DOI: 10.1111/gcb.13886
Scopus记录号: 2-s2.0-85039701850
论文题名: Detection of climate change-driven trends in phytoplankton phenology
作者: Henson S.A. ; Cole H.S. ; Hopkins J. ; Martin A.P. ; Yool A.
刊名: Global Change Biology
ISSN: 13541013
出版年: 2018
卷: 24, 期: 1 起始页码: e101
结束页码: e111
语种: 英语
英文关键词: bloom initiation
; bloom timing
; climate model
; climate warming
; ocean monitoring
; RCP8.5
; sustained observations
Scopus关键词: climate change
; climate effect
; climate modeling
; detection method
; environmental monitoring
; global warming
; phenology
; phytoplankton
; trend analysis
; climate change
; physiology
; phytoplankton
; population dynamics
; season
; temperature
; time factor
; Climate Change
; Phytoplankton
; Population Dynamics
; Seasons
; Temperature
; Time Factors
英文摘要: 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. © 2017 The Authors. Global Change Biology Published by John Wiley & Sons Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/110622
Appears in Collections: 影响、适应和脆弱性 气候变化事实与影响
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作者单位: National Oceanography Centre, Southampton, United Kingdom; Marine Scotland, Marine Laboratory, Aberdeen, United Kingdom; Bigelow Laboratory for Ocean Sciences, East Boothbay, ME, United States
Recommended Citation:
Henson S.A.,Cole H.S.,Hopkins J.,et al. Detection of climate change-driven trends in phytoplankton phenology[J]. Global Change Biology,2018-01-01,24(1)