Ning Jiang, Zhaoru Zhang, Ruifeng Zhang, Chuning Wang, Meng Zhou. The Connection of Phytoplankton Biomass in the Marguerite Bay Polynya of the Western Antarctic Peninsula to the Southern Annular Mode[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-023-2201-y
Citation:
Ning Jiang, Zhaoru Zhang, Ruifeng Zhang, Chuning Wang, Meng Zhou. The Connection of Phytoplankton Biomass in the Marguerite Bay Polynya of the Western Antarctic Peninsula to the Southern Annular Mode[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-023-2201-y
Ning Jiang, Zhaoru Zhang, Ruifeng Zhang, Chuning Wang, Meng Zhou. The Connection of Phytoplankton Biomass in the Marguerite Bay Polynya of the Western Antarctic Peninsula to the Southern Annular Mode[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-023-2201-y
Citation:
Ning Jiang, Zhaoru Zhang, Ruifeng Zhang, Chuning Wang, Meng Zhou. The Connection of Phytoplankton Biomass in the Marguerite Bay Polynya of the Western Antarctic Peninsula to the Southern Annular Mode[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-023-2201-y
School of Oceanography, Shanghai Jiao Tong University, Shanghai 200240, China
2.
Key Laboratory for Polar Science, Polar Research Institute of China, Ministry of Natural Resources, Shanghai 200136, China
Funds:
The Key Research & Development Program of the Ministry of Science and Technology of China under contract No. 2022YFC2807601; the National Natural Science Foundation of China under contract Nos 41941008 and 41876221; the Shanghai Science and Technology Committee under contract Nos 20230711100 and 21QA1404300; the Impact and Response of Antarctic Seas to Climate Change under contract No. IRASCC 1-02-01B; the National Key Research and Development Program of China under contract No. 2019YFC1509102; the Shanghai Frontiers Science Center of Polar (SCOPS); the Shanghai Pilot Program for Basic Research-Shanghai Jiao Tong University under contract No. 21TQ1400201.
Antarctic coastal polynyas are biological hotspots in the Southern Ocean that support the abundance of high-trophic-level predators and are important for carbon cycling in the high-latitude oceans. In this study, we examined the interannual variation of summertime phytoplankton biomass in the Marguerite Bay polynya (MBP) in the western Antarctic peninsula area, and linked such variability to the Southern Annular Mode (SAM) that dominated the southern hemisphere extratropical climate variability. Combining satellite data, atmosphere reanalysis products and numerical simulations, we found that the interannual variation of summer chlorophyll-a concentration (Chl-a) in the MBP is significantly and negatively correlated with the spring SAM index, and weakly correlated with the summer SAM index. The negative relation between summer Chl-a and spring SAM is due to weaker spring vertical mixing under a more positive SAM condition, which would inhibit the supply of iron from deep layers into the surface euphotic layer. The negative relation between spring mixing and spring SAM results from greater precipitation rate over the MBP region in positive SAM phase, which leads to lower salinity in the ocean surface layer. The coupled physical-biological mechanisms between SAM and phytoplankton biomass revealed in this study is important for us to predict the future variations of phytoplankton biomasses in Antarctic polynyas under climate change.
Figure 1. Location of the numerical model domain in the Southern Ocean (red box) (a), location of the Marguerite Bay polynya area in the model domain (black box) (b), and distribution of the satellite-observed summer-mean sea ice concentration in the Marguerite Bay averaged over 2001–2020 (c). The white dashed rectangle represents the selected box enclosing the polynya, and the red line represents the 20% contour of sea ice concentration. The Pal LTER CTD sampling stations within the polynya area are shown by the plus markers (yellow: 2001; blue: 2002; black: 2003; red: 2004; green: 2005 and 2015; white: 2007, 2008, 2011 and 2017; purple: 2014 and 2018. Stations in different years labeled by the same color are very close to each other, but not at the exact locations). Temperature and salinity data were collected at all stations in the 12 years mentioned above, while Chl-a data were only collected in 2001, 2003, 2005, 2007, 2014, 2015 and 2018.
Figure 2. Taylor diagrams of comparisons between modelled and observed physical parameters in the Marguerite Bay polynya. a. The comparison between modelled and observed temperature (from Pal LTER; Fig. 1). b. The comparison between modelled and observed salinity (from Pal LTER). c. The comparison between modelled and calculated mixed layer depth (MLD) based on temperature and salinity in-situ measurements. d. The comparison between modelled and satellite-observed spring sea ice concentration (SIC). e. The comparison between modelled and satellite-observed summer sea ice concentration. f. The comparison between modelled and satellite-observed summer sea surface temperature (SST). The red plus markers indicate the information of correlation coefficients, standard deviations and root mean square errors of the modeled data; the closer they are to the Obs, the smaller the errors between the modeled and observed data are.
Figure 3. The modelled and satellite-observed annual cycles of area-mean sea ice concentration in the Marguerite Bay polynya for the years 2001–2020. The correlation coefficient and p-value are shown.
Figure 4. Time series of the satellite-observed summer-mean chlorophyll-a (Chl-a) concentration averaged over the Marguerite Bay polynya (green lines) and the winter (a), spring (b), and summer (c) SAM indices (SAMI; black lines) for 2001–2020. The correlation coefficients, p-values, and standard deviations of Chl-a (vertical bars) are shown.
Figure 5. Time series of the modelled spring (a) and summer (b) mean mixed layer depth (MLD) and summer-mean chlorophyll-a (Chl-a) concentration averaged over the Marguerite Bay polynya for 2001–2020. The correlation coefficients, p-values, and standard deviations (vertical bars) are shown.
Figure 6. Spatial distributions of the modelled spring-mean mixed layer depth (MLD) in the Marguerite Bay polynya during years with low spring SAM index (SAMI) (a–e) and high spring SAM index (f–j), and time series of spring SAM index and spring MLD in the MBP for 2001–2020 (k). In a–j, the SAM indices are labeled in the upper left corner of the panels. In k, the correlation coefficient, p-value, and standard deviations of MLD (vertical bars) are also shown.
Figure 7. Time series of the modelled spring-mean sea surface salinity (SSS) and mixed layer depth (MLD) (a), sea surface salinity and surface density (SD) (b), and mixed layer depth and surface density (c) in the Marguerite Bay polynya for 2001–2020. The correlation coefficients, p-values, and standard deviations (vertical bars) are shown.
Figure 8. Time series of spring-mean precipitation rate (from ERA5) and sea surface salinity (SSS; modelled) (a), precipitation rate and SAM index (SAMI) (b), precipitation rate and surface air temperature (SAT; from ERA5) (c), and SAT and SAM index (d) for the Marguerite Bay polynya during 2001–2020. The correlation coefficients, p-values, and standard deviations (vertical bars) are shown.
Figure 9. The vertical profiles of chlorophyll-a (Chl-a) concentration at the 7 Pal LTER stations (Fig. 1) in 2001–2018.
Figure 10. The annual cycle of modelled mixed layer depth in the Marguerite Bay polynya for the years 2001–2020.
Figure 11. Schematic for the mechanisms linking the interannual variations of summer phytoplankton biomass in the Marguerite Bay polynya and spring SAM. The yellow dots represent iron (Fe), and the green dots represent phytoplankton biomass.
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