Detection of climate change-driven trends in phytoplankton phenology.

Stephanie A Henson,Andrew Yool,Adrian P Martin,Jason Hopkins,Harriet S Cole

doi:10.1111/gcb.13886

Stephanie A Henson, Andrew Yool + Show 3 more

Open Access

https://doi.org/10.1111/gcb.13886

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Abstract

The timing of the annual phytoplankton spring bloom is likely to be altered in response to climate change. Quantifying that response has, however, been limited by the typically coarse temporal resolution (monthly) of global climate models. Here, we use higher resolution model output (maximum 5days) to investigate how phytoplankton bloom timing changes in response to projected 21st century climate change, and how the temporal resolution of data influences the detection of long-term trends. We find that bloom timing generally shifts later at mid-latitudes and earlier at high and low latitudes by ~5days per decade to 2100. The spatial patterns of bloom timing are similar in both low (monthly) and high (5day) resolution data, although initiation dates are later at low resolution. The magnitude of the trends in bloom timing from 2006 to 2100 is very similar at high and low resolution, with the result that the number of years of data needed to detect a trend in phytoplankton phenology is relatively insensitive to data temporal resolution. We also investigate the influence of spatial scales on bloom timing and find that trends are generally more rapidly detectable after spatial averaging of data. Our results suggest that, if pinpointing the start date of the spring bloom is the priority, the highest possible temporal resolution data should be used. However, if the priority is detecting long-term trends in bloom timing, data at a temporal resolution of 20days are likely to be sufficient. Furthermore, our results suggest that data sources which allow for spatial averaging will promote more rapid trend detection.

Highlights

Phenology refers to the characteristics of naturally recurring events, such as the seasonal cycles of plants and animals
Phenology has been recognised by the Intergovernmental Panel on Climate Change (IPCC) as ‘perhaps the simplest process in which to track changes . . . in response to climate change’ (Rosenzweig et al, 2007)
Knowledge of contemporary interannual variability in phytoplankton phenology has principally originated from satellite ocean colour observations which provide the necessary temporal and spatial resolution to quantify the key features of the seasonal cycle

Summary

| INTRODUCTION

Phenology refers to the characteristics of naturally recurring events, such as the seasonal cycles of plants and animals. Long time series of data (>30 years) are expected to be necessary to distinguish a climate change-driven trend in phytoplankton. Knowledge of contemporary interannual variability in phytoplankton phenology has principally originated from satellite ocean colour observations which provide the necessary temporal and spatial resolution to quantify the key features of the seasonal cycle. The phytoplankton seasonal cycle is expected to be altered by climate change, principally via increasing stratification in response to warming (Doney, 2006). This is expected to lead to earlier bloom timing in subpolar regions, as light limitation is alleviated earlier in the growing season (Henson, Cole, Beaulieu, & Yool, 2013). We assess how the temporal and spatial resolution of observations may affect the ability to detect future trends in phytoplankton bloom initiation

| MATERIALS AND METHODS

| RESULTS

| Limitations

15. Arctic Ocean

Findings

| DISCUSSION