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Detection of climate change-driven trends in phytoplankton phenology

Detection of climate change-driven trends in phytoplankton phenology
Detection of climate change-driven trends in phytoplankton phenology
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 5 days) 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 ~5 days per decade to 2100. The spatial patterns of bloom timing are similar in both low (monthly) and high (5 day) 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 20 days are likely to be sufficient. Furthermore, our results suggest that data sources which allow for spatial averaging will promote more rapid trend detection.
1354-1013
e101–e111
Henson, Stephanie A.
d6532e17-a65b-4d7b-9ee3-755ecb565c19
Cole, Harriet S.
7e6479d0-4450-483c-a723-20f5e99fa515
Hopkins, Jason
1da01369-84a8-4c55-8577-ed6c37a07037
Martin, Adrian P.
9d0d480d-9b3c-44c2-aafe-bb980ed98a6d
Yool, Andrew
882aeb0d-dda0-405e-844c-65b68cce5017
Henson, Stephanie A.
d6532e17-a65b-4d7b-9ee3-755ecb565c19
Cole, Harriet S.
7e6479d0-4450-483c-a723-20f5e99fa515
Hopkins, Jason
1da01369-84a8-4c55-8577-ed6c37a07037
Martin, Adrian P.
9d0d480d-9b3c-44c2-aafe-bb980ed98a6d
Yool, Andrew
882aeb0d-dda0-405e-844c-65b68cce5017

Henson, Stephanie A., Cole, Harriet S., Hopkins, Jason, Martin, Adrian P. and Yool, Andrew (2018) Detection of climate change-driven trends in phytoplankton phenology Global Change Biology, 24, (1), e101–e111. (doi:10.1111/gcb.13886).

Record type: Article

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 5 days) 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 ~5 days per decade to 2100. The spatial patterns of bloom timing are similar in both low (monthly) and high (5 day) 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 20 days are likely to be sufficient. Furthermore, our results suggest that data sources which allow for spatial averaging will promote more rapid trend detection.

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Accepted/In Press date: 14 August 2017
e-pub ahead of print date: 4 September 2017
Published date: 1 January 2018

Identifiers

Local EPrints ID: 414604
URI: https://eprints.soton.ac.uk/id/eprint/414604
ISSN: 1354-1013
PURE UUID: fbbad97c-9109-4148-bff7-f71235459da0

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Date deposited: 05 Oct 2017 16:30
Last modified: 15 Jan 2018 17:31

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Contributors

Author: Harriet S. Cole
Author: Jason Hopkins
Author: Adrian P. Martin
Author: Andrew Yool

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