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

Hammond, Matthew L.; Beaulieu, Claudie; Sahu, Sujit K.; Henson, Stephanie A. ORCID: https://orcid.org/0000-0002-3875-6802. 2017 Assessing trends and uncertainties in satellite-era ocean chlorophyll using space-time modeling. Global Biogeochemical Cycles, 31 (7). 1103-1117. https://doi.org/10.1002/2016GB005600

Before downloading, please read NORA policies.

Preview

Text
AGU Publisher statement: An edited version of this paper was published by AGU. © 2017 American Geophysical Union. Further reproduction or electronic distribution is not permitted doi :10.1002/2016GB005600
Hammond_et_al-2017-Global_Biogeochemical_Cycles.pdf - Published Version
Download (901kB) | Preview

Official URL: https://doi.org/10.1002/2016GB005600

Abstract/Summary

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.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1002/2016GB005600
ISSN:	08866236
Additional Keywords:	Chlorophyll; Bayesian Inference; Spatio-Temporal modeling; Climate Change; Trend Detection; Phytoplankton
Date made live:	27 Jun 2017 12:54 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/517231

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1002/2016GB005600

ISSN:

08866236

Additional Keywords:

Chlorophyll; Bayesian Inference; Spatio-Temporal modeling; Climate Change; Trend Detection; Phytoplankton

Date made live:

27 Jun 2017 12:54 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/517231