Statistical modeling of global mean wave height considering principal component analysis of sea level pressures and its application to future wave height projection
February 11th, 2017
Risako Kishimoto, Tomoya Shimura, Nobuhito Mori, Hajime Mase
Received 2016/09/16, Accepted 2016/12/25, Published 2017/02/11
Risako Kishimoto1), Tomoya Shimura2), Nobuhito Mori2), Hajime Mase2)
1) Graduate School of Engineering, Kyoto University
2) Disaster Prevention Research Institute, Kyoto University
Future wave climate projection is important for climate impact assessment of the coastal hazards and environment. In this study, monthly averaged wave heights are estimated by a linear multi-regression model using atmospheric data as explanatory variables. The present statistical model considers local atmospheric information (wind speed at 10 m height, sea level pressure) and large scale atmospheric information obtained from principal component analysis (PCA) of the global sea level pressure field. The representation of swell in the lower latitude is greatly improved by introducing the large scale atmospheric information from the PCA. The present statistical model was applied to the results of the Japan Meteorological Research Institute’s Atmospheric General/Global Circulation Model (MRI-AGCM) climate change projection. The future change of wave heights shows an increase in the northern North Pacific Ocean and a decrease in the North Atlantic Ocean, middle latitude and tropics of the Pacific Ocean.
Copyright (c) 2017 The Author(s) CC-BY 4.0