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A quantile-based bias-correction method is applied to a seven-member dynamic ensemble of global wave climate simulations with the aim of reducing the significant wave height HS, mean wave period Tm, and mean wave direction (MWD) biases, in comparison with theERA5 reanalysis. The corresponding projected changes toward the end of the twenty-first century are assessed. SevenCMIP5EC-EARTHruns (single forcing) were used to force sevenwavemodel(WAM)realizations (singlemodel), following theRCP8.5 scenario (single scenario). The biases for the 1979-2005 reference period (present climate) are corrected using the empirical Gumbel quantile mapping and empirical quantile mapping methods. The same bias-correction parameters are applied to the HS, Tm (and wave energy fluxPw), andMWDfuture climate projections for the 2081-2100 period. The bias-corrected projected changes show increases in the annualmeanHS (14%), Tm (6.5%), and Pw (30%) in the Southern Hemisphere and decreases in the Northern Hemisphere (mainly in the North Atlantic Ocean) that are more pronounced during local winter. For the upper quantiles, the bias-corrected projected changes aremore striking during local summer, up to 120%, for Pw. After bias correction, the magnitude of the HS, Tm, and Pw original projected changes has generally increased. These results, albeit consistent with recent studies, show the relevance of a quantile-based bias-correction method in the estimation of the future projected changes in swave climate that is able to deal with the misrepresentation of extreme phenomena, especially along the tropical and subtropical latitudes. [ABSTRACT FROM AUTHOR]

In recent years, the impact of climate change on sea surface waves has received increasingly more attention by the climate community. Indeed, ocean waves reaching the coast play an important role in several processes concerning coastal communities, such as inundation and erosion. However, regional downscaling at the high spatial resolution necessary for coastal studies has received less attention. Here, we present a novel framework for regional wave climate projections and its application in the European region. Changes in the wave dynamics under different scenarios in the Northeast Atlantic Ocean and the Mediterranean are analyzed. The multi-model projection methodology is based on a statistical downscaling approach. The statistical relation between the predictor (atmospheric conditions) and the predictand (multivariate wave climate) is based on a weather type (WT) classification. This atmospheric classification is developed by applying the k-means clustering technique over historical offshore sea level pressure (SLP) fields. Each WT is linked to sea wave conditions from a wave hindcast. This link is developed by associating atmospheric conditions from reanalysis with multivariate local waves. This predictor–predictand relationship is applied to the daily SLP fields from global climate models (GCMs) in order to project future changes in regional wave conditions. The GCMs used in the multi-model projection are selected according to skill criteria. The application of this framework uses CMIP5-based wave climate projections in Europe. The low computational requirements of the statistical approach allow a large number of GCMs and climate change scenarios to be studied. Consistent with previous works on global wave climate projections, the estimated changes from the regional wave climate projections show a general decrease in wave heights and periods in the Atlantic Europe for the late twenty-first century. The regional projections, however, allow a more detailed spatial characterization of the projected changes under different climate scenarios. For example, changes in significant wave heights for the RCP8.5 scenario for the 2070–2099 time period indicate a general decrease of about 10 cm in Southern Europe (Portuguese, Spanish and French coasts) with respect to present conditions. This decrease is due to a higher occurrence of dominant and moderate Azores high pressure systems over the North Atlantic Ocean and a decrease in the persistence of intense low pressure systems at high latitudes. [ABSTRACT FROM AUTHOR]

The estimation of possible impacts related to climate change on the wave climate is subject to several levels of uncertainty. In this work, we focus on the uncertainties inherent in the method applied to project the wave climate using atmospheric simulations. Two approaches are commonly used to obtain the regional wave climate: dynamical and statistical downscaling from atmospheric data. We apply both approaches based on the outputs of a global climate model (GCM), ARPEGE-CLIMAT, under three possible future scenarios (B1, A1B and A2) of the Fourth Assessment Report, AR4 (IPCC, 2007), along the French coast and evaluate their results for the wave climate with a high level of precision. The performance of the dynamical and the statistical methods is determined through a comparative analysis of the estimated means, standard deviations and monthly quantile distributions of significant wave heights, the joint probability distributions of wave parameters and seasonal and interannual variability. Analysis of the results shows that the statistical projections are able to reproduce the wave climatology as well as the dynamical projections, with some deficiencies being observed in the summer and for the upper tail of the significant wave height. In addition, with its low computational time requirements, the statistical downscaling method allows an ensemble of simulations to be calculated faster than the dynamical method. It then becomes possible to quantify the uncertainties associated with the choice of the GCM or the socio-economic scenarios, which will improve estimates of the impact of wave climate change along the French coast. [ABSTRACT FROM AUTHOR]

One of the main sources of uncertainty in estimating climate projections affected by global warming is the choice of the global climate model (GCM). The aim of this study is to evaluate the skill of GCMs from CMIP3 and CMIP5 databases in the north-east Atlantic Ocean region. It is well known that the seasonal and interannual variability of surface inland variables (e.g. precipitation and snow) and ocean variables (e.g. wave height and storm surge) are linked to the atmospheric circulation patterns. Thus, an automatic synoptic classification, based on weather types, has been used to assess whether GCMs are able to reproduce spatial patterns and climate variability. Three important factors have been analyzed: the skill of GCMs to reproduce the synoptic situations, the skill of GCMs to reproduce the historical inter-annual variability and the consistency of GCMs experiments during twenty-first century projections. The results of this analysis indicate that the most skilled GCMs in the study region are UKMO-HadGEM2, ECHAM5/MPI-OM and MIROC3.2(hires) for CMIP3 scenarios and ACCESS1.0, EC-EARTH, HadGEM2-CC, HadGEM2-ES and CMCC-CM for CMIP5 scenarios. These models are therefore recommended for the estimation of future regional multi-model projections of surface variables driven by the atmospheric circulation in the north-east Atlantic Ocean region. [ABSTRACT FROM AUTHOR]

Through assessment of wind-wave directional spectra, we provide a comprehensive explanation of the projected 21st Century changes in the global wind-wave climatology under a high green-house emissions scenario. Using a seven-member wave climate projection ensemble, we estimate wave climate changes by comparing present and projected climatologies. Clustering techniques are applied to define regional patterns of change with homogenous behavior. The underlying mechanisms behind the changes are also explored by exploiting the relationship between the wave generation area and the effective energy flux propagating toward a target location. A robust transition from positive to negative trends in Southern Ocean westerly swells is observed around 45°S. The increasing signal found in the southernmost swells propagates north beyond 30°N, contributing significantly to the projected changes in tropical regions such as the tropical southern Atlantic and tropical southeastern Pacific. Results highlight the great complexity of the Pacific Ocean due to the convergence of multiple wave systems with different geneses. In the northern basins, the combined effect of the ice melting and a poleward shift of the storm track drives an increase of the northernmost westerly swells. This is countered by a decreasing trend projected in the main wave systems propagating in the North Atlantic Ocean. A poleward shift of trade-induced waves due to the Hadley cell expansion can also be observed globally, causing a clear dipole change pattern in the tropical southern Atlantic and tropical Indian oceans. • The effect of climate change on wave climate is assessed based on directional spectra. • The climate change signals of the main swell systems are described. • Sixteen ocean regions with a similar pattern of change are found. [ABSTRACT FROM AUTHOR]

Menendez, M., Mendez, F. J., and Losada, I. J. 2009. Forecasting seasonal to interannual variability in extreme sea levels. – ICES Journal of Marine Science, 66: 1490–1496.A statistical model to predict the probability of certain extreme sea levels occurring is presented. The model uses a time-dependent generalized extreme-value (GEV) distribution to fit monthly maxima series, and it is applied for a particular time-series record for the Atlantic Ocean (Newlyn, UK). The model permits the effects of seasonality, interannual variability, and secular trends to be identified and estimated in the probability distribution of extreme sea levels. These factors are parameterized as temporal functions (linear, quadratic, exponential, and periodic functions) or covariates (for instance, the North Atlantic Oscillation index), which automatically yield the best-fit model for the variability present in the data. A clear pattern of within-year variability and significant effects resulting from astronomical modulations (the nodal cycle and perigean tides) are detected. Modelling different time-scales helps to gain a better understanding of recent secular trends regarding extreme climate events, and it allows predictions to be made (for example, up to 2020) about the probability of the future occurrence of a particular sea level. [ABSTRACT FROM PUBLISHER]

This study focuses on the assessment of systematic biases in wave climate simulations, exploring different bias correction methods commonly used for climate impact variables (e.g. precipitation, temperature), and on the analysis of bias corrected wave climate projections. Four different bias correction methods are analyzed, two of which are applied to an 8-member dynamic multi-forcing global wave climate ensemble, to mitigate the significant wave height (H S) bias. The GOW2 (Global Ocean Waves 2) global wave hindcast is used as reference data for the calibration. Assuming that the statistical properties of the present climate biases are maintained in the future, these biases can be corrected by applying a bias correction model based on the reference data. Thus, the impact of climate change in the bias corrected future H S climate, is also investigated. A bias corrected 8-member ensemble is analyzed for the 2081–2100 period, under the RCP8.5 scenario. The results indicate the relevance of bias correction in both the estimation of ensemble mean H S projected changes towards the end of the 21st century, and in the ensemble spread magnitude. Outcomes support the need for a quantile based bias correction, able to deal with extreme events, which have a disproportionate impact in coastal processes. • Four different bias correction methods are described. • Two bias correction methods are applied to the H S wave parameter. • Future H S climate projections are corrected considering present climate biases. • The bias corrected H S projections show relevant differences compared to the originals. • Differences between corrected and original H S projections exceed 20% in the tropics. [ABSTRACT FROM AUTHOR]

Shoreline recession due to the combined effect of waves, surges, tides and sea level rise (SLR) is increasingly becoming a major threat to beaches, one of the main seaside tourist destinations worldwide. Given such an uncertain future climate and the climate-sensitive nature of many decisions that affect the long term, there is a growing need to shift current approaches towards probabilistic frameworks able to consider uncertainty. We develop a methodology to assess climate change-driven coastal erosion risk based on the IPCC risk framework (IPCC, 2014), in which risk results from the interplay of hazard, represented by the coastal dynamics, exposure, described by physical and socioeconomic settings, and vulnerability, associated with the susceptibility and adaptive capacity of the system. The methodology consists of a multitiered process in which these components are developed individually but are finally integrated to derive expected losses. A statistical method to generate beach erosion hazards must take into account that this impact involves two or more random variables (i.e., significant wave height, sea level), and that the erosion process depends on storm duration and antecedent beach conditions, which relies in turn on beach recovery between storms. Multiple simulations of a process-based approach that combine cross section–based equilibrium models and long-shore sinks allow the reconstruction of thousands of potential shoreline evolutions over the 21st century (Toimil et al., 2017).Exposure is defined by the users' preferences (i.e., when they would rather go to the beach), expressed in terms of effective utilization rates and their purchasing power, as an indicator of how much they value the time for recreation (Toimil et al., 2018). Concerning vulnerability, aspects related to the typology, the quality (i.e., water, sand and services), and the number and distribution of access points and parking facilities are considered (Toimil et al., 2018).We present a regional approach for the assessment of erosion risks triggered by climate-related extreme events and SLR. The framework is applied regionally over the Asturian coast, a 345 km coastline on the Cantabrian Sea (North of Spain), where the risk of loss of beach recreation as a proxy for tourism is provided in probabilistic terms.ReferencesIPCC (2014) Summary for policymakers. In Climate Change 2014: Impacts, Adaptation and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [eds. CB Field al.]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2014, pp. 1-32.Toimil A, Losada IJ, Camus P, Diaz-Simal P. (2017). Managing coastal erosion under climate change at the regional scale. Coastal Engineering, 128, 106–122. Toimil A, Diaz-Simal P, Losada IJ, Camus P (2018) Estimating the risk of loss of beach recreation value under climate change. Tourism Management 68:387-400. [ABSTRACT FROM AUTHOR]