Stephanie Henson

Harriet Cole, Claudie Beaulieu, Andrew Yool

Global warming impact on phytoplankton seasonal

cycles

• 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?

Motivation

How will global warming alter seasonality?

The canonical view (Doney, 2006)

Reduced mixing + nutrient limitation -> weaker seasonal cycle

Reduced mixing + light limitation -> seasonal cycle remains & earlier blooms

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

Phytoplankton seasonal cycle metrics

North Atlantic seasonal cycle of primary production (GFDL model – monthly output)

Timing of peak

Seasonal amplitude (max-min)

Trends in phytoplankton seasonality

Average % change per year, 2006-2090

Primary production Seasonal amplitude Timing of peak

Difference in days, 2006-2026 vs 2071-2090

Trends in phytoplankton seasonality

Decrease in PP, except Arctic

Decrease in seasonality, especially in North Atlantic

Peak PP ~ advances, particularly Arctic

Trends in drivers of seasonality

SST amplitude increases (highs get hotter quicker than the lows)

MLD seasonal amplitude decreases everywhere except the Arctic

Surface nitrate seasonal amplitude decreases almost everywhere

Average %

change/year

SST

MLD

NO3

ΔS

ST

/year

How much data is needed to detect a global warming trend?

n* 3.3N

11

2 / 3

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)

Detecting a trend in phytoplankton seasonality

n* - Number of years to detect a trend above natural variability

Mean PP – 34 years

Mean annual PP

Detecting a trend in phytoplankton seasonality

n* - Number of years to detect a trend above natural variability

Mean PP – 34 years; seasonal amplitude – 37 years

Mean annual PP Seasonal amplitude of PP

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)?

Ongoing work (Harriet Cole)

Effect on n* of calculating trends in bloom initiation with different model temporal resolution

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