1. Physical drivers of interannual variability in phytoplankton phenology
Harriet Cole1, Stephanie Henson2, Adrian Martin2, Andrew Yool2
1University of Southampton
2 National Oceanography Centre

2. Outline What is phenology and why is seasonality important?
Seasonality metric definition – bloom timing
Basin-wide relationships between bloom timing and physical drivers
Discussion – focus on subpolar North Atlantic and bloom initiation
Future work 2

3. Phytoplankton bloom phenology
Date of annually occurring features
Defined in bloom timing metrics
Peak
Point of slide: what is phenology, can sum up in metrics to measure objectively
Initiation
Termination

4. Why seasonality is important
Overlap with peak abundance in grazers
Point of slide: important for food chain
Match-mismatch hypothesis (Cushing, 1990)
Time

5. Why seasonality is important
Overlap with peak abundance in grazers
Carbon export – biological pump
Seasonal variability linked to magnitude of flux and fraction that is labile/refractory
Lutz et al. 2007
Point of slide: important for carbon export and climate

6. Key Questions Meteorological conditions modulate bloom magnitude
Subpolar North Atlantic - annual mean net heat flux, wind, TKE
Spatially quite strong but not seen interannually (Follows and Dutkiewicz, 2002)
Mean winter net heat flux and wind speed predictors for bloom initiation
Irminger Basin (Henson et al. 2006)
Does timing of change in physical environment influence bloom timing? – e.g. date the ML shoals/ML deepens
Do physical processes drive all of bloom timing?
Point of slide: bloom mag and weather mag, bloom timing and weather mag, bloom timing and weather timing 6

7. Critical depth vs. critical turbulence
Bloom starts when MLD becomes shallower than critical depth
Critical turbulence
Bloom starts when mixing rates become slower than phytoplankton growth and accumulation rates
Net heat flux becomes positive (Taylor and Ferrari, 2011)
Point of slide: examine two theories on bloom initiation, critical depth and critical turbulence
Huismann et al. 1999

8. Bloom timing metrics GlobColour – satellite-derived chlorophyll
Merges SeaWiFS, MODIS and MERIS
1x1 degree resolution, 8 day composites,
NASA Ocean Biogeochemical Model (NOBM)
Assimilates SeaWiFS, 8 day composites, – Nerger & Gregg, 2008
High fidelity to seasonal characteristics – Cole et al. 2012
No gaps – error on bloom initiation (30 days), peak (15 days) from gaps in satellite data
Initiation: rises 5% above annual median
Peak: maximum chlorophyll value
End: falls below 5% above annual median
Siegel et al., 2002
Point of slide: how did I define bloom metrics
+5%
Annual median 8

9. Physical data sources MLD Net heat flux Irradiance
T and S profiles (
density change of 0.03 kg m-3
Net heat flux
Satellite data + reanalysis products (NCEP/ECMWF) (
Irradiance
PAR data from MODIS (
Average ML irradiance
Point of slide: where did I get data

10. Average time series for North Atlantic
Point of slide: what does bloom initiation match with timing of changes

11. Physical timing metrics
Mixed layer depth
Timing of MLD max, MLD shoaling
PAR
ML PAR starts to increase, fastest increase, MLD shallower than euphotic zone depth
Net heat flux
Timing that NHF turns positive – Taylor and Ferrari, 2011
Point of slide: what things am I correlating with bloom initiation and why am I doing that.

12. Results
Bloom initiation more strongly correlated than peak and end with physical drivers
Basin-wide response seen in subpolar N. Atlantic
Patchy correlations in subpolar N. Pacific and S. Ocean
Point of slide: broad results, how does BI and N.Atlantic fit in with broader context

13. North Atlantic latitudinal gradients
Bloom initiation
r=0.76
r=0.69
Physical metric
Point of slide: spatial gradients match well. Nhf the best compared to MLD
r=0.58
r=0.86

14. North Atlantic interannual variability
6 30°x10° boxes in North Atlantic.
Brackets indicate correlation coefficient is not statistically significant at the 95% confidence interval
r=0.45
r=(0.36)
Point of slide: Interannual variability correlations – NHF is the winner, PAR is not correlated
(r=-0.12)
(r=-0.013)

15. North Atlantic vs. North Pacific
Point of slide: correlations much stronger in N. Atlantic
r=0.45
(r=-0.11)

16. Correlation map of bloom initiation and NHF turns positive
Point of slide: Broader spatial context. Basin wide response in North Atlantic, large patches of positive correlation in N.Pacific and S. Ocean
Coherent patches

17. Discussion
Bloom initiation – strongest relationship with changes in physical environment
Suggests biological processes more important for peak and end timing
Nutrient limitation, grazing, etc.
NHF better than MLD for predicting start of bloom - critical turbulence vs. critical depth
Basin-wide response seen in N. Atlantic both spatially and interannually
Why different to N. Pacific and S. Ocean?
Large scales – strong correlation, small scales - noisy
Point of slide: what does this all mean. Peak and end are not driven by physics, NHF is better predictor, why is N Atl different

18. Next steps
Impact of global warming on the seasonal cycle of phytoplankton
Climate change-driven trends in bloom timing using biogeochemical models
Final year – submitting in October
Point of slide: thesis 18

19. Summary Seasonality metrics develop to estimate bloom timing
Correlated with timing of changes in physical environment – spatially and interannually
Bloom initiation more strongly correlated than peak and end of bloom
NHF better predictor than MLD for onset of bloom
Basin-wide relationships weaker in N. Pacific and S. Ocean
Point of slide: sum up results 19

20. Thank you for listening!
Acknowledgments
GlobColour Project/ESA
NOBM/Giovanni
MODIS/NASA
Coriolis Project
WHOI OAflux Project
Liége Colloquium – travel grant
Thank you for listening!
Questions?
Point of slide

21. References
Cole, H., S. Henson, A. Martin and A. Yool (2012), Mind the gap: The impact of missing data on the calculation of phytoplankton phenology metrics, J. Geophys. Res., 117(C8), C08030, doi: /2012jc
Cushing, D. H. (1990), Plankton production and year-class strength in fish populations - an update of the match mismatch hypothesis, Adv. Mar. Biol., 26,
Follows, M. and S. Dutkiewicz (2002), Meteorological modulation of the North Atlantic spring bloom, Deep-Sea Research Part Ii-Topical Studies in Oceanography, 49(1-3),
Henson, S.A., I. Robinson, J.T. Allen and J.J. Waniek (2006), Effect of meteorological conditions on interannual variability in timing and magnitude of the spring bloom in the Irminger Basin, North Atlantic, Deep-Sea Research Part I-Oceanographic Research Papers, 53(10), , doi: /j.dsr
Lutz, M.J., K. Caldeira, R.B. Dunbar and M.J. Behrenfeld (2007), Seasonal rhythms of net primary production and particulate organic carbon flux to depth describe the efficiency of biological pump in the global ocean, Journal of Geophysical Research-Oceans, 112(C10), C10011, doi: /2006JC
Nerger, L. and W.W. Gregg (2008), Improving assimilation of SeaWiFS data by the application of bias correction with a local SEIK filter, Journal of Marine Systems, 73(1-2), , doi: /j.jmarsys
Siegel, D.A., S.C. Doney and J.A. Yoder (2002), The North Atlantic spring phytoplankton bloom and Sverdrup's critical depth hypothesis, Science, 296(5568), , doi: /science
Taylor, J.R. and R. Ferrari (2011), Shutdown of turbulent convection as a new criterion for the onset of spring phytoplankton blooms, Limnology and Oceanography, 56(6), , doi: /lo