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Assessing trends and uncertainties in satellite‐era ocean chlorophyll using space‐time modeling

Assessing trends and uncertainties in satellite‐era ocean chlorophyll using space‐time modeling
Assessing trends and uncertainties in satellite‐era ocean chlorophyll using space‐time modeling
The presence, magnitude, and even direction of long-term trends in phytoplankton abundance over the past few decades is still debated in the literature, primarily due to differences in the data sets and methodologies used. Recent work has suggested that the satellite chlorophyll record is not yet long enough to distinguish climate change trends from natural variability, despite the high density of coverage in both space and time. Previous work has typically focused on using linear models to determine the presence of trends, where each grid cell is considered independently from its neighbors. However, trends can be more thoroughly evaluated using a spatially resolved approach. Here a Bayesian hierarchical spatio-temporal model is fitted to quantify trends in ocean chlorophyll from September 1997 to December 2013. The approach used in this study explicitly accounts for the dependence between neighboring grid cells, which allows us to estimate trend by ‘borrowing strength’ from the spatial correlation. By way of comparison, a model without spatial correlation is also fitted. This results in a notable loss of accuracy in model fit. Additionally, we find an order of magnitude smaller global trend, and larger uncertainty, when using the spatio-temporal model: -0.023 ± 0.12 % yr-1 as opposed to -0.38 ± 0.045 % yr-1 when the spatial correlation is not taken into account. The improvement in accuracy of trend estimates, and the more complete account of their uncertainty emphasizes the solution that space-time modeling offers for studying global long-term change.
0886-6236
1103–1117
Hammond, Matthew L.
5c6509e8-0e13-4afa-9b8f-b8601462a93b
Beaulieu, Claudie
13ae2c11-ebfe-48d9-bda9-122cd013c021
Sahu, Sujit K.
e1809a9c-21ec-409a-884b-8e5f9041d4e4
Henson, Stephanie A.
d6532e17-a65b-4d7b-9ee3-755ecb565c19
Hammond, Matthew L.
5c6509e8-0e13-4afa-9b8f-b8601462a93b
Beaulieu, Claudie
13ae2c11-ebfe-48d9-bda9-122cd013c021
Sahu, Sujit K.
e1809a9c-21ec-409a-884b-8e5f9041d4e4
Henson, Stephanie A.
d6532e17-a65b-4d7b-9ee3-755ecb565c19

Hammond, Matthew L., Beaulieu, Claudie, Sahu, Sujit K. and Henson, Stephanie A. (2017) Assessing trends and uncertainties in satellite‐era ocean chlorophyll using space‐time modeling. Global Biogeochemical Cycles, 31 (7), 1103–1117. (doi:10.1002/2016GB005600).

Record type: Article

Abstract

The presence, magnitude, and even direction of long-term trends in phytoplankton abundance over the past few decades is still debated in the literature, primarily due to differences in the data sets and methodologies used. Recent work has suggested that the satellite chlorophyll record is not yet long enough to distinguish climate change trends from natural variability, despite the high density of coverage in both space and time. Previous work has typically focused on using linear models to determine the presence of trends, where each grid cell is considered independently from its neighbors. However, trends can be more thoroughly evaluated using a spatially resolved approach. Here a Bayesian hierarchical spatio-temporal model is fitted to quantify trends in ocean chlorophyll from September 1997 to December 2013. The approach used in this study explicitly accounts for the dependence between neighboring grid cells, which allows us to estimate trend by ‘borrowing strength’ from the spatial correlation. By way of comparison, a model without spatial correlation is also fitted. This results in a notable loss of accuracy in model fit. Additionally, we find an order of magnitude smaller global trend, and larger uncertainty, when using the spatio-temporal model: -0.023 ± 0.12 % yr-1 as opposed to -0.38 ± 0.045 % yr-1 when the spatial correlation is not taken into account. The improvement in accuracy of trend estimates, and the more complete account of their uncertainty emphasizes the solution that space-time modeling offers for studying global long-term change.

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Accepted/In Press date: 14 June 2017
e-pub ahead of print date: 11 July 2017
Organisations: Ocean and Earth Science, Physical Oceanography, National Oceanography Centre, Southampton Marine & Maritime Institute

Identifiers

Local EPrints ID: 411864
URI: http://eprints.soton.ac.uk/id/eprint/411864
ISSN: 0886-6236
PURE UUID: 24520322-c4fc-42c6-95d4-eb0cd13a6e91
ORCID for Matthew L. Hammond: ORCID iD orcid.org/0000-0002-8918-2351

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Date deposited: 27 Jun 2017 16:31
Last modified: 07 Oct 2020 05:17

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