Melisa Menéndez

Impacts on the Deep-Sea Ecosystem by a Severe Coastal Storm

Major coastal storms, associated with strong winds, high waves and intensified currents, and occasionally with heavy rains and flash floods, are mostly known because of the serious damage they can cause along the shoreline and the threats they pose to navigation. However, there is a profound lack of knowledge on the deep-sea impacts of severe coastal storms. Concurrent measurements of key parameters along the coast and in the deep-sea are extremely rare. Here we present a unique data set showing how one of the most extreme coastal storms of the last decades lashing the Western Mediterranean Sea rapidly impacted the deep-sea ecosystem. The storm peaked the 26th of December 2008 leading to the remobilization of a shallow-water reservoir of marine organic carbon associated with fine particles and resulting in its redistribution across the deep basin. The storm also initiated the movement of large amounts of coarse shelf sediment, which abraded and buried benthic communities. Our findings demonstrate, first, that severe coastal storms are highly efficient in transporting organic carbon from shallow water to deep water, thus contributing to its sequestration and, second, that natural, intermittent atmospheric drivers sensitive to global climate change have the potential to tremendously impact the largest and least known ecosystem on Earth, the deep-sea ecosystem.

Analyzing Monthly Extreme Sea Levels with a Time-Dependent GEV Model

A statistical model to analyze different time scales of the variability of extreme high sea levels is presented. This model uses a time-dependent generalized extreme value (GEV) distribution to fit monthly maxima series and is applied to a large historical tidal gauge record (San Francisco, California). The model allows the identification and estimation of the effects of several time scales—such as seasonality, interdecadal variability, and secular trends—in the location, scale, and shape parameters of the probability distribution of extreme sea levels. The inclusion of seasonal effects explains a large amount of data variability, thereby allowing a more efficient estimation of the processes involved. Significant correlation with the Southern Oscillation index and the nodal cycle, as well as an increase of about 20% for the secular variability of the scale parameter have been detected for the particular dataset analyzed. Results show that the model is adequate for a complete analysis of seasonal-to-interannual sea level extremes providing time-dependent quantiles and confidence intervals.

Evaluating the performance of CMIP3 and CMIP5 global climate models over the north-east Atlantic region

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.

A weather‐type statistical downscaling framework for ocean wave climate

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.

ESTELA: a method for evaluating the source and travel time of the wave energy reaching a local area

The description of wave climate at a local scale is of paramount importance for offshore and coastal engineering applications. Conditions influencing wave characteristics at a specific location cannot, however, be fully understood by studying only local information. It is necessary to take into account the dynamics of the ocean surface over a large ‘upstream’ wave generation area. The goal of this work is to provide a methodology to easily characterize the area of influence of any particular ocean location worldwide. Moreover, the developed method is able to characterize the wave energy and travel time in that area. The method is based on a global scale analysis using both geographically and physically based criteria. The geographic criteria rely on the assumption that deep water waves travel along great circle paths. This limits the area of influence by neglecting energy that cannot reach a target point, as its path is blocked by land. The individual spectral partitions from a global wave reanalysis are used to reconstruct the spectral information and apply the physically based criteria. The criteria are based on the selection of the fraction of energy that travels towards the target point for each analysed grid point. The method has been tested on several locations worldwide. Results provide maps that inform about the relative importance of different oceanic areas to the local wave climate at any target point. This information cannot be inferred from local parameters and agrees with information from other approaches. The methodology may be useful in a number of applications, such as statistical downscaling, storm tracking and grid definition in numerical modelling.

The effect of temporal dependence on the estimation of the frequency of extreme ocean climate events

The term ‘extreme ocean climate estimation’ refers to the assessment of the statistical distribution of extreme oceanographical geophysical variables. Components of the ocean climate are variables, such as the storm surge, wind velocity and significant wave height. Important characteristics of extreme ocean climate are the frequencies of the exceedances of ocean climate variables over selected thresholds. Assuming that exceedances are statistically independent of each other, their frequencies can be estimated using non-homogeneous Poisson processes. However, exceedances often exhibit temporal dependency because of the tendency of storms to gather in clusters. We assess the effect of these dependencies on the estimation of the rate of occurrence of extreme events. Using a database built under the HIPOCAS European project, which covers the Western Mediterranean Sea, we compare the performance of the non-homogeneous Poisson process approach versus a new model that allows for temporal dependency. We show that the latter outperforms the former in terms of the resulting goodness of fit and significance of the parameters involved.

A method for finding the optimal predictor indices for local wave climate conditions

In this study, a method to obtain local wave predictor indices that take into account the wave generation process is described and applied to several locations. The method is based on a statistical model that relates significant wave height with an atmospheric predictor, defined by sea level pressure fields. The predictor is composed of a local and a regional part, representing the sea and the swell wave components, respectively. The spatial domain of the predictor is determined using the Evaluation of Source and Travel-time of wave Energy reaching a Local Area (ESTELA) method. The regional component of the predictor includes the recent historical atmospheric conditions responsible for the swell wave component at the target point. The regional predictor component has a historical temporal coverage (n-days) different to the local predictor component (daily coverage). Principal component analysis is applied to the daily predictor in order to detect the dominant variability patterns and their temporal coefficients. Multivariate regression model, fitted at daily scale for different n-days of the regional predictor, determines the optimum historical coverage. The monthly wave predictor indices are selected applying a regression model using the monthly values of the principal components of the daily predictor, with the optimum temporal coverage for the regional predictor. The daily predictor can be used in wave climate projections, while the monthly predictor can help to understand wave climate variability or long-term coastal morphodynamic anomalies.

Pseudo-optimal parameter selection of non-stationary generalized extreme value models for environmental variables

Recent advances in the description of environmental and geophysical extreme events allow incorporating smooth time variations for the parameters of the GEV distribution using harmonic functions, long-term trends and covariates (North Atlantic Oscillation, El Nino, etc.). Most of the proposed models rely on the maximum likelihood estimation method for a given parameterization. However, finding the best parameter selection for each case is not an easy task, since the number of possible combinations grows exponentially with the number of possible parameters to be considered. This problem is usually overcome by assuming simplified models based on experience or using heuristic approaches, which are computationally very expensive. In this paper, a method to obtain a pseudo-optimal parameterization using the maximum likelihood method is presented. The proposed algorithm automatically selects the parameters which minimize the Akaike Information Criterion within an iterative scheme, including one parameter at a time based on a score perturbation criteria. The process is repeated until no further improvement in the objective function is achieved. The proposed method is applied for the adjustment of monthly maximum significant wave height at different locations around the Atlantic coast and results are compared with those obtained using an existing heuristic approach, showing an important reduction in computational time and comparable results in terms of fitting quality.

Changes in the mesoscale variability and in extreme sea levels over two decades as observed by satellite altimetry

A data set of precise radar altimeter sea surface heights obtained from the same 10 day repeat ground track has been analyzed to determine the magnitude of change in the ocean “mesoscale” variability over two decades. Trends in the standard deviation of sea surface height variability each year are found to be small (typically ∼0.5 percent/yr) throughout the global ocean. Trends in positive and negative extreme sea level in each region are in general found to be similar to those of mean sea level, with some small regional exceptions. Generalized Extreme Value Distribution (GEVD) analysis also demonstrates that spatial variations in the statistics of extreme positive sea levels are determined largely by the corresponding spatial variations in mean sea level changes, and are related to regional modes of the climate system such as the El Nino-Southern Oscillation. Trends in the standard deviation of along-track sea level gradient variability are found to be close to zero on a global basis, with regional exceptions. Altogether our findings suggest an ocean mesoscale variability that displays little change when considered over an extended period of two decades, but that is superimposed on a spatially and temporally varying signal of mean sea level change.

A wind chart to characterize potential offshore wind energy sites

Offshore wind industry needs to improve wind assessment in order to decrease the uncertainty associated to wind resource and its influence on financial requirements. Here, several features related to offshore wind resource assessment are discussed, such as input wind data, estimation of long-term and extreme wind statistics, the wind profile and climate variations.This work proposes an analytical method to characterize wind resource. Final product is a wind chart containing useful wind information that can be applied to any offshore sites. Using long-term time series of meteorological variables (e.g. wind speed and direction at different heights), the methodology is applied to five pilot sites in different countries along European Atlantic corridor and it is used to describe and compare offshore wind behavior.