The confidence level of the response patterns (Figures 2A,B) identified with the statistical analysis is assessed as very likely (i.e., within 90–100% probability range; IPCC Climate Change, 2013). During EP El Niño, at the global scale, the median chlorophyll impact is found to be −6.5%, mostly driven by a large decrease of −7.5% in the tropics (3,529 pixels), and limited increases in the Northern and Southern Hemispheres of +6.6 and +5.5% respectively (505 and 643 pixels respectively). During CP El Niño, global median chlorophyll impact is found to be −7.4%, which is the resultant of large decrease estimated for the tropics of −8.7% (1,948 pixels), and limited decrease estimated for the Northern Hemisphere of −4.9% (342 pixels) and increase for the Southern Hemisphere of +4.1% (500 pixels) (Supplementary Table 1 and Supplementary Figure 2). The impact values are estimated based on EP and CP indices equal to one (annual mean index values over the period 1997–2012 are presented in Figure 1). It is noteworthy that monthly impact values may be ~3–4 times higher, particularly during the peak of El Niño events, such as in December 1997 when the EP El Niño index reached value of 3.9, and in December 2009 when the CP El Niño index reached value of 2.6 (Yu, 2016).

The regions identified as most sensitive to the EP and CP El Niño climatic perturbations compare well with the locations where significant trends in chlorophyll have been estimated previously using contemporary satellite records (Vantrepotte and Mélin, 2011; Gregg and Rousseaux, 2014; Hoegh-Guldberg et al., 2014). Furthermore, the biological and physical response patterns to each type of El Niño observed in the present work over the satellite record of 1997–2012 are consistent with contemporary case studies of specific EP and CP El Niño events in the Equatorial Pacific (Turk et al., 2011; Gierach et al., 2012; Radenac et al., 2012), the Indian Ocean (Webster et al., 1999), the continental U.S. (Yu et al., 2012; Yu and Zou, 2013), and the global oceans (Behrenfeld et al., 2001; Messié and Chavez, 2012, 2013). In addition, the response patterns observed for the physical variables have also been shown to persist over decadal timescales during the period 1979–2014 (see Section Physical Forcing Mechanisms Associated with El Niño Variability). Finally, when both EP and CP indices are equal to one, the sum of the observed impacts observed in response to EP and CP El Niño (Supplementary Figure 3) is shown to be approximately equal to the impact observed using the MEI. This is coherent as the MEI encompasses effects of both EP and CP El Niño variations.

To understand the specific influence of the climatic perturbations on phytoplankton, we must first identify the mechanisms driving the biophysical interactions at the global and regional scales. Phytoplankton growth is light-limited at high-latitudes where annual mean nitrate concentration is high and monthly means of chlorophyll and PAR show positive correlation, and monthly means of chlorophyll and MLD show negative correlation (i.e., chlorophyll increases when MLD is shallower and light availability is higher; Figures 3A,B). In contrast, phytoplankton growth is nutrient-limited in the tropics and subtropics where light-availability is plentiful all-year-round, annual mean nitrate concentration is low (Figure 3C) and monthly means of chlorophyll and MLD show positive correlation (i.e., chlorophyll increases when MLD is deeper, and nutrient-rich deep waters are mixed with nutrient-poor surface waters, increasing nutrient availability for phytoplankton growth to occur). Further to nutrient supply from vertical mixing, the tropics display strong zonal surface currents (Figure 3D), which can increase horizontal advection of nutrient and, in turn, enhance phytoplankton growth. The latter mechanism can explain the weak correlation coefficients observed between monthly means of chlorophyll and MLD in some areas of the tropics and subtropics. Finally, the negative correlation shown between monthly means of chlorophyll and MLD in the eastern Equatorial Pacific is coherent with the observed high annual mean nitrate concentration (i.e., macronutrients are not limiting; Figure 3C), and previously reported iron limitation (i.e., limiting trace nutrient) occurring in the region (Gordon et al., 1997; Moore et al., 2013). In some specific areas of the North and Equatorial Pacific Ocean, and the Southern Ocean, known as High Nutrient-Low Chlorophyll (HNLC) regions, the low trace nutrients concentration (iron, manganese) present all year round, limit phytoplankton production.

The results presented in Figure 3 are consistent with the different physical regimes and global climatological relationships previously demonstrated between open ocean satellite surface observations of chlorophyll and subsurface parameters of MLD, thermocline and nutricline depths at global scale (Wilson and Coles, 2005; Messié and Chavez, 2012; Brewin et al., 2014) and in the Equatorial Pacific (Turk et al., 2011; Gierach et al., 2012; Radenac et al., 2012; Lee et al., 2014). In coastal regions, phytoplankton production can be modified further by local supply of nutrients through coastal upwelling, riverine input (e.g., Turner et al., 2003) or atmospheric dust deposition (Abram et al., 2003; Jickells et al., 2005). In Polar regions, changes in phytoplankton production are tightly coupled to variations in sea-ice extent and timing of retreat, which can affect light and nutrient availability (Kahru et al., 2011, 2016; Arrigo and van Dijken, 2015).

The results of the biological and physical responses to the EP and CP types of El Niño, which are statistically significant, are presented in Figures 2, 4, and with an estimate of uncertainty in the observational product in Figure 5. In the Equatorial Pacific Ocean, where ENSO activity is rooted, an EP El Niño event is generated when easterly trade winds weaken in the east and westerlies prevail in the west (Figures 2D, 4C), pushing warmer, nutrient-poor waters to the east (along the coast of Peru and Chile), reducing nutrient availability, leading to a decrease in chlorophyll and PP in the eastern Pacific of −12 ± 5% and −56 ± 21 TgC/y (Figures 2A,B, 5). In contrast, a CP El Niño event is generated when easterly trade winds in the east and westerlies in the west are enhanced (Figures 2D,4C), pushing warmer, nutrient-poor waters to the central Equatorial Pacific, reducing nutrient availability, which is associated with a decrease in chlorophyll and PP of −14 ± 5% and −68 ± 22 TgC/y (Figures 2A,B, 5). In both cases, regional decreases in phytoplankton are caused by variations in horizontal and vertical advective fluxes responsible for the transport of nutrients to the surface layer, which are driven by perturbation in the wind forcing (Ashok and Yamagata, 2009; Gierach et al., 2012; Messié and Chavez, 2012, 2013; Radenac et al., 2012). Enhanced advection is also observed during CP El Niño in the tropical Atlantic Ocean as the Equatorial easterlies intensify in the east (Figures 2D, 4C), bringing warmer, nutrient-poor waters to around 15°N (Richter et al., 2012), and leading to decreases in chlorophyll and PP of −8 ± 3% and −10 ± 5 TgC/y (Figures 2A,B, 5). In the Indian Ocean, Equatorial easterlies are found to intensify during EP El Niño, promoting horizontal advection of warmer and nutrient-poor waters to the western-side of the basin (Figure 2C) (Webster et al., 1999), resulting in decreases in chlorophyll and PP of −11 ± 4% and −82 ± 31 TgC/y (Figures 2A,B, 5), whereas in the eastern-side, the observed increases in chlorophyll and PP of +7 ± 3% and +13 ± 5 TgC/y (Figures 2A,B, 5) are likely to be driven by enhanced upwelling (Figure 2C) and atmospheric fallout from Indonesian fires and, further north in the basin, by enhanced nutrient supply from the Ganges and Brahmaputra rivers. The latter processes are consistent with the strong increase in fires (Wooster et al., 2012; Huijnen et al., 2016) and dust deposition reported off the west coast of Sumatra (Murtugudde et al., 1999; Abram et al., 2003), and the increased precipitation patterns observed over the Himalayas (Figure 4F). Furthermore, in some regions, these processes may not be sufficient to explain the observed variations in chlorophyll and PP, and other processes may be involved, such as atmospheric dust deposition from desert (e.g., EP El Niño impact in the Cape Verde Sea), extent and duration of sea-ice cover in the Artic (e.g., EP El Niño impact in the Bering and Labrador Seas), and iron limitation in HNLC regions (e.g., EP and CP El Niño impact in the Pacific sector of the Southern Ocean). Further information would be required to validate these forcing mechanisms.

The estimation of EP impact relies heavily on the El Niño event of 1997–1998, which was the single, important EP event that occurred within the relatively short time span of 15 years for which we have the OC-CCI data. To evaluate the impact that this single event had on our results, the correlation analyses have been rerun without the 1997–1998 event. In this case, the influence of EP El Niño on phytoplankton chlorophyll concentration remained significant in the Eastern Pacific Ocean and Western Indian Ocean, but not in the Eastern Indian Ocean region (Figure 5), indicating further that other regional climate oscillations are important drivers at basin scale (please see discussion in Section Implications for Climate Impact Research). In addition, the analyses of the EP and CP impact on physical processes are further validated on interannual to decadal timescales in Figure 4. The regression coefficients of the EP and CP impact estimated for the two periods 1979–2014 and 1997–2012 show similar spatial patterns and frequency distributions for the physical variables studied. This indicates that the results presented in Figure 2 (period 1997–2012) are not skewed to the 1997–1998 EP El Niño event, and that the impact patterns are stable over multi-decadal time scales (35-year), at least for the physical variables. Since phytoplankton dynamics are at the mercy of these physical conditions, we postulate that the inference may also hold for the biological variables studied here (e.g., Figures 2, 5).

Phytoplankton have a high turn-over rate, responding to changes in their environment at scales ranging from seconds to days, and illustrating well the first-level biological response to environmental changes. At the same time, because of decadal-scale variabilities in the physical forcing fields, it is generally understood that multi-decadal, uninterrupted data are needed to evaluate the impact of climate change on marine ecosystems. Such data are only rarely available from limited in situ time series stations (mostly coastal). Furthermore, satellite ocean-color sensors have provided barely two decades of uninterrupted data that can be used for climate research (Sathyendranath and Krasemann, 2014). In this context, El Niño variability, together with other large-scale inter-annual variations, provides an important vehicle to study how phytoplankton in the ocean (and hence the organisms at higher trophic levels) respond to climate variability and identify the driving processes. In turn, monitoring and analysis of long-term changes in these driving processes would help us to improve understanding of projected impact of long-term climate changes on the marine ecosystem.

In the present study, we have addressed potential issues related to the collection of continuous ocean-color time-series and processing of climate-quality products, to the study of biophysical interactions, El Niño remote-forcing mechanisms and their propagation, and the diversity of El Niño events by: (i) using the longest, error-characterized, biased-corrected, climate-quality controlled, global scale merged satellite ocean-color data product from ESA Ocean-Color Climate Change Initiative project; (ii) analyzing in synergy satellite ocean-color data record and reanalysis datasets to identify the dominant mechanisms driving the biophysical interactions; (iii) characterizing local and remote influences of El Niño types on key driving variables of SST, Sea Level, wind, and precipitation; (iv) analyzing annual mean signal centered around the peak timing of El Niño activity in boreal winter; and (v) selecting EP and CP indices, which are computed to enhance differences in SST anomalies from the two most eastern and central Pacific Niño1+2 and Niño 4 regions respectively.

Our work highlights the importance of maintaining a long time series of consistent ocean-color products, to be able to evaluate the impact of climate variability on the biological fields. For example, our results on the impact of EP events could be improved when additional EP events can be incorporated into the analyses, such that the results would no longer be so heavily dependent on a single EP event, as was the case here. More data from longer time series are also essential to explore non-linearities in biological responses, which could not be investigated here because of limited data availability.

Our analysis shows that the modification of global oceanic phytoplankton under climate change cannot be forecast with respect to changes in a single ocean property. Rather, a range of environmental properties may be involved (e.g., advection in three dimensions, wind, riverine input, atmospheric dust deposition, stratification) whose intensity may vary on a regional basis. The statistical approach to study El Niño impact applied here has permitted us to characterize a complex mosaic of biological responses illustrating that different forcing dominates in different regions. The biophysical processes driving phytoplankton production are summarized in Figure 6 in the form of an atlas of EP and CP El Niño impact. The influence of CP and EP El Niño events can be felt in the global oceans, although the affected regions are predominantly located in the tropics and subtropics encompassing 66–67% of the total areas affected, and the remaining 33–34% are areas located in high-latitudes. In the tropics and subtropics, 35–39% of the total affected areas showed a decrease in PP associated with reduced nutrient availability during CP and EP El Niño respectively, whereas in higher latitudes 19–20% of the affected areas showed an increase in PP associated with reduced light limitation (Figure 3). Even though, the percent of total affected areas are relatively similar between CP and EP El niño events, the regional differentiation is marked, and may be of opposing sign (e.g., along the coast of Peru and Chile, the Benguela upwelling, the Great Barrier Reef), or affected during an EP event but not during a CP event (e.g., in the tropical eastern and western Pacific). Several process-orientated studies have further highlighted the important role played by horizontal processes (together with vertical processes) in the supply of nutrients in the surface layer, and specifically demonstrated significant impacts in Winter new primary production in the North Pacific transition zone (Ayers and Lozier, 2010), inter-annual variations of chlorophyll concentration in the Equatorial Pacific (Gierach et al., 2012; Messié and Chavez, 2013; Dave and Lozier, 2015) and the Red Sea (Raitsos et al., 2015), and decadal variations in phytoplankton abundance in the North Atlantic Subpolar Gyre (Martinez et al., 2015). Thus, both the development of statistical methods to study climate impact, and the assessment of the future evolution of regional physical forcing processes may help us to understand phytoplankton responses to climate change and improve confidence in our projection of future ecosystem state (Bopp et al., 2013; Boyd et al., 2014). The first assessment based on a biogeochemical and ecosystem model output of chlorophyll response to the EP and CP types of El Niño was shown to compare well with remotely-sensed observations in the Equatorial Pacific (Lee et al., 2014). However, the response to El Niño variability projected from biogeochemical and ecosystem models is yet to be investigated at the global scale.

Notwithstanding the dominant influence of El Niño on global climate patterns, other driving factors may enhance or weaken the observed biophysical impact. Examining links between El Niño and inter-annual and decadal climate oscillations (Di Lorenzo et al., 2010; Izumo et al., 2010) may provide further insights toward improving projection of environmental properties and associated phytoplankton responses to climate forcing at global and regional scales. The regional variations associated with El Niño may be superimposed on long-term warming trends (Bopp et al., 2013; IPCC Climate Change, 2013; Boyd et al., 2014; Kumar et al., 2016) and regional-scale oscillations at sub-seasonal and seasonal scales associated with other large-scale climate modes of variability, such as the Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO; Martinez et al., 2009), the North Pacific Gyre Oscillation (NPGO; Di Lorenzo et al., 2008, 2010; Messié and Chavez, 2013), the monsoon and Indian Ocean Dipole (IOD; Saji et al., 1999; Ashok et al., 2007; Izumo et al., 2010; Brewin et al., 2012; Currie et al., 2013). As a result, the regional climate response is not a simple function of the strength and centroid location of an El Niño event. Further, the regional patterns observed using EP and CP El Niño indices may be sufficient to explain only a fraction of all the regional variations on a year-to-year basis (except perhaps where El Niño is likely to dominate the variability of the system such as in the Equatorial Pacific region). For instance, in the Indian Ocean, the ENSO and IOD indices can account for ~30% and 12% respectively of the regional variations in SST (Saji et al., 1999), and years of co-occurrence of positive IOD and El Niño events may provide positive feedbacks to the SST (Kumar et al., 2016). Therefore, some apparent differences will show between the observed impact of El Niño on biological and physical variables, and the corresponding anomalies.

Phytoplankton are at the base of the food chain and transfer energy to higher trophic levels. This transfer of energy has a knock-on effect on fisheries and dependent human societies especially in highly productive and coastal upwelling regions, as well as coral reef ecosystems. The larvae of many marine species graze on phytoplankton during this most vulnerable stage of their lives. Hence, changes in phytoplankton population associated with climate variability may propagate rapidly up the marine food chain and profoundly alter the functioning of marine ecosystems (Platt et al., 2003; Edwards and Richardson, 2004; Lo-Yat et al., 2011). In addition, changes in environmental conditions associated with EP and CP El Niño events have been shown to impact mesozooplankton community with variable time lags in the northern California Current, which in turn can affect top down control on phytoplankton, and disrupt the pelagic food chain (Fisher et al., 2015). Following EP and CP El Niño events, quite different impact on commercially important fisheries have been reported in anchovy catches in the Humboldt Current Large Marine Ecosystem (Jackson et al., 2011) and tuna catches in the Indian Ocean (Kumar et al., 2014). In coral reef ecosystems, changes in phytoplankton population and mass bleaching following an El Niño event can critically affect fisheries, recreation, and tourism services (Hoegh-Guldberg, 1999; Abram et al., 2003; Lo-Yat et al., 2011). Recent analysis in the Andaman Sea, southeast Bay of Bengal, has further demonstrated that differences both in intensity and timing of SST warming associated with EP and CP El Niño events, can determine the extent of mass coral bleaching (Lix et al., 2016). In this context, regional differentiation of the impact of each type of El Niño events (Figure 6) may provide important information to delineate and establish protected coral reef and fishing areas to facilitate their recovery.

The oceanic carbon sink is part of a very active, natural cycle, in which phytoplankton in the surface layer of the ocean fix, by photosynthesis, dissolved CO₂ in the water into organic matter, some of which subsequently sinks below the mixed layer. Through the associated decrease in the partial pressure of CO₂ in the surface ocean, phytoplankton contribute to the drawdown of dissolved CO₂ from the ocean surface layer (Hauck et al., 2015), which in turn help to modulate the increase in anthropogenic atmospheric CO₂. The estimated El-Niño-driven changes in PP at the regional scale can be considerable, reaching values of −57 ± 21 and−68 ± 22 TgC/y in the Eastern and Central Equatorial Pacific Ocean during EP and CP types of El Niño respectively (Figure 5). However, to provide a more complete picture on the influence on the carbon cycle, further investigations are required to quantify the impact of El Niño on carbon export and associated changes in air-sea CO₂ fluxes. The buffering action of the ocean in the carbon cycle is non-linear—it varies with the water temperature (solubility pump), alkalinity (carbonate pump), biological productivity and demineralization (biological pump); the impact on environmental and ecosystem properties must be evaluated at the appropriate scale to allow investigation of the underlying mechanisms driving the variability in the ocean carbon cycle.

As the frequency of extreme El Niño events and the relative frequency of occurrence of CP-El Niño/EP-El Niño are projected to increase under climate warming (Yeh et al., 2009; Lee and McPhaden, 2010; Cai et al., 2015), it is essential to refine our regional assessment of climate impact associated with El Niño variability. The atlas of impact of CP and EP types of El Niño on oceanic phytoplankton (Figure 6) can be used for societal benefit. It provides key climate impact information that can allow us to better inform fisheries management on possible risks and opportunities associated with El Niño events, and support more effectively mitigation and adaptation plans for local fisheries-dependent societies. The atlas information can also provide observational basis to test model predictions of the impact of climate change on the marine ecosystem. Finally, from a biogeochemical perspective, such insights on El Niño variability impact are needed to improve our understanding of the buffering capacity of the oceanic carbon cycle under climate change.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.