Unravelling Eastern Pacific and Central Pacific ENSO Contributions in South Pacific Chlorophyll-a Variability through Remote Sensing

Abstract

El Niño-Southern Oscillation (ENSO) is regarded as the main driver of phytoplankton inter-annual variability. Remotely sensed surface chlorophyll-a (Chl-a), has made it possible to examine phytoplankton variability at a resolution and scale that allows for the investigation of climate signals such as ENSO. We provide empirical evidence of an immediate and lagged influence of ENSO on SeaWiFS and MODIS-Aqua derived global Chl-a concentrations. We use 13 years of Chl-a remotely sensed observations along with sea surface temperature (SST) observations across the Tropical and South Pacific to isolate and examine the spatial development of Chl-a anomalies during ENSO: its canonical or eastern Pacific (EP) mode, and El Niño Modoki or central Pacific (CP) mode, using the extended empirical orthogonal function (EEOF) technique. We describe how an EP ENSO phase transition affects Chl-a, and identify an interannual CP mode of variability induced spatial pattern. We argue that when ENSO is analysed as a propagating signal by the EEOF, CP ENSO is found to be more influential on Chl-a interannual to decadal variability than the canonical EP ENSO. Our results cannot confirm the independence of the two ENSO modes but clearly demonstrate that both ENSO flavors manifest a distinct

Impact of El Niño Variability on Oceanic Phytoplankton

“…In this region, ENSO is recognized as the main driver of inter-annual phytoplankton variability. Tropical, as well as extra-tropical, influences of ENSO and ENSO Modoki have been demonstrated using statistical analyses based on a range of indices applied to ocean-color remotesensing observations (Yoder and Kennelly, 2003;Behrenfeld et al, 2006;Chavez et al, 2011;Vantrepotte and Mélin, 2011;Chavez, 2012, 2013;Racault et al, 2012Racault et al, , 2017Couto et al, 2013;Raitsos et al, 2015). One of the most widely-used environmental indices to characterize influence of ENSO on ocean biology is the Multivariate ENSO Index (MEI).…”

“…The main obstacles to distinguishing the ecosystem effects associated with El Niño variability may be summarized to arise from: (i) the challenges to construct continuous, synoptic-scale, long-term time-series of marine ecosystem state at high temporal and spatial resolutions for the global oceans (Sathyendranath and Krasemann, 2014); (ii) the diversity and complexity of the mechanisms driving the biophysical interactions in different oceanic sub-regions or provinces (Longhurst et al, 1995;Boyd et al, 2014); (iii) the difficulties to elucidate the roles of the local and remote-forcing mechanisms associated with different El Niño events (Cai et al, 2015;Capotondi et al, 2015); (iv) the issues of lag in the transmission of El Niños influences at higher latitudes and to other basins via different teleconnection mechanisms (Ashok et al, 2007;Couto et al, 2013); and finally (v) the broad ranges of meridional position, amplitude and evolution of SST anomalies observed during different El Niño events, which militate against consensus in the choice of method to estimate indices of El Niño variability (Capotondi et al, 2015). Here, we propose an original approach that overcome some of these obstacles based on climate-quality ocean-color products and state-of-the-art reanalysis datasets, and allow us to establish an atlas of the impact of CP and EP types of El Niño on primary producers in the global oceans.…”

Spatial–temporal variations in primary productivity and population dynamics of skipjack tuna Katsuwonus pelamis in the western and central Pacific Ocean

“…Based on the EOF analysis and previous studies [23,24], we generated a time series of the major changes in first primary productivity (PC1) through EOF analysis. The change in primary productivity was synchronized to increase or decrease with the change in the amount of nutrient transfer associated with the change in the intensity of the Pacific Gyre from east to west.…”

A Remote-Sensing-Driven System for Mining Marine Spatiotemporal Association Patterns

“…Spatiotemporal variations in marine environmental properties and their relationship with El Niño-Southern Oscillation (ENSO) make up a complex and interrelated system [3][4][5]. The complexities of such a system require analysis techniques that go beyond the conventional methods of spatiotemporal analysis, such as empirical orthogonal functions [6], canonical analysis [7], and singular value decomposition [8].…”