Trends in Satellite-Based Ocean Parameters through Integrated Time Series Decomposition and Spectral Analysis. Part I: Chlorophyll, Sea Surface Temperature, and Sea Level Anomaly

作者：Prasanjit Dash, Korak Saha, Paul M. DiGiacomo, Steven D. Miller, Huai‐Min Zhang, Rachel Lazzaro, SeungHyun Son · 发表于：Journal of Atmospheric and Oceanic Technology · 年份：2024 · DOI：10.1175/jtech-d-24-0007.1 · 被引用次数：15 · 研究领域：Oceanographic and Atmospheric Processes

Abstract This study investigated trends in satellite-based chlorophyll-a (Chl-a; 1998–2022), sea surface temperature (SST; 1982–2022), and sea level anomaly (SLA; 1993–2021) from the European Space Agency’s Climate Change Initiative records, integrating time series decomposition and spectral analysis. Trends in parameters signify prolonged increases, decreases, or no changes over time. These are time series in the same space as original parameters, excluding seasonalities and noise, and can exhibit nonlinearity. Trend rates approximate the pace of change per time unit. We quantified trends using conventional linear fit and three incrementally advancing methods for time series decomposition: simple moving average (SMA), seasonal-trend decomposition using locally estimated scatterplot smoothing (STL), and multiple STL (MSTL), across the global ocean, the Bay of Bengal, and the Chesapeake Bay. Challenges in decomposition include specifying accurate seasonal periods that are derived here by combining Fourier and wavelet transforms. Globally, SST and SLA trend upward, and Chl-a has no significant change, yet regional variations are notable. We highlight the advantage of extracting multiple periods with MSTL and, more broadly, decomposition’s role in disentangling time series components (seasonality, trend, and noise) without resorting to monotonic functions, thereby preventing overlooking episodic events. Illustrations include extreme events temporarily counteracting background tr...