Climate Variability and Phytoplankton Composition in the Pacific Ocean Rousseaux C.S., Gregg W.W.

1. Climate Variability and Phytoplankton Composition in the Pacific Ocean Rousseaux C.S., Gregg W.W. NASA Ocean Color Research Team Meeting, April 23-25 2012, Seattle, WA El Niño La Niña Chlorophyll a, NASA Ocean Color

2. 1997-98 El Niño Seabird abundance and anchoveta and sardine landings from Peru (Chavez et al. 2003)

3. When it comes to feeding fishes, all phytoplankton are not equal… Phytoplankton (Chlorophyll a) Cyanobacteria Coccolithophores Chlorophyll a Chlorophytes Diatoms

4. Are El Niño conditions unfavorable to all phytoplankton groups or only some?

5. NASA Ocean Biogeochemical Model (NOBM) • Clouds • Precipitation water • Relative humidity • Ozone Wind Stress Wind speed pCO2 Advection pCO2 Nutrients Phytoplankton Chloro, Cocco, Cyano Diatoms Fe, NO3, NH4 Si DIC Silica Detritus Mixing Herbivores Iron Detritus N/C Detritus

6. Validation Variable Global Difference % Correlation over Basins Nitrate 18.9% 0.905 P<0.05 Ammonia Not tested Not tested Silica 5.4% 0.952 P<0.05 Dissolved Iron 45% 0.933 P<0.05 Diatoms 15.5% 0.850 P<0.05 Chlorophytes -16.2% 0.020 NS Cyanobacteria 7.9% 0.970 P<0.05 Coccolithophores -2.6% 0.700 P<0.05 Total Chlorophyll vs In situ -17.1% 0.787 P<0.05 vs SeaWiFS -8.0% 0.618 P<0.05 vs Aqua 1.1% 0.469 NS Herbivores Not tested Not tested Detritus Not tested Not tested Diss. Inorganic Carbon 0.1% 0.972 P<0.05 pCO2 0.0% 0.765 P<0.05 Air-sea carbon flux 3.1% 0.741 P<0.05

7. Figure 2| Comparison of chlorophyll (mg m-3) from the assimilation model, the free-run model, and SeaWiFS. The assimilation and free-run chlorophyll distributions represent simulations for April 1, 2001. SeaWiFS data for the same day are shown for comparison, along with the monthly mean. Grey indicates land and coast, black indicates missing data, and white indicates sea ice. In situ data from Seabass and nodc

8. Data Assimilation In ocean biology, Two Classes: Variational (e.g., adjoint, 4DVar) Sequential (e.g., Kalman Filter) We use Sequential Methodologies, Conditional Relaxation Analysis Method Ensemble Kalman Filter Routinely assimilating SeaWiFS and Aqua Chlorophyll Data

9. Figure 3|Comparison of the free run, the multivariate and the univariate approach for chlorophyll and nutrients in the South Pacific Ocean. Time series of annual averages of (a) Chlorophyll, (b) Nitrate, (c) Silicate and (d) Iron. [Rousseaux & Gregg 2012]

10. MEI

14. How well does the NOBM compare to in situ data? Global Phytoplankton Relative Abundance 469 observations taken from figures in peer-reviewed papers; Available at GMAO Web site

15. How well does the NOBM compare to in situ data? Percentage difference between the NOBM and the in situ data. The number of observations used for the comparison is between parenthesis Only >20% in 3 cases

16. Intercomparison of model- and satellite-derived phytoplankton community composition Hirata et al. 2011

17. Percentage difference between Hirata’s method and the in situ data. The number of observations used for the comparison is between parenthesis Intercomparison of model- and satellite-derived phytoplankton community composition Percentage difference between Hirata’s method and the in situ data. The number of observations used for the comparison is between parenthesis Only >20% in 1 cases Regions where the satellite-derived approach is closer to the in situ data than the NOBM

19. Conclusion: Climate variability has most impact on the phytoplankton community composition in the Equatorial Pacific Large Shifts are observed both on temporal and spatial scale These shifts have potential important consequences for the carbon cycles and higher trophic levels Different methods provide different views of the impact climate variability has on the biology

20. NS P<0.05 P<0.05 P<0.05 Supporting data and publications: Google gmao, click Research, then Ocean Biology Modeling (http://gmao.gsfc.nasa.gov/research/oceanbiology)

21. Blue = NOBM; Green = Data Gregg and Casey, 2007, Deep-Sea Research II