1. 13 April 2006OCRT Meeting Newport RI 1 Meso- to Submesoscale Variability of Marine Biological Patchiness David M. Glover Woods Hole Oceanographic Institution Scott C. Doney (WHOI), Alise Wallis (Hunter College) and Norman Nelson (UCSB) This work is supported under NASA NRA-04-OES-02

2. 13 April 2006OCRT Meeting Newport RI 2 0.3 -10 km day - week 10 – 300 km week - months Provide global and systematic estimates of marine ecosystem responses to submesoscale and mesoscale physical forcing to help understand (model) the temporal and spatial variability of ocean primary productivity and new production.

3. 13 April 2006OCRT Meeting Newport RI 3 Goals Characterize the submesoscale component of ocean color variability; Determine the physical-biological mechanisms driving the high frequency ocean color variability; Quantify the seasonal and interannual variability in the mesoscale biological signals from a global multi-year time- series record.

4. 13 April 2006OCRT Meeting Newport RI 4 Biological Variability Physical processes on the submesoscale may enhance the biological signal (Levy et al., 2001; Abraham 1998; Martin 2003) –turbulent advection and stirring, ecosystem interactions, unresolved-scale upwelling We suggest ocean color variability is dominated by submesoscale processes in oligotrophic regimes (Doney et al., 2003) Objective is to characterize the variability on the submesoscale to gain insight into the processes occurring on this spatial scale.

5. 13 April 2006OCRT Meeting Newport RI 5 Semivariogram Analysis γ *(v) the semivariogram function Z(x) the geophysical data v the “lag” between data points N(v) the number of pairs of data points at lag v The closer two points are, the more similar they appear Robust semivariogram estimator Cressie and Hawkins (1980) Less sensitive to outlying data points

6. 13 April 2006OCRT Meeting Newport RI 6 Overall Variability → ← Resolved Variability ← Unresolved Variability ← Decorrelation Scale Geostatistical Jargon Translated

7. 13 April 2006OCRT Meeting Newport RI 7 (Doney et al., 2003) Spatial length scale (the point at which data become decorrelated) Resolved variability (geophysical signal) Unresolved variability (sub- scale or noise).

8. 13 April 2006OCRT Meeting Newport RI 8 19 February 2000

9. 13 April 2006OCRT Meeting Newport RI 9 Monthly smoothed mean field 30 day moving average 20 x 20 pixel block processed

10. 13 April 2006OCRT Meeting Newport RI 10 The anomaly field (daily minus the monthly mean field) Removes the seasonal, large scale trends Initially analyzed by quadrants

11. 13 April 2006OCRT Meeting Newport RI 11 2-D FFT Semivariograms (Marcotte, 1996)

12. 13 April 2006OCRT Meeting Newport RI 12 February 2000 1-D Semivariogram Sargasso Sea

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14. 13 April 2006OCRT Meeting Newport RI 14 One-dimensional semivariogram for the month of February 2000; Semivariogram analysis can extend out to lags of 250 km; NW quadrant around Bermuda only out to 50 km; submesoscale behavior of the semivariogram the data points fall off of the simple model at distances of less than approximately 10 km.

15. 13 April 2006OCRT Meeting Newport RI 15 Subtract the 1 km semivariance from the 10 km semivariance and divide by the total variability of the dataset minus the unresolved variability (at 1 km), this yields the percentage of submesoscale variability present.

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20. 13 April 2006OCRT Meeting Newport RI 20 Future Work Data MODIS AQUA (Chl, SST); Historical SeaWiFS (Chl); TOPEX and Jason- 1/2 (SLA); SeaWinds/QuikSCAT (U 10 ) Analysis Complete time series of meso- and sub- mesoscale; Improve calculation of daily anomalies; Perform cross- correlation and cross- semivariogram analysis

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22. 13 April 2006OCRT Meeting Newport RI 22 Not Quite Ready for Primetime Backup Slides

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25. 13 April 2006OCRT Meeting Newport RI 25 A similarity seen in physical and biological spatial decorrelation length scales; Suggests, but does not prove, a causative link; Does not directly address how physical turbulence governs biological spatial scales

26. 13 April 2006OCRT Meeting Newport RI 26 Robust Semivariogram Robust semivariogram estimator Cressie and Hawkins (1980) Less sensitive to outlying data points

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