The Experts below are selected from a list of 35040 Experts worldwide ranked by ideXlab platform
Fernando J Mendez - One of the best experts on this subject based on the ideXlab platform.
-
a multimodal Wave spectrum based approach for statistical downscaling of local Wave Climate
Journal of Physical Oceanography, 2017Co-Authors: Christie A. Hegermiller, Paula Camus, Jorge Perez, Ana Rueda, Jose A. A. Antolínez, Li H. Erikson, Patrick L. Barnard, Fernando J MendezAbstract:AbstractCharacterization 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., Pacific Ocean). 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, this study addresses these two challenges by improving upon the spatiotemporal definition of the atmospheric predictor used in the statistical downscaling of local Wave Climate. The improved methodology separates the local Wave spectrum into “w...
-
A Multimodal Wave Spectrum–Based Approach for Statistical Downscaling of Local Wave Climate
Journal of Physical Oceanography, 2017Co-Authors: Christie A. Hegermiller, Paula Camus, Jorge Perez, Ana Rueda, Jose A. A. Antolínez, Li H. Erikson, Patrick L. Barnard, Fernando J MendezAbstract:AbstractCharacterization 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., Pacific Ocean). 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, this study addresses these two challenges by improving upon the spatiotemporal definition of the atmospheric predictor used in the statistical downscaling of local Wave Climate. The improved methodology separates the local Wave spectrum into “w...
-
a multimodal Wave spectrum based approach for statistical downscaling of local Wave Climate
Journal of Geophysical Research, 2016Co-Authors: Paula Camus, Jorge Perez, Ana Rueda, Christie A. Hegermiller, Jose A. A. Antolínez, Li H. Erikson, Patrick L. Barnard, Fernando J MendezAbstract: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.
-
A weather‐type statistical downscaling framework for ocean Wave Climate
Journal of Geophysical Research: Oceans, 2014Co-Authors: Paula Camus, Melisa Menendez, Fernando J Mendez, Inigo J Losada, Cristina Izaguirre, Antonio Espejo, Verónica Cánovas, Jorge Perez, Ana Rueda, Raúl MedinaAbstract: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.
-
a weather type statistical downscaling framework for ocean Wave Climate
Journal of Geophysical Research, 2014Co-Authors: Paula Camus, Melisa Menendez, Fernando J Mendez, Inigo J Losada, Cristina Izaguirre, Antonio Espejo, Verónica Cánovas, Jorge Perez, Ana Rueda, Raúl MedinaAbstract: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.
Paula Camus - One of the best experts on this subject based on the ideXlab platform.
-
On the need of bias correction methods for Wave Climate projections
Global and Planetary Change, 2020Co-Authors: Gil Lemos, Melisa Menendez, Paula Camus, Mark Hemer, Alvaro Semedo, Mikhail Dobrynin, Pedro M. A. MirandaAbstract:Abstract 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 (HS) 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 HS 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 HS 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.
-
Robustness and uncertainties in global multivariate wind-Wave Climate projections
Nature Climate Change, 2019Co-Authors: Joao Morim, Paula Camus, Mark Hemer, Alvaro Semedo, Xiaolan L Wang, Nick Cartwright, Claire Trenham, Ian R. Young, Lucy Bricheno, M. Casas-pratAbstract:Understanding Climate-driven impacts on the multivariate global wind-Wave Climate is paramount to effective offshore/coastal Climate adaptation planning. However, the use of single-method ensembles and variations arising from different methodologies has resulted in unquantified uncertainty amongst existing global Wave Climate projections. Here, assessing the first coherent, community-driven, multi-method ensemble of global Wave Climate projections, we demonstrate widespread ocean regions with robust changes in annual mean significant Wave height and mean Wave period of 5–15% and shifts in mean Wave direction of 5–15°, under a high-emission scenario. Approximately 50% of the world’s coastline is at risk from Wave Climate change, with ~40% revealing robust changes in at least two variables. Furthermore, we find that uncertainty in current projections is dominated by Climate model-driven uncertainty, and that single-method modelling studies are unable to capture up to ~50% of the total associated uncertainty.
-
Statistical Wave Climate projections for coastal impact assessments
Earth's Future, 2017Co-Authors: Paula Camus, Melisa Menendez, Cristina Izaguirre, Antonio Espejo, I.j. Losada, Jorge PerezAbstract: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.
-
a multimodal Wave spectrum based approach for statistical downscaling of local Wave Climate
Journal of Physical Oceanography, 2017Co-Authors: Christie A. Hegermiller, Paula Camus, Jorge Perez, Ana Rueda, Jose A. A. Antolínez, Li H. Erikson, Patrick L. Barnard, Fernando J MendezAbstract:AbstractCharacterization 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., Pacific Ocean). 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, this study addresses these two challenges by improving upon the spatiotemporal definition of the atmospheric predictor used in the statistical downscaling of local Wave Climate. The improved methodology separates the local Wave spectrum into “w...
-
A Multimodal Wave Spectrum–Based Approach for Statistical Downscaling of Local Wave Climate
Journal of Physical Oceanography, 2017Co-Authors: Christie A. Hegermiller, Paula Camus, Jorge Perez, Ana Rueda, Jose A. A. Antolínez, Li H. Erikson, Patrick L. Barnard, Fernando J MendezAbstract:AbstractCharacterization 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., Pacific Ocean). 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, this study addresses these two challenges by improving upon the spatiotemporal definition of the atmospheric predictor used in the statistical downscaling of local Wave Climate. The improved methodology separates the local Wave spectrum into “w...
Nobuhito Mori - One of the best experts on this subject based on the ideXlab platform.
-
TRANSITIONAL IMPACTS OF ENSO ON Wave Climate IN COASTAL REGIONS
Coastal Engineering Proceedings, 2020Co-Authors: Itxaso Oderiz, Nobuhito Mori, Thomas Mortlock, Edgar Mendoza, Rodolfo SilvaAbstract:Amongst all the factors involved in coastal risk assessment, Climate variability is key, due to its potential for modifying the coast, particularly through increased seasonal risk of erosion-flooding on the coast (Toimil et al. 2020; Wahl and Plant 2015). The principal driver of interannual variability of the Wave Climate around the world is El Nio-Southern Oscillation (ENSO). Many researches have focused on the analysis of this phenomenon globally ( Stopa and Cheung 2014), its impacts on regional Wave Climate (Barnard et al. 2015, 2017; Oderiz et al. 2020; Reguero, Mendez, and Losada 2013) and their local coastal effects ( Mortlock and Goodwin 2016). This interest in ENSO impacts in Wave Climate is motivated by its capacity to cause coastal erosion (Barnard et al. 2015). Particularly, the temporal and spatial transition of ENSO is nowadays a current issue (Ha et al. 2012). On the worlds coasts, the ENSO impacts delay is not yet fully understood, nor integrated into engineering practices.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/DZbOYztPYW0
-
FUTURE CHANGES IN SPECTRAL Wave Climate AROUND JAPAN UNDER GLOBAL WARMING
Coastal Engineering Proceedings, 2020Co-Authors: Tomoya Shimura, Nobuhito MoriAbstract:Future projections of ocean Wave Climate related with global warming has been conducted for the assessment of Climate change impacts on coastal disaster, beach morphology, and coastal structure design. In this study, we conduct the high-resolution future Wave Climate projection in the East Asia region and detail analysis on Wave Climate based on two-dimensional Wave spectra in addition to conventional Wave statistics (significant Wave height). Future changes in Wave height, period and direction can be discussed consistently owing to analysis on the mean Wave spectra.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/FEYPZFRr5SQ
-
High-resolution Wave Climate hindcast around Japan and its spectral representation
Coastal Engineering, 2019Co-Authors: Tomoya Shimura, Nobuhito MoriAbstract:Abstract A 34-year high-resolution ocean surface Wave Climate hindcast around Japan is presented, and the Wave Climate around Japan is examined from a spectral point of view. The spectral Wave model is forced by the sea surface wind data obtained from the JRA-55 atmospheric reanalysis. The Wave Climate hindcast is validated by comparing it with buoy observations around Japan. The correlation coefficient of the significant Wave height is 0.9. The correlation coefficient of the mean Wave period is 0.8 for the Japan Sea and Pacific side of eastern Japan and 0.7–0.8 for the Pacific side of western Japan. The Wave Climate is represented by temporal-mean two-dimensional Wave spectra. The characteristics of the spectral Wave Climate are investigated by classifying them into three types. Distinctive characteristics of the mean Wave spectra along the Japan Sea are narrower band widths for both the period and direction. The mean Wave spectra along the Pacific side of eastern Japan are characterized by swells with a spectral peak propagating from the northeast. The distinctive spectral features corresponding to the Pacific side of western Japan are bi-modal peaks with long-period components typically generated by typhoons. The variability in the monthly mean Wave spectra is examined using an empirical orthogonal function (EOF) analysis. In the winter, it is found that the Wave height variability (EOF 1st mode) is related to Wave direction variability (EOF 2nd mode) at locations 1000 km apart via the sea level pressure variance over the North Pacific. In the summer, the EOF 1st mode corresponding to the Pacific side locations is dominated by the variability in typhoon-generated swells. The spectral Wave Climate representation provides new insight into the Wave Climate around Japan with clear relationships between the atmospheric conditions, the Wave height, direction, and period.
-
Wave Climate VARIABILITY AND RELATED Climate INDICES
Coastal Engineering Proceedings, 2018Co-Authors: Nobuhito Mori, Risako Kishimoto, Tomoya ShimuraAbstract:Climate change is highly expected to give significant impact on coastal hazards and environment. The future projections of Wave Climate under global warming scenarios have been carried out and shows changes in Wave heights depending on the regions (e.g., Hemer et al., 2013). Beside the long-term trends of Wave Climate, annual to decadal changes are also important to understand variability. For example, the North Atlantic Oscillation (NAO) is highly correlated to monthly mean Wave height along the western European coast. However, variability of Wave Climate is not well understood over the globe, quantitatively. Additionally, the standard coastal engineers regard stationary process for Wave environment for solving coastal problems. This study analyzes global Wave Climate variability for the last half century based on principal component analysis of atmospheric forcing (sea surface winds U10 and sea level pressure P) and Wave hindcast.
-
Future Projection of Ocean Wave Climate: Analysis of SST Impacts on Wave Climate Changes in the Western North Pacific
Journal of Climate, 2015Co-Authors: Tomoya Shimura, Nobuhito Mori, Hajime MaseAbstract:AbstractChanges in ocean surface Waves elicit a variety of impacts on coastal environments. To assess the future changes in the ocean surface Wave Climate, several future projections of global Wave Climate have been simulated in previous studies. However, previously there has been little discussion about the causes behind changes in the future Wave Climate and the differences between projections. The objective of this study is to estimate the future changes in mean Wave Climate and the sensitivity of the Wave Climate to sea surface temperature (SST) conditions in an effort to understand the mechanism behind the Wave Climate changes by specifically looking at spatial SST variation. A series of Wave Climate projections forced by surface winds from the MRI-AGCM3.2 were conducted based on SST ensemble experiments. The results yield future changes in annual mean Wave height that are within about ±0.3 m. The future changes in summertime Wave height in the western North Pacific (WNP), which are influenced by tro...
Tomoya Shimura - One of the best experts on this subject based on the ideXlab platform.
-
FUTURE CHANGES IN SPECTRAL Wave Climate AROUND JAPAN UNDER GLOBAL WARMING
Coastal Engineering Proceedings, 2020Co-Authors: Tomoya Shimura, Nobuhito MoriAbstract:Future projections of ocean Wave Climate related with global warming has been conducted for the assessment of Climate change impacts on coastal disaster, beach morphology, and coastal structure design. In this study, we conduct the high-resolution future Wave Climate projection in the East Asia region and detail analysis on Wave Climate based on two-dimensional Wave spectra in addition to conventional Wave statistics (significant Wave height). Future changes in Wave height, period and direction can be discussed consistently owing to analysis on the mean Wave spectra.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/FEYPZFRr5SQ
-
High-resolution Wave Climate hindcast around Japan and its spectral representation
Coastal Engineering, 2019Co-Authors: Tomoya Shimura, Nobuhito MoriAbstract:Abstract A 34-year high-resolution ocean surface Wave Climate hindcast around Japan is presented, and the Wave Climate around Japan is examined from a spectral point of view. The spectral Wave model is forced by the sea surface wind data obtained from the JRA-55 atmospheric reanalysis. The Wave Climate hindcast is validated by comparing it with buoy observations around Japan. The correlation coefficient of the significant Wave height is 0.9. The correlation coefficient of the mean Wave period is 0.8 for the Japan Sea and Pacific side of eastern Japan and 0.7–0.8 for the Pacific side of western Japan. The Wave Climate is represented by temporal-mean two-dimensional Wave spectra. The characteristics of the spectral Wave Climate are investigated by classifying them into three types. Distinctive characteristics of the mean Wave spectra along the Japan Sea are narrower band widths for both the period and direction. The mean Wave spectra along the Pacific side of eastern Japan are characterized by swells with a spectral peak propagating from the northeast. The distinctive spectral features corresponding to the Pacific side of western Japan are bi-modal peaks with long-period components typically generated by typhoons. The variability in the monthly mean Wave spectra is examined using an empirical orthogonal function (EOF) analysis. In the winter, it is found that the Wave height variability (EOF 1st mode) is related to Wave direction variability (EOF 2nd mode) at locations 1000 km apart via the sea level pressure variance over the North Pacific. In the summer, the EOF 1st mode corresponding to the Pacific side locations is dominated by the variability in typhoon-generated swells. The spectral Wave Climate representation provides new insight into the Wave Climate around Japan with clear relationships between the atmospheric conditions, the Wave height, direction, and period.
-
Wave Climate VARIABILITY AND RELATED Climate INDICES
Coastal Engineering Proceedings, 2018Co-Authors: Nobuhito Mori, Risako Kishimoto, Tomoya ShimuraAbstract:Climate change is highly expected to give significant impact on coastal hazards and environment. The future projections of Wave Climate under global warming scenarios have been carried out and shows changes in Wave heights depending on the regions (e.g., Hemer et al., 2013). Beside the long-term trends of Wave Climate, annual to decadal changes are also important to understand variability. For example, the North Atlantic Oscillation (NAO) is highly correlated to monthly mean Wave height along the western European coast. However, variability of Wave Climate is not well understood over the globe, quantitatively. Additionally, the standard coastal engineers regard stationary process for Wave environment for solving coastal problems. This study analyzes global Wave Climate variability for the last half century based on principal component analysis of atmospheric forcing (sea surface winds U10 and sea level pressure P) and Wave hindcast.
-
Future Projection of Ocean Wave Climate: Analysis of SST Impacts on Wave Climate Changes in the Western North Pacific
Journal of Climate, 2015Co-Authors: Tomoya Shimura, Nobuhito Mori, Hajime MaseAbstract:AbstractChanges in ocean surface Waves elicit a variety of impacts on coastal environments. To assess the future changes in the ocean surface Wave Climate, several future projections of global Wave Climate have been simulated in previous studies. However, previously there has been little discussion about the causes behind changes in the future Wave Climate and the differences between projections. The objective of this study is to estimate the future changes in mean Wave Climate and the sensitivity of the Wave Climate to sea surface temperature (SST) conditions in an effort to understand the mechanism behind the Wave Climate changes by specifically looking at spatial SST variation. A series of Wave Climate projections forced by surface winds from the MRI-AGCM3.2 were conducted based on SST ensemble experiments. The results yield future changes in annual mean Wave height that are within about ±0.3 m. The future changes in summertime Wave height in the western North Pacific (WNP), which are influenced by tro...
-
FUTURE PROJECTION OF OCEAN Wave Climate CHANGE USING MULTI-SST ENSEMBLE EXPERIMENTS
Coastal Engineering Proceedings, 2014Co-Authors: Tomoya Shimura, Nobuhito Mori, Tomohiro Yasuda, Hajime MaseAbstract:Long term changes in ocean Waves elicit a variety of impacts on a coastal environment. In order to assess the future changes in ocean Wave Climate, future projections of global Wave Climates have been carried out by previous studies. However, previously there has been little discussion about the causes behind changes in future Wave Climate and the differences between projections. The objective of this study is to estimate the future changes in mean Wave Climate and the sensitivity of the Wave Climate to Sea Surface Temperature (SST) conditions, in an effort to understand the mechanism behind the Wave Climate changes by specifically looking at spatial SST variation. A series of Wave Climate projections forced by surface winds from the MRI-AGCM3.2H were conducted based on SST ensemble experiments. The results show future changes in seasonal mean Wave heights that are within about ± 0.4 m depending on the region and season. The future changes in summertime Wave heights in the Western North Pacific (WNP) are highly sensitive to SST conditions that are influenced by tropical cyclone changes. The spatial variation of SST in the tropical Pacific Ocean is a major factor in the Wave Climate changes for the WNP during summer.
Jorge Perez - One of the best experts on this subject based on the ideXlab platform.
-
Statistical Wave Climate projections for coastal impact assessments
Earth's Future, 2017Co-Authors: Paula Camus, Melisa Menendez, Cristina Izaguirre, Antonio Espejo, I.j. Losada, Jorge PerezAbstract: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.
-
a multimodal Wave spectrum based approach for statistical downscaling of local Wave Climate
Journal of Physical Oceanography, 2017Co-Authors: Christie A. Hegermiller, Paula Camus, Jorge Perez, Ana Rueda, Jose A. A. Antolínez, Li H. Erikson, Patrick L. Barnard, Fernando J MendezAbstract:AbstractCharacterization 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., Pacific Ocean). 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, this study addresses these two challenges by improving upon the spatiotemporal definition of the atmospheric predictor used in the statistical downscaling of local Wave Climate. The improved methodology separates the local Wave spectrum into “w...
-
A Multimodal Wave Spectrum–Based Approach for Statistical Downscaling of Local Wave Climate
Journal of Physical Oceanography, 2017Co-Authors: Christie A. Hegermiller, Paula Camus, Jorge Perez, Ana Rueda, Jose A. A. Antolínez, Li H. Erikson, Patrick L. Barnard, Fernando J MendezAbstract:AbstractCharacterization 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., Pacific Ocean). 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, this study addresses these two challenges by improving upon the spatiotemporal definition of the atmospheric predictor used in the statistical downscaling of local Wave Climate. The improved methodology separates the local Wave spectrum into “w...
-
a multimodal Wave spectrum based approach for statistical downscaling of local Wave Climate
Journal of Geophysical Research, 2016Co-Authors: Paula Camus, Jorge Perez, Ana Rueda, Christie A. Hegermiller, Jose A. A. Antolínez, Li H. Erikson, Patrick L. Barnard, Fernando J MendezAbstract: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.
-
A weather‐type statistical downscaling framework for ocean Wave Climate
Journal of Geophysical Research: Oceans, 2014Co-Authors: Paula Camus, Melisa Menendez, Fernando J Mendez, Inigo J Losada, Cristina Izaguirre, Antonio Espejo, Verónica Cánovas, Jorge Perez, Ana Rueda, Raúl MedinaAbstract: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.
