GEOPHYSICAL RESEARCH LETTERS, VOL. 39, L15603, doi:10.1029/2012GL052912, 2012
Influence of the Pacific Decadal Oscillation on phytoplankton phenology and community structure in the western North Pacific Sanae Chiba,1 Sonia Batten,2 Kosei Sasaoka,3 Yoshikazu Sasai,1 and Hiroya Sugisaki4 Received 27 June 2012; accepted 27 June 2012; published 2 August 2012.
[1] Phytoplankton phenology and community structure in the western North Pacific were investigated for 2001–2009, based on satellite ocean colour data and the Continuous Plankton Recorder survey. We estimated the timing of the spring bloom based on the cumulative sum satellite chlorophyll a data, and found that the Pacific Decadal Oscillation (PDO)-related interannual SST anomaly in spring significantly affected phytoplankton phenology. The bloom occurred either later or earlier in years of positive or negative PDO (indicating cold and warm conditions, respectively). Phytoplankton composition in the early summer varied depending on the magnitude of seasonal SST increases, rather than the SST value itself. Interannual variations in diatom abundance and the relative abundance of non-diatoms were positively correlated with SST increases for March–April and May–July, respectively, suggesting that mixed layer environmental factors, such as light availability and nutrient stoichiometry, determine shifts in phytoplankton community structure. Our study emphasised the importance of the interannual variation in climate-induced warm–cool cycles as one of the key mechanisms linking climatic forcing and lower trophic level ecosystems. Citation: Chiba, S., S. Batten, K. Sasaoka, Y. Sasai, and H. Sugisaki (2012), Influence of the Pacific Decadal Oscillation on phytoplankton phenology and community structure in the western North Pacific, Geophys. Res. Lett., 39, L15603, doi:10.1029/2012GL052912.
1. Introduction [2] Large-scale climatic forcing could alter lower trophic levels not only in terms of biomass but also in its seasonality, e.g., the specific El Niño Southern Oscillation (ENSO)scale variation in bloom timing over the North Pacific [Sasaoka et al., 2011] and the Pacific Decadal Oscillation (PDO)-related decadal scale variation in seasonal peak abundance of phytoplankton in the western North Pacific [Chiba et al., 2008]. Phenological change in phytoplankton
1 Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan. 2 Sir Alister Hardy Foundation for Ocean Science, Nanaimo, British Columbia, Canada. 3 Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology, Yokosuka, Japan. 4 National Research Institute of Fisheries Science, Fisheries Research Agency, Yokohama, Japan.
Corresponding author: S. Chiba, Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology, 3173-25, Showa-machi, Kanazawa-ku, Yokohama, Kanagawa 236-0001, Japan. (
[email protected]) ©2012. American Geophysical Union. All Rights Reserved. 0094-8276/12/2012GL052912
can affect the production of local fisheries through the match/mismatch scenario between lower and higher trophic levels [Platt et al., 2003; Koeller et al., 2009]. Understanding the processes linking climate, physical environments and plankton phenology is therefore important for sustainable fisheries management. Ocean colour satellite observation is the most robust tool to detect phytoplankton phenology. [3] Climatic forcing also induces changes in plankton community structure and the dominant functional types in an ecosystem. Long-term variation in zooplankton community structure has been reported in many studies in the North Pacific [e.g., Ohman and Venrick, 2003; Peterson and Keister, 2003; Chiba et al., 2006; Mackas et al., 2007], yet only a few studies have been reported for phytoplankton [e.g., Chiba and Saino, 2002] due to a lack of community-level data. In the North Atlantic, an extensive long-term study on phytoplankton community structure was made using data from the Continuous Plankton Recorder (CPR) observations [Leterme et al., 2005; McQuatters-Gollop et al., 2007]. CPR is a useful observation tool by which we can obtain both temporally and spatially continuous data to provide phytoplankton taxonomic information. Any change in the dominant phytoplankton functional type is likely to also result in related changes in zooplankton, such as size composition and gelatinous abundance, and consequently determine the food quality for higher trophic levels [Peterson and Schwing, 2003]. [4] Considering that phytoplankton seasonality is determined by variations in the physical environment, one may reasonably assume that the same physical forcing also alters the phytoplankton community structure. Hence, any change in its phenology and dominant functional types might occur as a response to common climatic forcing. However, this relationship remains poorly investigated with only a few exceptions [e.g., Edwards and Richardson, 2004]. [5] The CPR observation of phytoplankton has been conducted since 2000 across the subarctic North Pacific [Batten et al., 2006]. Using data of satellite ocean colour and the CPR, we investigated mechanisms that determine phytoplankton bloom timing and community structure in the western North Pacific in relation to the PDO.
2. Materials and Methods [6] We investigated phytoplankton phenology and community structure in the region southwest of the Kuril Islands with a boundary of 40 N and 155 E (Figure 1). The environment can be characterised by that of the Oyashio domain with an extensive spring bloom and high biological productivities [Limsakul et al., 2002]. [7] Mean seasonal and interannual phytoplankton abundance for the area was estimated using SeaWiFS level 3 data for February 2000 to August 2009 obtained from the NASA
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Figure 1. The study region. Satellite and CPR data were taken in the area covered by the shaded triangle. Goddard Space Flight Center (GSFC) website (http:// oceancolor.gsfc.nasa.gov/). Original SeaWiFS data with a 9 km spatial and daily temporal resolution were regridded to 1 1 and 10 day composites. The mean spatial coverage (number of grids with more than one datum for each 10 day period) was 69% (SD: 23) of the total grids (90) over the research area. SeaWiFS data accuracy was in good agreement with in situ data for the North Pacific [Gregg and Casey, 2004]. [8] The cumulative sum (CUSUM) technique [Greve et al., 2001] was applied to detect the timing of the bloom. Daily chlorophyll a (Chl a) was estimated by linearly interpolating 10 day composites of area mean satellite Chl a data, which was summed from 01 February to 31 August and where the value at the end of August was assumed to be 100% CUSUM. We defined the dates on which CUSUM Chl a reached 40% of the value of 31 August as the middle of the bloom period. Note that these definitions are arbitrary and the 40% CUSUM date would not necessarily coincide with a scientifically defined mid or peak of bloom. We used CUSUM instead of Gaussian curve fitting method [e.g., Platt et al., 2003] to examine phenology because the CUSUM method can give the date of the middle of bloom even when the single Chl a peak was not clearly detectable, as often seen in field observation timeseries. [9] The North Pacific CPR survey has been conducted along a transect between Vancouver and Northern Japan three times a year during the April–October period since 2001. To determine the phytoplankton community structure, phytoplankton abundance data obtained by the CPR survey were analysed for the period 2001–2008. Phytoplankton data taken by early summertime transect conducted in June was used, and in 2006, the mean value of data from two independent transects in May and July was used. We estimated an
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area mean phytoplankton abundance for four categories: total diatoms, pennate diatoms, centric diatoms and non-diatoms that were mostly dinoflagellates. We used four phytoplankton community indices, diatom abundance, non-diatom abundance, pennate diatom ratio to total diatom abundance and non-diatom ratio to total phytoplankton abundance to examine the interannual variation in each category. Phytoplankton colour index (CI) of CPR samples was compared to the satellite summertime Chl a (May–July) to examine the consistency of the area mean Chl a and phytoplankton abundance along the CPR transect. Detailed configuration and the towing procedure of the CPR, and the methods used to analyse phytoplankton CI and abundance of CPR samples, were reported by Batten et al. [2003]. [10] To investigate the possible mechanism linking climatic forcing, physical environment and the observed phytoplankton variations, we compared PDO (annual) and SST variables to phytoplankton time-series. The PDO index (http://jisao.washington.edu/pdo/website) and area mean sea surface temperature (SST) variables were closely related to the Aleutian Low dynamics that dominate wind stress over the NP [Trenberth and Hurrell, 1994] and thus influence the upper water environment. [11] For SST, Advanced Very High Resolution Radiometer (AVHRR) Pathfinder level 3 SST data (Version 5) for 2000–2009 were obtained from NASA JPL–PO.DAAC (Jet Propulsion Laboratory–Physical Oceanography Distributed Active Archive Center) website (http://podaac.jpl.nasa. gov/SeaSurfaceTemperature/AVHRR-Pathfinder). Original daily averaged SST at a 4 km resolution was regridded to 1 1 resolution to obtain sufficient data coverage with at least one datum per day per grid. Area mean SST variables used for the correlation analysis were monthly mean SST (March, April, May, June and July), seasonal mean SST (March–May, April–June, May–July) and the magnitude of monthly and seasonal SST increases (Delta SST March– April, April–May, May–June, June–July, March–May, April– June, May–July). All time series of phytoplankton indices and SST variables were normalised (mean = 0, SD = 1) prior to correlation analysis, and the significance was tested by obtaining values of Pearson’s r. Degree of freedom was not adjusted for autocorrelation during the analysis because summertime phytoplankton data were considered to be an annually independent population that was unlikely to be affected by its abundance in the previous or following years.
3. Results and Discussion 3.1. Phytoplankton Phenology and PDO [12] Satellite Chl a data showed a clear interannual variation in phytoplankton seasonality with high abundance in April–June (Figure 2a), which is roughly consistent to the field observation in the Oyashio region [Limsakul et al., 2002]. However, the extent of missing data (31% of grids per 10 days) due to the cloud coverage and episodic events, such as eddy-induced production increases, may be responsible for the observed high Chl a in the post-bloom season in July–August 2002. [13] The Julian days of 40% CUSUM ranged ca. one month from 107 to 142 for 2000–2009. The production peak was observed later in 2004 and 2005 and earlier for 2006– 2008 as indicated by the 40% CUSUM date. The PDO had a significant correlation with the 40% CUSUM date (r = 0.706,
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Figure 2. Phytoplankton seasonality and annual PDO. (a) Seasonal and interannual variation in the area average chlorophyll a (Chl a) (mg/m3) based on the 1 1 gridded, 10 days composite satellite ocean colour data. Red line indicates the date for 40% of CUSUM Chl a. (b) The PDO and anomaly of time series 40% CUSUM date. P < 0.05), where the 40% CUSUM date was delayed in years with a positive PDO and was early in years with a negative PDO (Figure 2). The average 40% CUSUM date for 2000– 2009 was 130 (SD: 10), occurring in mid-May. As the PDO was negatively correlated with the SST in May for each 1 1 grid over this study area during 2000–2009 (Figure S1 in the auxiliary material).1 Figure 2 indicates that bloom timing was either early or late in years with warmer or cooler springs, respectively. [14] A positive or negative PDO is indicative of strong or weak AL conditions, respectively [Trenberth and Hurrell, 1994], which subsequently generate cool or warm winter conditions in the Oyashio region [Miller et al., 2004]. A cool (warm) winter during the positive (negative) PDO years is associated with a deeper (shallower) mixing of the upper layer in the Oyashio region [Chiba et al., 2008] (Figure S2 in the auxiliary material). Thus, these results are consistent with Sverdrup’s theory of bloom timing [Sverdrup, 1953], which suggests that a bloom is initiated when the Mixed Layer Depth (MLD) reaches a critical depth; thus, a deep (shallow) MLD tends to result in a delayed (early) bloom. [15] Based on the seasonal observation of phytoplankton abundance in the Oyashio region for the 1960s–1990s, Chiba et al. [2008] reported that abundance was high in winter and 1 Auxiliary materials are available in the HTML. doi:10.1029/ 2012GL052912.
low in spring during the warm decade after 1990. They speculated that this might indicate a shift in phytoplankton seasonality although they could not show further evidence to support this notion from the seasonal observation data. The result of the present study clearly demonstrated the influence of the PDO-related cool–warm anomaly on phytoplankton phenology, supporting the Chiba et al. [2008] hypothesis. 3.2. Community Structure [16] A significant relationship was not detected between the interannual variation of the summertime phytoplankton community structure and either of the PDO and PDO-related monthly/seasonal average SSTs. Phytoplankton community was correlated to the extent of monthly/seasonal SST increases (Table S1 in the auxiliary material). [17] Diatom abundance was greatest in 2001 and declined afterward (Figure 3a), with interannual variation being significantly correlated with Delta SST (March–April) (r = 0.757, P < 0.05). Likewise, non-diatom abundance had a weak positive correlation (r = 0.657, P < 0.1) with Delta SST (May– July) and the non-diatom ratio to total phytoplankton abundance had a significant positive correlation (r = 0.836, P < 0.01) with Delta SST (May–July) (Figure 3b). No significant correlations were detected between any other phytoplankton indices and SST variables. Although the possibility of Type-1 error in the detection of two significant correlations among 4 16 pairs of time series cannot be eliminated, these
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Figure 3. Anomaly of phytoplankton indices and area mean SST variables with information of mid-day of the CPR sampling (Julian Day: JD). (a) Diatom abundance and extent of the SST increase between March and April (Delta SST March–April). (b) Non-diatom ratio to total phytoplankton abundance and extent of the SST increase between May and July (Delta SST May–July). Phytoplankton and SST time series were normalized to have a mean = 0 and SD = 1. For 2006, phytoplankton data are the mean of two CPR transects conducted in May and July. results suggested that phytoplankton response to seasonality in upper water physical environments was specific to taxonomic groups. The possible bias caused by interannual differences in sampling timing was ignored because the mid-day of sampling showed no significant correlation (P > 0.05) with either phytoplankton or SST variables. However, the lowest diatom abundance in 2006 might be due to differences in sampling timing (average of May and July transects, no June data) for that year. [18] Since we obtained similar interannual variations between CPR CI and the area mean Chl a in the May–July average (Figure S3 in the auxiliary material), we assumed that the satellite data represented the phytoplankton abundance around the CPR sampling line. The non-diatom ratio was high for 2004–2006 and low both before and after this period. Because a seasonal Chl a peak was observed in June for 2004–2006, while it occurred during April–May in other years (Figure 2a), the increase in non-diatom species was considered to contribute to the peaks in those years. Conversely, diatoms were considered likely to contribute to the relatively high Chl a in May and June 2001 after the extensive April bloom occurred (Figure 2a). [19] Although the correlation was not significant (P > 0.05) between the ratio of non-diatom to total phytoplankton cells and either the PDO or CUSUM 40% data, the time series showed that a similar interannual variation was high in 2004– 2006, the period with late bloom and a cool winter–spring conditions. This result suggested that the phytoplankton
phenology detected by satellite Chl a data not only indicated a change in seasonality of total phytoplankton abundance but also implied a shift in its community structure to some extent. [20] This led to the question as to what mechanism influenced the phytoplankton composition due to the extent of seasonal SST increase rather than the monthly/seasonal mean SST itself. The spring bloom in the Oyashio region is characterised by a high abundance of diatoms [Liu et al., 2004]. Assuming that seasonal SST increases indicate upper water stratification and a shoaling of the MLD, the results of our study suggest that stratification increased in the early spring during the warm years, which might provide favourable conditions with ample nutrients and light availability in the surface mixed layer to allow for the formation of an extensive diatom bloom. Alternatively, a large SST increase in late spring to summer after a cool winter indicates that rapid stratification occurred with nutrient depletion within a mixed layer. Such a condition is more likely to alter nutrient stoichiometry, becoming more favourable for non-diatoms rather than diatoms. [21] In the Oyashio region, silicates as well as other major nutrients were reported to be above the depleted level under post-bloom conditions [Saito et al., 2002], while other studies suggest that the subsurface iron supply might be limiting primary production in this area [Ono et al., 2002]. Thus, iron depletion might have a negative influence on diatom growth, whereas non-diatoms might benefit from the reduction of competitors. Moreover, nutrient concentration
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within the mixed layer in the Oyashio region is determined by the extent of not only wintertime vertical mixing, but also lateral mixing with water from Okhotsk that influences concentrations of subsurface nutrients [Tadokoro et al., 2009]. This may be why the PDO itself failed to account for observed seasonal/interannual changes in the phytoplankton community.
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by the North Pacific Research Board (NPRB), Canadian Department of Fisheries and Oceans and the Sir Alister Hardy Foundation for Ocean Science (SAHFOS), Grant-in-Aid for Scientific Research (A) (21241012) of the Japan Society for the Promotion of Science (JSPS), and UK-Japan Joint Research Project of JSPS. [26] The Editor thanks Li Zhai and an anonymous reviewer for assisting in the evaluation of this paper.
References 4. Conclusion [22] This study demonstrated the link between large-scale climatic forcing and the lower trophic level ecosystem in an oceanic region of the North Pacific. We conclude that seasonal and interannual SST variables related to the PDO signal are useful indicators of the variability of bloom timing. A possible relationship between the PDO-induced SST anomaly and phytoplankton bloom timing has been suggested for the Oyashio region based on seasonal shipboard observation data [Chiba et al., 2008], and we have demonstrated that the relationship is plausible using satellite data. Note, however, that the key mechanism proposed is likely driven by seasonal mixed layer processes rather than by the direct effects of temperature changes. We hence emphasise the importance of a better understanding of mixed layer dynamics, together with the variation of nutrients and light availability, and its influence on this ecosystem. [23] We also detected that phenological change occurred along with a change in phytoplankton community structure. This is particularly important in terms of the links to the higher trophic levels in the Oyashio region, which holds one of most productive fisheries in the world. A warming trend would influence zooplankton production and fish recruitment through either a reduction of available food through the match/mismatch scenario as a result of changes in phytoplankton seasonality and/or deterioration in the quality of food sources as a result of changes in the functional type of dominant phytoplankton. Recent studies have developed an algorithm to discriminate phytoplankton functional types (PFT) using ocean colour data [Sathyendranath et al., 2004]. Analysis of the CPR data of phytoplankton and zooplankton communities combined with satellite data could provide detailed processes linking spatio-temporal variation in primary and secondary production. [24] Marine ecosystem models have started to take account of PFT to understand ecosystem responses to ongoing and future environmental perturbations. For example, a model projection suggests that warming would induce a shift in the timing of the spring bloom and reduce the biomass of diatom and non-diatoms to a different extent [Hashioka and Yamanaka, 2007]. The results of our study provide useful data to be used for validation of these PFT model as well as to emphasise the importance of incorporating plankton functional types into ecosystem models. As functional biodiversity and its response to common climatic and anthropogenic forcing are regionally specific, further improvement of conventional PFT models through close international collaboration between biological oceanographers and modellers are highly recommended. [25] Acknowledgments. We acknowledge the captain and crew of the volunteer ship, Skaubryn, which towed the CPR, and the analysts who conducted the microscopic observation on phytoplankton. This study was supported by a consortium for the North Pacific CPR survey coordinated by the North Pacific Marine Science Organization (PICES), and funded
Batten, S. D., R. Clark, J. Flinkman, G. Hays, E. John, A. W. G. John, T. Jonas, J. A. Lindley, D. P. Stevens, and A. Walne (2003), CPR sampling: The technical background, materials and methods, consistency and comparability, Prog. Oceanogr., 58, 193–215, doi:10.1016/j. pocean.2003.08.004. Batten, S. D., K. D. Hyrenbach, W. J. Sydeman, K. H. Morgan, M. F. Henry, P. Y. Y. Yen, and D. W. Welch (2006), Characterising mesomarine ecosystems of the North Pacific, Deep Sea Res., Part II, 53, 270–290, doi:10.1016/j.dsr2.2006.01.004. Chiba, S., and T. Saino (2002), Interdecadal change in the upper water column environment and spring diatom community structure in the Japan Sea: An early summer hypothesis, Mar. Ecol. Prog. Ser., 231, 23–35, doi:10.3354/meps231023. Chiba, S., K. Tadokoro, H. Sugisaki, and T. Saino (2006), Effects of decadal climate change on zooplankton over the last 50 years in the western North Pacific, Global Change Biol., 12, 907–920, doi:10.1111/ j.1365-2486.2006.01136.x. Chiba, S., M. N. Aita, K. Tadokoro, T. Saino, H. Sugisaki, and K. Nakata (2008), From climate regime shifts to lower-trophic level phenology: Synthesis of recent progress in retrospective studies of the western North Pacific, Prog. Oceanogr., 77, 112–126, doi:10.1016/j.pocean.2008.03.004. Edwards, M., and A. J. Richardson (2004), Impact of climate change on marine pelagic phenology and trophic mismatch, Nature, 430, 881–884, doi:10.1038/nature02808. Gregg, W. W., and N. W. Casey (2004), Global and regional evaluation of the SeaWiFS chlorophyll data set, Remote Sens. Environ., 93, 463–479, doi:10.1016/j.rse.2003.12.012. Greve, W., U. Lange, F. Reiners, and J. Nast (2001), Predicting the seasonality of North Sea Zooplankton, Senckenb. Marit., 31, 263–268, doi:10.1007/BF03043035. Hashioka, T., and Y. Yamanaka (2007), Ecosystem change in the western North Pacific associated with global warming using 3D-NEMURO, Ecol. Modell., 202, 95–104, doi:10.1016/j.ecolmodel.2005.12.002. Koeller, P., et al. (2009), Basin-scale coherence in phenology of shrimps and phytoplankton in the North Atlantic Ocean, Science, 324, 791–793, doi:10.1126/science.1170987. Leterme, S. C., M. Edwards, L. Seuront, M. J. Attrill, P. C. Reid, and A. W. G. John (2005), Decadal basin-scale changes in diatoms, dinoflagellates, and phytoplankton color across the North Atlantic, Limnol. Oceanogr., 50, 1244–1253, doi:10.4319/lo.2005.50.4.1244. Limsakul, A., T. Saino, J. I. Goes, and T. Midorikawa (2002), Seasonal variability in the lower trophic level environments of the western subtropical Pacific and Oyashio Waters—A retrospective study, Deep Sea Res., Part II, 49, 5487–5512, doi:10.1016/S0967-0645(02)00208-4. Liu, H., K. Suzuki, and H. Saito (2004), Community structure and dynamics of phytoplankton in the western subarctic Pacific Ocean: A synthesis, J. Oceanogr., 60, 119–137, doi:10.1023/B:JOCE.0000038322.79644.36. Mackas, D. L., S. Batten, and M. Trudel (2007), Effects on zooplankton of a warmer ocean: Recent evidence from the northeast Pacific, Prog. Oceanogr., 75, 223–252, doi:10.1016/j.pocean.2007.08.010. McQuatters-Gollop, A., D. E. Raitsos, M. Edwards, and M. J. Attrill (2007), Spatial patterns of diatom and dinoflagellate seasonal cycles in the NE Atlantic Ocean, Mar. Ecol. Prog. Ser., 339, 301–306, doi:10.3354/ meps339301. Miller, A. J., F. Chei, S. Chiba, J. R. Moisan, and D. J. Neilson (2004), Decadal-scale climate and ecosystem interaction of the North Pacific Ocean, J. Oceanogr., 60, 163–188, doi:10.1023/B:JOCE.0000038325.36306.95. Ohman, M. D., and E. L. Venrick (2003), CalCOFI in the changing ocean, Oceanography, 16, 76–85, doi:10.5670/oceanog.2003.34. Ono, T., K. Tadokoro, T. Midorikawa, J. Nishioka, and T. Saino (2002), Multi-decadal decrease of net community production in western subarctic North Pacific, Geophys. Res. Lett., 29(8), 1186, 4 pp., doi:10.1029/ 2001GL014332. Peterson, W. T., and J. E. Keister (2003), Interannual variability in copepod community composition at a coastal station in the northern California Current: A multivariate approach, Deep Sea Res., Part II, 50, 2499–2517, doi:10.1016/S0967-0645(03)00130-9. Peterson, W. T., and F. B. Schwing (2003), A new climate regime in northeast Pacific ecosystems, Geophys. Res. Lett., 30(17), 1896, doi:10.1029/ 2003GL017528.
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Platt, T., C. Fuentes-Yaco, and K. T. Frank (2003), Spring algal bloom and larval fish survival, Nature, 423, 398–399, doi:10.1038/423398b. Saito, H., A. Tsuda, and H. Kasai (2002), Nutrient and plankton dynamics in the Oyashio region of the western subarctic Pacific Ocean, Deep Sea Res., Part II, 49, 5463–5486, doi:10.1016/S0967-0645(02)00204-7. Sasaoka, K., S. Chiba, and T. Saino (2011), Climatic forcing and phytoplankton phenology over the subarctic North Pacific from 1998 to 2006, as observed from ocean color data, Geophys. Res. Lett., 38, L15609, doi:10.1029/2011GL048299. Sathyendranath, S., L. Watts, E. Devred, T. Platt, C. Caverhill, and H. Maass (2004), Discrimination of diatoms from other phytoplankton
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using ocean-colour data, Mar. Ecol. Prog. Ser., 272, 59–68, doi:10.3354/ meps272059. Sverdrup, H. U. (1953), On conditions for the vernal blooming of phytoplankton, J. Cons. Int. Explor., 18, 287–295. Tadokoro, K., T. Ono, I. Yasuda, S. Osafune, A. Shiomoto, and H. Sugisaki (2009), Possible mechanisms of decadal‐scale variation in PO4 concentration in the western North Pacific, Geophys. Res. Lett., 36, L08606, doi:10.1029/2009GL037327. Trenberth, K. E., and J. W. Hurrell (1994), Decadal atmosphere-ocean variations in the Pacific, Clim. Dyn., 9, 303–319, doi:10.1007/BF00204745.
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