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Impact of Pacific Climate Variability on Ocean Circulation, Marine Ecosystems & Living Resources

Impact of Pacific Climate Variability on Ocean Circulation, Marine Ecosystems & Living Resources. Francisco Chavez MBARI Lead PI Dick Barber, Duke University Co PI

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Impact of Pacific Climate Variability on Ocean Circulation, Marine Ecosystems & Living Resources

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  1. Impact of Pacific Climate Variability on Ocean Circulation, Marine Ecosystems & Living Resources Francisco Chavez MBARI Lead PI Dick Barber, Duke University Co PI Fei Chai, University of Maine Co PI Yi Chao, JPL of Calif. Inst. of Tech. Co PI David Foley, NOAA SW Fish. CenterCollaborating PI Supported by Earth Science Research Program; Carbon Cycle and Ecosystems Focus Area; Biodiversity and Ecological Forecasting Section.

  2. Usual* justification of ocean ecosystem development: - better management of living resources - helping to achieve sustainability, and - optimizing societal investment in fishery infrastructure But…

  3. For example, a proposal funded by NSF in May 1975 said: “The goal of the Coastal Upwelling Ecosystems Analysis Program (CUEA) is to understand the coastal upwelling ecosystem well enough to predict its response far enough in advance to be useful to mankind.”

  4. What was CUEA? A big, multi-institutional, multi-disciplinary, multi-agency, long-term project… Big = 14 mil $ from 1972 to 1980; ~24 PI’s, ~14 institutions, four agencies (NSF/NOAA/ONR/NASA) Peru field work in 1976/1977 with 4 US ships, an NCAR plane, a NASA radiometer, several shore-based met stations plus a lot support from Peru in people, logistics and diplomacy CUEA was a successful interdisciplinary basic research project; but did notdeliver any “useful to mankind” product. Why?

  5. three sources of systemic model error: theory - understanding, equations resolution - time/space realism initialization - initial state realism

  6. In the 70’s there were: Two specific biological “theory” deficiencies: food web structure + Fe, Two specific physical “theory” deficiencies: remote forcing + decadal variability Plus 3 fatal technical constraints*: computing power, observing power (satellites) information handling infrastructure • *Tech constraints unconceivable, at the time • Absolutely fatal • Orders of magnitude changes required to fix

  7. In 2008 we think this is the status of ocean forecast modeling • Two specific biological “theory” deficiencies: = FIXED • food web structure + Fe • Two specific physical “theory” deficiencies: = ½ FIXED • remote forcing+ decadal variability • Plus 3 fatal technical constraints: = ALL FIXED • computing power, observing power (satellites and moorings) • information infrastructure

  8. computational power revolution. The potential consequences of Moore’s Law for operational forecast modeling are impressive: increased time and space resolution, new concepts (i.e., assimilation), scale convergence, scale expansion, spatial nesting, reanalysis, model complexity, near real-time modeling, retrospective modeling, etc., etc., etc.

  9. Model complexity re need for diatom and picophytoplankton response to perturbations pico-micro steady-state rates shift-up, but with small biomass change Diatomsbloom, crash and export; rates and biomass change (first +, then --) picophytoplankton Chl (mg m-3) diatoms -1 0 1 2 3 4 5 6 7 8 9 10 11 Days since first Fe Addition From Barber and Hiscock, GBC, 2006

  10. Remote Forcing: El Nino’s influence California Current System (J. Ryan, MBARI)

  11. Sardine Landings Japan California Peru South Africa 1925 1950 1975 Chavez et al. Science (2003) 1925 1950 1975 2000

  12. Scale convergence of eddy kinetic energy of model and observationsin a coastal upwelling system Observation 2.5-km 5-km 10-km 20-km Eddy kinetic energy (cm2s-2) Drifter Model Resolution (km)

  13. Different physical and biological response to the same initial physical perturbation

  14. A Nine Month Forecast of Peru Coastal Chl that is PDG for the first 5 months!

  15. The same nine month forecast embedded in a longer record

  16. European Centre for Medium-Range Weather Forecasts (ECMWF)

  17. Conclusion: The deficiencies in theory and technology that prevented “useful” operational marine ecosystem forecasts for resource management in the past are now being surmounted. Its time to test this new, and still primitive, forecasting capability in ecosystem-based management of living resources. Peru is the best place to start.

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