Global warming impact on phytoplankton seasonal cycles

1. Global warming impact on phytoplankton seasonal cycles Stephanie Henson Harriet Cole, Claudie Beaulieu, Andrew Yool

2. Motivation • Seasonal cycle of phytoplankton is relevant to higher trophic levels and carbon export • How will phytoplankton seasonality change with global warming and why? • A previous study suggested it takes ~ 30 years to detect a global warming trend in primary production • Could seasonality be a ‘shortcut’ to detecting effects of climate change?

3. How will global warming alter seasonality? Reduced mixing + nutrient limitation -> weaker seasonal cycle Reduced mixing + light limitation -> seasonal cycle remains & earlier blooms The canonical view (Doney, 2006)

4. How will phytoplankton seasonality change with global warming? Take coupled climate model simulations using IPCC CMIP5 models run with the RCP8.5 scenario 2006-2100: • Canadian Centre for Climate Modelling and Analysis CanESM2 • NOAA Geophysical Fluid Dynamics Laboratory GFDL-ESM2M • Met Office Hadley Centre HadGEM2-CC • Institut Pierre Simon Laplace • IPSL-CM5A-MR • Max Planck Institute • MPI-ESM-LR • National Oceanography Centre • NEMO-MEDUSA

5. Phytoplankton seasonal cycle metrics Timing of peak North Atlantic seasonal cycle of primary production (GFDL model – monthly output) Seasonal amplitude (max-min)

6. Trends in phytoplankton seasonality Primary production Seasonal amplitude Timing of peak Difference in days, 2006-2026 vs 2071-2090 Average % change per year, 2006-2090

7. Trends in phytoplankton seasonality Decrease in PP, except Arctic Decrease in seasonality, especially in North Atlantic Peak PP ~ advances, particularly Arctic

8. Trends in drivers of seasonality SST SST amplitude increases (highs get hotter quicker than the lows) ΔSST/year MLD seasonal amplitude decreases everywhere except the Arctic MLD Average % change/year Surface nitrate seasonal amplitude decreases almost everywhere NO3

9. How much data is needed to detect a global warming trend? Signal (i.e. trend) has to exceed noise (i.e. natural variability) • n*: number of years required to detect trend • N : standard deviation of the noise (residuals after trend removed) • : estimated trend • : auto-correlation of the noise (AR(1)) Weatherhead et al. (1998)

10. Detecting a trend in phytoplankton seasonality Mean annual PP n* - Number of years to detect a trend above natural variability Mean PP – 34 years

11. Detecting a trend in phytoplankton seasonality Mean annual PP Seasonal amplitude of PP n* - Number of years to detect a trend above natural variability Mean PP – 34 years; seasonal amplitude – 37 years

12. Effect of model temporal resolution • Used monthly mean model output here • But phenological changes may only be observable at higher temporal resolution • How does changing the model temporal resolution alter n* (number of years to detect trend)?

13. Ongoing work (Harriet Cole) Effect on n* of calculating trends in bloom initiation with different model temporal resolution

14. Conclusions • Seasonal amplitude of PP decreases; timing of peak advances  transformation of bloom regions to non-bloom regions • Due to decreased mixing and nutrient supply • Arctic is an exception: increased seasonality and earlier peak, but reduced mixing  effect of ice melt? • Seasonality metrics are not necessarily a shortcut to detecting a trend • For some regions > monthly resolution data required to detect phenological change Henson et al. (2010); Beaulieu et al. (2013); Henson et al. (in press) – all Biogeosciences