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

• DOI: 10.1175/jtech-d-24-0007.1
• Publication Date: 2024-09-18T15:20:40Z
• Authors: Prasanjit Dash; Korak Saha; Paul DiGiacomo; Steven D. Miller; Huai-Min Zhang; Rachel Lazzaro; Seunghyun Son
• Keywords: Anomaly (physics); Sea-surface height; Spectral Analysis

Abstract This study investigated trends in satellite-based chlorophyll-a (Chl-a; 1998–2022), sea surface temperature (SST; 1982–2022), and level anomaly (SLA; 1993–2021) from the European Space Agency’s Climate Change Initiative records, integrating time series decomposition spectral analysis. Trends parameters signify prolonged increases, decreases, or no changes over time. These are same space as original parameters, excluding seasonalities noise, can exhibit nonlinearity. Trend rates approximate pace of change per unit. We quantified using conventional linear-fit three incrementally advancing methods for decomposition: simple moving average (SMA), seasonal-trend locally estimated scatterplot smoothing (STL), multiple STL (MSTL), across global ocean, Bay Bengal, Chesapeake Bay. Challenges include specifying accurate seasonal periods that derived here by combining Fourier Wavelet Transforms. Globally, SST SLA trend upwards, Chl-a has significant change, yet regional variations notable. highlight advantage extracting with MSTL and, more broadly, decomposition’s role disentangling time-series components (seasonality, trend, noise) without resorting to monotonic functions, thereby preventing overlooking episodic events. Illustrations extreme events temporarily counteracting background trends, e . g ., 2010–2011 drop due La Niña-induced rainfall land. The continuous analysis clarifies warming hiatus debate, affirming sustained warming. Decadal grid cell also mapped. ubiquitously SLA, whereas globally low but coasts boundary currents.