Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2012;7(1):e30436.
doi: 10.1371/journal.pone.0030436. Epub 2012 Jan 19.

Climatic control of upwelling variability along the western North-American coast

Affiliations

Climatic control of upwelling variability along the western North-American coast

Diego Macias et al. PLoS One. 2012.

Abstract

The high biological production of the California Current System (CCS) results from the seasonal development of equatorward alongshore winds that drive coastal upwelling. While several climatic fluctuation patterns influence the dynamics and biological productivity of the CCS, including the El Niño-Southern Oscillation (ENSO), the Pacific Decadal Oscillation index (PDO) and the North Pacific Gyre Oscillation (NPGO), the mechanisms of interaction between climatic oscillations and the CCS upwelling dynamics have remained obscure. Here, we use Singular Spectral Analysis (SSA) to reveal, for the first time, low-frequency concordance between the time series of climatic indices and upwelling intensity along the coast of western North America. Based on energy distributions in annual, semiannual and low-frequency signals, we can divide the coast into three distinct regions. While the annual upwelling signal dominates the energy spectrum elsewhere, low-frequency variability is maximal in the regions south of 33°N. Non-structured variability associated with storms and turbulent mixing is enhanced at northerly locations. We found that the low-frequency signal is significantly correlated with different climatic indices such as PDO, NPGO and ENSO with the correlation patterns being latitude-dependent. We also analyzed the correlations between this upwelling variability and sea surface temperature (SST) and sea level pressure (SLP) throughout the North Pacific to visualize and interpret the large-scale teleconnection dynamics in the atmosphere that drive the low-frequency coastal winds. These results provide new insights into the underlying mechanisms connecting climatic patterns with upwelling dynamics, which could enhance our prediction and forecast capabilities of the effects of future oceanographic and climatic variability in the CCS.

PubMed Disclaimer

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Position of the CUI estimates along the western coast of USA.
Inset, map of North Pacific Ocean and box showing the study area.
Figure 2
Figure 2. Upwelling index at 39°N.
Gray line, monthly CUI; continuous black line, annual signal; broken black line, low-frequency signal.
Figure 3
Figure 3. Results of the SSA analysis of the CUI at 39°N.
a) Main eigenvalues of the series. b) Three first pure signals; continuous black line, annual signal; broken black line, semi-annual signal; gray line, low-frequency signal.
Figure 4
Figure 4. Analysis of the annual signal at 39°N.
a) Climatologic (gray) and SSA (black) annual cycles of the CUI at 39°N. b) Percentage of difference between both annual cycles.
Figure 5
Figure 5. Characteristics of the annual signal of the CUI at 39°N calculated with the SSA.
a) Maximum (black line) and minimum (dotted line) values of the annual signal. b) Integrated value of the water upwelled only because of the annual signal. c) Day of the year of the maximum upwelling due to the annual signal.
Figure 6
Figure 6. Low-frequency signal of the CUI at 39°N and different climatic indices.
PDO (upper panel), NPGO (central panel) and MEI (lower panel). Climatic indices are indicated by gray bars and the low frequency CUI signal as a black line.
Figure 7
Figure 7. Percentage of energy associated to each signal detected in the SSA analysis.
Latitudinal distribution of the energy of the annual (black triangles), semiannual (crosses) and low-frequency (gray circles) signals along the Western North American coast.
Figure 8
Figure 8. Correlation coefficients of the different low-frequency signals of the CUI with the climatic indices (a) and SST and SLP anomalies (b) along the western US coast.
Panel a) Gray circles, PDO; black triangles, NPGO; red squares, MEI. Panel b) Gray circles, SST anomalies; black triangles, SLP anomalies. Insignificant correlations are shown with circled symbols; correlations significant at 0.05>p>0.01 are presented with an asterisk, and the rest are significant at p<0.01.The number in brackets is the month of the year in which the maximum correlation between both series is achieved.
Figure 9
Figure 9. R-value distributions throughout the North Pacific.
SST anomalies (left column) and SLP anomalies (right column) versus low frequency CUI signal at 27°N (upper panel), 42°N (central panel) and 54°N (lower panel). Black stars indicate the position of the CUI estimates.

Similar articles

Cited by

References

    1. Walsh JJ. Importance of continental margins in the marine biogeochemical cycling of carbon and nitrogen. Nature. 2001;350
    1. Smith SV, Hollibaugh JT. Coastal metabolism and the oceanic organic carbon balance. Rev Geophys. 1993;31:75–89.
    1. Muller-Karger FE, Varela R, Thunell R, Luerssen R, Hu C, et al. The importance of continental margins in the global carbon cycle. Geophysical Research Letters. 2005;32:L01602.
    1. Berger WH, Smetacek VS, Wefer G. Berlin: 1989. Productivity of the ocean : present and past : report of the Dahlem Workshop on Productivity of the Ocean, Present and Past.
    1. Rebstock GA. Long-term change and stability in the California Current System: lessons from CalCOFI and other long-term data sets. Deep-Sea Research II. 2003;50:2583–2594.

Publication types