Role of Atmospheric Indices in Describing Inshore Directional Wave Climate in the United Kingdom and Ireland
| Autor: | Andy Saulter, Gerd Masselink, Guillaume Dodet, Adam A. Scaife, Tim Scott, Bruno Castelle, Robert Jak McCarroll, Nick Dunstone |
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| Přispěvatelé: | School of Biological and Marine Sciences, Plymouth University, Environnements et Paléoenvironnements OCéaniques (EPOC), Observatoire aquitain des sciences de l'univers (OASU), Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Sciences et Technologies - Bordeaux 1-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Océanographie Physique et Spatiale (LOPS), Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS), United Kingdom Met Office [Exeter], College of Engineering, Mathematics and Physical Sciences [Exeter] (EMPS), University of Exeter, Université Sciences et Technologies - Bordeaux 1 (UB)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Sciences et Technologies - Bordeaux 1 (UB)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-École Pratique des Hautes Études (EPHE), Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS), ANR-17-CE01-0014,SONO,Marier les objectifs de défense côtière avec ceux de la protection du milieu naturel grâce aux dunes sableuses(2017) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
010504 meteorology & atmospheric sciences
Earthquake Source Observations Biogeosciences 01 natural sciences Volcanic Effects Global Change from Geodesy Ionospheric Physics Volcanic Hazards and Risks Oceans Sea Level Change Earth and Planetary Sciences (miscellaneous) GE1-350 Disaster Risk Analysis and Assessment Earthquake Interaction Forecasting and Prediction QH540-549.5 General Environmental Science Gravity Methods climate indices Climate and Interannual Variability Wave climate Seismic Cycle Related Deformations Tectonic Deformation Climate Impact Earthquake Ground Motions and Engineering Seismology Explosive Volcanism Time Variable Gravity Earth System Modeling Atmospheric Processes Seismicity and Tectonics Ocean Monitoring with Geodetic Techniques Ocean/Atmosphere Interactions Mathematical Geophysics Atmospheric Probabilistic Forecasting Regional Modeling Atmospheric Effects [SDE.MCG]Environmental Sciences/Global Changes Volcanology Hydrological Cycles and Budgets Decadal Ocean Variability Land/Atmosphere Interactions Earthquake Dynamics Magnetospheric Physics Geodesy and Gravity Global Change wave direction Air/Sea Interactions [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environment Numerical Modeling Solid Earth [SDU.STU.OC]Sciences of the Universe [physics]/Earth Sciences/Oceanography Gravity anomalies and Earth structure Geological inshore wave climate Ocean/Earth/atmosphere/hydrosphere/cryosphere interactions Water Cycles Modeling Avalanches Volcano Seismology Benefit‐cost Analysis seasonal forecasting Computational Geophysics Regional Climate Change Subduction Zones Transient Deformation Natural Hazards Abrupt/Rapid Climate Change Informatics Surface Waves and Tides Atmospheric Composition and Structure 010502 geochemistry & geophysics Volcano Monitoring long term prediction Range (statistics) Seismology Climatology Exploration Geophysics Ecology Ocean Predictability and Prediction Radio Oceanography Coastal Processes Gravity and Isostasy Marine Geology and Geophysics Physical Modeling Regression Oceanography: General Policy Estimation and Forecasting Space Weather Cryosphere Impacts of Global Change Oceanography: Physical Research Article coastal evolution Risk Oceanic Theoretical Modeling Satellite Geodesy: Results Radio Science Tsunamis and Storm Surges Paleoceanography Climate Dynamics Ionosphere Long-term prediction Monitoring Forecasting Prediction 0105 earth and related environmental sciences Wave power Numerical Solutions Climate Change and Variability Continental Crust Effusive Volcanism Climate Variability General Circulation Policy Sciences Climate Impacts Mud Volcanism Air/Sea Constituent Fluxes Environmental sciences Mass Balance Interferometry Ocean influence of Earth rotation 13. Climate action Seasonal forecasting Volcano/Climate Interactions Environmental science Hydrology Prediction Sea Level: Variations and Mean Forecasting |
| Zdroj: | Earth's Future Earth's Future, American Geophysical Union, 2021, 9 (5), pp.e2020EF001625. ⟨10.1029/2020EF001625⟩ Earth's Future, Vol 9, Iss 5, Pp n/a-n/a (2021) Earth's Future, 2021, 9 (5), pp.e2020EF001625. ⟨10.1029/2020EF001625⟩ Earths Future (2328-4277) (American Geophysical Union (AGU)), 2021-05, Vol. 9, N. 5, P. e2020EF001625 (21p.) |
| ISSN: | 2328-4277 |
| DOI: | 10.1029/2020EF001625⟩ |
| Popis: | Improved understanding of how our coasts will evolve over a range of time scales (years‐decades) is critical for effective and sustainable management of coastal infrastructure. A robust knowledge of the spatial, directional and temporal variability of the inshore wave climate is required to predict future coastal evolution and hence vulnerability. However, the variability of the inshore directional wave climate has received little attention, and an improved understanding could drive development of skillful seasonal or decadal forecasts of coastal response. We examine inshore wave climate at 63 locations throughout the United Kingdom and Ireland (1980–2017) and show that 73% are directionally bimodal. We find that winter‐averaged expressions of six leading atmospheric indices are strongly correlated (r = 0.60–0.87) with both total and directional winter wave power (peak spectral wave direction) at all studied sites. Regional inshore wave climate classification through hierarchical cluster analysis and stepwise multi‐linear regression of directional wave correlations with atmospheric indices defined four spatially coherent regions. We show that combinations of indices have significant skill in predicting directional wave climates (R 2 = 0.45–0.8; p Key Points Over 70% of inshore wave climates analyzed throughout the United Kingdom and Ireland were directionally bimodalCombinations of winter atmospheric indices NAO, WEPA, SCAND, and EA are significantly correlated with directional wave climate in all regionsRegression models using multiple winter atmospheric indices enable skillful reconstructions of directional wave climate in all regions |
| Databáze: | OpenAIRE |
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