Influence of Climate Variability on Extreme Ocean Surface Wave Heights Assessed from ERA-Interim and ERA-20C

Abstract

Extreme ocean surface wave heights significantly affect coastal structures and offshore activities and impact many vulnerable populations of low-lying islands. Therefore, better understanding of ocean wave height variability plays an important role in potentially reducing risk in such regions. In this study, global impacts of natural climate variability such as El Niño–Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), and Pacific decadal oscillation (PDO) on extreme significant wave height (SWH) are analyzed using ERA-Interim (1980–2014) and ECMWF twentieth-century reanalysis (ERA-20C; 1952–2010) datasets for December–February (DJF). The nonstationary generalized extreme value (GEV) analysis is used to determine the influence of natural climate variability on DJF maxima of SWH (Hmax), wind speed (Wmax), and mean sea level pressure gradient amplitude (Gmax). The major ENSO influence on Hmax is found over the northeastern North Pacific (NP), with increases during El Niño and decreases during La Niña, and its counter responses are observed in coastal regions of the western NP, which are consistently observed in both Wmax and Gmax responses. The Hmax response to the PDO occurs over similar regions in the NP as those associated with ENSO but with much weaker amplitude. Composite analysis of different ENSO and PDO phase combinations reveals stronger (weaker) influences when both variability modes are of the same (opposite) phase. Furthermore, significant NAO influence on Hmax, Wmax, and Gmax is observed throughout Icelandic and Azores regions in relation to changes in atmospheric circulation patterns. Overall, the response of extreme SWH to natural climate variability modes is consistent with seasonal mean responses.

Changes in extreme precipitation in the Yangtze River basin and its association with global mean temperature and ENSO

“…This method was further refined by Wang and Swail (2001). The iterative-based MK method has been shown to be efficient in detecting trends, especially in extreme meteorological time series data (Kumar et al, 2016). In this study, we adopted the iterative-based MK method detailed in Wang and Swail (2001) to detect trends in extreme precipitation.…”

Global changes of extreme coastal wave energy fluxes triggered by intensified teleconnection patterns

“…Studies based on satellite altimeter [Izaguirre et al, 2011] and reanalysis data [Dodet et al, 2010;Plomaritis et al, 2015;Kumar et al, 2016] revealed strong relationships between wind-wave climate and large-scale teleconnection patterns, such as the Antarctic Oscillation (AAO), the El Niño-Southern Oscillation (ENSO), and the North Atlantic Oscillation (NAO). Some of these indices are expected to intensify under increased anthropogenic forcing [Zheng et al, 2013;Cai et al, 2014], but it is not well understood how wind-wave variability and especially extreme waves will relate to these climate indices in a warmer world.…”

Long‐Term Variations in Nearshore Wave Climate and Littoral Drift at Ganpatipule Coast, Eastern Arabian Sea and its Link to Pacific Climate Variability

“…With the availability of the global reanalysis dataset from European Centre for Medium-Range Weather Forecasts Re-Analysis-Interim (ERA-I; Dee et al, 2011), now it is possible to study the long-term changes in different atmospheric and ocean parameters. Several researchers (e.g., Harley et al, 2010;Hemer et al, 2010;Kumar et al, 2016) across the globe has used ERA-I datasets to examine the interannual wave climate variability. Previous studies along the eastern Arabian sea (Kumar & Naseef, 2015;Shanas & Sanil Kumar, 2014b, 2014c suggest that ERA-I datasets give a good agreement with the measured data.…”

“…Several researchers studied interannual variability of wave climate and their response to wellknown climate indices (e.g, Hemer et al, 2010;Izaguirre et al, 2011;Splinter et al, 2012). For coastal and ocean engineering applications, the relationship between wave climate variability and teleconnection patterns along the Atlantic, Pacific, and the Indian Ocean were discussed by many (Izaguirre et al, 2011(Izaguirre et al, , 2010Kumar et al, 2016;Semedo et al, 2011;Shimura et al, 2013). Earlier studies have shown that on interannual time scales, the Pacific Ocean variability modulates Indian Ocean (Du et al, 2009).…”

“…A warm (cold) phase of PDO is associated with deficit (excess) rainfall (Krishnamurthy & Krishnamurthy, 2014) and a corresponding increase (decrease) in the surface air temperature over the Indian subcontinent (Krishnan & Sugi, 2003). Impact of ENSO and PDO on surface waves were investigated by Kumar et al (2016). However, across the North Indian Ocean, there have been limited studies on the impacts of regional atmospheric circulation patterns (Indian Ocean Dipole) on the wave climate (Anoop et al, 2016).…”