· Various satellite time series were used to evaluate the large-scale space-time variability in the North Pacific.
· As shown by several authors (Esaias et al. 1999; Behrenfeld et al., 2005) one way to differentiate between various oceanographic provinces is to use the standard deviation of surface chlorophyll. Different levels of variability tend to characterize functionally different oceanic regions.
· Fig. 1. Standard deviation of the merged (OCTS-SeaWiFS-MODIS-Aqua) monthly Chl time series. Different oceanographic regions such as the oligotrophic gyres (blue), highly variable upwelling or seasonal production cycle areas (red) and intermediate variability areas (green to yellow) are evident. By thresholding the standard deviation value we can create coarse masks for different oceanographic regions.
· Fig. 2. One possible partition of the North Pacific into five different regions according to their surface Chl variability. The smaller separate patches may be treated separately or merged with their large neighboring areas.
· Another way to find characteristic spatio-temporal domains is to use the EOF analysis. First, monthly anomalies were created for the merged Chl time series and then EOF analysis was applied to the monthly anomalies. The following figure shows the results.
Fig. 3. First
principal component of the monthly Chl anomalies time series in North Pacific. Yellow
and red show positive values and blue show negative areas. The patterns on this
image are quite different from the standard deviation patterns of Fig. 1. For
example, while the Bering Sea and the
· Fig. 4. The temporal evolution of the first principal component (EOF1) shows close resemblance with the Extratropical Northern Oscillation index (NOI, Schwing et al., 2002). Note the strong El Nino event of 1997-1998, and the weak events of 2003 and 2005 corresponding to the minima.
· The previous area of interest covered the whole Northern Pacific. We now concentrate on the eastern North Pacific.
Fig. 5. Chlorophyll
anomaly during the height of the 1997/1998 El Niño. Red shows significant
positive anomalies and blue shoes significant negative anomalies. The increased
chlorophyll-a concentration off
· We now apply EOF analysis on Chl anomalies in the eastern North Pacific. The results are very similar to the EOF analysis as applied to the whole North Pacific.
· Fig. 6. First principal component of the monthly Chl anomalies time series in eastern North Pacific. Green to yellow to red show positive values and blue to purple show negative areas. Positive values in this mode mean decreased Chl and negative values mean increased Chl during El Niño. These patterns are very similar to patterns in Fig. 3 but show more detail in Eastern Pacific. For example, we see that the near-shore zone of Baja California behaves differently from offshore Baja (first noted in Kahru and Mitchell, 2000). The increased Chl during El Niño occurs only offshore while the near-shore Chl is suppressed.
· We now select the following characteristics areas (masks) according to PC1 of Chl anomalies in Fig. 3 in order to show time series of Chl in these characteristic domains:
o 1. The northern part of the oligotrophic gyre (orange)
· Fig. 7. Selected characteristics areas (masks) according to PC1 of Chl anomalies in Fig. 3.
Time series of
export flux (EF) in these 4 contrasting areas shows opposite response to El
Nino. While areas 1 and 3 have increased net production (NPP) and EF during the
El Nino, areas 2 and 4 have suppressed NPP and EF. It is interesting that the
nearby areas off
Fig. 5. Export
flux in the 4 areas (see above) calculated from satellite detected Chl, SST and
PAR and using the VGPM model of Behrenfeld and Falkowski (1997) and the export
flux model of Laws (2004). As predicted by the selection of the areas according
to their contrasting PC1 values, they show opposite responses to El Nino. While
El Nino suppresses Chl, NPP and EF off the
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