K. E. Frey1, J. C. Comiso2, L. W. Cooper3, J. M. Grebmeier3, and L. V. Stock2
1Graduate School of Geography, Clark University, Worcester, MA, USA
2Cryospheric Sciences Laboratory, Goddard Space Flight Center, NASA, Greenbelt, MD, USA
3Chesapeake Biological Laboratory, University of Maryland Center for Environmental Science, Solomons, MD, USA
(See erratum, published in 2021)
- Satellite estimates of ocean primary productivity (i.e., the rate at which marine algae transform dissolved inorganic carbon into organic material) showed higher values for 2019 (relative to the 2003-18 mean) for seven of the nine investigated regions (with the Barents Sea and North Atlantic the only regions showing lower than average values).
- All regions continue to exhibit positive trends over the 2003-19 period, with the strongest trends in the Eurasian Arctic, Barents Sea, and Greenland Sea.
- During May 2019, a ~1500 km long region along the sea ice edge in the Greenland Sea showed on average ~18 times higher chlorophyll-a concentrations than the same month of the previous years (2003-18) on record.
- Unprecedented declines of sea ice in the Bering Sea in late winter 2018 and 2019 have been associated with shifts in the timing and intensity of phytoplankton blooms. Both years showed increases in chlorophyll biomass in March in the St. Lawrence Island Polynya region, associated with earlier spring break-up of sea ice. However, 2019 (unlike 2018) showed a more characteristic phytoplankton bloom in May, which may be associated with the resurgence of ice cover in April 2019.
Autotrophic single-celled algae living in sea ice (ice algae) and water column (phytoplankton) are the main primary producers in the Arctic Ocean. Through photosynthesis, they transform dissolved inorganic carbon into organic material. Consequently, primary production provides a key ecosystem service by providing energy to the entire food web in the oceans. Primary productivity is strongly dependent upon light availability and the presence of nutrients, and thus is highly seasonal in the Arctic. In particular, the melting and retreat of sea ice during spring are strong drivers of primary production in the Arctic Ocean and its adjacent shelf seas, owing to enhanced light availability and stratification (Barber et al. 2015; Leu et al. 2015; Ardyna et al. 2017). Recent declines in Arctic sea ice extent (see essay Sea Ice) have contributed substantially to shifts in primary productivity throughout the Arctic Ocean. However, the response of primary production to sea ice loss has been both seasonally and spatially variable (e.g., Tremblay et al. 2015; Hill et al. 2018).
Here we present satellite-based estimates of algal chlorophyll-a (occurring in all species of phytoplankton), based on the color of the ocean, and subsequently provide calculated primary production estimates. These results are shown for ocean areas with less than 15% sea ice concentration and, therefore, do not include production by sea ice algae or under-ice phytoplankton blooms.
Measurements of the algal pigment chlorophyll (e.g., chlorophyll-a) serve as a proxy for the amount of algal biomass present as well as overall plant health. The complete, updated MODIS-Aqua satellite chlorophyll-a record for the northern polar region for the years 2003-19 can serve as a time series against which individual years can be compared. For this reporting, a reference period of 2003-18 was chosen to calculate the 2019 anomalies to maximize the length of the satellite-based time series.
The 2019 data show a distribution of both positive and negative anomalies in chlorophyll-a concentrations in a number of locations across the Arctic Ocean region, where patterns are spatially and temporally heterogeneous (Fig. 1). These patterns are often associated with the timing of the seasonal break-up and retreat of the sea ice cover (Fig. 2): positive anomalies tend to occur in regions where the break-up is relatively early, while negative anomalies tend to occur in regions where the break-up is delayed. The most notable positive anomalies in 2019 occurred during May, with high concentrations of chlorophyll-a occurring along the ice edge in the Greenland Sea (Figs. 1a and 3). In particular, this regional positive anomaly of chlorophyll-a concentrations extended nearly 1500 km in length from Svalbard at ~80° N southward to central East Greenland at 70° N and exhibited on average ~18 times higher concentrations than previous years on record (Fig. 3b). Before this observed anomaly, the highest concentration in chlorophyll-a for this region had been in June 2007 (Fig. 3b), but it was only two-thirds as high as in 2019. Furthermore, not only was the intensity of the spring bloom unprecedented in 2019, but also the timing: peak seasonal phytoplankton blooms in this region typically occur in June (Fig. 3b) but were shifted one month earlier to May in 2019. Additional widespread positive anomalies occurred in the Bering Sea in May, June, and July, which are discussed in greater detail below. Some of the strongest negative anomalies in chlorophyll-a concentrations (i.e., low primary productivity) occurred in the Barents Sea in May (Fig. 1a) and in the Beaufort Sea in July (Fig. 1c). The relatively low chlorophyll-a concentrations in the Barents Sea in May are associated with increases in sea ice cover in that region throughout the season (Fig. 2a-c).
Mean monthly chlorophyll-a
concentrations during 2019, shown as a percent of the 2003-18 average for (a) May, (b) June, and (c) July. The black regions represent areas where no data are available (either owing to >15% sea ice concentrations or cloud cover). Satellite-based chlorophyll-a
data across the pan-Arctic region were derived using the MODIS-Aqua Reprocessing 2018.0, OCx algorithm: http://oceancolor.gsfc.nasa.gov
Fig. 2. Sea ice concentration anomalies (%) in 2019 (compared to a 2003-18 mean reference period) for (a) May, (b) June, and (c) July. Satellite-based sea ice concentrations were derived from the Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager/Sounder (SSMIS) passive microwave instruments, calculated using the Goddard Bootstrap (SB2) algorithm (Comiso et al. 2017a,b).
Fig. 3. (a) Mean chlorophyll-a anomalies surrounding Greenland in May 2019, shown as a percent of the 2003-18 average. The white line denotes the mean sea ice extent (i.e., sea ice concentration at 15%) for May 2019. The red box denotes the area along the sea ice edge in the Greenland Sea for which data are shown in (b). (b) Mean chlorophyll-a concentrations for the region in the Greenland Sea highlighted in (a) from 2003-19, where May 2019 showed on average ~18 times higher concentrations than previous years on record. Phytoplankton blooms are distinctly seasonal in high-latitude seas, with peak production occurring once light levels and stratification are sufficient (with melting snow and the break-up of sea ice) and then dissipate once nutrients in the water column are depleted.
As noted above, some of the largest positive anomalies in chlorophyll-a concentrations observed in 2019 occurred over the shelf region of the Bering Sea during May and June (Fig. 1a,b). During July, these positive anomalies extended more widely across the northern Bering Sea and into the Chukchi Sea (Fig. 1c). In particular, focusing on the Distributed Biological Observatory Site 1 (DBO1; Moore and Grebmeier 2018) in the St. Lawrence Island Polynya (SLIP) region, 2019 experienced similar declines in sea ice that also occurred in 2018 (Frey et al. 2018; Stabeno and Bell 2019) (Fig. 4a). However, while sea ice had disappeared from this region by the end of March in both 2018 and 2019, 2019 in contrast showed a slight resurgence of ice during the month of April before it declined again a few weeks later by the end of April. These general patterns of early sea ice break-up at DBO1 are associated with upticks in March chlorophyll-a concentrations that were observed in both 2018 and 2019 (Fig. 4b). In 2018, it was apparent that the typical large blooms that are produced in April and May did not occur; instead, we observed a redistribution of chlorophyll biomass in March (~275% increase over the 2003-17 average) and June (~500% increase over the 2003-17 average) (Frey et al. 2018). In contrast, the typical seasonal bloom of phytoplankton occurred as expected in May 2019 (Fig. 4b), which may be associated with the resurgence of sea ice cover in the region in April. In particular, May 2019 showed a ~360% increase in chlorophyll-a concentrations over the 2003-18 average, with similar percentage increases persisting through the season including ~140% (June), ~200% (July), and ~220% (August) (Fig. 4b).
Fig. 4. (a) Daily time series of sea ice concentration in the Bering Sea region at the DBO1/SLIP region (red square within the map inset), highlighting the 1981-2010 mean, 2012 (one of the highest winters/springs of sea ice cover on record), and 2018 and 2019 (the lowest winters/springs of sea ice cover on record). (b) Mean chlorophyll-a concentrations for the DBO1/SLIP region from 2003-19.
Having knowledge of how regions experience changes in chlorophyll-a concentrations alongside dramatic losses of sea ice cover provides insight into what to expect with future sea ice declines. While many of these observed patterns are directly linked to sea ice variability (and therefore light availability), it is important to note that there are other dominant factors at play that add to the complexity of observed chlorophyll-a concentrations such as the distribution and availability of nutrients (e.g., Giesbrecht et al. 2019). The impacts of sea ice decline on specific water column phytoplankton properties, such as community composition and carbon biomass (Neeley et al. 2018), as well as broader ecosystem responses (Duffy-Anderson et al. 2019), are critical to continue to monitor.
Chlorophyll-a concentrations give an estimate of the total standing stock of algal biomass. However, rates of primary production (i.e., the production of organic carbon via photosynthesis) provide a different perspective since not all algae present in the water column are necessarily actively producing, and can be estimated by combining remotely sensed chlorophyll-a concentrations with sea surface temperatures, incident solar irradiance, and mixed layer depths (see Fig. 5 for references to details of the method for estimation). Estimates of ocean primary productivity for nine regions (and the average of these nine regions) across the Arctic, relative to the 2003-18 reference period, indicate above-average primary productivity for 2019 in all regions except for the Barents Sea and North Atlantic (Fig. 5, Table 1). In the longer term, positive trends in primary productivity occurred in all regions during the period 2003-19 (Fig. 5, Table 1). Statistically significant positive trends occurred in the Eurasian Arctic, Barents Sea, Greenland Sea, Baffin Bay/Labrador Sea, North Atlantic, and for the average of the nine regions. The steepest trends were found for the Eurasian Arctic (12.85 g C/m2/yr/dec, or a ~35.5% increase), the Barents Sea (10.62 g C/m2/yr/dec, or a ~22.7% increase), and the Greenland Sea (7.33 g C/m2/yr/dec, or a ~20.5% increase).
Fig. 5. Primary productivity (2003-19, March-September only) in nine different regions of the Northern Hemisphere (for a definition of the regions see Comiso 2015), as well as the average of these nine regions, derived using chlorophyll-a concentrations from MODIS-Aqua data, the NOAA 1/4° daily Optimum Interpolation Sea Surface Temperature dataset (or daily OISST) that uses satellite sea surface temperatures from AVHRR, and additional parameters. Values are calculated based on the techniques described by Behrenfield and Falkowski (1997) and represent net primary productivity (NPP). Additional information regarding these data can be found in Table 1.
Table 1. Linear trends, statistical significance, percent change and primary productivity anomalies in 2019 (March-September) in the nine regions (and overall average) as shown in Fig. 5. Utilizing the Mann-Kendall test for trend, values in bold are significant at the 95% confidence level. The percent change was estimated from the linear regression of the 17-year time series.
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November 20, 2019