Detection of climate change-driven trends in phytoplankton phenology

Henson, Stephanie A. ORCID: https://orcid.org/0000-0002-3875-6802; Cole, Harriet S.; Hopkins, Jason; Martin, Adrian P. ORCID: https://orcid.org/0000-0002-1202-8612; Yool, Andrew ORCID: https://orcid.org/0000-0002-9879-2776. 2018 Detection of climate change-driven trends in phytoplankton phenology. Global Change Biology, 24 (1). e101-e111. 10.1111/gcb.13886

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Abstract/Summary

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.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1111/gcb.13886
ISSN:	1354-1013
Date made live:	24 Aug 2017 10:22 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/517662

Item Type:

Publication - Article

Digital Object Identifier (DOI):

10.1111/gcb.13886

ISSN:

1354-1013

Date made live:

24 Aug 2017 10:22 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/517662