Cristina Izaguirre

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.

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.

Statistical wave climate projections for coastal impact assessments

The authors acknowledge the support of the Spanish Ministerio de Economia y Competitividad (MINECO) and European Regional Development Fund (FEDER) under Grant BIA2015-70644-R (MINECO/FEDER, UE). The authors are grateful to Nicolas Ripoll for his help in the performing the statistical simulations. The DAC data is produced by CLS Space Oceanography Division and distributed by Aviso, with support from Cnes (http://www.aviso.altimetry.fr/). The CMIP5 sea level pressure data are available at http://cmip-pcmdi.llnl.gov/cmip5/data_portal.html. Mean sea level projections are available at ftp://ftp.icdc.zmaw.de/ar5_sea_level_rise/Global multimodel wave climate projections are obtained at 1.0° × 1.0° scale from 30 Coupled Model Intercomparison Project Phase 5 (CMIP5) global circulation model (GCM) realizations. A semi-supervised weather-typing approach based on a characterization of the ocean wave generation areas and the historical wave information from the recent GOW2 database are used to train the statistical model. This framework is also applied to obtain high resolution projections of coastal wave climate and coastal impacts as port operability and coastal flooding. Regional projections are estimated using the collection of weather types at spacing of 1.0°. This assumption is feasible because the predictor is defined based on the wave generation area and the classification is guided by the local wave climate. The assessment of future changes in coastal impacts is based on direct downscaling of indicators defined by empirical formulations (total water level for coastal flooding and number of hours per year with overtopping for port operability). Global multimodel projections of the significant wave height and peak period are consistent with changes obtained in previous studies. Statistical confidence of expected changes is obtained due to the large number of GCMs to construct the ensemble. The proposed methodology is proved to be flexible to project wave climate at different spatial scales. Regional changes of additional variables as wave direction or other statistics can be estimated from the future empirical distribution with extreme values restricted to high percentiles (i.e., 95th, 99th percentiles). The statistical framework can also be applied to evaluate regional coastal impacts integrating changes in storminess and sea level rise.

Introducing marine climate variability into life cycle management of coastal and offshore structures

Coastal and offshore structures are subject to a life cycle process including several different phases. From the planning and design phase to the re-use or demolition phase marine climate information is extremely important to achieve cost effective functionality and technical quality. The complete life cycle may span over several decades what requires site-specific marine climate information at different time scales and including its natural variability. We present an integrated methodology to generate marine climate information relevant for life cycle management of coastal and offshore structures including short-term, seasonal, long-terms and very longterm information. An application of the methodology to a harbour is presented.

Multi-sectoral, high-resolution assessment of climate change consequences of coastal flooding

In the context of growing concern about the threat of flooding posed by climate change in coastal areas, the Spanish plan for coastal adaptation to climate change gave rise to stringent requirements on risk consequence estimates at the regional scale O (100 km). Within this framework, we propose a methodology that combines high space-time resolution climate information (reanalysis databases and projections), local data on exposure that accounts for the most relevant sectors, site-specific vulnerability functions, and flood risk consequence valuation, gridded at 5 m. This approach involves efficient multiple-forcing flood modeling, in which the connection between climate change and potential inundation is primarily established through the definition of a total water level index. This research tackles challenging issues, including the importance of incorporating the effects of existing coastal defenses and local wave effects in port areas, dealing with data at different spatial scales and sectors in an integrated way, and the impact of discounting. The results provide insights into the possible consequences of inaction for a range of future scenarios based on changes in climate and socio-economics over the most relevant sectors. With the goal of prioritizing adaptive action and the efficient assignment of funds, we propose a weight-based integration of the sectoral value-at-risk through the application of Bayesian techniques and expert judgment. The methodology described here was applied to a pilot case study on the coast of Asturias in northern Spain.

Is the extreme wave climate in the NE Pacific increasing

Recent studies reveal important trends in mean values, high percentiles and extreme events of significant wave height (SWH) in the northeast (NE) Pacific using buoy measurements. However, the spatial variability of these trends has not been well addressed since the sparse and uneven coverage of wave buoys makes it difficult to assess spatial patterns. In this work, we analyze the long-term variability of extreme significant wave height along the northeast Pacific using two time-dependent extreme value models and three different datasets from buoys, satellite missions and hindcast databases. Significant long-term trends of extreme SWH have been detected reinforcing the previous studies. We also demonstrate an impact of El Niño on extreme wave heights in the NE Pacific as well as important correlations with mid-latitudinal climate patterns (e.g. PNA index).

Spatial and temporal variability of nearshore wave energy resources along Spain: Methodology and results

A global framework, than can be applied worldwide, has been developed to determine waver energy resources at shallow water with high spatial and long temporal resolution in order to analyze the seasonality, interannual variability (correlation of wave power with climate-related indices, such as the North Atlantic Oscillation) and spatial long-term trends along the Spanish coast. The proposed methodology combines instrumental and numerical databases, wave numerical models (dynamical downscaling) and sophisticated mathematical tools (statistical downscaling), which includes a selection algorithm and an interpolation technique to reconstructed time series. The validation of the results in deep water and coastal buoys locations confirm the ability of the methodology developed.