Application of Non-stationary Extreme Value Analysis to Satellite-Observed Sea Surface Temperature Data for Past Decades

Eun-Young Lee,Kyung-Ae Park

doi:10.3389/fmars.2021.798408

Abstract

Extreme value analysis (EVA) has been extensively used to understand and predict long-term return extreme values. This study provides the first approach to EVA using satellite-observed sea surface temperature (SST) data over the past decades. Representative EVA methods were compared to select an appropriate method to derive SST extremes of the East/Japan Sea (EJS). As a result, the peaks-over-threshold (POT) method showed better performance than the other methods. The Optimum Interpolation Sea Surface Temperature (OISST) database was used to calculate the 100-year-return SST values in the EJS. The calculated SST extremes were 1.60–3.44°C higher than the average value of the upper 5th-percentile satellite-observed SSTs over the past decades (1982–2018). The monthly distribution of the SST extremes was similar to the known seasonal variation of SSTs in the EJS, but enhanced extreme SSTs exceeding 2°C appeared in early summer and late autumn. The calculated 100-year-return SSTs were compared with the simulation results of the Coupled Model Intercomparison Project 5 (CMIP5) climate model. As a result, the extreme SSTs were slightly smaller than the maximum SSTs of the model data with a negative bias of –0.36°C. This study suggests that the POT method can improve our understanding of future oceanic warming based on statistical approaches using SSTs observed by satellites over the past decades.

Highlights

Global warming and climate change have become widespread over time
Prior to applying the POT analysis to the entire East/Japan Sea (EJS) region, we tested whether this method could successfully resolve the 100year-return sea surface temperature (SST) value as an extreme value at an arbitrary position
Prior to the application of the non-stationary method, we investigated whether the satellite SST data can be treated as non-stationary data through significance tests of the long-term trend of the SSTs within the 95-percent confidence level

Summary

Introduction

Global warming and climate change have become widespread over time. In addition to the warming air temperature and land surface, the upper-ocean temperature of the global ocean has increased over the past decades (Baker et al, 2004; Sutton et al, 2007; Stott, 2016). Along with the warming of the global ocean, regional seas have experienced significant warming at significant rates (IPCC Climate Change, 2014). The influence of warming has induced more pronounced climate extremes. As the scale of warming increases, the possibility of coastal disasters is steadily growing (IPCC Climate Change, 2014; Stott, 2016). It is necessary to predict the magnitude of such warming along with the extremes of sea surface temperatures (SSTs).

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