# Presentation: 501890

## 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

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## 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.

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## 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.

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## 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.

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## 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

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## Geostatistical Jargon Translated

**Overall Variability ****→**

**← ****Resolved Variability**

**← ****Unresolved Variability**

**← ****Decorrelation Scale**

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## Spatial length scale (the point at which data become decorrelated)

**(Doney *****et al*****., 2003)**

- Resolved variability (geophysical signal)

- Unresolved variability (sub-scale or noise).

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## 19 February 2000

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## Monthly smoothed mean field 
30 day moving average 
20 x 20 pixel block processed

- Monthly smoothed mean field
- 30 day moving average
- 20 x 20 pixel block processed

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## The anomaly field (daily minus the monthly mean field)
Removes the seasonal, large scale trends
Initially analyzed by quadrants

- The anomaly field (daily minus the monthly mean field)
- Removes the seasonal, large scale trends
- Initially analyzed by quadrants

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## 2-D FFT Semivariograms (Marcotte, 1996)

**2-D FFT Semivariograms (Marcotte, 1996)**

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## February 2000 1-D Semivariogram Sargasso Sea

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## Slide 13

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## One-dimensional semivariogram for the month of February 2000;

**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.**

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## 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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## Slide 16

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## Slide 17

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## Slide 18

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## Slide 19

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## Future Work

- _Data_
- MODIS AQUA (Chl, SST);
- Historical SeaWiFS (Chl);
- TOPEX and Jason-1/2 (SLA);
- SeaWinds/QuikSCAT  (U10)

- _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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## Slide 21

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## Not Quite Ready for Primetime Backup Slides

**Not Quite Ready for Primetime Backup Slides**

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## Slide 23

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## Slide 24

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## 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

- 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

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## Robust Semivariogram

- Robust semivariogram estimator
- Cressie and Hawkins (1980)
- Less sensitive to outlying data points

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## Slide 27

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## Slide 28

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## Slide 29

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