1. Equatorial Pacific primary productivity: Spatial and temporal variability and links to carbon cycling Pete Strutton College of Oceanic and Atmospheric Sciences Oregon State University

2. Outline Physical processes that impact productivity –El Niño –Tropical instability waves (TIWs) –Kelvin waves (MJO) How might these processes change as climate changes? What would be the resulting impact on carbon budgets? What do the models say? Variability at longer time-scales: PDO

5. Physical and biological setting: 1997-2004

8. Comparison of satellite and in situ data The in situ database documents large-scale trends but misses ephemeral events

9. Phytoplankton community composition Elevated nutrients in the upwelling zone relative to warm pool Moderately high chlorophyll concentrations –0.2 to 0.3 mg m -3 cf 0.05 to 0.1 mg m -3 in the warm pool & gyres Dominated by small planktonic species –Prochlorococcus and Synechococcus –Competitive advantage in low nutrient environment because of large surface area : volume –Survive on recycled N (as NH 4 ) and Fe An additional diatom component, reliant on Fe and Si inputs Export flux driven largely by the diatoms

10. Variability of chlorophyll and nutrients: 1997-2004

11. El Niño-La Niña mixed layer chlorophyll variability

12. Species composition as a function of nutrients Figure adapted from Dugdale et al., 2002

13. El Niño-La Niña mixed layer nutrient variability

15. El Niño-La Niña source nutrient variability

16. No change in source nutrients as a function of El Nino Probably not true for iron (source is the EUC)

17. Increased dominance of El Niño/La Niña? Almost no change in source nutrients.

18. Impact of tropical instability waves (TIWs)

19. Strutton et al., GRL, 2001.

21. 0.4 0.5 0.6 0.7 0.8 0.9 Growth 0.1 0.2 0.3 5 Day 21 Day 400 500 600 700 800 900 1000 1100 1200 1300 130 140 150 160 170 180 1900.03 0.04 0.05 0.06 0.07 0.08 Depth  f Date 12/1/961/1/972/1/973/1/974/1/975/1/97 3.0 3.5 4.0 4.5 5.0 25 26 27 28 29 NO 3 SST ( o C) A B C chl [mg m -3 ] depth [m] SST [°C] 1  prod [mgC m -2 d -1 ] growth [d -1 ] ff NO 3 [  M]

22. Impact of TIWs and Kelvin waves TIWs –Enhanced chlorophyll at the equator –Averaged over Wyrtki Box, essentially no difference –Evidence for enhanced diatom production and export –TIWs should become less dominant in an ‘El Niño climate’ Kelvin waves –Small decrease in chlorophyll –Evidence for reduced diatom production and export –Enhanced or diminished in an ‘El Niño climate’? –Impact possibly diminished for a deeper thermocline

23. Life in a more El Niño- or La Niña-like world Satellites can provide chl, but we need satellites + models to quantify changes in export

24. The system has been modeled as a chemostat - limiting nutrient(s) fed in via upwelling Can reproduce the general surface chlorophyll patterns well Ability to reproduce processes and the subsurface structure heavily dependent on the physics and available data Controls on new production –Depends on the type of physical forcing –Nutricline variability: El Nino and Kelvin waves –Variability in upwelling velocity: TIWs and short-term wind events Barely enough export data to know if they are getting it right What do the models say?

25. Global importance of equatorial Pacific productivity Figure courtesy of Mike Behrenfeld, OSU

26. Global importance of equatorial Pacific productivity From Behrenfeld et al., Science, 2001. Dec98-Feb99 Jun99-Aug99 98/99-97/98 1999-1998

27. El Niño to La Niña transition, 1997-2000 Increase in ocean NPP from ~50 to 53 PgC year -1 Largely due to increases in: –Equatorial Pacific and Atlantic –Coastal upwelling regions (Canary, Arabian Sea) –Patagonian shelf and regions downstream Terrestrial productivity: –approximately constant, globally –regionally variable (Amazonia) Global importance of equatorial Pacific productivity

29. The PDO’s impact on the equatorial Pacific From Chavez et al., Science, 2003.

30. TAO array and satellites provide excellent synoptic view of broad physics and surface chlorophyll But, to predict the future we need better models For this we need more data: –Iron! –Phytoplankton community composition in response to nutrient fluxes –Spatial and temporal variability of export (TIWs) –Mixing and upwelling vs thermocline variability for fueling productivity Also need a better understanding of feedbacks Productivity and export: Knowns and unknowns