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What mechanisms lead to the enhancement of sea surface chlorophyll-a in the Arafura Sea?

G. Napitupulu, M.R. Ramadhan, S. Nurdjaman, I.M. Radjawane,
A.A. Lukman, A.Y. Yuliardi, M. Napitupulu and S. Nurfitri

Abstract: 

Sea surface clorophyll-a (SSC) represents a fundamental indicator of oceanic primary production and serves as a widely used proxy for phytoplankton biomass. Although upwelling is recognised as the dominant driver of SSC variability, precipitation and river discharge can substantially modulate its spatial and temporal distribution. This study examines the spatiotemporal dynamics of SSC in the Arafura Sea, utilising satellite-derived oceanographic data from 1998 to 2022, with a focus on the influences of upwelling, precipitation, and oceanographic conditions. Seasonal analysis reveals that SSC peaks during the SE monsoon (June-August), coinciding with intensified upwelling, as indicated by positive Ekman pumping velocity (EPV), reduced sea surface height, and lowered sea surface temperature (SST). Coastal regions consistently exhibit higher SSC, driven by riverine discharge and precipitation-enhanced nutrient input. Regional correlation analysis confirms that offshore EPV and increased precipitation are the dominant mechanisms driving SSC enhancement during the SE monsoon. Climatological correlation analysis further identifies a strong positive relationship between SSC and EPV in key regions, with a maximum correlation coefficient of 0.85. The spatial distribution of SSC exhibits a pronounced inshore-offshore gradient across both monsoon seasons, reflecting the interplay between coastal upwelling, stable river discharge, and nutrient runoff. The empirical orthogonal function analysis of interannual variability suggests that SSC fluctuations are influenced by additional drivers beyond the El-Niño Southern Oscillation, including SST co-variability and anomalous precipitation patterns over central and north-western Papua. These findings show that phytoplankton variability in the Arafura Sea is chiefly controlled by local ocean, atmosphere, and land interactions, underscoring their importance for ecosystem forecasting.