Biological oceanography Remote Sensing

Studying plankton from an atmospheric satellite

Behrenfeld, M. J., Hu, Y., O’Malley, R. T., Boss, E. S., Hostetler, C. A., Siegel, D. A., … & Rodier, S. (2016). Annual boom-bust cycles of polar phytoplankton biomass revealed by space-based lidar. Nature Geoscience. doi:10.1038/ngeo2861

Studying phytoplankton, the ocean’s microscopic primary producers, is a challenging task. The amount of plankton in a given area can change in a matter of hours or days due to a myriad of factors: the availability of nutrients, physical oceanographic and climate process, the list goes on and on. The short time and spatial scales of changes in plankton density makes it very difficult to estimate the population. But understanding how these populations change is of great importance in many areas of ocean research. After all, phytoplankton form the base of almost every marine ecosystem, transport carbon dioxide out of the atmosphere to the deep ocean, and produce much of oxygen we breathe.

Figure 1 – Artists rendition of the CALIOP sensor orbiting the earth. The instrument discussed in this article is housed in the black cylinder pointing toward the Earth’s surface (Image courtesy of CNES)

One popular method for observing phytoplankton over large geographic areas is satellite based ocean color remote sensing. These instruments look at very particular bands, or colors, of light coming out of the ocean that are related to the amount of plankton in the region. For decades, scientists have used such measurements to do amazing research. But ocean color sensors are not perfect; they are highly sensitive to cloud cover and day-night cycles.

Dr. Michael Behrenfeld from Oregon State University figured out a way to use a different satellite based instrument to overcome those limitations. He and his collaborators from across the United States took advantage of a tool called the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) sensor. Originally designed for atmospheric research, the CALIOP shoots a laser at Earth and records how much light comes back. Behrenfeld was able to tease out the amount of plankton in a given region using that reflected light.

Behrenfeld and his team analyzed CALIOP data collected between 2006 and 2015 to look at plankton populations near the poles. The polar regions are among the most productive, plankton-rich environments on Earth, but are largely understudied. Ocean color devices that are capable of looking at these regions are limited by weather, sea-ice, and long periods of sunless winter. The CALIOP data, on the other hand, allowed the researchers to fill in the late autumn to early spring timeframe that older instruments entirely missed.

The result is the first complete record of seasonal polar phytoplankton biomass. The nearly ten-year long time series gave Behrenfeld’s group a peak at the mechanisms that drive changes in the phytoplankton population. Their findings confirmed the conventional wisdom that the biomass is highest in the warm summer months and lowest in the depths of winter.

Perhaps Behrenfeld’s most interesting conclusions, however, came from looking at anomalies in the total phytoplankton biomass over an entire region (Figure 2). Surprisingly, the deviations in biomass from the average appear to be driven by different processes in each hemisphere. In the Arctic, the biomass anomalies are driven purely by the concentration of phytoplankton. Antarctic phytoplankton populations, however, seem to be forced by the extent of the ice-free ocean area surrounding the continent.

Figure 2 – Plots of biomass anomalies against possible forcing factors for both hemispheres. The black lines in all the plots show the biomass anomaly as computed from CALIOP data. The blue lines in the top row plot the ice-free area anomaly. The red lines in the bottom plots illustrate the changes in phytoplankton concentration. Notice how well the biomass in the south pole tracks changes in the ice-free area. Likewise, note how the changes in biomass are mirrored by shifts in concentration in the north.

Behrenfeld suggests two reasons for these different forcing factors. One possibility is that surface nutrients, which phytoplankton need to grow, are generally more available in the polar north. Second, the annual differences in ice-free ocean area in the pole were much greater in the southern hemisphere. So in the north, phytoplankton could take advantage of favorable growth conditions due to higher nutrient availability. Conversely, the phytoplankton of the south needed more ice-free area to experience higher growth.

These contrasting situations highlight just how much both ecological and physical processes influence changes in phytoplankton stock in polar regions. Behrenfeld points out that this tight coupling raises questions about what changes in ice cover and nutrient availability mean for future polar ecosystem trajectories. He does not offer any particular answers to these questions, indicating that a lot of work is yet to be done.

While the CALIOP sensor was not designed for oceanographic research, it turned out to be useful for measuring phytoplankton concentrations. Ideally, a similar instrument specifically designed to observe plankton would be launched on a future Earth observation satellite. Combining those measurements with other satellite data and ocean-based sampling would provide scientists with a more complete picture of how changes in the environment affect phytoplankton populations and, by extension, how the global carbon cycle might shift in the future ocean.

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