Remote sensing of Southern Ocean phytoplankton blooms in a warming world.

• Main Author: Montie, Shinae
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: University of Canterbury 2020
• Subjects: Ross Sea; Southern Ocean; Antarc*
• Online Access: https://dx.doi.org/10.26021/6364; https://ir.canterbury.ac.nz/handle/10092/100142

Marine phytoplankton are important ubiquitous phototrophs that play an essential role in biogeochemical cycles, mediate global climate, are at the base of food webs and fuel fisheries worldwide. Since 2006, ca. 60-90% of the increase in global ocean heat, associated with the burning of fossil fuels has occurred in the Southern Ocean (SO) alone. Being unicellular, short-lived and fast growing, phytoplankton can respond rapidly to changes in sea surface temperature (SST). Concurrent with long-term small increases in average SST, according to the latest IPCC Special Report on the Oceans and Cryosphere (2019), the more dramatic increases in short term warming events, that is marine heatwaves (MHWs), are very likely to also be attributable to global warming. Little is known about how oceanic warming coupled with MHWs will affect phytoplankton distribution and abundances in the SO. This research aims to address this research gap by quantifying the effects of SST anomalies and MHWs on chlorophyll-a (chl-a) concentrations, a proxy for phytoplankton biomass, using satellite measurements of ocean colour and remote sensing applications. First, I correlated SST and chl-a anomalies on a pixel-by-pixel scale for the entire Ross Sea region of the SO over 20 years. The Ross Sea is the most productive region in Antarctica’s coastal zone, accounting for ~30% of total annual primary production. Therefore, a recent observed decrease in chl-a in this region warranted further research scrutiny. Both positive and negative correlations between SST and chl-a anomalies were found. Based on Anova and post hoc Tukey tests, I found that correlations for different zones varied systematically across monthly, seasonal and annual timescales. Highly significant differences occurred between months and seasons, more specifically, between March and December, and autumn and summer, representing the coldest and warmest periods in the year accounted for during this study. Second, I identified all extreme summer MHW events across the SO over a 16 year time period, and correlated the associated temperature anomalies to chl-a concentrations using a ‘control vs. impact’ experimental design. A relatively new MHW identification procedure, based on Hobday et al. (2016), was used to identify 19 events that could be analysed from remote sensing images. MHWs were here defined as anomalously warm events during which temperatures exceed the 90th percentile and persisted for >5 days, although my study focused only on ‘extreme’ summer MHWs where temperatures were four times higher than the 90th percentile of the climatological SST. Based on Anova and correlation analyses, I found that these extreme summer MHWs increased chl-a in the SO, and that this increase was stronger in regions that had lower sea surface temperatures and higher cover of winter ice. The results outlined here suggest that a focus on average changes over long periods and over wide areas could overlook ecologically important short-term changes associated with anomalously short-term warming events, such as MHWs. These short-term events, superimposed on long-term climate changes, may eventually reach a tipping point in the SO with large-scale shifts to entire communities at the base of the food web. Therefore, it remains a fundamental challenge to understand and model variability in phytoplankton abundances and community structure. Furthermore, it is important that future research from the SO adopt this relatively new MHW approach to study phytoplankton dynamics and to ensure consistency within this rapidly expanding literature.