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This work represents the first attempt to study the behavior of sea level extreme events nearshore using satellite altimetry data. A novel approach based on the application of a nonstationary extreme model to nontidal residual monthly maxima from remote data is presented. With the aim of countering the lack of accuracy, an extreme scale factor to correct satellite data is defined. The extreme scale factor depends on local morphology and its definition makes use of the information provided by 14 tide gauges along the North American East Coast. The methodology is validated using eight additional tide gauge records along the study region. Low differences on values associated to high return periods using satellite and tide gauge data input are found for all the validated locations. Besides, a regional study of the climate variability of extreme sea levels from satellite is also presented. The found climate patterns strongly agree with the historical climate variations from in situ data. Results show a great potential of this approach, especially at those locations where in situ information is not available. ; The work reported here was partially supported by the Spanish Government through the grant PORTIO (BIA2015-70644-R); project ECLISEA, part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by UC-IHC, HZG, BRGM, NCSRD, and CNRS and cofunding by the European Union (Grant 690462), and the US DOD Strategic Environmental Research and Development Program (SERDP Project RC-2644).

ABSTRACT: Adaptation requires planning strategies that consider the combined effect of climatic and non-climatic drivers, which are deeply uncertain. This uncertainty arises from many sources, cascades and accumulates in risk estimates. A prominent trend to incorporate this uncertainty in adaptation planning is through adaptive approaches such as the dynamic adaptive policy pathways (DAPP). We present a quantitative DAPP application for coastal erosion management to increase its utilisation in this field. We adopt an approach in which adaptation objectives and actions have continuous quantitative metrics that evolve over time as conditions change. The approach hinges on an adaptation information system that comprises hazard and impact modelling and systematic monitoring to assess changing risks and adaptation signals in the light of adaptation pathway choices. Using an elaborated case study, we force a shoreline evolution model with waves and storm surges generated by means of stochastic modelling from 2010 to 2100, considering uncertainty in extreme weather events, climate variability and mean sea-level rise. We produce a new type of adaptation pathways map showing a set of 90-year probabilistic trajectories that link changing objectives (e.g., no adaptation, limit risk increase, avoid risk increase) and nourishment placement over time. This DAPP approach could be applied to other domains of climate change adaptation bringing a new perspective in adaptive planning under deep uncertainty. ; Alexandra Toimil acknowledges the financial support from the FENIX Project funded by the Government of Cantabria. This research was also funded by the Spanish Government through the grant RISKCOADAPT (BIA2017-89401-R).

ABSTRACT:Future projections of coastal erosion, which are one of the most demanded climate services in coastal areas, are mainly developed using top-down approaches. These approaches consist of undertaking a sequence of steps that include selecting emission or concentration scenarios and climate models, correcting models bias, applying downscaling methods, and implementing coastal erosion models. The information involved in this modelling chain cascades across steps, and so does related uncertainty, which accumulates in the results. Here, we develop long-term multi-ensemble probabilistic coastal erosion projections following the steps of the top-down approach, factorise, decompose and visualise the uncertainty cascade using real data and analyse the contribution of the uncertainty sources (knowledge-based and intrinsic) to the total uncertainty. We find a multi-modal response in long-term erosion estimates and demonstrate that not sampling internal climate variability?s uncertainty sufficiently could lead to a truncated outcomes range, affecting decision-making. Additionally, the noise arising from internal variability (rare outcomes) appears to be an important part of the full range of results, as it turns out that the most extreme shoreline retreat events occur for the simulated chronologies of climate forcing conditions. We conclude that, to capture the full uncertainty, all sources need to be properly sampled considering the climate-related forcing variables involved, the degree of anthropogenic impact and time horizon targeted. ; AT acknowledges the financial support from the FENIX Project by the Government of Cantabria. This research was also funded by the Spanish Government via the grant RISKCOADAPT (BIA2017-89401-R).

In this work, it is evaluated the accessibility of a floating platform, by means of a catamaran vessel equipped with a fender. The two bodies are modelled as a constrained multi-body system in the frequency domain. Transfer functions are calculated for the motions and forces of the system. Access is possible when no slip conditions occur at the fender, and when the relative rotations between the two bodies are within certain tolerance limits. Four response variables are defined to impose such conditions. In a short-term sea state the extreme maximum crest height of these variables is computed, assuming that response crest heights follow a Rayleigh distribution. Each of the extreme values is compared to a specific threshold, to determine whether access is possible or not. Accessibility is calculated for a sample platform located off the coast of Scotland using hindcast data for the period 1980-2013. Average accessibility resulted to be 23.7%. A strong seasonality is ascertained, together with a large variation of accessibility, due to the variability of wave climate. ; The authors would like to acknowledge the projects "OceaNET", which received funding from the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no 607656, and "Plataformas multiuso para aplicaciones offshore" of the University of Cantabria (code: 56.JS05.64061).

ABSTRACT: Coastal flood risks are rising rapidly. We provide high resolution estimates of the economic value of mangroves forests for flood risk reduction every 20 km worldwide. We develop a probabilistic, process-based valuation of the effects of mangroves on averting damages to people and property. We couple spatially-explicit 2-D hydrodynamic analyses with economic models, and find that mangroves provide flood protection benefits exceeding $US 65 billion per year. If mangroves were lost, 15 million more people would be flooded annually across the world. Some of the nations that receive the greatest economic benefits include the USA, China, India and Mexico. Vietnam, India and Bangladesh receive the greatest benefits in terms of people protected. Many (>45) 20-km coastal stretches particularly those near cities receive more than $US 250 million annually in flood protection benefits from mangroves. These results demonstrate the value of mangroves as natural coastal defenses at global, national and local scales, which can inform incentives for mangrove conservation and restoration in development, climate adaptation, disaster risk reduction and insurance ; We thank the supporting provided by the World Bank and the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) on the basis of a decision adopted by the German Bundestag. We also acknowledge financial support from the Spanish Ministry of Economy and Innovation (BIA2014-59718-R). Authors are grateful to the useful contributions provided by Borja González Reguero (University of Santa Cruz California), Antonio Espejo, Sheila Abad and Pedro Díaz Simal (IH Cantabria). Pelayo Menéndez acknowledge to the FPI grant from the Spanish Ministry of Economy and Innovation (BES-2015-074343). The authors acknowledge to the National Plan "RISKOADAPT" from the Spanish Ministry of Sciences, Innovation and Universities (BIA2017-89401-R).

Traditional approaches for assessing wave climate variability have been broadly focused on aggregated or statistical parameters such as significant wave height, wave energy flux, or mean wave direction. These studies, although revealing the major general modes of wave climate variability and trends, do not take into consideration the complexity of the wind-wave fields. Because ocean waves are the response to both local and remote winds, analyzing the directional full spectra can shed light on atmospheric circulation not only over the immediate ocean region, but also over a broad basin scale. In this work, the authors use a pattern classification approach to explore wave climate variability in the frequency–direction domain. This approach identifies atmospheric circulation patterns of the sea level pressure from the 31-yr long Climate Forecast System Reanalysis (CFSR) and wave spectral patterns of two selected buoys in the North Atlantic, finding one-to-one relations between each synoptic pattern (circulation type) and each spectral wave energy distribution (spectral type). Even in the absence of long-wave records, this method allows for the reconstruction of longterm wave spectra to cover variability at several temporal scales: daily, monthly, seasonal, interannual, decadal, long-term trends, and future climate change projections. ; The authors are grateful to Puertos del Estado (Spanish Ministry of Public Works and Infrastructures) for providing us the instrumental buoy data. This work was partially funded by the project IMAR21 (CT M2010-15009) from the Spanish Government.

Coral reefs can provide significant coastal protection benefits to people and property. Here we show that the annual expected damages from flooding would double, and costs from frequent storms would triple without reefs. For 100-year storm events, flood damages would increase by 91% to $US 272 billion without reefs. The countries with the most to gain from reef management are Indonesia, Philippines, Malaysia, Mexico, and Cuba; annual expected flood savings exceed $400?M for each of these nations. Sea-level rise will increase flood risk, but substantial impacts could happen from reef loss alone without better near-term management. We provide a global, process-based valuation of an ecosystem service across an entire marine biome at (sub)national levels. These spatially explicit benefits inform critical risk and environmental management decisions, and the expected benefits can be directly considered by governments (e.g., national accounts, recovery plans) and businesses (e.g., insurance). ; We gratefully acknowledge support from the World Bank Wealth Accounting and Valuation of Ecosystems (WAVES) Program, the Lyda Hill Foundation, Science for Nature and People Partnership, Lloyd's Tercentenary Research Foundation, a Pew Fellowship in Marine Conservation to MWB, the German International Climate Initiative (IKI) of the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) and the Spanish Ministry of Economy and Innovation (BIA2014-59718- R).

Wave climate characterization at different time scales (long-term historical periods, seasonal prediction, and future projections) is required for a broad number of marine activities. Wave reanalysis databases have become a valuable source of information covering time periods of decades. A weather-type approach is proposed to statistically downscale multivariate wave climate over different time scales from the reanalysis long-term period. The model calibration is performed using historical data of predictor (sea level pressure) and predictand (sea-state parameters) from reanalysis databases. The storm activity responsible for the predominant swell composition of the local wave climate is included in the predictor definition. N-days sea level pressure fields are used as predictor. K-means algorithm with a postorganization in a bidimensional lattice is used to obtain weather patterns. Multivariate hourly sea states are associated with each pattern. The model is applied at two locations on the east coast of the North Atlantic Ocean. The validation proves the model skill to reproduce the seasonal and interannual variability of monthly sea-state parameters. Moreover, the projection of wave climate onto weather types provides a multivariate wave climate characterization with a physically interpretable linkage with atmospheric forcings. The statistical model is applied to reconstruct wave climate in the last twentieth century, to hindcast the last winter, and to project wave climate under climate change scenarios. The statistical approach has been demonstrated to be a useful tool to analyze wave climate at different time scales. ; The work was partly funded by the project iMar21 (CTM2010-15009) from the Spanish Government and the FP7 European projects CoCoNet (287844) and Mermaid (288710).