Trends in primary production in the canary current upwelling system-A regional perspective comparing remote sensing models

ORIGINAL RESEARCH published: 14 November 2017 doi: 10.3389/fmars.2017.00370 Frontiers in Marine Science | www.frontiersin.org 1November 2017 | Volume 4 | Article 370 Edited by: Ananda Pascual, Mediterranean Institute for Advanced Studies (CSIC), Spain Reviewed by: Gabriel Navarro, Instituto de Ciencias Marinas de Andalucía (CSIC), Spain Eric Machu, Institut de Recherche Pour le Développement, Senegal *Correspondence: Markel Gómez-Letona [email protected] Specialty section: This article was submitted to Ocean Observation, a section of the journal Frontiers in Marine Science Received: 11 May 2017 Accepted: 31 October 2017 Published: 14 November 2017 Citation: Gómez-Letona M, Ramos AG, Coca J and Arístegui J (2017) Trends in Primary Production in the Canary Current Upwelling System—A Regional Perspective Comparing Remote Sensing Models. Front. Mar. Sci. 4:370. doi: 10.3389/fmars.2017.00370 Trends in Primary Production in the Canary Current Upwelling System—A Regional Perspective Comparing Remote Sensing Models Markel Gómez-Letona1*, Antonio G. Ramos2, Josep Coca2and Javier Arístegui1 1Instituto de Oceanografía y Cambio Global, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain, 2División de Robótica y Oceanografía Computacional, IUSIANI, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain After Bakun (1990) formulated his hypothesis of upwelling intensification caused by increasing global warming, contradictory results have been published on whether primary productivity is increasing or decreasing in Eastern Boundary Upwelling Ecosystems (EBUE). The present work is focused in comparing three net primary production (NPP) models—the VGPM (Vertically Generalized Production Model), the Eppley-VGPM and the CbPM (Carbon-based Production Model)—in the Canary Current (CanC) EBUE during the 1998–2015 period, making use of both SeaWiFS (Sea-viewing Wide Field-of-view Sensor) and MODIS (MODerate-resolution Imaging Spectroradiometer) derived data. We looked for the first time for seasonal to interannual trends of NPP under a regional perspective, with the aim of searching for temporal patterns that could support or reject the intensification hypothesis. According to previous studies based on the seasonality of the upwelling regime, the CanC EBUE was divided into three subregions: a seasonal upwelling zone (SUZ; 13–20◦N), a permanent upwelling zone (PUZ; 20–26◦N) and a weak permanent upwelling zone (WPUZ; 26–33◦N). Although differences in the output of the models are important, both at regional and subregional scales, our analyses do not show significant increasing trends in NPP with any of the productivity models used. Our results are in accordance with previous published studies that indicate, that unlike other EBUE, winds have weakened (or at least not intensified) in the CanC upwelling over time scales ranging up to 60 years. Nevertheless, the comparison made in this work shows disagreements between some of the best-known NPP models and calls for a validation effort in this region. Seasonal to decadal anomalies of NPP and sea-surface temperature (SST) are estimated and analyzed in relation to selected climate indices, yielding only significant correlations between SST and the North Atlantic Oscillation (NAO) indices. Keywords: canary current EBUE, upwelling, primary production model, Chl-a, decadal trends, climate indices Gómez-Letona et al. Primary Production Trends in the CanC INTRODUCTION Over recent decades increasing trends in global warming have been evident in most oceanic regions, both at surface and deep layers (Levitus et al., 2000, 2005; Gille, 2002; Ihara et al., 2008; Hansen et al., 2010; Gouretski et al., 2012; Nieves et al., 2015). Impacts of rising temperatures on marine ecosystems may have direct effects on the ranges of distribution and vulnerability of organisms, but also on the productivity of ecosystems, their management and their services (Aalst et al., 2014). Ocean surface warming leads to enhanced stratification of the water column, reducing vertical mixing and hence limiting the supply of colder, nutrient-enriched waters to the euphotic zone, where primary production takes place. Behrenfeld et al. (2006) were the first to provide evidence of a reduction on global average net primary production (NPP), derived from remote sensing ocean-color data, of 190 TgC·year−1for the 1999–2006 period, associated with a global warming trend during the same period. Boyce et al. (2010) combined in situ chlorophyll a (Chl-a) measurements with sea water transparency to found also a global significant decline of Chl-a (about −0.020 mg·m−3·◦C−1) over the twentieth century, related to increasing sea surface temperatures (SST). Nevertheless, other authors have found both increasing and decreasing trends on phytoplankton biomass and primary productivity, related to multi-decadal climatic oscillations over the last century, showing variable regional patterns (Martinez et al., 2009; Chavez et al., 2011). Overall, present observations suggest that NPP will increase with global warming at high latitudes, but this enhancement will be offset by a decrease in the open-ocean at temperate and tropical latitudes (Behrenfeld et al., 2006; Martinez et al., 2009; Chavez et al., 2011). Moreover, the poor and uncertain information on the productivity trends at coastal and near-shore regions, which largely contribute to the global productivity, reduce confidence in future global projections (Aalst et al., 2014). Indeed, although the global ocean is projected to warm under climate change, the surface waters of eastern boundary upwelling ecosystems (EBUE)—contributing nearly 25% to global fish production—may lead to cooling rather than warming, causing enhanced productivity. Bakun (1990) proposed that EBUEs would tend to cool, as the result of the intensification of wind-driven upwelling. According to his hypothesis, the stronger continental land mass warming compared to the ocean would cause an increase in the cross-shore atmospheric pressure gradient, intensifying coastal upwelling, hence enhancing primary productivity. Analyses of sea-surface temperatures, wind trends and chlorophyll provide however contradictory evidences to support Bakun’s hypothesis in all EBUEs (Snyder et al., 2003; McGregor et al., 2007; Demarcq, 2009; Narayan et al., 2010; Barton et al., 2013; Cropper et al., 2014; Sydeman et al., 2014; Oerder et al., 2015; Varela et al., 2015; Wang et al., 2015). Moreover, it is unclear to what extent wind stress can offset the increased stratification in upwelling regions due to surface warming, and how other ecosystem drivers (like the progressive acidification and deoxygenation) could affect organisms’ responses and ecosystem productivity in EBUEs. The present work aims to shed some light on this controversy by analyzing spatiotemporal trends on physical and biological variables in the Canary Current (CanC) upwelling region, which is located along the northwestern African coast (Figure 1) and constitutes one of the four major EBUEs in the planet, along with the California, Humboldt and Benguela systems (Carr and Kearns, 2003; Chavez and Messié, 2009). We use satellite data to explore seasonal to interannual trends in sea-surface temperature, chlorophyll and primary production, for the 1998– 2015 period. Past observations indicate that the CanC has been warming at both local and regional scales since the early 1980s, with a decrease in chlorophyll over the last two decades (Arístegui et al., 2009; Belkin, 2009; Demarcq, 2009). In this study we have further extended the analyses to sub-regional scales and introduce a trend analysis on NPP employing various wellknown models, an effort that had not being done yet for this upwelling system. DATA AND METHODS Study Area Although the large marine ecosystem of the CanC, in its broadest sense, spans from the northwest tip of the Iberian Peninsula to south of Senegal (Arístegui et al., 2009), we refer here to the CanC region as the area extending from northern Morocco to Senegal. The CanC constitutes the eastern boundary of the North Atlantic subtropical gyre and flows parallel to the Northwest African continental margin until ∼15–21◦N, where it detaches from the coast and merges into the westward North Equatorial Current (NEC). The CanC region displays a great geographical variability, resulting in highly diverse upwelling environments (Arístegui et al., 2009). For instance, the nutrient content of upwelled waters differs depending on the source of the water mass: North Atlantic Central Waters (NACW) and South Atlantic Central Waters (SACW) dominate north and south of Cape Blanc (21◦N), respectively, with SACW providing higher concentrations of nutrients than the NACW. Mesoscale activity plays also an important role in upwelling dynamics, as changes in shelf width and the presence of capes (e.g., Capes Ghir, Juby, and Bojador) enhance the formation of recurrent filaments that export organic matter from the upwelling into the oligotrophic waters of the North Atlantic subtropical gyre. Besides, the perturbation of the main flow by the Canary Archipelago induces the generation of mesoscale eddies downstream the islands (at about 27–28◦N), many of them drifting westwards to the open ocean along the path of an “eddy corridor” (Sangrà et al., 2009). Dust deposition is also a major feature, especially in the southern part of the study area, as the transport of mineral aerosols from the Sahara desert and the Sahel makes the CanC one of the regions with the highest dust deposition rates in the world (Mahowald et al., 2005). Despite this high diversity of environments, three main subregions are identified for the purpose of this study according to their upwelling intensity throughout the year, based on previous regional descriptions (Wooster et al., 1976; Van Camp et al., 1991; Cropper et al., 2014): a seasonal upwelling zone (SUZ) along the Senegalese-Mauritanian coast, which expands from 13 to 20◦N, a permanent upwelling zone (PUZ), from 20 to 26◦N, and a weak permanent upwelling zone (WPUZ), from 26 to Frontiers in Marine Science | www.frontiersin.org 2November 2017 | Volume 4 | Article 370 Gómez-Letona et al. Primary Production Trends in the CanC FIGURE 1 | CanC upwelling region and its main geographical features. The three upwelling subregions –the weak permanent (WPUZ), permanent (PUZ), and seasonal upwelling zones (SUZ)– are identified along with the geographical domains selected to estimate climatological anomalies (black boxes). 200 m and 2,000 m isobaths are represented by gray contour lines. Bathymetry data corresponds to a 2′global dataset derived by Smith and Sandwell (1997). 33◦N (Figure 1). Notice, however, that these latitudinal bands represent approximate boundaries, as in practice they might vary from year to year due to the meridional shift of the trade wind system. While the WPUZ and PUZ present year-round upwelling (peaking during summer in the northern section), the SUZ exhibits winter upwelling followed by a downwelling period, especially during summer months, due to the appearance of the onshore monsoonal winds. Sea-Surface Temperature (SST) SST from daily Reynolds analyses that combine AVHRR and in situ data (at a weekly temporal and 1/4◦spatial resolutions) was provided by the Copernicus Marine Environment Monitoring Service (CMEMS, http://marine.copernicus.eu/), product reference GLOBAL_REP_PHYS_001_013. The dataset covers the period from 01/1993 to 12/2014. Net Primary Production (NPP) The main goal of this study is to look at seasonal to decadal trends in productivity in the CanC region. For this purpose three well-known NPP models are selected to compare them: the VGPM, the Eppley-VGPM and the CbPM (r2014), all three provided by the Oregon State University (OSU, http://www. science.oregonstate.edu/ocean.productivity/) with temporal and spatial resolutions of 8 days and 1/12◦, respectively. In order to study the longest possible time period, we employ data derived both from SeaWiFS and MODIS; the former expands the 01/1998–12/2007 period, whereas the latter the 01/2003–12/2015 one. The VGPM is a Chl-based model developed by Behrenfeld and Falkowski (1997). Using chl-a as a biomass proxy, it estimates NPP as a function of the maximum daily net primary production found within a given water column (PB opt), which is intended to represent the variation of light-saturated Frontiers in Marine Science | www.frontiersin.org 3November 2017 | Volume 4 | Article 370 Gómez-Letona et al. Primary Production Trends in the CanC photosynthetic efficiencies. The PB opt is defined by a complex empirical, SST-dependent polynomial expression of 7th degree: PB opt = −3.27 ·10−8T7+3.4132 ·10−6T6−1.348 ·10−4T5 +2.462 ·10−3T4−0.0205T3+0.0617T2 +0.2749T+1.2956 Estimates are integrated to the whole water column by a volume function that depends on the depth of the euphotic zone (zeu) and a term that accounts for the vertical decrease in light (as photosynthetically active radiation, PAR), which is empirically parametrized. Thus, multiplying by the daily hours of light (hPAR) the following expression is obtained: NPP =chl ·PB opt ·hPAR ·0.66125 ·PAR PAR +4.1·zeu The Eppley-VGPM represents a modified version of the VGPM, replacing the original polynomial description of PB opt with the exponential function described by Morel (1991), based on the curvature of the temperature-dependent growth function of Eppley (1972): PB opt =1.54 ·100.0275·SST−0.07 The CbPM was developed by Behrenfeld et al. (2005) and modified by Westberry et al. (2008). The key difference from the previous two models is that instead of considering chl-a as a proxy of phytoplankton biomass it employs organic carbon estimates. Phytoplankton carbon (Cphyto) is calculated from an empirical relationship that relates it to the particulate backscattering coefficient (bbp): Cphyto =13000 ·bbp −0.00035 The chosen physiological rate was phytoplankton growth (µ), which is estimated based on chl:C ratios: µ=µmax ·chl :Csat −ε chl :CN,Tmax −ε·1−e−5I where µmax is the maximum growth rate (set at 2 div·day−1 after Banse, 1991), chl :Csat are the observed satellite-derived chl:C ratios and εis the intercept between chl:C and µ. Chl:C ratios under nutrient replete conditions (chl :CN,Tmax ) were empirically parametrized. Besides, the modified version of the CbPM includes a modification of chl:C ratios for depths under the nitracline. The last term accounts for the light-dependent growth. See Westberry et al. (2008) for details. Finally, light change through the water column is parametrized as follows: Ig=I0·e −k490·MLD 2 where I0is cloud-corrected surface PAR, k490 the light attenuation coefficient at 490 nm and MLD the mixed layer depth. Thus, CbPM NPP is given by the product of the three terms: NPP =Cphyto ·µ·Ig Chlorophyll-a (Chl-a) Chl-a was provided by the European Space Agency (ESA) through the Ocean Colour–Climate Change Initiative (OC– CCI) portal (http://esa-oceancolour-cci.org/) with a temporal resolution of 8 days and 4 km2spatial resolution (v3.1) for the 1998–2015 period. This product consists in globally merged data from MERIS (MEdium Resolution Imaging Spectrometer), Aqua-MODIS, SeaWiFS and VIIRS (Visible Infrared Imaging Radiometer Suite), contributing to the reduction of cloud cover and bias correction. Besides, the individual datasets of SeaWiFS and MODIS Chl-a employed in the NPP estimates were downloaded from the OSU portal. Chl-a is usually employed as a proxy for phytoplankton biomass, although it is worth to note that the “carbon:Chl-a ratio” may vary among different species of phytoplankton as well as their metabolic states. Linear Fits Linear fits are computed in order to estimate the change rate of the different variables throughout the time period analyzed. The Theil-Sen slope estimator was chosen for this purpose, since it is a robust method against outliers that generate departures from normality and homoscedasticity of the residuals. The method, developed by Theil (1950) and later extended by Sen (1968), yields a simple linear regression of a set of data points, estimating the median slope among all lines connecting pairs of points. Although it is a robust method, correction for temporal autocorrelation is required to correctly estimate the significance of the calculated slopes. This is achieved making use of the modified Mann-Kendall trend test developed by Hamed and Ramachandra Rao (1998), which corrects Mann-Kendall test Z statistics and, consequently, p-values for autocorrelated data. The significance boundary is set at the usual 0.05 level. Correlations As datasets in the present work exhibit departures from normality and extreme values, we use the Spearman’s rank correlation coefficient (also known as Spearman’s ρ), a non-parametric method that is robust to outliers. The significance boundary chosen was the usual 0.05 level. Seasonal Climatological Anomalies Seasonal climatological anomalies are estimated within each upwelling subregion to better study fluctuations of the different variables and thus support the trend analysis. The selected areas are shown in Figure 1. The box approach is chosen taking into account the different spatial resolution of some of the datasets, and in order to include the oceanward extensions of the coastal upwelling. The procedure followed for their computation involves three steps: (1) Averaging datasets in order to obtain seasonal time series, dividing each year into four quarters: (i) winter, corresponding to January, February and March (JFM); (ii) spring, corresponding to April, May and June (AMJ); (iii) summer, corresponding to July, August and September (JAS); and (iv) autumn, corresponding to October, November and Frontiers in Marine Science | www.frontiersin.org 4November 2017 | Volume 4 | Article 370 Gómez-Letona et al. Primary Production Trends in the CanC December (OND); (2) Calculating mean values for each season, i.e., seasonal climatologies (this is done independently for each sensor’s time-series); and (3) Subtracting the corresponding seasonal climatology from each measured seasonal value. In order to estimate the anomalies for all the three upwelling subregions, datasets are also spatially averaged prior to the computation of climatologies. Climate Indices Five climate indices are selected to look at correlations with the studied variables: The Multivariate ENSO Index (MEI) and the Southern Oscillation Index (SOI). Although MEI and SOI both describe the El Niño-Southern Oscillation (ENSO) they do it in distinct ways: while SOI is based on the observed sea-level pressure (SLP) differences between Tahiti and Darwin (Australia), MEI takes six observed variables into account: SLP, zonal, and meridional components of the surface wind, SST, surface air temperature and total cloudiness fraction of the sky. Despite the fact that MEI and SOI characterize the same phenomena both are analyzed in order to make stronger assumptions supported by two different indices. Both MEI and SOI were provided by the National Oceanic and Atmospheric Administration (NOAA) at http://www.esrl.noaa.gov/psd/enso/mei/ and https:// www.ncdc.noaa.gov/teleconnections/enso/indicators/soi/, respectively. The station-based North Atlantic Oscillation (NAO-SB) and the principal component-based North Atlantic Oscillation (NAO-PC). The NAO-SB is based on the difference of normalized SLP between Lisbon (Portugal) and Stykkisholmur/Reykjavik (Iceland) whereas the NAO-PC is the leading Empirical Orthogonal Function (EOF) of the SLP anomalies over the ocean area, covered between 20◦and 80◦N and 90◦W-40◦E. Like with MEI/SOI, two different indices for the same mode of variability are chosen in order to support a more consistent analysis. Both NAO indices were provided by the National Center for Atmospheric Research (NCAR) at https://climatedataguide.ucar. edu/climate-data. The Eastern Atlantic Pattern (EA) represents a southwarddisplaced NAO-like index. It also consists of a north-south dipole of anomalies, though these are shifted southeastward relative to those of NAO. Nevertheless, the south center of the dipole has a strong subtropical link related to the variations of the subtropical ridge. This fact differentiates the EA from the NAO and allows to characterize the variability of the CanC upwelling as it is located in subtropical latitudes. EA data was also provided by NOAA at http://www.cpc.ncep.noaa.gov/data/ teledoc/ea.shtml. Following the same approach as Arístegui et al. (2004), correlations between ENSO indices and the different variables are searched comparing the 1st and 2nd quarter of a year of each variable with the 4th quarter of the previous year of the SOI/MEI, since this quarter tends to exhibit the greatest correlations. Correlations are also calculated independently for each of the quarters with no lag applied for all the five indices to assess if short-term responses existed. RESULTS Linear Trends Trends in SST Trends computed with SST data show a significant (p<0.001) overall warming tendency for the CanC EBUE during the 1993–2014 time period (Figure 2), although the increase in temperature is more evident in the southern part of the study area (around 0.4◦C·decade−1) rather than in the northern one (about 0.2◦C·decade−1). Nevertheless, in contrast to the open-ocean, nearly all of the water located over the shelf (<200 m depth) shows no significant change in temperature. Furthermore, some spots present slightly negative trends, coinciding with some of the major upwelling cores, i.e., Cape Blanc (21◦N), Dakhla (24◦N), and Cape Ghir (30◦N). At subregional level (Table 1), the WPUZ and PUZ present similar non-significant warming trends over the FIGURE 2 | Mean SST (A) and SST trend (B) for the 1993–2014 period. In (B) black dots correspond to significant trends (p<0.05). 200 and 2,000 m isobaths are represented by gray contour lines. Frontiers in Marine Science | www.frontiersin.org 5November 2017 | Volume 4 | Article 370 Gómez-Letona et al. Primary Production Trends in the CanC TABLE 1 | Average trends for SST (1993–2014), Chl-a and NPP (VGPM, Eppley-VGPM, and CbPM), for 1998–2007 (SeaWiFS) and 2003–2015 (MODIS) periods in each of the upwelling subregions over the shelf (0–200m), continental slope (200–2,000 m) and open-ocean region (>2,000 m, as far as 28◦W). Area SST [◦C·decade−1] Chl-a [mgChl·m−3·decade−1] VGPM [mgC·m−2·decade−1] Eppley-VGPM [mgC·m−2·decade−1] CbPM [mgC·m−2·decade−1] ESA OC-CCI SeaWiFS MODIS SeaWiFS MODIS SeaWiFS MODIS SeaWiFS MODIS Weak permanent upwelling zone Shelf 0.088 0.088*−0.025 0.013 10.03 −22.54 70.77 0.46 −460.24** −113.52* Slope 0.204* 0.011 −0.019 −0.006 −28.38 0.42 13.23 14.24 −81.61** −187.47** Open–ocean 0.217** 0.004 −0.003 0.006 −22.69 10.44 1.53 13.26 7.44 −100.25** Permanent upwelling zone Shelfa0.125 0.171 0.176 −0.378** 104.06 −283.95* 156.29 −152.72 −414.81*−100.11 Slope 0.315** 0.125 0.085 −0.203 −11.97 −120.16 64.05 −61.51 81.35 −353.93** Open-ocean 0.387** 0.012 −0.053 0.021 −120.04 46.00 −54.94 39.59 33.97** −155.17** Seasonal upwelling zone Shelfa0.300** 0.835 0.059 −1.660** −74.81 −529.77* 172.58 −354.74 3.29 −97.87** Slope 0.441** 0.142*−0.368 −0.186 −323.05 −85.11 −130.56 −19.37 93.63** −184.25** Open-ocean 0.473** 0.006 −0.212 0.021 −271.70* 12.53 −134.25 24.98 −2.55 −191.48** Significance reads as follows: bold** (p <0.01), italics*(p <0.05). aBanc d’Arguin zone was excluded from the mean estimates due to its special characteristics (shallow, warm waters with high turbidity). shelf (0.09 and 0.13◦C·decade−1, respectively), whereas the SUZ exhibits a significant average trend of 0.3◦C·decade−1. However, in all three instances, the warming trends in the open-ocean and slope areas are higher than over the shelf. Trends in NPP and Chl-a Computed NPP trends show very different results depending on the model and the dataset (which comprise both the time period and sensor factors). Both the VGPM (Figure 3) and Eppley-VGPM (Figure 4) exhibit similar trends, although these are more marked in the former. For the SeaWiFS dataset two patterns can be clearly identified: an increasing trend over the shelf near Cape Blanc (around 600–800 mgC·m−2·decade−1) and a decreasing one around it. Nevertheless, note that only in some patches the trends are significant (p<0.05). Regarding the two other upwelling subregions, both present zones of slight increase/reduction of NPP. Average trend values for the VGPM over the shelf in the SUZ, PUZ and WPUZ are −75, 105, and 10 mgC·m−2·decade−1, respectively, whereas the EppleyVGPM shows increases of 175, 160, and 70 mgC·m−2·decade−1; in neither case are they significant (Table 1). On the other hand, the MODIS datasets are dominated by decreases in NPP (Figures 3,4). VGPM mean trends over the shelf are −530, −285 and −25 mgC·m−2·decade−1in the SUZ, PUZ and WPUZ, respectively, the first two being significant (p<0.05). Similarly, the Eppley-VGPM yields non-significant changes in NPP of −355, −155 and 0 mgC·m−2·decade−1for the same zones (Table 1). In general, the CbPM (Figure 5) exhibits different trends relative to the chlorophyll-based VGPM models. The SeaWiFS dataset registers a marked significant decreasing trend exceeding −400 mgC·m−2·decade−1(p<0.001) over most of the WPUZ and PUZ (Figure 5 and Table 1). On the other hand, the SUZ barely shows any change. Nevertheless, it is worth noting the highly significant (p<0.001) positive trend off Cape Blanc, with broad areas exceeding 200 mgC·m−2·decade−1. The MODIS dataset yields different tendencies: no apparent changes are registered for most of shelf waters over the entire study area (Figure 5). However, mean trends result in significant decreases of −100 and −110 mgC·m−2·decade−1for the SUZ and WPUZ, respectively, and a non-significant drop of −100 mgC·m−2·decade−1for the PUZ (Table 1). Off the continental break, significant declines in NPP are observed widespread (Figure 5). These results reveal that absolute values and trends in NPP greatly vary depending on the chosen dataset and, especially, model. Absolute NPP mean values show marked differences between Chl-based models and CbPM. In the most productive areas (e.g., Cape Blanc) these discrepancies are magnified, VGPM/Eppley-VGPM showing fourfold NPP values relative to CbPM. Regarding decadal change rates in NPP, a detailed comparison of the average trends in the shelf portion of each of the upwelling zones (Table 1) is shown in Figure 6. For the SeaWiFS dataset, the VGPM and Eppley-VGPM show no significant changes, while the CbPM presents remarkable reductions in the WPUZ and PUZ regions (Table 1). The three models only agree in the SUZ, where NPP estimates do not show any clear trend. Nonetheless, a common feature in the VGPM, Eppley-VGPM and CbPM is the marked seasonality of NPP in all three upwelling zones, reflecting the annual cycle in solar irradiance. On the other hand, for the MODIS dataset all three models agree fairly well in a qualitative sense, exhibiting decreases in all the upwelling zones. Notably, CbPM trends shift from marked decreases in SeaWiFS data to more gentle ones in the MODIS dataset. VGPM and Eppley-VGPM also show changes among SeaWiFS and MODIS datasets, as Frontiers in Marine Science | www.frontiersin.org 6November 2017 | Volume 4 | Article 370 Gómez-Letona et al. Primary Production Trends in the CanC FIGURE 3 | Mean VGPM (A,C) and VGPM trend (B,D) maps for SeaWiFS (1998–2007, left column) and MODIS (2003–2015, right column) datasets. In (B,D), black dots correspond to significant trends (p<0.05). 200 and 2,000 m isobaths are represented by contour lines. overall increases are registered in the former and decreases in the latter. When comparing SeaWiFS and MODIS trends in their shared period (2003–2007), both VGPM and EppleyVGPM show trends that qualitatively match, except in some areas between the Canary Islands and the Cape Verde archipelagos (Supplementary Figure 1), where weak, non-significant trends shift from positive to negative (and viceversa). CbPM trends, on the other hand, exhibit large areas of opposing trends in the open ocean, but agree in the shelf area north of Cape Blanc (21◦N). Thus, SeaWiFS and MODIS trends agree reasonably well between 2003 and 2007 for areas close to the upwelling. However, in terms of absolute NPP values it is interesting to notice that SeaWiFS always yields higher values for CbPM than MODIS, whereas the opposite is true for the VGPM and Eppley-VPGM models. Chl-a trends exhibit no significant changes except for some areas north of Cape Ghir (30◦N) and around Cap Vert (14◦N) (Supplementary Figure 2). This is due to the fact that increases in the first part of the time-series are followed by a stagnant period until ∼2010, when the Chl-a starts falling. Average OC-CCI Chl-a trend values over the shelf differ between upwelling zones, being 0.09, 0.17, and 0.83 mg Chl·m−3·decade−1in the WPUZ, PUZ, and SUZ, respectively (Table 1), although only the first one is significant at p< 0.05. However, time-series (Figure 7) seem to show an initial increasing period followed by a decreasing last one. When looking at the individual Chl-a datasets differences are observed between them. SeaWiFS Chl-a shows no significant changes, with trends of 0.06, 0.18 and −0.03 mgChl·m−3·decade−1in the SUZ, PUZ, and WPUZ, respectively (Table 1). On the other hand, for MODIS Chl-a significant negative trends (p<0.01) of −1.66 and −0.38 mgChl·m−3·decade−1are registered in the SUZ and PUZ, respectively, whereas no significant variation is observed in the WPUZ. Overall, OC-CCI exhibits lower Chl-a concentrations than MODIS and SeaWiFS, although being very close to the latter from 2002 to 2007. Frontiers in Marine Science | www.frontiersin.org 7November 2017 | Volume 4 | Article 370 Gómez-Letona et al. Primary Production Trends in the CanC FIGURE 4 | Mean Eppley-VGPM (A,C) and Eppley-VGPM trend (B,D) maps for SeaWiFS (1998–2007, left column) and MODIS (2003–2015, right column) datasets. In (B,D) black dots correspond to significant trends (p<0.05). 200 and 2000 m isobaths are represented by contour lines. Seasonal Climatological Anomalies The most apparent aspect about seasonal climatological anomalies is that, in general, there is considerable spatial variability between the different upwelling zones studied. SST is, however, the variable which exhibits lower subregional variability (Figure 8). In all the three zones SST anomalies (reaching or exceeding ±0.5◦C) follow a similar pattern, with remarkable periods of negative anomalies between 1993–1995 and 1998–2003 (with the exception of 2002), and positive anomalies between 2003–2007 and 2009–2011. NPP (Figure 9) and Chl-a anomalies present differences depending on upwelling zone and the production model. Overall, the VGPM and Eppley-VGPM show alternate positive and negative anomalies, whereas the CbPM displays a more regular pattern, especially in the MODIS dataset, where anomalies switch from positive to negative halfway through the 2003–2015 period. Regarding spatial variability, overall anomalies increase southwards, from the WPUZ to the PUZ and SUZ, especially for the VGPM and Eppley-VGPM, evidencing differences in the degree of the changes experienced by each upwelling zone. Variability Associated with Climate Modes The correlation analyses between the ENSO indices and the lagged SST yield no significant results. On the other hand, correlations between SST and various climate indices for the North Atlantic and Pacific Oceans with no lag applied show some significant results (Table 2). Significant negative correlations of SST with the NAO are found widespread (especially with NAOSB and to a lesser extent with NAO-PC) during winter, spring and autumn, particularly at the PUZ and WPUZ. On the contrary, EA, SOI, and MEI show only sporadic significant correlations with SST. Frontiers in Marine Science | www.frontiersin.org 8November 2017 | Volume 4 | Article 370 Gómez-Letona et al. Primary Production Trends in the CanC FIGURE 5 | From top to bottom, mean CbPM (A,C) and CbPM trend (B,D) maps for SeaWiFS (1998–2007, left column) and MODIS (2003–2015, right column) datasets. In (B,D) black dots correspond to significant trends (p<0.05). 200 and 2,000 m isobaths are represented by contour lines. Regarding the NPP models, VGPM and Eppley-VGPM show some high, significant positive correlations with NAO indices when the MODIS dataset is considered, especially during winter (Table 3). This would agree with the SST correlations, as higher temperatures (less upwelling) would be related to lower NPP, and vice versa. CbPM shows only some occasional significant correlations with the climate indices both for SeaWiFS and MODIS datasets. DISCUSSION Since Bakun (1990) proposed his upwelling intensification theory more than two decades ago, it has become a matter of increasing interest, especially in the last few years, as growing attention has been paid to global warming and more accurate satellite and model data have become available. Several studies have been published (see references in the following sections) addressing this subject, but it has not been possible to reach a consensus. Different results, both supporting and rejecting Bakun’s hypothesis, suggest opposite trends even for the same EBUEs. Linear Trends Trends in SST Computed SST trends reflect a non-surprising overall warming of the ocean, which has been extensively described in the literature over the last years (Levitus et al., 2000, 2005; Hansen et al., 2006, 2010; Domingues et al., 2008; Lyman et al., 2010; Gouretski et al., 2012). Warming trends calculated in the present work for this specific oceanic region are slightly weaker than those found in the literature. Demarcq (2009) used AVHRR pathfinder v5 SST data to estimate trends for the 1998–2007 period. Although the pattern is similar, his average trends were stronger than those obtained in the present work. This might be explained in part by Frontiers in Marine Science | www.frontiersin.org 9November 2017 | Volume 4 | Article 370 Gómez-Letona et al. Primary Production Trends in the CanC CONCLUSIONS The principal aim of the present work was to analyze interannual trends of NPP at subregional scale in the CanC upwelling region. Estimated SST trends show a widespread warming in the region, with the exception of some restricted areas around Cape Blanc, Dakhla and Cape Ghir, which show significant decreases. On the other hand, OC-CCI Chl-a shows no significant change over the shelf (except in certain areas), apparently due to a marked increase in ∼2002 and generalized decreases during the last part of the studied period, a fact confirmed by trend analyses of individual the SeaWiFS and MODIS datasets. Furthermore, NPP trends estimated for the 1998–2007 (SeaWiFS) and 2003–2015 (MODIS) periods show differences between upwelling zones, but overall present either significant decreases or non-significant changes, suggesting that no upwelling intensification is being experienced. Nonetheless, NPP models yield different average results for upwelling zones, as well as different interannual trends, especially for the SeaWiFS dataset. While the CbPM exhibit marked, significant decreases in the shelf region of the WPUZ and PUZ, the VGPM and Eppley-VGPM show nonsignificant, subtle changes, although disparities are reduced when the MODIS dataset is considered. In sum, this seems to indicate that no intensification of the upwelling is occurring. These discrepancies in NPP estimates probably arise from errors in input variables such as overestimated Chl-a concentrations due to mineral aerosols, which are an important feature of the study area. Modeled NPP and in situ data comparisons carried out in the California Current have shown that the VGPM correlates better with in situ measurements than the CbPM. Here a comparison with historical data seems to indicate that the Eppley-VGPM is the model that best suits in situ PP measurements. However, considering (1) the reduced amount of in situ measurements, (2) the different time periods covered by them and by model NPP estimates, and (3) the errors introduced to NPP estimates by the input variables, discussing the performance of the models themselves is a difficult task. Consequently, a similar comparative analysis with an extended and more recent data of in situ PP is needed for the CanC upwelling region to elucidate which model reproduces better the actual variability in NPP. The variability associated with climate modes could be of importance to predict future perturbations in the CanC EBUE, as changes in wind patterns related to variations of climate indices seem to regulate upwelling intensity. An individualized study for each season has proved crucial in this regard as distinct responses have been registered depending on the period of the year. The absence of consensus in bibliography, however, does not allow drawing definitive conclusions, being necessary further studies. Finally, the results from the present work need to be interpreted with caution, since extrapolating to longer periods can be misleading. Although long SST time series are relatively common, this is not the case for variables such as satellite-derived NPP. Further improvements on NPP models and approaches to combine different Chl-a/NPP datasets, along with recent, more accurate data, will help to build climate data records of satellite-derived biological parameters, eventually allowing for a better comprehension of the evolution of upwelling ecosystems at regional and subregional scales under a climate warming scenario. AUTHOR CONTRIBUTIONS MG-L carried out the analysis of the data, made the figures, and wrote the manuscript. JA acted as an advisor during the development of the study, and contributed to the discussion and the writing of the manuscript. AR acted as an advisor during the development of the study, and contributed to the discussion and the writing of the manuscript. JC participated in the analyses of the data and reviewed the manuscript. FUNDING This is a contribution to the FLUXES project (CTM2015-69392C3-1-R; Coordinator: JA) funded by the Spanish government (Plan Nacional I+D). ACKNOWLEDGMENTS We would like to thank the data providers mentioned in the Methods section for freely providing the datasets employed in the present work. SUPPLEMENTARY MATERIAL The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmars. 2017.00370/full#supplementary-material Supplementary Figure 1 | Trend agreement between SeaWiFSand MODIS-based NPP estimates for their common period (2003–2007). If both SeaWiFSand MODIS-based estimates yield either positive or negative trends, agree. If they yield opposed trends, differ. 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The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Frontiers in Marine Science | www.frontiersin.org 18 November 2017 | Volume 4 | Article 370