Assessing trends and uncertainties in satellite‐era ocean chlorophyll using space‐time modeling. Issue 7 (11th July 2017)

• Record Type: Journal Article
• Authors: Hammond, Matthew L.; Beaulieu, Claudie; Sahu, Sujit K.; Henson, Stephanie A.
• Is Part Of: Global biogeochemical cycles. Volume 31:Issue 7(2017:Jul.)
• Page Start: 1103
• Page End: 1117
• Publication Date: 2017-07-11
• Subjects: chlorophyll -- Bayesian inference -- spatiotemporal modeling -- climate change -- trend detection -- phytoplankton; Biogeochemical cycles -- Periodicals; Electronic journals; 577.1405
• DOI: 10.1002/2016GB005600
• Languages: English
• ISSNs: 0886-6236

Abstract

Abstract: The presence, magnitude, and even direction of long‐term trends in phytoplankton abundance over the past few decades are 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 spatiotemporal 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 spatiotemporal 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 emphasize the solution that space‐time modeling offers for studying global long‐term change. Key Points: We provide probabilistic estimates of trends, and their errors, in satellite derived ocean chlorophyll using Bayesian space‐time modeling We show that including spatial correlation in a statistical model improves fit accuracy and provides a more complete uncertainty assessment We show that the global trend in ocean chlorophyll from a space‐time model is reduced with implications for studying global long‐term change