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Regional Fisheries Oceanography of the California Current System pp 77–138Cite as

Scales of Variability Relevant to Fisheries in the Southern California Current System

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Abstract

The concepts that have received considerable attention by researchers and that have guided trends in the interpretation of research results relevant to CalCOFI can be organized according to the temporal scales that they encompass, notably the decadal (10–100+ years), inter-annual (1–5 years), seasonal (1 month to 1 year) and weather (less than 1 month) scales. At the decadal scale are regime shifts, the basin hypothesis of range contraction and expansion, cycles in fish and plankton assemblages, the alternation of dominance in small pelagic fish populations, and geographical shifts of assemblages with secular trends in climate. Inter-annual scales are dominated by the effects of El Niño-Southern Oscillation events and by mesoscale variability and associated advection. At seasonal scales, I discuss upwelling and production, the spring transition and phenology (in Chap. 2), fish migration, and variability in small pelagic fish spawning habitat. At the shortest daily to weekly time frames, or weather scale, the key concepts include mixing, stability and recruitment, mortality and patchiness.

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Notes

  1. 1.

    PDO time series data are available from the Joint Institute for the Study of the Atmosphere and Oceans, University of Washington.

  2. 2.

    Gershunov and Barnett [182] use the term NPO, or North Pacific Oscillation, to refer to the same phenomena that Mantua et al. [356] call the PDO, or Pacific Decadal Oscillation.

  3. 3.

    The PDO was described from sea surface temperature anomaly (SSTa) field by Mantua et al. [356] in contrast to Di Lorenzo et al. [138] who described the PDO in terms of the sea surface height anomaly (SSHa) field, but both these fields are highly correlated.

  4. 4.

    Climate “noise” comes from the realization of random processes created from the sum of individual sinusoids of different frequencies, amplitudes and phases. A red noise spectrum is one where the power (i.e. the average-squared amplitude) increases as frequency decreases (or wavelength increases). In contrast, for a white noise process, the power is constant as a function of frequency [432]. Different realizations of a stationary red noise process can produce “runs” where a time series anomaly will have the same sign (i.e. be either above or below the mean) for extended periods [479].

  5. 5.

    Hubb’s conclusions were based on the Pacific Railroad Survey off San Diego between 1853–1857 and more extensive collections off Monterey Bay and southern California conducted by Gilbert and Jordan around 1880 (MacCall [344] attributed to Hubbs [226]).

  6. 6.

    The 26.4 isopycnal is used to show the influence of the California Undercurrent or flows of southern origin (see Sect. 2.3).

  7. 7.

    At the sea surface, the geostrophic velocities can be calculated from time-averaged sea surface heights at two measurement locations relative to an assumed level surface (which is generally 500 m or 200 m in the CalCOFI atlases). The mass of water at a given measurement location is a function of both the sea surface height and the density of the water. The total mass of water determines the pressure difference at a given depth. The geostrophic velocity is proportional to the difference in pressure at two locations. Near the coast, the water column stands higher at the surface relative to the geoid, and the greater mass of water creates higher pressure. The geostrophic balance between pressure and Coriolis forces (i.e. no flow relative to the earth) causes the current to flow to the right of the down-pressure gradient, i.e. creates a northward flowing current. Further offshore, sea surface height is greater toward the west, and the geostrophic balance again deflects flow to the right of the down-pressure gradient, which in this case, is a southward-flowing current (see [546] for detail).

  8. 8.

    Bakun considered that the locations off Baja California were influenced by the Gulf of California waters to the east which changed the ocean-atmosphere interactions that create the upwelling favorable winds, so that these areas did not experience the same conditions as locations further to the north which have continental land mass to the east.

  9. 9.

    Based on springtime samples collected over 32 years in the period 1951–2005 when both southern and central California were surveyed.

  10. 10.

    Roemich and McGowan [475, 476] do not state in their methods whether they used total zooplankton displacement volume including larger individual organisms with biovolumes > 5 ml, or whether they used small plankton volumes which exclude these larger individuals. Lavaniegos and Ohman [298] stated that they used total zooplankton displacement volumes.

  11. 11.

    Their study area was the regions from San Francisco to south-central Baja California, extending offshore to about CalCOFI station 80 (their figure 1 [82]).

  12. 12.

    It is unclear what a “near maximum” as opposed to a maximum is.

  13. 13.

    Dollars are not inflation adjusted.

  14. 14.

    0.24 million or 240,000 metric tons

  15. 15.

    The environmental variables used by Hsieh et al. [224] were surface temperatures averaged over the 75-station CalCOFI pattern, the upwelling index anomaly from the central Southern California Bight (33N, 119W) [40], and three climate indices: the Southern Oscillation Index [561], the Pacific Decadal Oscillation Index [356], and the North Pacific Index [562].

  16. 16.

    Temperature was from 6 core CalCOFI lines on the quarterly surveys including stations as far offshore as station 90, and all standard depths (0, 10, 20, 30, 50, 75, 100, 125, 150, 200, 250, 300, 400, and 500 m). Quarterly means were calculated and the long term quarterly averages removed to create anomalies, which were then averaged to create annual “SST” anomalies. It is unclear whether Hsieh et al. [225] used all of these depths, or just some of these depths to estimate “SST”.

  17. 17.

    For an example, see the comparison of inter-annual variability in SST anomalies of − 0.2 to + 2.2∘C for April and May 1982 and 1983 versus a temperature anomaly of 4 ∘C in July 1983 in warm water intruding along the coast of the Southern California Bight attributed to El Niño [160].

  18. 18.

    Chondrophores are now separated from Siphonophores and are represented by two genera, Velella and Porpita [459].

  19. 19.

    Quoting from Singh et al. [508]: “Using EOF and AHC analyses on SSS, and comparing with ENSO-related SST features, we showed that the eastern Pacific (EP) El Niño events in SSS appear in 1982–1983, 1991–1992, and 1997–1998, the central Pacific (CP) El Niño events in 1977–1978, 1986–1988, 1990–1991, 1992–1995, 2002–2003, 2004–2005 and 2006–2007, the EP La Niña events in 1985–1986, 1988–1989, 1995–1996, 1999–2001, 2005–2006 and 2007–2008, and the CP La Niña events in 1983–1984 and 1998–1999. It should be noted, however, that clearly differentiating the two EP and CP El Niño and La Niña flavors is a difficult exercise, depending on the method used, and noting that some years may even be classified as “mixed events”.

  20. 20.

    According to a National Center for Atmospheric Research (NCAR) web page (http://climatedataguide.ucar.edu/guidance/nino-sst-indices), these values are computed by following: (a) Calculate the area-averaged total SST from Niño X region; (b) Compute a monthly climatology (e.g., 1950–1979) for area averaged total SST from Niño X region, and subtract the climatology from the area averaged total SST time series to obtain anomalies; (c) Smooth the anomalies with a 5-month running mean; (d) Normalizing the smoothed values by its standard deviation over the climatological period.

  21. 21.

    According to a National Center for Atmospheric Research (NCAR) web page (http://climatedataguide.ucar.edu/guidance/nino-sst-indices), the TNI computation follows: (a) Compute the area averaged total SST from Niño 1+2 region; (b) Compute area averaged total SST from Niño 4 region; (c) Compute monthly climatologies (e.g., 1950–1979) for area averaged total SST from Niño 1+2 region, and Niño 4 region, and subtract climatologies from area averaged total SST time series to obtain anomalies; (d) Normalize each time series of anomalies by their respective standard deviations over the climatological period; (e) Define the raw TNI as Niño 1+2 normalized anomalies minus Niño 4 normalized anomalies; (f) Smooth the raw TNI with a 5-month running mean; (g) Normalize the smoothed TNI by its standard deviation over the climatological period.

  22. 22.

    Note that 2005 was also an anomalously warm year in the northern California Current System with delayed wind-driven upwelling [497]. Conditions were close to ENSO-neutral (Bivariate ENSO index) for much of the year, so warm years are not just associated with ENSO events.

  23. 23.

    See Sect. 3.1.4

  24. 24.

    Zeidberg et al. [595] refer to this rather loosely as a “stock recruitment relationship” although CPUE is only a coarse proxy for the biomass of market squid, as they explain in their paper, and they do not measure recruitment.

  25. 25.

    This size threshold was chosen to reflect the size at which larvae are expected to begin schooling behavior, on the assumption that aggregated larvae were those that had occupied suitable habitat and experienced lower mortality than the dispersed early larvae. For the analysis Logerwell and Smith (2001) simulated a Poisson distribution with the same mean as the frequency distribution of larvae (for all surveys combined and for each survey) and then selected larval densities of 8.75+ mm sized larvae that were more abundant than the maximum density from the simulated Poisson distribution (i.e. those larval densities that were considered non-random).

  26. 26.

    To test the association between larvae and eddies at small scales Logerwell and Smith (2001) selected surveys where more than 10 larvae were caught (17 of 56 surveys) and compared the median density of larvae at stations in eddies and away from eddies using a Mann-Whitney U-test. To test the relationship between survivor abundance and environmental conditions including eddies Logerwell and Smith (2001) divided the survey area into regions, calculated the mean sardine survivor density, egg density, 10 m temperature, chlorophyll a, and zooplankton displacement volume for 1983–1998 and eddy density for 1987–1998, and tested for difference between regions using ANOVA.

  27. 27.

    During spring cruises from 1997 to 2012 fish eggs were collected from 3 m depth with the Continuous Underway Fish Egg Sampler (CUFES), manually identified and counted, and converted to densities (eggs m−3). Sea surface temperature (SST) imagery were gridded to the same resolution [579], and densities of sardine, anchovy and jack mackerel eggs were overlaid on the SST (Fig. 4.47).

  28. 28.

    Ahlstrom [6] found sardine and anchovy larvae in the upper 90 m, but 80–90 % of them were in the upper 50 m.

  29. 29.

    A reasonable alternative approach when the glider time series is long enough (it is currently only 6–8 years long) would be to revise the model using integrated acoustic backscatter at 420kHz as a direct proxy for secondary production.

  30. 30.

    Asch and Checkley (2013) reported that “Spring observations of dynamic height in the southern CCE ranged between 68 and 108 cm. Reflecting the equatorward flow along the California coast, low dynamic heights (75–80 cm) were usually observed nearshore, while high dynamic heights (95–105 cm) occurred offshore” [32].

  31. 31.

    The mean differences in distance offshore for four cruises between January to May were only 30–60 km [205]. For comparison, the width of wind-driven upwelling off central California ranges from 15–50 km, so small cross-shelf distances can make a substantial difference in environment in some areas.

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    Sam McClatchie

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McClatchie, S. (2014). Scales of Variability Relevant to Fisheries in the Southern California Current System. In: Regional Fisheries Oceanography of the California Current System. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7223-6_4

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