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The South Pacific region is an area affected by characteristic precipitation patterns undergoing extreme events such as tropical cyclones and droughts. First, a daily weather typing of precipitation is presented, based on principal component analysis and k-means clustering using precipitation and atmospheric circulation variables derived from sea-level pressure and wind reanalysis fields. As a result, five weather types (WTs) are presented, able to capture distinct precipitation spatiotemporal patterns, interpretable in terms of salient regional climate features. Second, we undertake the calibration of the TRMM precipitation product using a set of rain gauge stations as reference and scaling and empirical quantile mapping (eQM) as calibration techniques. Furthermore, we build upon the weather-type classification to compare the results with a WTconditioned calibration approach. Overall, our results underpin the need of adjusting the existing TRMM biases, mostly relevant for the upper tail of their distribution, and advocate the use of correction techniques able to deal with quantile-dependent biases-such as eQM-instead of a simple scaling, in order to obtain a more realistic representation of extreme precipitation events. The conditioning has shown only a marginal added value over the simple approach, although this minor improvement may prove relevant for applications focused on extreme event analysis. Furthermore, the weather types created can be applied to a wide variety of conditioned analyses in this region. AFRICULTURES, Grant/Award Number: 774652; Beach4Cast, Grant/Award Number: PID2019-107053RB-I00; CORDyS, Grant/Award Number: PID2020-116595RB-I00; INDECIS, Grant/Award Number: 690462

Tropical cyclones are associated with extreme winds, waves, and storm surge, being among most destructive natural phenomena. Developing capability for a rapid impact estimate is crucial for coastal applications and risk preparedness. When predicting waves characteristics associated to tropical cyclones, the traditional approach involves a two-step procedure (a) a Holland-type wind vortex model and (b) numerical simulations using a wave generation model, using buoy and satellite measurements for validation. In this work, we take advantage of the increasing amount of remote sensing observational data and propose a new empirical model to estimate the wind wave footprint of tropical cyclones. For this purpose, we construct a dataset with over a million satellite observations of waves triggered by tropical cyclones assuming a circular shape of the TC influence area and defining composites of significant wave height as a function of representative parameters of the track characteristics like the minimum pressure, its forward velocity, and its latitude. The validation against buoy data confirms the usefulness of the model for a first and rapid estimation of the wave footprint, although an underestimation of the most extreme events is observed due to the relatively small number of observations recorded. Due to its efficiency, the model can be applied for rapid estimations of wave footprints in operational systems, reconstruction of historical or synthetic events and risk assessments. This work would not have been possible without funding from the Strategic Environmental Research and Development Program's grant DOD/SERDP RC-2644 and from the Spanish Ministry of Science and Innovation, project Beach4cast PID2019-107053RB-I00. Ana Rueda funded by a Juan de la Cierva Incorporación Scholarship (IJC2020-04390). Laura Cagigal is funded by a scholarship from the University of Auckland. Open access publishing facilitated by The University of Auckland, as part of the Wiley - The University of Auckland agreement via the Council of Australian University Librarians.

In the South Pacific region, the precipitation patterns are mostly driven by a number of processes operating at spatial and temporal scales. One of the most important features is the South Pacific Convergence Zone (SPCZ).Five Daily Weather Types (WT) of precipitation are presented, based on Principal Component Analysis (PCA) and k-means clustering using ERA5 precipitation and atmospheric circulation variables such as mean sea-level pressure (SLP), day-to-day difference of mean daily SLP or northward and eastward 10-m wind component fields, able to capture distinct precipitation spatio-temporal patterns, interpretable in terms of salient regional climate features such as the SPCZ state and tropical cyclone activity. We then undertake a weather-type conditioned calibration of the TRMM (Tropical Rainfall Measuring Mission) product using in-situ rain gauge records from the PACRAIN database as reference. “Conditioning” is here based on applying separate statistical corrections for each of the generated WTs, since biases might be dependent on specific atmospheric situations that can be partially captured by the clustering procedure, thus adapting the correction factors to specific synoptic conditions. Our results indicate that the WT-conditioned calibration provides an overall marginal added value over the unconditioned approach, although it makes a significant difference for a better correction of extreme rainfall events, critical in many impact studies. The approach can be extended to compound extreme events, in which several variables are involved (e.g. precipitation, sea level, wind, etc.), in order to better preserve multi-variable consistency.

Coastal compound flooding events occur when extreme events of rainfall, river discharge and sea level coincide and collectively increase water surface elevation, exacerbating flooding. The meteorological conditions that generate these events are usually low-pressure systems that generate high winds and intense rainfall. In this study, we identify the types of synoptic atmospheric conditions that are typically associated with coastal compound events using a weathertype approach, for the North Atlantic coastlines (encompassing northwest Europe and the east coast of the United States). Compound events are identified along the estuaries of the study region from 1980 to 2014 based on an impact function defined by water surface elevation that resulted from the combination of river discharge and sea level. We find that compound events are more frequent along European as opposed to U.S. coastlines. In both cases, they are associated with a few dominant weather patterns. European hotspots of compound events are concentrated in the west coast of United Kingdom, the northwest coast of the Iberian Peninsula and around the Strait of Gibraltar. These areas share the same weather patterns which represent the main pathways of storms that cross the North Atlantic Ocean. In the case of U.S. locations, the areas with highest number of compound events are located mainly in the Gulf of Mexico and along Mexico and along the mid-eastern U.S. coastlines. In these areas, compound events are produced by transitional weather patterns, which describe storms that travel northward parallel to the coastline. Splitting the occurrence of compound events in the corresponding weather types discriminates the interannual variability based on the relationship with dominant climate indices in the North Atlantic Ocean. This research forms part of the CHANCE project, which is supported by awards from the UK Natural Environment Research Council (NE/S010262/1) and US National Science Foundation (1929382). We would like to thank Dirk Eilander for providing support about the use of dataset of simulated water levels and discharge at river mouth locations globally which is available on Zenodo(doi: 10.5281/zenodo.3665734).

Through alteration of wave-generating atmospheric systems, global climate changes play a fundamental role in regional wave climate. However, long-term wave-climate cycles and their associated forcing mechanisms remain poorly constrained, in part due to a relative dearth of highly resolved archives. Here we use the morphology of former shorelines preserved in beach-foredune ridges (BFR) within a protected embayment to reconstruct changes in predominant wave directions in the Subtropical South Atlantic during the last ~3000 years. These analyses reveal multi-centennial cycles of oscillation in predominant wave direction in accordance with stronger (weaker) South Atlantic mid- to high-latitudes mean sea-level pressure gradient and zonal westerly winds, favouring wave generation zones in higher (lower) latitudes and consequent southerly (easterly) wave components. We identify the Southern Annular Mode as the primary climate driver responsible for these changes. Long-term variations in interhemispheric surface temperature anomalies coexist with oscillations in wave direction, which indicates the influence of temperature-driven atmospheric teleconnections on wave-generation cycles. These results provide a novel geomorphic proxy for paleoenvironmental reconstructions and present new insights into the role of global multi-decadal to multi-centennial climate variability in controlling coastal-ocean wave climate. A.P.S. and A.H.F.K. acknowledge the Brazilian Ministry of Environment for funding through the SMC-Brazil project. A.H.F.K. acknowledges the Rede Clima, INCT MarCOI “Oceanografia Integrada e Usos Multiplos da Plataforma Continental e Oceano Adjacente—Centro de Oceanografia Integrada (COI)”, CNPQ (PQ2- CNPQ 301963/2015-0, PQ1D CNPQ 301597/2018-9, CNPQ 441545/2017-3) and, CAPES Brazil-Finance Code 001 and Capes/PROEX 88881.146046/2017-01 for financial support. F.J.M. acknowledges the funding from the Spanish Ministry of Science and Innovation through the project PID2019-107053RB-I00. A.P.S. acknowledges the CESM1(CAM5) Last Millennium Ensemble Community Project and supercomputing resources provided by NSF/CISL/Yellowstone. C.J.H. acknowledges that this paper is Contribution 3957 of the Virginia Institute of Marine Science, William & Mary.

Extreme value analysis seeks to assign probabilities to events which deviate significantly from the mean and is thus widely employed in disciplines dealing with natural hazards. In terms of extreme sea levels (ESLs), these probabilities help to define coastal flood risk which guides the design of coastal protection measures. While tide gauge and other systematic records are typically used to estimate ESLs, combining systematic data with historical information has been shown to reduce uncertainties and better represent statistical outliers. This paper introduces a new method for the incorporation of historical information in extreme value analysis which outperforms other commonly used approaches. Monte-Carlo Simulations are used to evaluate a posterior distribution of historical and systematic ESLs based on the prior distribution of systematic data. This approach is applied at the German town of Travemünde, providing larger ESL estimates compared to those determined using systematic data only. We highlight a potential to underestimate ESLs at Travemünde when historical information is disregarded, due to a period of relatively low ESL activity for the duration of the systematic record.

Wave climatologies from historical and projected simulations of the ACCESS1.0, MIROC5 and CNRM-CM5 Global Circulation Models (GCM) were sourced from the Coordinated Ocean Wave Climate Project (COWCLIP) and downscaled using the SWAN wave model. Biases between GCM's historical simulations and a regional hindcast were assessed, and the two best-performing models (ACCESS1.0, MIROC5) had their projections analysed. A general increase in wave height and period was observed along the south/west, together with a decrease in (Formula presented.) along the north/east coasts. The projected near-term (NEA21C) period shows mostly a (Formula presented.) increase, whilst for the long-term (END21C) period, increased and decreased (Formula presented.) are present. The areas of statistically significant changes are larger in the END21C than in the NEA21C period. The wave direction change is counter-clockwise along the west and clockwise along the east coasts. This study is a first assessment of historical and projected GCM-forced waves along New Zealand and the database we generated can be of great value for renewable energy research, risk assessment and the mitigation of future coastal hazards.

Numerical models for tides, storm surge, and wave runup have demonstrated ability to accurately define spatially varying flood surfaces. However these models are typically too computationally expensive to dynamically simulate the full parameter space of future oceanographic, atmospheric, and hydrologic conditions that will constructively compound in the nearshore to cause both extreme event and nuisance flooding during the 21st century. A surrogate modeling framework of waves, winds, and tides is developed in this study to efficiently predict spatially varying nearshore and estuarine water levels contingent on any combination of offshore forcing conditions. The surrogate models are coupled with a time-dependent stochastic climate emulator that provides efficient downscaling for hypothetical iterations of offshore conditions. Together, the hybrid statistical-dynamical framework can assess present day and future coastal flood risk, including the chronological characteristics of individual flood and wave-induced dune overtopping events and their changes into the future. The framework is demonstrated at Naval Base Coronado in San Diego, CA, utilizing the regional Coastal Storm Modeling System (CoSMoS; composed of Delft3D and XBeach) as the dynamic simulator and Gaussian process regression as the surrogate modeling tool. Validation of the framework uses both in-situ tide gauge observations within San Diego Bay, and a nearshore cross-shore array deployment of pressure sensors in the open beach surf zone. The framework reveals the relative influence of large-scale climate variability on future coastal flood resilience metrics relevant to the management of an open coast artificial berm, as well as the stochastic nature of future total water levels. This work was funded by the Strategic Environmental Research Development Program (DOD/SERDP RC-2644). Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. F. J. Mendez, A. Rueda, and L. Cagigal acknowledge the partial funding from the Spanish Ministry of Science and Innovation, project Beach4cast PID2019-107053RB-I00. The authors thank the Scripps Center for Coastal Studies for their efforts to deploy, recover, and process surf zone pressure sensor data used as validation in this study. The authors thank Melisa Menendez for sharing GOW2 hindcast data for Southern California. The authors thank the sea-level rise projection authors for developing and making the sea-level rise projections available, multiple funding agencies for supporting the development of the projections, and the NASA Sea-Level Change Team for developing and hosting the IPCC AR6 Sea-Level Projection Tool.

Populated coastlines influenced by tropical cyclone (TC) prone areas call for flood risk hazard assessments, including knowledge on the probability of occurrence of major TC-induced significant wave heights. Due to the scarcity of TC historical records, extreme value analyses often rely on fitting generalized extreme value distribution functions to extrapolate longer return periods. This paper describes a methodology that allows to obtain deterministic estimations of the tail probability distribution using long collections of high-fidelity tracks that reproduce similar historical diversity and frequency trends. Given the large dimensionality of the problem (spatiotemporal variability of track geometry and intensity), we implement a track parameterization to easily identify storms in a parametric space. A hybrid approach significantly reduces computational resources by enabling to narrow the number of non-stationary numerically simulated cases forced with vortex-type wind fields parameterized using the Holland Dynamic Model. The proposed surrogate model, HyTCWaves, is trained with a selected subset of maximum significant wave height (MSWH) spatial fields to which a Principal Component Analysis and interpolation functions are performed. Results show a useful approximation of spatialbased regional extreme value distribution of MSWH induced by TCs. The proposed model is applied to the target location of Majuro atoll. This work has been partially funded by the Beach4Cast PID2019-107053RB-I00 project, granted by the Spanish Ministry of Science and Innovation. AR acknowledge the funding from Juan de la Cierva-Incorporación IJC2020-043907-I/ MCIN/AEI/10.13039/501100011033 and “NextGenerationEU”/PRTR .

Wave-induced flooding is a major coastal hazard for the low-lying atolls of the Pacific. These flooding events are expected to increase over time, which may cause significant coastal damage in some locations. Coastal flooding analysis (forensic or forecasted) is particularly challenging in these small islands due to the co-occurrence of several swells and local seas propagating in a complex configuration of archipelagos. Therefore, assessing the contribution of swells and wind seas on the flooding hazards that threaten the atoll islands requires the spectral characterization of the wave climate, since integrated wave parameters do not accurately represent the wave conditions in these environments. On the other hand, the relative short records of wave conditions, represent only a small fraction of the possible range of combinations that could produce a wave-induced flooding event. For these reasons, we propose the analysis of all the spectral energy arriving toward a study site, by isolating and parameterizing each swell train. Then, taking into account the link with large-scale climatic patterns (i.e., El Niño Southern Oscillation), we present a new multi-modal seas emulator capable of generating infinitely long time series of synthetic individual swell trains and seas. This new climate-based emulator allows a better understanding of swell behavior in the Pacific, and the generation of multimodal wave conditions to populate the historical records as a key point to perform robust coastal flood risk assessments considering climate variability.

In this study, a general methodology that is based on numerical models and statistical analysis is developed to assist in the definition of marine litter cleanup and mitigation strategies at an estuarine scale. The methodology includes four main steps: k-means clustering to identify representative metocean scenarios; dynamic downscaling to obtain high-resolution drivers with which to force a transport model; numerical transport modelling to generate a database of potential litter trajectories; and a statistical analysis of this database to obtain probabilities of litter accumulation. The efficacy of this methodology is demonstrated by its application to an estuary along the northern coast of Spain by comparing the numerical results with field data. The necessary criteria to ensure its applicability to any other estuary were provided. As the main conclusion, the developed methodology successfully assesses the litter distribution in estuaries with minimum computational effort.

Shorelines respond to a number of "drivers" operating on a variety of time-scales. For some time-scales (e.g., seasonal), the driver-shoreline relationship is often evident; however, at longer timescales (e.g., multiannual), the shoreline changes may be superimposed on changes at shorter time-scales and thus are diffcult to identify. Here, we predict shoreline evolution from storm events to decadal timescales, using a novel approach based on the Complete Ensemble Empirical Mode Decomposition. This approach identifies and links the primary time-scales in the model drivers (large-scale sea level pressure [SLP] and/or waves) with the same time-scales in the shoreline position. The multiscale approach reproduced shoreline changes at two beaches more skillfully than a common shoreline model when SLP and wave information were used in combination. In addition, the analysis can be applied to climate indices, providing the opportunity to link longer time-scales with climate patterns (e.g., El Niño Southern Oscillation).

A high resolution partitioned wave hindcast of New Zealand waters is presented together with validation results against 9 buoy deployments that are representative of the local wave climate. An analysis of integrated and partitioned mean wave parameters was conducted together with a study of the correlation between wave height anomalies and atmospheric indices. A directional spatial analysis was also performed to identify and quantify wave systems that are not detectable by in situ buoys or hindcast integrated parameters. The presented study outlines a framework of how partitions can be analyzed in order to improve our understanding of the local wave climate in terms of wind-sea and swell waves.

Reliable predictions and accompanying uncertainty estimates of coastal evolution on decadal to centennial time scales are increasingly sought. So far, most coastal change projections rely on a single, deterministic realization of the unknown future wave climate, often derived from a global climate model. Yet, deterministic projections do not account for the stochastic nature of future wave conditions across a variety of temporal scales (e.g., daily, weekly, seasonally, and interannually). Here, we present an ensemble Kalman filter shoreline change model to predict coastal erosion and uncertainty due to waves at a variety of time scales. We compare shoreline change projections, simulated with and without ensemble wave forcing conditions by applying ensemble wave time series produced by a computationally efficient statistical downscaling method. We demonstrate a sizable (site-dependent) increase in model uncertainty compared with the unrealistic case of model projections based on a single, deterministic realization (e.g., a single time series) of the wave forcing. We support model-derived uncertainty estimates with a novel mathematical analysis of ensembles of idealized process models. Here, the developed ensemble modeling approach is applied to a well-monitored beach in Tairua, New Zealand. However, the model and uncertainty quantification techniques derived here are generally applicable to a variety of coastal settings around the world.

Variability in global wave climate has been observed to occur in response to climate changes influencing the wave-generating zones. This highlights the need for an improved understanding of long-term wave-climate cycles, considering the multi-decadal variability of the atmospheric patterns and large-scale climate drivers. In this study, a novel use of the morphology of former shorelines preserved in beach-foredune ridges was applied to reconstruct changes in predominant wave directions in the Subtropical South Atlantic during the Late Holocene. A 3km wide semi-continuous sequence of beach-foredune ridges preserved within the Pinheira Strandplain (Santa Catarina State, Brazil) was mapped in order to extract the orientation of the former shorelines and derive a 3000-year record of inferred mean wave direction. The mean wave direction series was compared to ~1000 years of decadal means of mid-latitude mean sea-level pressure gradients (∆MSLP) and zonal westerly wind velocities estimated from the CESM1-CAM5 “Last Millennium Ensemble (LME)”, and to 2000 years of air-surface temperature anomalies for Southern Hemisphere. Results showed that multi-centennial cycles of oscillation in predominant wave direction occurred in accordance with stronger (weaker) South Atlantic mid-latitude mean sea-level pressure and zonal westerlies winds, favouring wave generation zones in higher (lower) latitudes and consequent southerly (easterly) wave climate dominance. It was identified the Southern Annular Mode as the main climate driver responsible for these changes, responding for 43% of the variance in the Subtropical South Atlantic atmospheric patterns in the last 1000 years. Long-term variations in interhemispheric air-surface temperature offsets, coincident with oscillations in wave direction, may have influenced wave-generation patterns similarly to the seasonal behaviour observed over recent decades. Periods of relatively warmer Southern Hemisphere (SH) as compared with Northern Hemisphere (NH) (e.g., during 400–800 CE and the Little Ice Age) favours the predominance of easterly wave energy flux along the eastern South American coast, whereas periods with equivalent NH-SH temperature anomalies (e.g., Medieval Warm Period) or with colder relative SH (last ~150 years) support an increase in the influence of the southerly wave energy flux over the South Atlantic. These results provide a novel geomorphic proxy for paleoenvironmental reconstructions and present new insights into the role of multi-decadal to multi-centennial climate variability on controlling coastal ocean wave climate.

Tropical coral reef-lined coasts are exposed to storm wave-driven flooding. In the future, flood events during storms are expected to occur more frequently and to be more severe due to sea-level rise, changes in wind and weather patterns, and the deterioration of coral reefs. Hence, disaster managers and coastal planners are in urgent need of decision-support tools. In the short-term, these tools can be applied in Early Warning Systems (EWS) that can help to prepare for and respond to impending storm-driven flood events. In the long-term, future scenarios of flooding events enable coastal communities and managers to plan and implement adequate risk-reduction strategies. Modeling tools that are used in currently available coastal flood EWS and future scenarios have been developed for open-coast sandy shorelines, which have only limited applicability for coral reef-lined shorelines. The tools need to be able to predict local sea levels, offshore waves, as well as their nearshore transformation over the reefs, and translate this information to onshore flood levels. In addition, future scenarios require long-term projections of coral reef growth, reef composition, and shoreline change. To address these challenges, we have formed the UFORiC (Understanding Flooding of Reef-lined Coasts) working group that outlines its perspectives on data and model requirements to develop EWS for storms and scenarios specific to coral reef-lined coastlines. It reviews the state-of-the-art methods that can currently be incorporated in such systems and provides an outlook on future improvements as new data sources and enhanced methods become available.

International audience; Beaches around the world continuously adjust to daily and seasonal changes in wave and tide conditions, which are themselves changing over longer timescales. Different approaches to predict multi-year shoreline evolution have been implemented; however, robust and reliable predictions of shoreline evolution are still problematic even in short-term scenarios (shorter than decadal). Here we show results of a modelling competition, where 19 numerical models (a mix of established shoreline models and machine learning techniques) were tested using data collected for tairua beach, new Zealand with 18 years of daily averaged alongshore shoreline position and beach rotation (orientation) data obtained from a camera system. in general, traditional shoreline models and machine learning techniques were able to reproduce shoreline changes during the calibration period (1999-2014) for normal conditions but some of the model struggled to predict extreme and fast oscillations. During the forecast period (unseen data, 2014-2017), both approaches showed a decrease in models' capability to predict the shoreline position. this was more evident for some of the machine learning algorithms. A model ensemble performed better than individual models and enables assessment of uncertainties in model architecture. Research-coordinated approaches (e.g., modelling competitions) can fuel advances in predictive capabilities and provide a forum for the discussion about the advantages/disadvantages of available models. Quantitative prediction of beach erosion and recovery is essential to planning resilient coastal communities with robust strategies to adapt to erosion hazards. Over the last decades, research efforts to understand and predict shoreline evolution have intensified as coastal erosion is likely to be exacerbated by climatic changes 1-5. The social and economic burden of changes in shoreline position are vast, which has inspired development of a growing variety of models based on different approaches and techniques; yet current models can fail (e.g. predicting erosion in accreting conditions). The challenge for shoreline models is, therefore, to provide reliable, robust and realistic predictions of change, with a reasonable computational cost, applicability to a broad variety of systems, and some quantifiable assessment of the uncertainties.

Wave climate is referred to the long-term statistics of sea state parameters. It is the statistical characterization of a sequence of pseudo-stationary wave conditions, which are characterised by the aggregated parameters of significant wave height, mean (or peak) period and mean direction, over several years. The magnitude and frequency of these parameters condition the beach shape and evolution. The understanding of how these parameters change based on weather and climate conditions helps to understand and predict changes on the beach morphodynamics. Wave climate varies along the globe, although it is possible to find areas with similar wave forcing and therefore similar expected responses in their shorelines.

The environment of the sub-Antarctic ecoregion of Magallanes is highly heterogenous due to the influence of three oceans (Pacific, Atlantic, and Southern) and the effects of postglacial events such as the Last Glacial Maximum. In the sub-Antarctic ecoregion of Magallanes, the presence of two morphotypes of Durvillaea antarctica has recently been recorded that are related to the specific hydrodynamic configuration of the sites in the region. This study investigates the photosynthetic activity and pigment composition during two periods of the year in these two morphotypes of D. antarctica. One of them has broad and laminar fronds and occurs in wave-protected environments, while the other morphotype is characterized by cylindrical and elongated fronds and inhabits wave-exposed environments. The adult specimens of the “elongated-cylindrical” morphotype were collected in Seno Otway (53.1° S, 71.5° W) and the specimens of the “laminar” morphotype in Bahia el Aguila, San Isidro (53.7° S, 70.9° W). ETRmax, α, and Ek as parameters of the ETR-E curves were higher for the “laminar” than the “elongated-cylindrical” morphotype, resulting in significant values. The concentration of fucoxanthin was statistically higher for the morphotype “laminar” compared to the morphotype “elongated-cylindrical.” Both morphotypes exhibited different photosynthetic activities, perhaps attributed to their morphology, floatation capacity, and environment.

Deterministic dynamical modeling of future climate conditions and associated hazards, such as flooding, can be computationally-expensive if century-long time-series of waves, sea level variations, and overland flow patterns are simulated. To alleviate some of the computational costs, local impacts of individual coastal storms can be explored by first identifying particular events or scenarios of interest and dynamically modeling those events in detail. In this study, an efficient approach to selecting storm events for subsequent deterministic detailed modeling of coastal flooding is presented. The approach identifies locally relevant scenarios derived from regional datasets spanning long time-periods and covering large geographic areas. This is done by identifying storm events from global climate models using a robust, yet computationally simple approach for calculating total water level proxies at the shore, assuming a linear superposition of the important processes contributing to the overall total water level. Clustering of the total water level time-series is used to define coherent coastal cells where similar return period water level extrema occur in response to region-wide storms. Results show that the more severe but rare coastal flood events (e.g., the 100-year (yr) event) typically occur from the same storm across the region, but that a number of different storms are responsible for the less severe but more frequent local extreme water levels (e.g., the 1-yr event). This new ‘storm selection’ approach is applied to the Southern California Bight, a region of varying shoreline orientations that is subject to wave refraction across complex bathymetry, and shadowing, focusing, diffraction, and dissipation of wave energy by islands. Results indicate that wave runup dominates total water level extremes at this study site, highlighting the importance of downscaling global-scale models to nearshore waves when seeking accurate projections of local coastal hazards in response to climate change.

Rising seas coupled with ever increasing coastal populations present the potential for significant social and economic loss in the 21st century. Relatively short records of the full multidimensional space contributing to total water level coastal flooding events (astronomic tides, sea level anomalies, storm surges, wave run‐up, etc.) result in historical observations of only a small fraction of the possible range of conditions that could produce severe flooding. The Time‐varying Emulator for Short‐ and Long‐Term analysis of coastal flood hazard potential is presented here as a methodology capable of producing new iterations of the sea‐state parameters associated with the present‐day Pacific Ocean climate to simulate many synthetic extreme compound events. The emulator utilizes weather typing of fundamental climate drivers (sea surface temperatures, sea level pressures, etc.) to reduce complexity and produces new daily synoptic weather chronologies with an auto‐regressive logistic model accounting for conditional dependencies on the El Niño Southern Oscillation, the Madden‐Julian Oscillation, seasonality, and the prior two days of weather progression. Joint probabilities of sea‐state parameters unique to simulated weather patterns are used to create new time series of the hypothetical components contributing to synthetic total water levels (swells from multiple directions coupled with water levels due to wind setup, temperature anomalies, and tides). The Time‐varying Emulator for Short‐ and Long‐Term analysis of coastal flood hazard potential reveals the importance of considering the multivariate nature of extreme coastal flooding, while progressing the ability to incorporate large‐scale climate variability into site specific studies assessing hazards within the context of predicted climate change in the 21st century.

Gainza, J.; Rueda, A.; Camus, P.; Tomas, A.; Mendez, F.J.; Sano, M., and Tomlinson, R., 2018. A meta-modelling approach for estimating long-term wave run-up and total water level on beaches. Wave run-up is defined as the maximum vertical extent of wave up-rush on a beach or structure above the sea water level from wave breaking. Wave run-up is responsible for beach and dune erosion and can be an important component of coastal flooding. Run-up can be estimated using either empirical formulations or sophisticated wave-breaking models with high computational demand. On the other hand, meta-models are efficient approximations of physical-process models that enable researchers to obtain long-term time series of wave dynamics. These hybrid models are developed by combining statistical techniques and numerical models. In this study, a methodology to transform offshore sea conditions to long-term time series of wave run-up is described. The methodology combined the construction of two meta-models of offs...

Shifts in the frequency of typical meteorological patterns in an ocean basin, over interannual to decadal time scales, cause shifts in the patterns of wave generation. Therefore, ocean basin-scale climate shifts produce shifts in the wave climates affecting the coastlines of the basin. We present a hybrid methodology for downscaling observed (or predicted) climate shifts into local nearshore wave climates and then into the associated coastline responses. A series of statistical analyses translate observed (or predicted) distributions of meteorological states into the deep water wave climate affecting a coastal region and dynamical modeling combined with statistical analyses transform the deep water wave climate into the nearshore wave climate affecting a particular coastline. Finally, dynamical modeling of coastline evolution hindcasts (or predicts) how coastline shapes respond to climate shifts. As a case study, we downscale from meteorological hindcast in the North Atlantic basin since 1870 to the responses of the shape of the coast of the Carolinas, USA. We test the hindcasts using shoreline change rates calculated from historical shorelines, because shifts in coastline shape equate to changes in the alongshore pattern of shoreline change rates from one historical period to another. Although limited by the availability of historical shorelines (and complicated by historical inlet openings), the observations are consistent with the predicted signal of ocean basin-scale climate change. The hybrid downscaling methodology, applied to the output of global climate models, can be used to help forecast future patterns of shoreline change related to future climate change scenarios. This work was partially funded by the “U.S. National Science Foundation, Coupled Natural Human Systems Program.” J. A. A. Antolínez is indebted to the MEC (Ministerio de Educación, Cultura y Deporte, Spain) for the funding provided in the FPU (Formación del Profesorado Universitario) studentship (BOE-A-2013-12235). J. A. A. Antolínez and F. J. Méndez acknowledge the support of the Spanish “Ministerio de Economia y Competitividad” under grant BIA2014-59643-R.

One approach to identifying and mapping the state of marine biophysical conditions is the identification of large-scale ecological units for which conditions are similar and the strategies of management may also be similar. Because biological processes are difficult to directly record over large areas, abiotic characteristics are used as surrogate parameters. In this work, the Mediterranean Sea was classified into homogeneous spatial areas based on abiotic variables. Eight parameters were selected based on salinity, sea surface temperature, photosynthetically active radiation, sea-wave heights and depth variables. The parameters were gathered in grid points of 0.5° spatial resolution in the open sea and 0.125° in coastal areas. The typologies were obtained by data mining the eight parameters throughout the Mediterranean and combining two clustering techniques: self-organizing maps and the k-means algorithm. The result is a division of the Mediterranean Sea into seven typologies. For these typologies, the classification recognizes differences in temperature, salinity and radiation. In addition, it separates coastal from deep areas. The influence of river discharges and the entrance of water from other seas are also reflected. These results are consistent with the ecological requirements of the five studied seagrasses ( Posidonia oceanica , Zostera marina , Zostera noltei , Cymodocea nodosa , Halophila stipulacea ), supporting the suitability of the resulting classification and the proposed methodology. The approach thus provides a tool for the sustainable management of large marine areas and the ability to address not only present threats but also future conditions, such as climate change.

Hydrological variability over seasonal and multi-annual timescales strongly shapes the ecological structure and functioning of floodplain ecosystems. The current IPCC climate scenario foresees an increase in the frequency of extreme events. This, in conjunction with other anthropogenic disturbances (e.g., river regulation or land-use changes) poses a serious threat to the natural functioning of these ecosystems. In this study we aimed to i) evaluate the long-term variability of the flooded area of the third largest floodplain lake in the Brazilian Pantanal using remote sensing techniques, and ii) analyze the possible factors influencing this variability. Changes in open-water and riparian floodplain-wetland vegetation areas were mapped by applying an ad hoc-developed remote-sensing method (including a newly developed normalized water index, NWI) to 221 Landsat-Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+) images, acquired between 1984 and 2011. Added to the lake's natural swing between riparian floodplain-wetland vegetation expansion and retraction, our analyses revealed large interannual changes, grouped into three main periods within the studied time interval. Moreover, our results indicate that this floodplain-lake system is losing open-water area, paired with an increase in riparian floodplain-wetland vegetation. The system's long-term dynamics are not all climate related, but are the result of a combination of drivers. The start of the Manso dam's operation upstream of the studied system, and the subsequent river regulation because of the dam operation, coupled with climatic oscillation appear to be responsible for the observed changes. However, other factors which were not considered in this study might also be important in this process and contributing to the reduction of the system's resilience to droughts (e.g., land-use changes). This study illustrates the serious conservation risks that the Pantanal faces in the near future, given the current climate-change scenario and the accumulation of dam building projects in this region.

This study presents the design, development, scheme and field validation of an early-alert ocean wave system. It is designed to automate, improve, analyse, design and manage the daily construction activities of any harbour at construction stage. The objective is threefold: a) maximise construction safety, with regards to well-known hazards which occur during construction, especially breakwaters that interact with high-energy sea states, b) optimise the transport, by means of specialised vessels, of the refill material, and c) to minimise the construction delay and disruption on a daily basis, thanks to short-term construction forecasting (96 h). The system, known as PATO, offers short-term sea states characteristics, at any point near harbour structures, and relevant wave-structure interaction parameters at any harbour construction stage. The system is able to assist harbour project managers by providing accurate ocean wave data through a user-friendly interface.

The coastal marine ecosystems of the sub-Antarctic ecoregion of Magallanes in southern Chile are strongly characterized by environmental heterogeneity with a unique and high biodiversity of benthic macroalgae. The kelp Durvillaea antarctica (Phaeophyceae) is abundant in the intertidal in this region. Two morphotypes, one with elongated-cylindrical (“morphotype EC”) and one with laminar (“morphotype L”) blades, have been collected from a wave-protected site at the Strait of Magellan and the wave-exposed shores of the isolated Otway Sound. Morphometric and physiological (by chlorophyll fluorescence) analyses showed significant differences between the two morphotypes regarding the morphology, the maximum electron transport rates (ETRmax), and the chlorophyll c concentration. These morphological and physiological differences can be linked to their hydrodynamic environment. The presence of two different species in the region of Magallanes based on the measured parameters is discussed. This study may be useful for future genetic and biotechnological investigations and draws attention to D. antarctica as a species of commercial value among the marine resources of Chile.

In order to promote the use of sub-Antarctic macroalgae as food, three species of seaweeds, Macrocystis pyrifera, Pyropia columbina, and Durvillaea antarctica, were used as ingredients in a variety of dishes such as cochayuyo bread, cochayuyo hamburgers, cochayuyo fettuccine, huiro fritters, huiro breadsticks, and luche-parsley pesto. Subsequently, the nutritional value of each of the dishes was analyzed. The chemical composition, including carbohydrates, proteins, lipids, sodium, and the fatty acid (FA) content, was examined. In general, the prepared food varieties demonstrated low lipid contents (4.1 to 8.5% dry weight, dw), moderate concentrations of protein (6.9 to 12.7% dw), and highly variable contents of total dietary fiber (1.3 to 18.9% dw). The fatty acid patterns were different for all prepared dishes, and the FA profile exhibited a beneficial contribution of polyunsaturated fatty acids (PUFAs), with a preponderance of linoleic acid and α-linolenic. The chemical composition of the dishes was compared to that of similar and commonly consumed food like white bread, hamburgers, fettuccine made of dough enriched with eggs, fritters, breadsticks, and green pesto. The results suggest that macroalgae can be used as an ingredient in prepared food where they may contribute successfully to a more balanced diet and to maintain the organoleptic characteristics in commercially attractive prepared food like cochayuyo bread, cochayuyo hamburger, cochayuyo fettuccine, huiro breadsticks, and luche-parsley pesto.

Characterization of wave climate by bulk wave parameters is insufficient for many coastal studies, including those focused on assessing coastal hazards and long-term wave climate influences on coastal evolution. This issue is particularly relevant for studies using statistical downscaling of atmospheric fields to local wave conditions, which are often multimodal in large ocean basins (e.g. the Pacific). Swell may be generated in vastly different wave generation regions, yielding complex wave spectra that are inadequately represented by a single set of bulk wave parameters. Furthermore, the relationship between atmospheric systems and local wave conditions is complicated by variations in arrival time of wave groups from different parts of the basin. Here, we address these two challenges by improving upon the spatiotemporal definition of the atmospheric predictor used in statistical downscaling of local wave climate. The improved methodology separates the local wave spectrum into “wave families,” defined by spectral peaks and discrete generation regions, and relates atmospheric conditions in distant regions of the ocean basin to local wave conditions by incorporating travel times computed from effective energy flux across the ocean basin. When applied to locations with multimodal wave spectra, including Southern California and Trujillo, Peru, the new methodology improves the ability of the statistical model to project significant wave height, peak period, and direction for each wave family, retaining more information from the full wave spectrum. This work is the base of statistical downscaling by weather types, which has recently been applied to coastal flooding and morphodynamic applications. This article is protected by copyright. All rights reserved.

Ocean-basin-scale climate variability produces shifts in wave climates and water levels affecting the coastlines of the basin. Here we present a hybrid shoreline change?foredune erosion model (A COupled CrOss-shOre, loNg-shorE, and foreDune evolution model, COCOONED) intended to inform coastal planning and adaptation. COCOONED accounts for coupled longshore and cross-shore processes at different timescales, including sequencing and clustering of storm events, seasonal, interannual, and decadal oscillations by incorporating the effects of integrated varying wave action and water levels for coastal hazard assessment. COCOONED is able to adapt shoreline change rates in response to interactions between longshore transport, cross-shore transport, water level variations, and foredune erosion. COCOONED allows for the spatial and temporal extension of survey data using global data sets of waves and water levels for assessing the behavior of the shoreline at multiple time and spatial scales. As a case study, we train the model in the period 2004?2014 (11 years) with seasonal topographic beach profile surveys from the North Beach Sub-cell (NBSC) of the Columbia River Littoral Cell (Washington, USA).We explore the shoreline response and foredune erosion along 40 km of beach at several timescales during the period 1979?2014 (35 years), revealing an accretional trend producing reorientation of the beach, cross-shore accretional, and erosional periods through time (breathing) and alternating beach rotations that are correlated with climate indices. J. A. A. Antolínez and F. J. Méndez acknowledge the support of the Spanish “Ministerio de Economia y Competitividad” under Grant BIA2014-59643-R.

International audience; Predictions of shoreline change are of great societal importance, but models tend to be tested and tuned for the specific site of interest. To overcome this issue and test the ability of numerical models to simulate shoreline change over the medium scale (order of years) we have organized a non-competitive competition where participants were given data to train their model (1999-2014) and data to predict seasonal to inter-annual future changes (2014-2017). Participants were shown the observed shoreline changes only after submission of their modelling results. Overall, 19 numerical models were tested, the vast majority falling in the broad categories of "hybrid models" or "machine learning". Models were able to reproduce the mean characteristics of shoreline change but often failed to reproduce the observed rapid changes induced by storms.

Wave-induced flooding is a major coastal hazard on tropical islands fronted by coral reefs. The variability of shape, size, and physical characteristics of the reefs across the globe make it difficult to obtain a parameterization of wave run-up, which is needed for risk assessments. Therefore, we developed the HyCReWW metamodel to predict wave run-up under a wide range of reef morphometric and offshore forcing characteristics. Due to the complexity and high dimensionality of the problem, we assumed an idealized one-dimensional reef profile, characterized by seven primary parameters. XBeach Non-Hydrostatic was chosen to create the synthetic dataset, and Radial Basis Functions implemented in MATLAB® were chosen for interpolation. Results demonstrate the applicability of the metamodel to obtain fast and accurate results of wave run-up for a large range of intrinsic reef morphologic and extrinsic hydrodynamic forcing parameters, offering a useful tool for risk management and early warning systems.

The assessment of coastal flood risk on a reef-lined coastline presents several challenges. From the probabilistic side, we need to consider all possible events that could occur in the system, taking into account the different contributions of waves, storm surges, and tides that contribute to the total water level. To estimate reliable flood extents, we need to accurately model the complex wave processes that occur across the reef. To explore the multivariate nature of coastal flooding, we rely on a climate emulator that accounts for climate variability and simulates time series of all the variables involved. Due to the computational constraints to numerically simulate thousands of events, we explore the feasibility of using a recently developed tool, the HyCreWW (Hybrid Coral Reef Wave and Water level) meta-model to estimate wave run-up and flooding extents. Limitation of a 1D assessment are analyzed and results compared with 2D modeling.

We present an ensemble Kalman filter shoreline change model to predict long-term coastal evolution due to waves, sea-level rise, and other natural and anthropogenic processes responsible for sediment transport. The model utilizes ensemble simulations to improve both reliability (via data assimilation) and uncertainty quantification. Coastal change projections exhibit significant differences when simulated with and without ensemble wave conditions. Many long-term coastal change projections rely on a single realization of the future wave climate, often derived from atmospheric conditions simulated by a global climate model. Yet, the single realization approach does not account for the stochastic nature of future wave conditions across a variety of temporal scales (e.g., daily, weekly, seasonally, and interannually). Here, by applying ensemble time series of wave forcing conditions, we demonstrate a sizable increase in model uncertainty compared with the unrealistic case of model projections based on a single realization (e.g., a single time series) of wave forcing.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/V-VwC-cIiQ0

A regression-guided classification is implemented in statistical downscaling models based on weather types for downscaling multivariate wave climate and modelling extreme events. The semi-supervised method classifies the atmospheric circulation conditions (predictor) and the estimations from a regression model linking the circulation with local marine climate (filtered predictand). A weighted factor controls the influence of the predictor and predictand in the weather patterns to improve the performance of the classification to reflect local marine climate characteristics. In addition to the analysis of the variance explained by the predictor and the predictand, the selection of the optimal value of the weighted factor is based on the predictand response in order to avoid subjectivity in the solution. The statistical models using the guided classification are applied in the North Atlantic. The new technique reduces the dispersion of the multivariate predictand within weather types and improves the model skill to downscale waves and to reproduce extremes.

Coastal floods often coincide with large waves, storm surge and tides. Thus, joint probability methods are needed to properly characterize extreme sea levels. This work introduces a statistical downscaling framework for multivariate extremes that relates the non-stationary behavior of coastal flooding events to the occurrence probability of daily weather patterns. The proposed method is based on recently-developed weather-type methods to predict extreme events (e.g., significant wave height, mean wave period, surge level) from large-scale sea-level pressure fields. For each weather type, variables of interest are modeled using Generalized Extreme Value (GEV) distributions and a Gaussian copula for modelling the interdependence between variables. The statistical dependence between consecutive days is addressed by defining a climate-based extremal index for each weather type. This work allows attribution of extreme events to specific weather conditions, enhancing the knowledge of climate-driven coastal flooding.

Seasonal environmental changes may significantly influence macroalgal diversity and biomass. Cryptogam species richness increases towards the poles, especially in sub-Antarctic environments. Yet, subpolar seaweed biodiversity and ecophysiology remain understudied even though it is essential for the management and sustainability of endemic species of significant economic interest (e.g., Gigartina skottsbergii). We evaluate the seasonality and ecophysiology of the different life phases of the rhodophyte G. skottsbergii by analyzing variation in fluorescence yield and photosynthetic pigment composition. There were significant seasonal differences in maximum relative electron transport rate (rETRmax) between gametophyte and tetrasporophyte phase, and between reproductive and vegetative specimens. Photosynthetic efficiency (α) was not significantly different between reproductive states of G. skottsbergii. We found significant differences in mean concentrations of allophycocyanin (APC), phycocyanin (PC), and chlorophyll a (Chl a) between gametophyte and tetrasporophyte phases. Results obtained provide new insight into seasonal acclimation patterns of an ecologically important species, which can be used for the design of appropriate management and cultivation strategies of G. skottsbergii towards the restoration of natural populations in fragile, subpolar regions where some of the last, relatively undisturbed communities of G. skottsbergii still remain.

17 pages, 6 figures, 1 table, supporting information https://dx.doi.org/10.1002/2015PA002883, data will be available at the Scientific Earth Drilling Information Service (http://sedis.iodp.org), Pangaea website (http://www.pangaea.de), and NOAA Paleoclimatology (https://www.ncdc.noaa.gov/data-access/paleoclimatology-data)

Extreme wave heights are climate-related events. Therefore, special attention should be given to the large-scale weather patterns responsible for wave generation in order to properly understand wave climate variability. We propose a classification of weather patterns to statistically downscale daily significant wave height maxima to a local area of interest. The time-dependent statistical model obtained here is based on the convolution of the stationary extreme value model associated to each weather type. The interdaily dependence is treated by a climate-related extremal index. The model's ability to reproduce different time scales (daily, seasonal, and interannual) is presented by means of its application to three locations in the North Atlantic: Mayo (Ireland), La Palma Island, and Coruna (Spain).

In recent years, wave reanalyses have become popular as a powerful source of information for wave climate research and engineering applications. These wave reanalyses provide continuous time series of offshore wave parameters; nevertheless, in coastal areas or shallow water, waves are poorly described because spatial resolution is not detailed. By means of wave downscaling, it is possible to increase spatial resolution in high temporal coverage simulations, using forcing from wind and offshore wave databases. Meanwhile, the reanalysis wave databases are enough to describe the wave climate at the limit of simulations; wind reanalyses at an adequate spatial resolution to describe the wind structure near the coast are not frequently available. Remote sensing synthetic aperture radar (SAR) has the ability to detect sea surface signatures and estimate wind fields at high resolution (up to 300 m) and high frequency. In this work a wave downscaling is done on the northern Adriatic Sea, using a hybrid methodology and global wave and wind reanalysis as forcing. The wave fields produced were compared to wave fields produced with SAR winds that represent the two dominant wind regimes in the area: the bora (ENE direction) and sirocco (SE direction). Results show a good correlation between the waves forced with reanalysis wind and SAR wind. In addition, a validation of reanalysis is shown. This research demonstrates how Earth observation products, such as SAR wind fields, can be successfully up-taken into oceanographic modeling, producing similar downscaled wave fields when compared to waves forced with reanalysis wind.