %) when sea level variations are included. At the end of the 21st century, extreme significant wave heights are larger by up to +40 % (+60 cm), mainly due to the effect of tides and mean sea level rise. The estimates provided in this study only partially represent the processes responsible for the sea-level–wave non-linear interactions due to model limitations in terms of resolution and the processes included.

A Real-Time Spatiotemporal Machine Learning Framework for the Prediction of Nearshore Wave Conditions

Abstract The safe and successful operation of offshore infrastructure relies on a detailed awareness of ocean wave conditions. Ongoing growth in offshore wind energy is focused on very large-scale projects, deployed in ever more challenging environments. This inherently increases both cost and complexity and therefore the requirement for efficient operational planning. To support this, we propose a new machine learning framework for the short-term forecasting of ocean wave conditions to support critical decision-making associated with marine operations. Here, an attention-based long short-term memory (LSTM) neural network approach is used to learn the short-term temporal patterns from in situ observations. This is then integrated with an existing, low computational cost spatial nowcasting model to develop a complete framework for spatiotemporal forecasting. The framework addresses the challenge of filling gaps in the in situ observations and undertakes feature selection, with seasonal training datasets embedded. The full spatiotemporal forecasting system is demonstrated using a case study based on independent observation locations near the southwest coast of the United Kingdom. Results are validated against in situ data from two wave buoy locations within the domain and compared to operational physics-based wave forecasts from the Met Office (the United Kingdom’s national weather service). For these two example locations, the spatiotemporal forecast is found to have an accuracy of R2 = 0.9083 and 0.7409 in forecasting 1-h-ahead significant wave height and R2 = 0.8581 and 0.6978 in 12-h-ahead forecasts, respectively. Importantly, this represents respectable levels of accuracy, comparable to traditional physics-based forecast products, but requires only a fraction of the computational resources. Significance Statement Spectral wave models, based on modeling the underlying physics and physical processes, are traditionally used to generate wave forecasts but require significant computational cost. In this study, we propose a machine learning forecasting framework developed using both in situ buoy observations and a surrogate regional numerical wave model. The proposed framework is validated against in situ measurements at two renewable energy sites and found to have very similar 12-h forecasting errors when benchmarked against the Met Office’s physics-based forecasting model but requires far less computational power. The proposed framework is highly flexible and has the potential for offering a low-cost, low computational resource approach for the provision of short-term forecasts and can operate with other types of observations and other machine learning algorithms to improve the availability and accuracy of the prediction.

Regional wave model climate projections for coastal impact assessments under a high greenhouse gas emission scenario

In the future, shifts in wind storms across the North and Baltic Seas are highly unpredictable, challenging the projection of wave conditions for managing coastal hazards. Moreover, regional sea level rise (SLR), with very large uncertainty, complicates the situation for stakeholders seeking recommendations for climate adaptation plans. The purpose of this study is to examine the change of the storm surge and wind wave components of the water level due to climate change in a low tidal range Køge Bay near the entrance of the Baltic Sea. Under a high greenhouse gas emission scenario RCP8.5, we employed a regional climate model (HIRHAM) forced wave model (WAM) and focused on the wave model results during the “storm surge conditions” (exceeding 20 years storm surge events) and “stormy conditions” (exceeding 90th percentile of wave heights). We find that the change in both wave height and period in the future is negligible under “stormy conditions”. Nevertheless, under “storm surge conditions” when considering SLR, the simulated wave height is projected to double in the near future (mid-century) under RCP 8.5, and the wave period may also increase by about 1.5 seconds. This is because some high significant wave height events in the future are associated with the storm surge events when considering SLR. The findings suggest that the combined effects of mean sea level rise, storm surge and waves are likely to increase the risk to a bay with geography and exposure comparable to Køge Bay. As a result, the future plan for climate engineering protection should place a premium on the additional wave energy protection associated with storm surges.

Assess the Typhoon-driven Extreme Wave Conditions in Manila Bay through Numerical Simulation and Statistical Analysis

Philippines is the most exposed country in the world to typhoons, with an average of twenty tropical cyclones entering the Philippine Area yearly. This study develops numerical models to simulate the extreme waves driven by typhoons, in an attempt to provide design wave conditions for a hypothetical land reclamation project in Manila Bay. A total number of 36 tropical storms passing through Manila Bay are selected within a 200 km search radius of the project for the period from 1977 to 2016. The typhoon-driven storm waves in Manila Bay are then simulated using a third-generation spectral wave model, driven by a hybrid wind field which combines both the typhoon information from JTWC (Joint Typhoon Warning Center) and the background information from CFSR (Climate Forecast System Reanalysis). A nested wave modelling scheme is adopted, which comprises of a regional wave model and a local wave model. The regional wave model encompasses all the typhoon tracks of the selected tropical storms, providing the wave inputs (spectrum) at the boundaries for the local wave model which covers the Manila Bay with a focus on the project locations. Model results of the maximum significant wave heights Hm0, the associated mean energy wave period Tm-1,0, and wave direction at the project locations are extracted from the numerical simulation. Extreme value analysis is finally conducted, using the moment ratio diagram to select the best distribution functions, to determine the extreme wave conditions corresponding to different return periods.

Ocean Swell Comparisons Between Sentinel‐1 and WAVEWATCH III Around Australia

AbstractAn intercomparison between directional wave spectra derived from Sentinel‐1 Synthetic Aperture Radar (SAR) satellites and a WAVEWATCH III model hindcast in the wider Australian region is presented. The coastal buoy network around Australia is considerably sparse, and only a handful of buoy measurements exist in deeper oceans. National and regional scale wave models require validation and verification through intercomparisons with available observations to be confidently adopted and improved. In the absence of dense in‐situ measurements, satellite‐derived surface wave data provide an invaluable and independent source of observations. Satellite altimeters provide well‐calibrated significant wave height data, but do not resolve wave directions, and are also not the platform of choice for resolving wave periods. SAR satellites that routinely map ocean surface waves fill this gap as they are able to measure directional wind‐wave spectra of long period waves. This constitutes an important satellite data stream for better understanding the propagation of swell waves across ocean basins, and for comparisons with national and down‐scaled wave models. However, both SAR wave measurements as well as wave models do not represent the truth and need qualification before regarding them as reliable data sets. The aim of this article is to perform an intercomparison of a wave model hindcast with SAR‐derived wave information in the wider Australian region. The comparisons are done in the context of mean wave climate and its seasonal variability, and demonstrate good agreement for wave heights and periods. Valuable insights into possible sources of disagreement are given.

ENSEMBLE WAVE CLIMATE PROJECTIONS BASED ON CMIP5 MODELS

A warming climate has the potential to not only raise sea level but also exacerbate coastal hazards due to changes in storm frequency and intensity. Along open coasts where wave energy is often the dominant process dictating shoreline positions, changes in mean and extreme wave conditions are likely to alter long-term geomorphic evolution patterns. The Coordinated Ocean Wave Climate Project (COWCLIP) is to provide infrastructure to support a systematic, community-based framework that allows for validation and inter-comparison of wave projections. Here, the primary aims are to 1) present quantitative evaluations of projected global scale wave conditions and 2) to present the framework and preliminary results of regional wave modeling that will provide projections of nearshore wave conditions for use in long-term geomorphic change analyzes.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/Y6BEHq5wZXw

Modelling wave growth in narrow fetch geometries: The white-capping and wind input formulations

This paper investigates the performance of three different wave model source term packages in narrow fetch geometries. The packages are used to model the sea state in a complex coastal system with narrow fjords on the west coast of Norway. The modelling system is based on the Simulating WAves Nearshore (SWAN) wave model that is forced with winds from a nested atmospheric model and wave spectra from a regional wave model at the boundaries. The performances of the recent ST6, and two older SWAN white-capping and wind input packages, are evaluated by comparing modelled spectra and integrated wave parameters against five wave buoys. The comparison covers long-term statistics and two case studies of narrow fetch geometries (i) without swell and (ii) with swell-wind sea conditions. SWAN’s original saturation-based approach performs best in the fjord system. In narrow fetch geometry without swell, all packages overestimate the wave energy. ST6 shows the highest sensitivity to fetch geometry and local wind changes. The results indicate that the ST6 white-capping is too weak to balance its strong wind input.

An Application of NEMOVAR for Regional Wave Model Data Assimilation

The benefits of applying data assimilation to a regional wave model for the northwest European continental shelf have been assessed. The assimilation method uses a 3D variational scheme commonly used in ocean model data assimilation, NEMOVAR, coupled to a regional configuration of WAVEWATCH III deployed on a Spherical Multiple-Cell grid with rotated pole. Significant wave height forecast skill was positively impacted over lead times of up to 12 hours, in both offshore and coastal areas of the model. Assimilation experiments tested the impacts of applying both satellite altimeter and in-situ platform observations of significant wave height as the state variable. Addition of in-situ data introduced an improvement in model skill versus assimilating altimeter measurements only, but these improvements were relatively short-lived in lead time (on the order of a few hours). Extending observation coverage into the coastal zone introduced a benefit for only the first 1-2 hours of forecast lead time at coastal sites. Although impacts on significant wave height forecasts were substantial at short lead times, the relatively short ‘system memory’ of the assimilation suggests that, in the case of the northwest European shelf region, the best application of wave data assimilation is in a rapidly cycling short range forecast system used to generate nowcasts, or for a reanalysis product.

Quantifying processes contributing to marine hazards to inform coastal climate resilience assessments, demonstrated for the Caribbean Sea

Abstract. Scientific evidence is critical to underpin the decisions associated with shoreline management, to build climate-resilient communities and infrastructure. We explore the role of waves, storm surges and sea level rise for the Caribbean region with a focus on coastal impacts in the eastern Caribbean islands. We simulate past extreme events and a worst-case scenario, modelling the storm surges and waves, suggesting a storm surge might reach 1.5 m, depending on the underwater topography. Coastal wave heights of up to 12 m offshore and up to 5 m near the coast of St Vincent are simulated with a regional wave model. We deliver probabilistic sea level projections for 2100, with a low-probability–high-impact estimate of possible sea level rise up to 2.2 m, exceeding the 1.8 m global estimate for the same scenario. We introduce a combined vulnerability index, which allows for a quantitative assessment of relative risk across the region, showing that sea level rise is the most important risk factor everywhere but wave impacts are important on windward coasts, increasing to the north, towards the main hurricane track. Our work provides quantitative evidence for policy-makers, scientists and local communities to actively prepare for and protect against climate change.

Southern African Wave Model Sensitivities and Accuracies

Numerous studies have identified the complexities of the wave climatology around the South African coast, but limited studies have investigated these complex dynamics in the available literature. Several freely available parameterized wave boundary conditions are produced around southern Africa. However, none of these are fully spectral outputs from global or larger regional spectral wave models. This constraint results in local engineering and scientific organizations, reconstructing their own spectral boundary conditions. For coastal models, this is a reasonable assumption, assuming that the single parameterization is accurate and a representation of a non-multimodal sea state. The South African Weather Service (SAWS) Marine unit recently launched their coupled, operational wave and storm surge forecasting model. The aim of the SAWS Wave and Storm Surge (SWaSS) platform was to provide accurate, high-resolution coastal forecasts for the entire South African coastline. The present investigation thus presents the validation of the spectral wave component of the coupled system, developed in Delft3D. Various wave boundary reconstructions are investigated together with the two most used and well-known whitecapping formulations. Validation is performed with both in situ wave-rider buoy data (at nine locations along the coastline) and regional remotely sensed, along track, altimetry data. Full model performance statistics are provided, and the accuracy of the model is discussed.

Predicting site-specific storm wave run-up

Storm wave run-up causes beach erosion, wave overtopping, and street flooding. Extreme runup estimates may be improved, relative to predictions from general empirical formulae with default parameter values, by using historical storm waves and eroded profiles in numerical runup simulations. A climatology of storm wave run-up at Imperial Beach, California is developed using the numerical model SWASH, and over a decade of hindcast spectral waves and observed depth profiles. For use in a local flood warning system, the relationship between incident wave energy spectra E(f) and SWASH-modeled shoreline water levels is approximated with the numerically simple integrated power law approximation (IPA). Broad and multi-peaked E(f) are accommodated by characterizing wave forcing with frequency-weighted integrals of E(f). This integral approach improves runup estimates compared to the more commonly used bulk parameterization using deep water wave height H_0 and deep water wavelength L_0 Hunt (Trans Am Soc Civ Eng 126(4):542–570, 1961) and Stockdon et al. (Coast Eng 53(7):573–588, 2006. https://doi.org/10.1016/j.coastaleng.2005.12.005). Scaling of energy and frequency contributions in IPA, determined by searching parameter space for the best fit to SWASH, show an H_0L_0 scaling is near optimal. IPA performance is tested with LiDAR observations of storm run-up, which reached 2.5 m above the offshore water level, overtopped backshore riprap, and eroded the foreshore beach slope. Driven with estimates from a regional wave model and observed beta _f, the IPA reproduced observed run-up with <30% error. However, errors in model physics, depth profile, and incoming wave predictions partially cancelled. IPA (or alternative empirical forms) can be calibrated (using SWASH or similar) for sites where historical waves and eroded bathymetry are available.

Future Changes in Significant Wave Heights in the East/Japan Sea

Jang, C.J.; Kim, K.H.; Jung, H.; Do, K.; Yoo, J.; Kim, Y.S., and Lim, H.S., 2020. Future changes in significant wave heights in the East/Japan Sea. In: Malvarez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 1443-1448. Coconut Creek (Florida), ISSN 0749-0208.Future changes in wave climate play a crucial role in both coastal safety by changing coastal environments including erosion and marine ecosystem mainly by modulating nutrient input through vertical mixing changes. In this study, we investigated future changes in significant wave heights (SWHs) in the East/Japan Sea by using a regional wave model forced with future winds in the mid-21 century based on the RCP4.5 scenario. The future wind field was obtained from a regional climate model (SNU-MM5) contributing to CORDEX-EA (COordinated Regional climate Downscaling EXperiment-East Asia) project. In general, the mean SWHs are projected to increase, with a seasonally contrasting pattern: a greater increase in summer than in winter. In addition to the seasonal contrast, a basin-wide difference is also noticeable: larger changes in the northern basin than in the southern basin. The 99th percentile SWHs, a measure of extreme SWHs, show a significant spatial variation, with a maximum increase near Vladivostok, where a considerable intensification of wind is projected. Our findings suggest that the projected future changes in SWHs in the East/Japan Sea can be considerably different depending on seasons and basins.

Sixteen years of bathymetry and waves at San Diego beaches

Sustained, quantitative observations of nearshore waves and sand levels are essential for testing beach evolution models, but comprehensive datasets are relatively rare. We document beach profiles and concurrent waves monitored at three southern California beaches during 2001–2016. The beaches include offshore reefs, lagoon mouths, hard substrates, and cobble and sandy (medium-grained) sediments. The data span two energetic El Niño winters and four beach nourishments. Quarterly surveys of 165 total cross-shore transects (all sites) at 100 m alongshore spacing were made from the backbeach to 8 m depth. Monthly surveys of the subaerial beach were obtained at alongshore-oriented transects. The resulting dataset consists of (1) raw sand elevation data, (2) gridded elevations, (3) interpolated elevation maps with error estimates, (4) beach widths, subaerial and total sand volumes, (5) locations of hard substrate and beach nourishments, (6) water levels from a NOAA tide gauge (7) wave conditions from a buoy-driven regional wave model, and (8) time periods and reaches with alongshore uniform bathymetry, suitable for testing 1-dimensional beach profile change models.

Numerical modelling of waves and surge from Cyclone Chapala (2015) in the Arabian Sea

Cyclones cause significant loss of life and damage to properties, ecosystems and marine structures and facilities. Cyclone modelling results are used for deriving robust design conditions for coastal and marine structures and facilities. The results are also used for emergency planning and decision-making to estimate potential loss of life, damage to properties and marine facilities and to develop rescue and mitigation measures and plan clean-up operations. Royal HaskoningDHV (hereafter RHDHV) has set up regional tidal hydrodynamic and wave models covering the Arabian Sea to provide data to address the above issues. Over the course of a number of studies a total of 30 major cyclones have been identified in the Arabian Sea since 1945. Cyclone Chapala (28 October – 4 November 2015) was found to be the second strongest cyclone event since 1945 (the strongest being Cyclone Gonu in 2007). As less information is available on Cyclone Chapala, this paper has concentrated on this event to illustrate the use of numerical modelling to simulate waves and surge generated by cyclones. The MIKE21 model suite has been used to investigate this cyclone and sample results from the wave and surge modelling are presented in this paper for illustration purposes. The models could be used to simulate any cyclone generated within the Arabian Sea. The methodology described in this paper for modelling waves and surge in the Arabian Sea could also be applied to simulate this type of events at other sites around the world.

Extreme Wave Analysis by Integrating Model and Wave Buoy Data

Estimating the extreme values of significant wave height (HS), generally described by the HS return period TR function HS(TR) and by its confidence intervals, is a necessity in many branches of coastal science and engineering. The availability of indirect wave data generated by global and regional wind and wave model chains have brought radical changes to the estimation procedures of such probability distribution—weather and wave modeling systems are routinely run all over the world, and HS time series for each grid point are produced and published after assimilation (analysis) of the ground truth. However, while the sources of such indirect data are numerous, and generally of good quality, many aspects of their procedures are hidden to the users, who cannot evaluate the reliability and the limits of the HS(TR) deriving from such data. In order to provide a simple engineering tool to evaluate the probability of extreme sea-states as well as the quality of such estimates, we propose here a procedure based on integrating HS time series generated by model chains with those recorded by wave buoys in the same area.

Numerical Modelling of Waves from the 1991 Cyclone in the Bay of Bengal (Bangladesh)

Cyclones cause significant loss of life and damage to properties, marine structures/facilities and ecosystems. Cyclone modelling results are used for deriving robust design conditions for coastal and marine structures and facilities. They are also used for emergency planning and decision-making to estimate potential loss of life, damage to properties and marine facilities and to develop rescue and mitigation measures and plan clean-up operations. Royal HaskoningDHV (hereafter RHDHV) has set up a regional wave model covering the Bay of Bengal and its wider surroundings to address these issues. A list of major cyclones in Bay of Bengal that have affected the Bangladeshi coastline since 1970 was initially identified. The 1991 Cyclone (22-30 April 1991) was found to be the second strongest cyclone affecting the coastal areas of Bangladesh since 1945 (after the Bhola Cyclone on 7-13 November 1970). This paper concentrates on the 1991 Cyclone to illustrate the use of numerical modelling to simulate waves generated by cyclones. The MIKE21 Spectral Wave Model of DHI was used in the study. Sample results of waves from the modelling study are presented in this paper for illustration purposes. The model could be used to simulate any cyclone originating in the Bay of Bengal and its surroundings. The methodology described in this paper for modelling cyclone waves in the Bay of Bengal could also be applied to simulate cyclones at other sites around the world.

Bulk versus Spectral Wave Parameters: Implications on Stokes Drift Estimates, Regional Wave Modeling, and HF Radars Applications

AbstractAccurate estimates of Stokes drift are necessary to quantify Lagrangian transport and upper-ocean mixing. These can be estimated from directional wave spectra. Here, a methodology for the reconstruction of such spectra is developed using partitioned bulk wave parameters provided by global wave models. These reconstructed spectra agree well with global wave model–simulated full spectra. Regional wave model simulations with reconstructed spectra as open boundary conditions lead to more accurate estimates of bulk wave parameters in the coastal ocean. Furthermore, the reconstructed directional spectra can be used to improve high-frequency (HF) radar–derived surface Lagrangian current estimates. Stokes drift vertical profiles from complete directional spectra are more accurate, and therefore coupled ocean circulation and wave models should incorporate spectral estimates for wave–current interaction studies. Based on model simulations conducted here, it is recommended that regional wave modeling studies use partitioned rather than bulk wave parameter products from global wave simulations to reconstruct complete directional spectra for open boundary conditions. Finally, this study shows that inclusion of the peak spectral energy for each partition improves the ability to reconstruct more accurately directional spectra and surface Stokes drift. It is recommended that the global wave model hindcast/forecast include this additional bulk parameter.

Downscaling wind and wavefields for 21st century coastal flood hazard projections in a region of complex terrain

AbstractWhile global climate models (GCMs) provide useful projections of near‐surface wind vectors into the 21st century, resolution is not sufficient enough for use in regional wave modeling. Statistically downscaled GCM projections from Multivariate Adaptive Constructed Analogues provide daily averaged near‐surface winds at an appropriate spatial resolution for wave modeling within the orographically complex region of San Francisco Bay, but greater resolution in time is needed to capture the peak of storm events. Short‐duration high wind speeds, on the order of hours, are usually excluded in statistically downscaled climate models and are of key importance in wave and subsequent coastal flood modeling. Here we present a temporal downscaling approach, similar to constructed analogues, for near‐surface winds suitable for use in local wave models and evaluate changes in wind and wave conditions for the 21st century. Reconstructed hindcast winds (1975–2004) recreate important extreme wind values within San Francisco Bay. A computationally efficient method for simulating wave heights over long time periods was used to screen for extreme events. Wave hindcasts show resultant maximum wave heights of 2.2 m possible within the Bay. Changes in extreme over‐water wind speeds suggest contrasting trends within the different regions of San Francisco Bay, but 21th century projections show little change in the overall magnitude of extreme winds and locally generated waves