Trends in Satellite-Based Ocean Parameters through Integrated Time Series Decomposition and Spectral Analysis. Part II: NOAA/NCEI Blended Seawinds

Abstract Seawind is essential in studying extreme weather and climate events globally over the oceans. It has significant impacts through air–sea interactions, upper ocean mixing, and energy flux generation. The sea surface wind is also a critical element in blue economy strategic planning, offshore renewable energy, marine transportation, marine ecosystem, and fisheries. As per the Intergovernmental Panel on Climate Change (IPCC) working group report, there is low confidence level in wind trends due to insufficient evidence. This study uses signal decomposition, namely, the multiple seasonal-trend decomposition using locally estimated scatterplot smoothing (MSTL) on NCEI blended seawinds, version 2.0 (NBSv2.0; 1988–2022), to derive the nonlinear dynamic trend of global blended sea surface winds showing variations of 0.3–0.8 m s −1 and a global rate (linear approximation) of 0.022% ± 20% m s −1 decade −1 . Implementing MSTL requires specifying periods, which is achieved using time-dependent spectral wavelet analysis to extract significant seasonalities in the dataset. The calculated average trend rates are notably higher for the Southern Hemisphere oceans than for the Northern Hemisphere, with peaks ∼0.1–0.15 m s −1 decade −1 around the higher midlatitudes. Conversely, the tropical and near-equatorial bands show either a decreasing trend rate or weakly increasing trends. Areas with significantly increasing trend rates are mainly located in the west of the North Atlantic and the North Pacific, the Arctic, and the eastern tropical Pacific Ocean (ETPO)/central Pacific oceans, and a decreasing trend is visible over the rest of the Northern Hemisphere (specifically over the North Indian and the Northern Pacific oceans). In contrast, the Southern Hemisphere has mostly increasing trend rates except for the tropical southern Indian Ocean. Significance Statement Trends in global ocean winds are highly sensitive to the input data. Consequently, per the IPCC working group report, the trends reported over global winds are of low confidence. This study emphasizes that the trend of global neutral winds is a dynamic quantity that varies with time, and the globally reported trends (∼0.08–1.2 m s −1 decade −1 ) represent a linear approximation of this dynamic behavior. Most of these studies use an ordinary least squares fit to estimate this linear approximation. On the contrary, this analysis uses 35 years of high-quality and stable blended sea wind product developed from multiple satellites and estimates a more realistic long-term nonlinear trend by filtering out quasi-seasonal and high-frequency signals from the input data using a time series decomposition. This study leverages decomposition-based methods to reveal and quantify global sea wind trends, with a particular emphasis on understanding their variations across different latitude bands. This analysis provides global trend rate maps and explores the geospatial distributions of these trends. Our findings will assist in strategizing offshore wind energy development, understanding air–sea interactions, and disaster management.