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Cite this article
* Kahru Mati,
* Lee Zhongping,
* Mitchell B. Greg and
* Nevison Cynthia D.
2016Effects of sea ice cover on satellite-detected primary production in the
Arctic OceanBiol. Lett.122016022320160223http://doi.org/10.1098/rsbl.2016.0223
SECTION
* Abstract
* 1. Background
* 2. Methods
* 3. Conclusion
* Ethics
* Data accessibility
* Authors' contributions
* Competing interests
* Funding
* Acknowledgement
* Footnotes
Supplemental Material
You have accessArctic biota
EFFECTS OF SEA ICE COVER ON SATELLITE-DETECTED PRIMARY PRODUCTION IN THE ARCTIC
OCEAN
Mati Kahru
Mati Kahru
http://orcid.org/0000-0002-1521-0356
Scripps Institution of Oceanography, University of California, San Diego, La
Jolla, CA 92093, USA
mkahru@ucsd.edu
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Zhongping Lee
Zhongping Lee
School for the Environment, University of Massachusetts Boston, Boston, MA
02125, USA
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B. Greg Mitchell
B. Greg Mitchell
Scripps Institution of Oceanography, University of California, San Diego, La
Jolla, CA 92093, USA
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Cynthia D. Nevison
Cynthia D. Nevison
University of Colorado, Boulder, CO 80309, USA
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Mati Kahru
Mati Kahru
http://orcid.org/0000-0002-1521-0356
Scripps Institution of Oceanography, University of California, San Diego, La
Jolla, CA 92093, USA
mkahru@ucsd.edu
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Zhongping Lee
Zhongping Lee
School for the Environment, University of Massachusetts Boston, Boston, MA
02125, USA
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B. Greg Mitchell
B. Greg Mitchell
Scripps Institution of Oceanography, University of California, San Diego, La
Jolla, CA 92093, USA
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Cynthia D. Nevison
Cynthia D. Nevison
University of Colorado, Boulder, CO 80309, USA
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Published:01 November 2016https://doi.org/10.1098/rsbl.2016.0223
ABSTRACT
The influence of decreasing Arctic sea ice on net primary production (NPP) in
the Arctic Ocean has been considered in multiple publications but is not well
constrained owing to the potentially large errors in satellite algorithms. In
particular, the Arctic Ocean is rich in coloured dissolved organic matter (CDOM)
that interferes in the detection of chlorophyll a concentration of the standard
algorithm, which is the primary input to NPP models. We used the quasi-analytic
algorithm (Lee et al. 2002 Appl. Opti. 41, 5755−5772.
(doi:10.1364/AO.41.005755)) that separates absorption by phytoplankton from
absorption by CDOM and detrital matter. We merged satellite data from multiple
satellite sensors and created a 19 year time series (1997–2015) of NPP. During
this period, both the estimated annual total and the summer monthly maximum
pan-Arctic NPP increased by about 47%. Positive monthly anomalies in NPP are
highly correlated with positive anomalies in open water area during the summer
months. Following the earlier ice retreat, the start of the high-productivity
season has become earlier, e.g. at a mean rate of −3.0 d yr−1 in the northern
Barents Sea, and the length of the high-productivity period has increased from
15 days in 1998 to 62 days in 2015. While in some areas, the termination of the
productive season has been extended, owing to delayed ice formation, the
termination has also become earlier in other areas, likely owing to limited
nutrients.
1. BACKGROUND
Decrease in the summer extent of Arctic sea ice is well known and has been
correlated with the apparent increase in net primary production (NPP) in the
Arctic Ocean [1–3]. However, many parts of the Arctic Ocean, particularly the
shelf areas, are rich in coloured dissolved organic matter (CDOM) [4], which is
interfering with the remote detection of chlorophyll a (Chla, mg m−3), the
primary input to most NPP models. Estimates of NPP using standard satellite Chla
are therefore to be treated with caution. In addition to the changes in the
magnitude of NPP, the seasonal timing of NPP and other biological processes may
also be changing [5] and may have consequences for the Arctic food webs.
2. METHODS
We applied the quasi-analytic algorithm [6,7] to daily binned level-3 spectral
remote sensing reflectance at approximately 9 km spatial resolution of multiple
ocean colour sensors (OCTS, 1996–1997, version 2014.0; SeaWiFS, 1997–2010,
version 2014.0; MERIS, 2002–2012, ESA second processing; MODISA, 2002–2016,
version 2014.0). The daily time series is incomplete for years 1997 and 2016,
which are therefore excluded from annual calculations. The spectral absorption
and backscattering coefficients derived with the quasi-analytic algorithm (QAA)
at 440 and 490 nm wavelengths were merged from different sensors and composited
over 5 day periods. Polar oceans are notorious for their cloudiness, which
prevents the remote detection of in-water bio-optical variables. While solar
radiation can change drastically from day to day and affect NPP, in-water
components are temporally less variable and were assumed to be constant during
each 5 day period. Daily estimates of NPP were created from daily solar radiance
and 5 day composites of in-water bio-optical properties. The vertically
generalized production model (VGPM [8]) is a well-known model that ranks among
the best in model-to-model comparisons [2,9,10]. We applied the VGPM to the
Arctic Ocean with Chla derived from phytoplankton absorption at 440 nm [11],
merged photosynthetically active radiation (PAR [12]), the depth of the euphotic
zone calculated from the total absorption and backscattering coefficients at 490
nm [13] and using daily optimally interpolated sea surface temperature [14]. PAR
was derived by merging estimates from all ocean colour sensors, and filling
remaining gaps using an empirical relationship between PAR and surface incoming
shortwave irradiance from geostationary and polar orbiting satellites [15].
Daily NPP estimates were composited into 5 day periods by averaging valid data
during each 5 day period on a grid of 0.25°. Temporal interpolation between
composites was used to fill missing pixels. Spatial interpolation was used to
fill remaining missing neighbouring pixel values if the corresponding ice
concentration [16] was below 15% using daily sea ice fraction. Sea ice coverage
was obtained from NASA Team algorithm datasets (v. 1.1,
http://nsidc.org/data/nsidc-0051.html) derived from passive microwave data. More
details are provided in the electronic supplementary material. Global 5 day
datasets of oceanic NPP are available [17].
In the processing of ocean colour data, pixels with solar zenith angle more than
70° (and sensor zenith angle more than 60°) are excluded. During the winter
season, this creates large areas with no ocean colour data (figure 1a). While we
can assume no photosynthesis in the polar night (poleward of the polar circle at
approximately 65.5° N), the region of missing data starts already poleward of
about 50°. The area of missing ocean colour data in open water reaches over 11
million km2 every winter in the Northern Hemisphere and is slightly increasing
owing to decreasing ice cover. NPP for pixels that had valid PAR but no ice and
no ocean colour data after interpolation and extrapolation was calculated
assuming a low Chla value (0.1 mg m−3) with estimated PAR. While the total area
of this gap filling was large, the effect on total NPP was relatively small
owing to the low PAR.
Figure 1. (a) Missing ocean colour data during the winter season (January 2015)
shown in white, ice cover (pink to purple) in the north and the detected
chlorophyll a (blue to yellow) in the south. Monthly time series of NPP (b) and
daily NPP per open water area (c) between latitudes 66° N and 84° N. (d) Annual
pan-Arctic NPP. (e) Monthly anomaly of open water area in the Arctic Ocean
(between 66° N and 84° N).
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(A) INTERANNUAL TIME SERIES OF NET PRIMARY PRODUCTION IN THE ARCTIC OCEAN
The monthly maximum in total integrated pan-Arctic NPP (figure 1b) has increased
approximately 47% from the first half of the time series (1998–2006) to the
second half of the series (2007–2015) and is currently estimated at
approximately 0.15 PgC month−1. NPP increased sharply from 2006 to 2007, and
interannual changes both before and after that were much smaller. In contrast to
the increasing NPP, the productivity per open water area (figure 1c) has
declined by 12.9% from 1998–2006 to 2007–2015, particularly in the period
2009–2010. The annual pan-Arctic NPP (figure 1d) has increased more smoothly but
the increase is also almost 47%. It appears that both the summer maximum and the
annual total reached a plateau in 2011–2012. These changes in NPP are inversely
correlated with the extent of sea ice and positively with the open water area.
Years with large positive open water anomaly, e.g. 2007 and 2012 (figure 1e),
have also large positive anomalies in NPP.
Monthly NPP anomalies (calculated by subtracting the climatological monthly mean
from the value of the current month) are positively correlated with anomalies in
open water area (figure 2a,b) during the summer months with the strength of the
correlation being highest in July. The effectiveness of open water to generate
additional NPP (i.e. the slope of the regression between open water anomaly and
NPP anomaly) is highest in June, followed by July and May.
Figure 2. Relationships between anomalies of open water area and pan-Arctic NPP
between latitudes 66° N and 84° N in June (a) and July (b). Spatial trends in
interannual changes in ice concentration (left panel) and in NPP (right panel)
for the months of June (c) and August (d). Blue means decrease and red means
increase (both at 95% significance). The trends are calculated for the periods
of, respectively, 1979–2015 for ice concentration and 1998–2015 for NPP. (e)
Changes in the timing of the start (filled circles, mean slope −4.5 d yr−1) and
end (open circles, mean slope +1.4 d yr−1) of the open water period (ice
fraction less than 0.15) in northern Barents Sea. (f) Changes in the start
(filled circles, mean slope −3.0 d yr−1) and end (open circles, mean slope −0.3
d yr−1) of the high-productivity (NPP ≥0.5 gC m−2 d−1) period in northern
Barents Sea.
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The areas where interannual changes in ice concentration and NPP have occurred
are different from month to month. In June (figure 2c), the decrease in ice
concentration has occurred primarily in the northeastern Barents Sea and between
Greenland and the North American continent. There is a good correspondence
between areas of June decrease in ice concentration and the increase in NPP, but
the area of increased NPP seems to be larger, as if the effects of decreased ice
have ‘spilled over’ to a larger area. In July and August, the areas of
increasing NPP move gradually to the east (to the Kara and Laptev seas) and the
correspondence between the decrease in ice and increase in NPP becomes low. In
August (figure 2d), the decrease in ice concentration has occurred primarily off
Siberia and in the Beaufort and Chukchi seas, whereas major increases in NPP
occurred primarily in different areas (e.g. Barents and Laptev seas).
Owing to earlier ice retreat and later freeze-up (figure 2e), the duration of
the ice-free period has increased in many areas, e.g. in the Barents Sea. In the
northern Barents Sea, the mean trend towards earlier ice retreat has been at a
rate of −4.6 ± 0.6 d yr−1 and the freeze-up has become later at a mean rate of
1.4 ± 0.3 d y−1. As a result, the ice-free period there has increased 3.5-fold,
from approximately 80 days in 1979 to approximately 289 days in 2015 (standard
error of the estimate is 40 days). In the same area, the start of the productive
period with high NPP (greater than or equal to 0.5 gC m−2 d−1) has advanced at a
mean rate of −3.0 ±0.6 d y−1 (figure 2f) but, in contrast to the later formation
of ice, the timing of the end of the high-productive period has not changed
significantly. This can be explained by the fact that after the end of the
spring bloom, primary production is limited by nutrients and the apparent
decrease in satellite-detected NPP may be accentuated by the sinking of the Chla
maximum. Still, the length of time between the start and end of the
high-productivity period has increased from approximately 15 days in 1998 to 62
days in 2015 (standard error of the estimate is 13 days). For the whole Artic
Ocean between latitudes 66° N and 84° N, the mean start of the high-productivity
(greater than or equal to 0.4 gC m−2 d−1) season has advanced at a mean rate of
−0.4 ± 0.1 d yr−1, and the end is delayed at a mean rate of 0.6 ± 0.1 d yr−1.
The delay may be related to the appearance of autumn blooms in some areas [18].
3. CONCLUSION
* — The summer monthly maximum and the annual pan-Arctic NPP have increased by
47% from the first half of the time series (1998–2006) to the second half of
the series (2007–2015) but changes after 2011 have been minor. The specific
productivity per open water area has decreased by 12.9% from the first half
to the second half of the series.
* — The monthly anomalies in NPP are positively correlated with the summer
anomalies in open water area: open water area in June has the strongest
effect on increasing NPP, followed by July and May.
* — The areas of interannual increase in NPP correspond well to the areas of
decreased ice concentration in June, but the correspondence is weak in July
and August.
* — The high-productivity period starts earlier and extends longer when
averaged over the whole Arctic Ocean. In an area of the most dramatic change,
the northern Barents Sea, sea ice has been retreating earlier at a mean rate
of −4.5 d yr−1 and freeze-up is later at 1.4 d yr−1. The high-productivity
season is also starting earlier at a mean rate of −3.0 d yr−1 but the
termination is not becoming later.
ETHICS
Analyses did not directly involve animal subjects.
DATA ACCESSIBILITY
Datasets of 5 day NPP used in this analysis are available in HDF4 format from
the Dryad Digital Repository (http://dx.doi.org/10.5061/dryad.34f4q) [17].
AUTHORS' CONTRIBUTIONS
M.K. created the time series, performed the analysis and wrote the manuscript.
Z.L., C.D.N and B.G.M conceived the study, contributed to interpreting results,
critically edited the manuscript, gave final approval of the version to be
published and agree to be held accountable for its content.
COMPETING INTERESTS
We declare we have no competing interests.
FUNDING
Financial support was provided by NASA grants NNX14AL80G, NNX14AM15G and by
Hanse-Wissenschaftskolleg to M.K.
ACKNOWLEDGEMENT
We thank NASA Ocean Color Processing Group, PO-DAAC, ESA OC-CCI group, NOAA
NCDC, National Snow and Ice Data Center Distributed Active Archive Center and CM
SAF for satellite data.
FOOTNOTES
One contribution to the special feature ‘Effects of sea ice on Arctic biota’.
Electronic supplementary material is available online at
https://dx.doi.org/10.6084/m9.figshare.c.3573246.
© 2016 The Author(s)
Published by the Royal Society. All rights reserved.
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THIS ISSUE
November 2016
Volume 12Issue 11
*
Article Information
* DOI:https://doi.org/10.1098/rsbl.2016.0223
* PubMed:27881759
* Published by:Royal Society
* Online ISSN:1744-957X
History:
* Manuscript received20/03/2016
* Manuscript accepted31/10/2016
* Published online01/11/2016
* Published in print30/11/2016
License:
© 2016 The Author(s)
Published by the Royal Society. All rights reserved.
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Keywords
* primary production
* ocean colour
* sea ice
* global change
--------------------------------------------------------------------------------
Subjects
* ecology
* environmental science
--------------------------------------------------------------------------------
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