Arctic amplification of global warming has accelerated mass loss of Arctic
land ice over the past decades and led to increased freshwater discharge into
glacier fjords and adjacent seas. Glacier freshwater discharge is typically
associated with high sediment load which limits the euphotic depth but may
also aid to provide surface waters with essential nutrients, thus having
counteracting effects on marine productivity. In situ observations from a few
measured fjords across the Arctic indicate that glacier fjords dominated by
marine-terminating glaciers are typically more productive than those with only
land-terminating glaciers. Here we combine chlorophyll a from
satellite ocean color, an indicator of phytoplankton biomass, with glacier
meltwater runoff from climatic mass-balance modeling to establish a
statistical model of summertime phytoplankton dynamics in Svalbard (mid-June
to September). Statistical analysis reveals significant and positive
spatiotemporal associations of chlorophyll a with glacier runoff for
7 out of 14 primary hydrological regions but only within 10 km
distance from the shore. These seven regions consist predominantly of the
major fjord systems of Svalbard. The adjacent land areas are characterized by
a wide range of total glacier coverage (35.5 % to 81.2 %)
and fraction of marine-terminating glacier area
(40.2 % to 87.4 %). We find that an increase in specific
glacier-runoff rate of 10 mm water equivalent per 8 d period
raises summertime chlorophyll a concentrations by
5.2 % to 20.0 %, depending on the region. During the annual peak discharge we estimate that glacier runoff increases chlorophyll a by 13.1 % to 50.2 % compared to situations with no runoff. This suggests that glacier runoff is an
important factor sustaining summertime phytoplankton production in Svalbard
fjords, in line with findings from several fjords in Greenland. In contrast,
for regions bordering open coasts, and beyond 10 km distance from the
shore, we do not find significant associations of chlorophyll a with
runoff. In these regions, physical ocean and sea-ice variables control
chlorophyll a, pointing at the importance of a late sea-ice breakup
in northern Svalbard, as well as the advection of Atlantic water masses along
the West Spitsbergen Current for summertime phytoplankton dynamics. Our method
allows for the investigation and monitoring of glacier-runoff effects on primary
production throughout the summer season and is applicable on a pan-Arctic
scale, thus complementing valuable but scarce in situ measurements in both
space and time.
Introduction
The Arctic cryosphere is experiencing rapid transitions due to Arctic
amplification of global warming. Climate change is reflected in changing
oceanic and atmospheric circulation patterns, permafrost degradation, decline
in sea-ice thickness and extent, and shrinking glaciers . Over the past few decades, glaciers and ice caps in the
Arctic have retreated and lost mass at accelerating rates
e.g.,, including glaciers in Svalbard
. A long-term trend of increased mass loss is also observed
for the Greenland ice sheet despite a temporary slowdown of mass loss in
2013–2017 . Ice mass loss in the form of glacial meltwater
runoff or frontal ablation, i.e., iceberg calving and submarine melt,
constitutes a significant source of freshwater being discharged into glacial
fjords and adjacent seas . This glacier freshwater discharge
has implications for the physical oceanographic conditions and the biogeochemistry of water masses , which affects the biological productivity in the fjords and the
ocean e.g.,.
Arctic marine ecosystems display strong seasonal cycles in productivity and
functioning due to the pronounced seasonality of environmental variables such as
solar radiation, sea-ice concentration, sea-surface temperature and salinity,
as well as terrestrial freshwater input . Marine primary
production, i.e., the generation of phytoplankton biomass, ultimately depends
on the availability of light and the supply of essential, “limiting”
nutrients . Seasonal changes in any of these factors lead
to periods of high or low primary production . A
characteristic “phytoplankton spring bloom” follows the rapid increase in
incoming solar radiation after the polar night, combined with high initial
nutrient levels and the development of a weak stratification
. The persistence of sea ice, with or
without snow cover, may delay the penetration of light into the water column
and thus the phytoplankton spring bloom . Stratification during spring bloom is due to freshwater input
mainly from melting of sea ice, as well as solar heating. Stratification
ensures that the phytoplankton remains within the euphotic zone, i.e., the
upper part of the water column where sufficient light is available for
photosynthesis. Stratification favors primary production at an initial stage
but also limits nutrient supply from intermediate-depth water
. Nutrient depletion and increased grazing
pressure by a growing zooplankton population terminate the spring bloom and
lead to post-spring bloom minima in phytoplankton concentrations
. New production of
phytoplankton during summer requires a supply of limiting nutrients to the
euphotic zone either by mobilization of nutrients from deeper water layers or
input from external sources, such as dust storms , coastal
erosion and river discharge .
Recent studies have shown that tidewater glaciers sustain high primary
production throughout summer in Greenland fjords and coastal waters
. In
Godthåbsfjord, a sub-Arctic tidewater glacier fjord in southwest Greenland,
observed a secondary peak in primary production, or
“summer bloom”, that coincided with substantial runoff from the Greenland Ice
Sheet. This summer bloom may be of similar magnitude or even exceed the
spring bloom. Similar findings are available from Glacier Bay, Alaska
.
Glacial freshwater discharge enters the fjord or coastline either via
pro-glacial rivers fed by runoff from land-terminating glaciers or via
frontal ablation and runoff from marine-terminating glaciers
. These tidewater glaciers are typically highly crevassed
so that most of the meltwater percolates into the glacier and is discharged
subglacially at the glacier grounding line, where it is injected into the
fjord at depth . Glacier runoff can have counteracting
effects on the productivity of Arctic fjords
e.g.,. Glacier runoff may be a direct source of
nutrients to downstream ecosystems, for example bioavailable iron, nitrogen,
phosphate or silicate . However,
glacial meltwater is generally characterized by low nutrient concentrations in
comparison with the ambient seawater . In addition, glacier runoff is typically associated with high
sediment loads which limit the light
penetration into the water column and thereby the extent of the euphotic
zone. In Svalbard, the euphotic depth may vary from less than about
0.3 m within subglacial discharge plumes near glacier calving fronts
to more than 30 m in the outer parts of the fjords
. Poor light
conditions near glacier fronts thus limit primary production
. With
increasing distance from the glaciers or pro-glacial river, light conditions
become more favorable as progressively more sediments settle
out. Phytoplankton growth will then mainly depend on the supply of limiting
nutrients to the euphotic zone .
The effect of glacier runoff on vertical mixing provides an indirect
mechanisms by which to fertilize the marine ecosystem. Subglacial discharge
drives buoyant upwelling of plumes near the calving front of tidewater
glaciers, which leads to the entrainment of large volumes of ambient seawater from
all depth levels , thereby supplying nutrient-depleted
surface layers with nutrients from nutrient-rich deep water layers
. A study by
suggests that this “nutrient pump” may provide the euphotic zone with 2
orders of magnitudes more nutrients than what is directly supplied by the
glacial meltwater. Glacier runoff may also enhance the general estuarine
circulation within fjords and embayments, which is considered to have positive
effects on biological productivity . Down-fjord katabatic winds facilitate the export of
brackish/low-density surface water out of the fjord, which leads to a
compensating return flow of nutrient-rich saline water at depth
. In
either case, positive effects of glacier runoff on primary productivity are
expected to occur only where suspended particles have settled deeper into the
water column and light conditions in surface waters become more favorable
.
In situ studies across the Arctic show a large variability in marine primary
production in response to glacier runoff for individual fjord systems due to
distinct fjord geometry, the presence and depth of an entry sill, glacier
configuration of marine- and land-terminating glaciers, and oceanographic
conditions and climatic setting e.g.,. Glacial fjords dominated by tidewater glaciers appear to have a
higher productivity than those dominated by land-terminating glaciers
, underpinning the importance of subglacial
upwelling. A study by revealed low primary production in a northeast Greenland fjord dominated by land-terminating glaciers as glacier runoff
limited light availability and enhanced stratification. Nevertheless, this low
productivity was sustained throughout the ice-free season, well into fall. In
Svalbard, glacier runoff is known to affect the distribution and species
composition of phytoplankton , but it is a matter
of debate whether or not glacier runoff facilitates higher productivity during
summer .
The current knowledge about the impacts of glacier runoff on marine primary
production is largely based on in situ observations. While providing valuable
information about the measured variables at specific locations, in situ
observations are often limited in space and time, typically capturing a
snapshot of the situation at the surveyed site. This highlights the need for
innovative long-term monitoring programs of proglacial marine ecosystems
. In addition, efforts should be taken to upscale local
in situ observations in space and time. This can be achieved by the
application of modeling approaches and/or satellite remote sensing.
This study aims to investigate the overall effects of glacier runoff on
phytoplankton dynamics and marine primary productivity in Svalbard, focusing
on a regional rather than local scale. We utilize a 10-year time series of
glacier runoff from high-resolution climatic mass balance simulations of all
glaciers in Svalbard for the time period 2003–2013 and
chlorophyll a concentrations from satellite ocean color, an
indicator of phytoplankton biomass . Chlorophyll a products and other physical ocean variables,
including sea-surface temperature (SST) and sea-ice fraction (SIF), are
available through the Copernicus Marine Service
(CMEMS). We use a statistical model to identify significant associations of
chlorophyll a with runoff while accounting for the potentially
confounding effects of physical ocean and sea-ice variables that may covary
with runoff. We focus on the summer melt period, from mid-June to September,
anticipating that this period follows the termination of the spring
bloom. Specifically, we investigate whether there are significant associations
between runoff and chlorophyll a in coastal waters around Svalbard,
and if there are spatial variations in association strength, e.g., with respect
to regional characteristics or distance to coast.
Research region
The Svalbard archipelago in the Eurasian Arctic is bordered by the Barents Sea
to the east, the Greenland Sea to the west and the Arctic Ocean to the north
(Fig. ). The climate in Svalbard is relatively warm,
given its high Arctic location. This is due to the West Spitsbergen Current
(WSC), an extension of the North Atlantic Current, which transports warm
Atlantic Water up north along the West Spitsbergen Shelf
Fig. a. The eastern
side of Svalbard is dominated by the East Spitsbergen Current (ESC), which
transports cold Arctic Water clockwise around the southern tip of Spitsbergen
. It continues northwards on the West
Spitsbergen Shelf, forming a coastal current which is subsequently freshened
by the export of brackish surface water from the fjords
Fig. a.
(a) Map of Svalbard with 14 primary hydrological regions (two-digit ID number) and one subregion (155 – Kongsfjorden and 156 – Krossfjorden) shown in different colors. Black outlines indicate secondary hydrological regions. The bathymetry is shown in shades of gray (IBCAO dataset). Adjacent seas and major currents are plotted according to and in which the red arrows delineates the West Spitsbergen Current (WSC) and pathways of Atlantic Water, and the blue arrows the Arctic Coastal Current (ACC), originating as East Spitsbergen Current (ESC), and other pathways of Arctic Water. (b) Regional time series of annual cumulative glacier runoff extracted from climatic mass-balance simulations by .
From 1971 to 2017, Svalbard has experienced strong atmospheric warming by
3–5 ∘C, evident in all seasons but most
pronounced during winter and spring . Strong atmospheric
warming is attributed to a general decline in sea ice and an increase in
sea-surface temperatures . Climate projections under medium
to high emission scenarios indicate that air temperatures may rise by
7–10 ∘C by 2071–2100, as compared to 1971–2000, which may lead
to a 5-fold increase in glacier mass loss .
Glaciers and ice caps cover 57 % (34 000 km2) of the total
land area in Svalbard. Tidewater glaciers drain 68 % of the
glacierized area and have a combined total calving-front length of ∼740km. The degree of glacier coverage and the size
of individual glaciers reflect the general climatic gradient across
Svalbard. Glaciers in the southern and western parts, characterized by
relatively warm atmospheric and oceanic conditions, are generally smaller than
glaciers in the northeastern parts of Svalbard, where colder climatic
conditions prevail. Consequently, the total glacier coverage is lower in the
southern and western parts, with a minimum in the dry central parts of
Spitsbergen . Overall, glaciers in Svalbard have been loosing
mass since the 1960s, with a pronounced increase in mass loss since the
2000s . A compilation of available mass balance
assessments for the period 2000–2019 reveals a total mass balance of -8±6Gta-1, of which -7±4Gta-1 are attributed to
the climatic mass balance and -2±7Gta-1 to the poorly
constrained frontal ablation, i.e., iceberg calving and submarine melt
. The climatic mass balance simulation by ,
from which we extract glacier runoff, is included in this reconciled mass
balance estimate. For the period 2003–2013, found a mean
annual mass balance of about -8.7 Gt, which is well within the error
margins of the consensus estimate by .
Fjords in Svalbard are affected by terrestrial freshwater discharge, on the one
hand, and the exchange of water masses with the adjacent shelf, on the other
hand . Glacier
ablation constitutes the major component of the terrestrial freshwater
discharge into Svalbard fjords . During the
summer melt season, glacier runoff enters the fjord in the form of surface
runoff and subglacial discharge, in addition to iceberg calving and submarine
melt. This freshwater mixes with ambient fjord water to form a layer of
brackish surface waters, its thickness typically decreasing from the head
towards the mouth of the fjord . The exchange of water
masses between the fjords and the shelf depends on stratification and
wind-stress, as well as the presence or absence of a topographic barrier,
e.g., in the form of a shallow sill at the fjord mouth . The
dominating wind field in Svalbard fjords is down-fjord due to katabatic winds
and orographic steering of the large-scale wind field . This drives brackish surface water out of the fjord and a
compensating inflow of Atlantic Water from the shelf, thereby stimulating
estuarine circulation and vertical mixing of water masses . In addition to wind-stress, the circulation in
broad fjords, such as found in Svalbard, is influenced by rotational dynamics
or “Coriolis” effects . Changes in
atmospheric circulation patterns since the early 2000s have caused repeated
overflow of the WSC onto the West Spitsbergen Shelf and inflow of warm saline
Atlantic Water into some of the major fjords, with implications for
water mass composition and heat content, significantly reducing sea-ice
production during wintertime .
For our regional-scale assessment of glacier runoff effects on phytoplankton
dynamics and marine primary production, we consider 14 primary drainage basins
or hydrological regions of Svalbard (Fig. a), following the
most recent Svalbard glacier inventory . The
identification system follows , in which the first digit
represents one out of five major areas: (1) Spitsbergen, (2) Nordaustlandet,
(3) Barentsøya, (4) Edgeøya and (5) Kvitøya, the latter of which is not
included in this study. The second and third digits indicate the primary and
secondary drainage basins, respectively. For each hydrological region, we
distinguish between different marine zones, defined by their distances from
the coast, namely 0 to 10, 10 to 20 and
20 to 50 km. The innermost zone contains most of the fjords, which
typically have a width of less than 20 km. The outer regions beyond
10 km distance from the coast extend into the open ocean. Along the
western and northern side of Spitsbergen, the 50 km offshore distance
contour line corresponds approximately with the shelf edge. In addition to the
primary hydrological regions, we consider one subregion near the research hub
of Ny-Ålesund in northeast Spitsbergen (15). The Kongsfjorden–Krossfjorden system
consists of two secondary drainage basins, Kongsfjorden (155) and
Krossfjorden (156), and serves as a key site for interdisciplinary studies on
glacier–ocean interactions, focusing on physical oceanographic conditions in
response to glacier runoff and their implications for the marine ecosystem
.
Material and methodsClimatic glacier mass balance and meltwater runoff
We extracted regional glacier meltwater runoff from a 10-year simulation of
the climatic mass balance of all glaciers in Svalbard, later referred to as
glacier runoff or simply runoff. The coupled atmosphere–glacier model was run
over the time period September 2003 to September 2013 . The
glacier model computes the climatic mass balance (CMB), i.e., the mass fluxes
at the surface of the glacier mainly due to deposition of snow during the
accumulation season (typically October to May) and surface melt followed by
runoff during the ablation season (typically June to September). The CMB model
is implemented into the Weather Research and Forecasting model (WRF), which
provides precipitation and other meteorological variables to the CMB model that are required to compute the climatic mass balance, considering the surface energy
balance. WRF is a mesoscale atmospheric model . In
Svalbard it has been applied to study boundary layer processes
and atmosphere–land interactions over
both tundra and glaciers . Coupled model simulations were run over all of Svalbard at
3 km horizontal resolution using sea-surface temperature and sea-ice
concentration from the Operational Sea Surface Temperature and Sea Ice
Analysis (OSTIA) and ERA-Interim climate reanalysis data as boundary
conditions. Results were validated against field observations of
meteorological conditions and in situ measurements of snow accumulation and
surface-mass balance across the archipelago .
For grid cells covered by glaciers, the land-surface scheme of WRF was
replaced by a modified version of the CMB model by , specifically adjusted for Arctic conditions . The
model simulates the development of multi-year snowpacks and their transition
into firn and ice. The CMB model employs meteorological variables generated by
WRF, near-surface temperature, humidity, pressure, wind speed and incoming
radiation to solve the surface energy balance and determine the energy
available for melt. Solid precipitation and surface and subsurface melt
then yield the column-specific mass balance over 17 layers down to
20 m depth. Variables are computed at a 20 s temporal
resolution and are then aggregated into daily values.
Daily glacier runoff is determined as the difference between a production and
a retention term of liquid water at or near the glacier surface. Production of
liquid water is given as the sum of surface melt, internal melt and rain
(liquid precipitation). Meltwater retention is the sum of internal refreezing
within the snow and firn, superimposed ice formation, i.e., water refreezing on
top of impermeable ice, and liquid water storage or, more precisely, the change
in liquid water content. Meltwater production is highest at lower glacier
elevation but not restricted to the ablation area. At higher elevation within
the accumulation area, locally produced meltwater may be stored in the snow
and firn column, thus reducing or preventing runoff. Runoff from each region
is first computed in absolute terms (Gt; Fig. b) and then
normalized by the associated area of the sea (km2), up to a defined
distance from the coast (10, 20 or 50 km). This yields specific runoff
received by the sea in terms of millimeter water equivalent (RUNOFF, in
mmw.e.), i.e., the same units as used for expressing precipitation amounts or
specific glacier mass balance. Note that our CMB model does not include a
scheme for transport and routing of meltwater. The exact location of meltwater
input to the fjords and ocean is therefore unknown. However, this does not
compromise our regional-scale analysis, in which all glacier runoff generated
within a primary hydrological region drains into the same associated fjord
system or adjacent sea. Similarly, the glacier model does not distinguish
between surface runoff and subglacial discharge.
Mean specific climatic net mass balance of Svalbard glaciers for the period
2003–2013 was negative, -257 mmw.e.yr-1, which
corresponds to a mean annual mass loss of about 8.7 Gt. Interannual variability in climatic mass balance is large
and dominated by a high variability in summer ablation. This is closely
reflected in the annual cumulative runoff curves for the various hydrological
regions (Fig. b). Regional glacier runoff is a function of the
total regional glacier area and region-specific ablation. On average,
Svalbard-wide specific glacier ablation and thus total annual glacier runoff
amounted to 919 mmw.e. and 31.2 Gt, respectively, with a
minimum in summer 2008 (673 mmw.e.; 22.9 Gt) and a maximum in
summer 2013 (1508 mmw.e.; 51.3 Gt).
Ocean data
Chlorophyll a concentration (CHL, in mgm-3) in
near-surface waters was quantified using satellite data from the European
Space Agency (ESA) Ocean Colour Climate Change Initiative (CCI). We used
Arctic reprocessed version L4 data obtained from the Copernicus Marine
Environment Monitoring Service (CMEMS), providing 8 d means of merged,
bias-corrected remote sensing reflectance at 1 km resolution from 1998
to 2014 (see “Data availability” section below). This product merges
reflectance data from SeaWiFS, MODIS-Aqua and MERIS sensors by realigning the
spectra to those of the SeaWiFS sensor. Chlorophyll a is estimated
from the OC5ci algorithm, which is a combination of two ocean color
algorithms for chlorophyll retrieval. The first is developed for clear waters
in the open ocean, where ocean color is dominated by chlorophyll a,
i.e., the green pigment contained in phytoplankton biomass (case-1 waters; CI;
). The second is optimized for optically
complex coastal waters, influenced by terrestrial runoff and hence suspended
sediments and colored dissolved organic matter (case-2 waters; OC5;
). For Svalbard, chlorophyll a observations are
typically limited to late March to early September each year.
As key environmental variables other than RUNOFF we consider sea-surface temperature (SST, in ∘C), mixed-layer depth, a measure
of stratification (MLD, in m) and sea-ice fraction (SIF, [0
1]). Daily means of these variables at 12.5 km resolution for the years
1998–2014 were extracted from the TOPAZ4 Arctic Ocean Physics Reanalysis
(version V0.3) obtained from CMEMS. The TOPAZ4 reanalysis uses the Hybrid
Coordinate Ocean Model (HYCOM), an operational general ocean-circulation model
that assimilates remotely sensed sea level anomalies, sea-surface temperature,
sea-ice concentration and Lagrangian sea-ice velocities (winter only, since
2002), as well as temperature and salinity profiles from Argo floats using a
100-member deterministic version of the ensemble Kalman filter
. A rigorous quality assessment of the TOPAZ4 dataset can be
found in .
Statistical analysis
All data (CHL, RUNOFF, SST, MLD, SIF) were
first aggregated into regional time series with the same 8 d temporal
resolution as CHL. For each of the 14 hydrological regions (plus one
subregion), we constructed three time series of different spatial scale and
near-shore influence: 0–10, 10–20 and 20–50 km distance from
land. The main emphasis is on 0–10 km from land as this covers the major
fjord systems where we expect the largest potential RUNOFF effects.
To test if associations between RUNOFF and CHL were
statistically significant we restricted the data to late summer (13 June to
15 October, i.e., annual 8 d periods 21 to 36). This period includes
the main glacier summer melt period (mid-June to September) and is expected to
start after termination of the phytoplankton spring bloom. For each region and
spatial scale we considered the following generic model:
log(CHLr,t)=αr+βr⋅log(CHLr,t-1)+cr⋅er,t+εr,t.
Here log(CHLr,t) is the natural logarithm of CHL in
region r (and a given distance interval from land) at time t, αr is
the intercept, βr is the auto-regressive effect of CHL in the
previous time step, cr is a row vector with coefficients for
environmental effects, er,t is a column vector with the
environmental covariate values, εr,t is a normally and
independently distributed error term with variance σr2/nr,t,
and nr,t is the number of CHL observations that were averaged to
calculate CHLr,t. By weighting the error variance with sample
size, region–time combinations with few CHL observations, e.g., due to
cloud cover, have less influence on results than region–time combinations with
many observations.
To determine which environmental variables to include for each region, we used
a two-step approach. We first found the best model without RUNOFF,
using data for all years 1998–2014 (whereas RUNOFF was only available
from September 2003 to September 2013). Variables were selected stepwise by
adding terms if it led to a lower value of the information criterion AICC,
i.e., the Akaike information criterion corrected for small sample size
. The AICC helps to find the best trade-off between the
goodness-of-fit of a model and the simplicity of the model; a model with lower
AICC is preferred over a model with higher AICC. Terms only marginally
significant (P>0.05) were removed from the model. Nine candidate variables
were considered at this step: (1) SSTr,t, (2)
SSTr,t-1, (3) SSTdt=SSTr,t-SSTr,t-1, (4) log(MLDr,t), (5)
log(MLDr,t-1), (6) log(MLDdt)=log(MLDr,t)-log(MLDr,t-1), (7) SIFr,t,
(8) SIFr,t-1, and (9) SIFdt=SIFr,t-SIFr,t-1. The difference variables SSTdt and
log(MLDdt) were included as possible indicators of mixing of
deeper nutrient-rich water masses into the surface layer. The difference
variable SIFdt was included as an indicator of the sea-ice breakup
and the associated increase in light levels in the water column. We then added
RUNOFF and RUNOFFt-1 to the model selected in the first
step but only if leading to lower AICC (for the reduced period with RUNOFF
data) and only if the association was significant at P<0.05. A summary of
all regional models, including model equations, parameter estimates with
standard errors and statistical significance, can be found in the Appendix
(Tables A1–A3).
To assess if key model assumptions were met, we checked if residuals were
independent and approximately normally distributed. Specifically, Pearson
residuals (i.e., residuals standardized to unit standard deviation) from the
final model for each region were explored for independence by plotting the
autocorrelation function and the partial autocorrelation function and for
approximate normality by plotting quantile-quantile normal plots. The
residuals from the final model for each region were uncorrelated in time and
approximately normally distributed, with a possible exception of region 22 in
the analysis for 0–10 km from the coast, which showed indications of
unequal variance. We also checked if results were strongly influenced by a few
outlying observations. Outliers were identified as residuals more than 3.3× standard deviations away from zero, which is expected to occur by
chance for 1 out of 1000 normally distributed cases, i.e., for about 2–3 out
of the >2000 observations analyzed. Within 10 km distance from the
coast, 13 residuals distributed among 10 regions were identified as
outliers. A similar number of outliers existed for the other distances from
the coast. If outliers were identified, we refitted the model excluding the
outliers. Since the removal of outliers had little influence on parameter
estimates for RUNOFF effects, we kept them in the present model (all
the coefficients remained statistically significant at P<0.05). All
statistical analyses were performed using the R programming environment
().
Results
We first present regional associations of CHL with glacier runoff
(Sect. ) before moving on to associations with
physical-ocean and sea-ice variables
(Sect. ). Interpretation of these results will
be discussed in the following section (Sect. 5). Our statistical model
identifies the environmental variables that best explain the observed regional
summertime CHL (Figs. and A1–A3). The model
considers instantaneous and delayed associations of CHL with a set of
predictor variables, based on variable values during the current and previous
8 d time step marked by an index “t” and “t-1”,
respectively. In addition, the model inspects associations of CHL with
the rate of change in selected environmental variables (index “dt”). Note
that the associations that we hereafter discuss are partial effects, i.e., the
association of CHL with each predictor variable, while accounting for
all other predictor variables selected in the model. Specifically, the
statistical model estimates the joint effects of all selected predictor
variables on CHL, and the partial effect of a variable represents the
expected effect of that variable if all other variables are kept constant. As
a model control run, we test the auto-correlation of CHL in the current
and previous time step. This “null model” reveals a significant positive
association in all regions regardless of distance from the coast, as expected
(Figs. a and A1–A3). In other words, if there is high
CHL in the previous 8 d time step, then it is likely that
CHL will also be high in the present time step.
Regional significance of environmental variables and their association with
the predicted chlorophyll a concentrations within 0–10, 10–20 and
20–50 km from the coast: CHL during previous 8 d period
(a); glacier runoff, RUNOFF, during the current and previous
8 d period (b, c); current, previous and change in sea-ice
fraction, SIF (d–f; denoted by index t, t-1 and
dt); sea-surface temperature, SST (g–i); and mixed-layer
depth, MLD (j–l). Positive associations are indicated by red shades and negative by blue. The intensity of the color showing the level of significance of the association (1: P<0.05; 2: P<0.01; 3: P<0.001).
Association of summertime chlorophyll a with glacier runoff
We find significant positive associations of CHL with RUNOFF in
half of the primary hydrological regions (7 out of 14), namely eastern
Spitsbergen (Region 11), southern Spitsbergen (12), Van Mijen- and
Van Keulenfjorden (13), Isfjorden (14), Wijde- and Woodfjorden (16), and
Wahlenbergfjorden (22) in Nordaustlandet and Edgeøya (31) in southeast
Svalbard (Figs. and A1). A positive association also
exists for the subregion of Kongsfjorden–Krossfjorden (155), whereas no
significant association exists for northwest Spitsbergen (15) as a whole. Positive
associations are mainly restricted to within 10 km distance from the
coast, indicating that the RUNOFF effect on CHL is mainly
limited to within the fjords. Fjords in Svalbard have a maximum width of
typically less than 20 km and are thus entirely covered by this range.
Beyond 10 km distance from the coast, as well as for regions
characterized by open coastal conditions, the significant positive association of
CHL with RUNOFF vanishes (Figs. b, A2 and
A3). At 10–20 km, there is no significant association, while at
20–50 km there is a weak negative association for southern
Spitsbergen (12) and a weak positive association for eastern Spitsbergen (11) and
Barentsøya (41). The latter regions all border Storfjorden, which forms a
large, 40–80 km wide embayment between eastern Spitsbergen to the
west and Barentsøya and Edgeøya to the east. There are only a few
delayed associations of CHL with RUNOFF
(Fig. c). For Edgeøya (31) a positive association is
present at 10–50 km, in addition to the instantaneous response within
10 km distance from the coast (Fig. b). For
neighboring Barentsøya (41) a weak positive association exists for the
10–20 km zone. CHL shows a negative delayed association with
RUNOFF at 0–10 km for Wijdefjorden (16) and within
20–50 km off northeast Nordaustlandet (25).
Glacier configuration and runoff characteristics for primary hydrological regions of Svalbard, including subregion Kongsfjorden–Krossfjorden.
a Specific runoff rate, CHL and CHL increase are based on marine area within 10 km from the coast.
b Mean and mean annual maximum values are derived from annual 8 d periods 21–36 during 10 subsequent summers 2004–2013.
c Mean chlorophyll a increase per 10 mmw.e.8d-1 and at annual maximum runoff. Numbers in squared brackets provide the plausible range at 95 % confidence interval. d Glacier area drained through tidewater glaciers (TWG area).
We find that regions that display significant positive associations between
CHL and RUNOFF within 10 km distance from the coast have
a 26 % higher mean summertime chlorophyll a content and a
19 % higher mean annual maximum chlorophyll a content than regions
without such associations (Table 1). Our statistical model suggests that an
increase in specific runoff of 10 mmw.e.8d-1 raises summertime chlorophyll a
concentrations in these regions by 5.2 % to 20.0 %, or
9.3 % on average, with a standard deviation
of 4.6 % (Table 1). During the annual peak discharge we estimate that
runoff increases chlorophyll a by 13.1 % to 50.2 %
or 28.4±13.5% on average, compared to situations with no
runoff.
Association of summertime chlorophyll a with physical ocean and sea-ice variables
There are both negative and positive associations of CHL with the
physical ocean and sea-ice variables, although only for a limited number of
regions. Concerning sea-ice variables, the current sea-ice fraction
(SIF) has little association with CHL
(Fig. d). However, there is a delayed positive
association of CHL with SIF in northern Svalbard, mainly within
10 km from the coast (regions 15, 16, 23;
Fig. e) but also 10–20 km (16) and
20–50 km (21), while CHL is negatively associated with a
change in SIF at 0–10 and 10–20 km (regions 12, 15, 17, 21,
24, 31, 41; Fig. f).
Moving on to sea-surface temperature (SST), current SST has a
few positive associations at 20–50 km distance from the shore
(regions 12, 14 and 17) and negative associations north of Nordaustlandet at
0–10 and 10–20 km distance from the coast (24, 25;
Fig. g). There is a positive delayed association of
CHL and SST along the entire west coast of Spitsbergen at 0–10
and/or 10–20 km distance from the coast (12, 13, 14, 15;
Fig. h), as well as in Hinlopen Strait off northeast
Spitsbergen (17). There is a negative instantaneous association of CHL
with SST north of Nordaustlandet (25). The association of CHL
with a change in SST is negative all around Edgeøya (31) and
Barentsøya (41), as well as western Nordaustlandet (23), and weakly positive
in the outer region of northeast Spitsbergen (17) at 20–50 km distance from
the coast (Fig. i).
Mixed-layer depth shows some positive association with CHL at the outer
regions along the west coast of Spitsbergen (13, 14, 15) and Hinlopen (17;
Fig. j). The delayed association between CHL and
MLD is negative in two northern regions (16, 21) within 10 km
from the coast and positive at 10–20 and 20–50 km for Isfjorden (14)
and eastern Spitsbergen (11), respectively (Fig. k). The
change in MLD has a few both positive and negative associations
(Fig. l).
Discussion
We first discuss the observed associations of summertime CHL with the
environmental variables and provide physical and biological explanations. We
start with the associations of summertime CHL with RUNOFF
(Sect. ) before moving on to ocean and sea-ice variables
which point at the effect of persistent sea-ice coverage and the influence of
the West Spitsbergen Current (Sect. ). We then
describe the seasonal evolution of chlorophyll a in relation to
environmental variables (Sect. ). Finally, we
discuss challenges related to the use of remotely sensed
chlorophyll a as a proxy of phytoplankton biomass
(Sect. ).
Glacier-runoff effects on marine primary production
Our study suggests that the overall effect of glacier runoff on marine primary
production is positive for 7 out of 14 hydrological regions in Svalbard. These
regions represent the major fjord systems rather than coastal
regions. Positive association is generally restricted to within
10 km distance from the coast; i.e. it does not extend far outside the
fjords and onto the shelf. The primary hydrological regions have highly
variable glacier coverage, ranging from 34.5 % for Isfjorden in
central Spitsbergen to 90.3 % for southeast Austfonna on Nordaustlandet
(Table 1). For regions which display significant and positive associations
between CHL and RUNOFF, glacier characteristics in terms of
glacier coverage, glacier area drained by tidewater glaciers and total
calving-front length are on average ∼10% smaller compared to
regions without associations between CHL and RUNOFF. Regions
which display significant and positive associations between CHL and
RUNOFF are also characterized by a highly variable fraction of
tidewater glacier-drained area, ranging from 40.2 % for Isfjorden to
87.4 % for southern Spitsbergen, with a regional mean of 62.3±21.0%. This is slightly less than the corresponding mean value of
66.4±21.0% in the other regions. Mean specific runoff rates
per marine area within 10 km distance from the coast range from
4.2 mmw.e.8d-1 for Barentsøya to
24.2 mmw.e.8d-1 for Kongsfjorden–Krossfjorden
(Table 1). Despite the slightly smaller average glacier coverage, regions with
RUNOFF effect on CHL have higher specific runoff rates that
exceed those in the other regions by 46 % and 69 % for mean
specific runoff rates and specific mean annual peak runoff rates,
respectively.
Field observations across the Arctic show that glacial fjords dominated by
tidewater glaciers have generally higher productivity than those dominated by
land-terminating glaciers . Runoff from marine-terminating
glaciers is generally thought to enhance marine primary production through
buoyant upwelling of subglacial discharge plumes e.g.,,
whereas runoff from land-terminating glaciers is thought to limit primary
production, as a high amount of suspended particles lowers light availability,
while surface freshening leads to strong stratification, thereby restricting
nutrient availability in surface waters
e.g.,. Consequently, one might expect that regions with a
high fraction of tidewater glaciers yield significant positive associations
between CHL and RUNOFF, whereas regions with a low fraction of
tidewater glaciers yield weaker positive or potentially negative
associations. However, we do not find a clear relationship between the
fraction of tidewater glaciers and the sign or strength of associations
between CHL and RUNOFF (Table 1). This indicates that a fraction
of tidewater glaciers above ∼40% is sufficient to provide
upwelling of subglacial discharge plumes capable of stimulating regional-scale
marine primary production. Alternatively, other mechanisms by which glacier
runoff stimulates marine primary productivity may play a role.
While our method allows us to assess the overall effect of glacier runoff on
regional-scale phytoplankton dynamics, it does not reveal the specific
mechanism(s) by which the effect is achieved. We suggest that the positive
association between CHL and RUNOFF could be explained by several
processes, which may act independently or in combination, dependent on
regional characteristics: (1) buoyant upwelling of subglacial discharge plumes
at the calving front of tidewater glaciers (a few tidewater glaciers may be
sufficient to fuel primary production in the entire fjord system); (2) glacier
runoff may enhance the general estuarine circulation; and (3) glacier runoff
may provide a direct source of limiting nutrients. The first two points are
considered indirect effects and the third a direct effect of glacier runoff on
marine primary production.
Considering the first mechanism, buoyant upwelling of subglacial discharge
plumes is associated with the entrainment of large volumes of ambient
seawater from deep to intermediate depth. This process is considered to
deliver significant quantities of nutrients to surface waters
. These nutrients are
first expected to enhance primary production some distance away from the
glacier front, where light conditions become more favorable as progressively
more suspended particles have settled deeper into the water column
. Glacier erosion rates, the
amount and size of suspended particles, and thus glacier runoff effects on
light regime are controlled by the glacier bedrock lithology, as well as
subglacial drainage-system configuration and total discharge
. Tidewater glaciers in Svalbard are grounded at shallow
depth compared to those in Greenland. Entrainment factors are therefore
expected to be significantly smaller for Svalbard than for Greenland as they
scale with the depth at which subglacial discharge enters the water column
. Nevertheless, found nutrient
upwelling in Kongsfjorden to be a significant source of nutrients to the
euphotic zone as comparably small discharge volumes were sufficient for the
plume to reach the surface , and plumes were present for a
long period during summer . In addition, upwelling of ammonium
released from the shallow seafloor of Kongsfjorden was found to be a
significant source of bioavailable nitrogen .
The second mechanism concerns the estuarine circulation, driven by down-fjord
katabatic winds, which facilitates the export of relatively fresh or
“brackish” surface waters out of the fjord
e.g.,. This outflow of surface waters will induce a
compensating return flow of warm and saline water masses from the shelf area
at intermediate depth .
used a high-resolution ocean-circulation model, forced with glacial freshwater
discharge, to simulate water exchange in Kongsfjorden, Svalbard. Simulations
revealed that glacial freshwater discharge drives a strong outflow in the
upper surface layer and a significant compensating inflow of Atlantic Water in
the upper 15–20 m, which was enhanced in times of peak discharge. The
volume flux was strongly influenced by the local wind field. Vertical mixing
by wind stress and tidal forcing provides a mechanism of bringing nutrients
from intermediate water into the euphotic zone where they become available for
phytoplankton, fueling primary production. Svalbard fjords are considered
broad fjords, where rotational “Coriolis” effects play a role
. These rotational dynamics may contribute to
vertical mixing of surface and intermediate depth waters, thereby enhancing
the effect of the general estuarine circulation on nutrient availability in
surface waters.
The third candidate mechanism concerns the direct fertilization of seas by
nutrients contained in glacier runoff. In light of the reported low
concentrations of nutrients in glacier meltwater compared to ambient seawater
, we believe that indirect
effects dominate over direct effects. While recent studies have focused
primarily on the role of subglacial discharge plumes, we cannot exclude that
also the enhancement of the general estuarine circulation may contribute to
the observed positive effect of glacier runoff on marine primary
productivity. The strong climatic warming trend which is currently observed in
Svalbard is expected to lead to a widespread
transition from marine to land-terminating glaciers. Glacier runoff from
land-terminating glaciers may still promote estuarine circulation and
constitute a potential, although limited source of nutrients. On the other
hand, freshly exposed glacier forelands may supply arctic fjords with
nutrients mobilized by eolian or fluvial processes . Nevertheless, widespread tidewater-glacier retreat would lead
to a reduction and eventually loss of subglacial plume dynamics, with
significant implications for fjord circulation and biogeochemistry, possibly
rendering Svalbard fjords less productive .
To this end, we can highlight some differences between regions with
significant positive associations between CHL and RUNOFF, namely
the major fjord systems in Svalbard, and regions without such associations,
i.e., regions characterized by open ocean conditions. While our method does not
reveal the specific mechanism(s) by which the association is achieved, the
fjord systems receive more freshwater per marine area compared to open
coastal regions, as is evident in their specific runoff rates
(Table 1). Furthermore, enhancement of estuarine circulation only applies
within the fjords but not at the open coast. We expect that residence times
of water masses are higher inside the fjords than along the open
coast. Potential direct or indirect enhancements of nutrient availability
through glacier runoff may thus be of lower magnitude and/or attenuate more
quickly so that no effect on primary production is revealed at the
spatiotemporal scale used in our study. With the exception of a single weak
negative association between CHL and RUNOFF off the coast of
southern Spitsbergen (region 12 at 20–50 km distance from the coast)
and two weak negative delayed associations for Wijdefjorden (16;
0–10 km) and north off Nordaustlandet (25; 20–50 km), we
generally find significant positive associations between CHL and
RUNOFF. This indicates that on a regional scale, positive effects of
glacier runoff on primary production may outweigh negative local impacts, such
as reduced availability of light and persistent stratification. Significant
positive effects are, however, largely restricted to the fjord systems and do
not extend far out of the mouth of the fjords and onto the shelves.
The role of ocean and sea-ice variables on summertime CHLLate spring bloom in northern Svalbard
The northern regions of Svalbard show a positive delayed association of
CHL with SIF (Fig. e). This suggests high
CHL in response to previously high SIF. The exact timing and
breakup of sea ice is highly variable. It depends not only on the initial
sea-ice extent, thickness and stability but also wind conditions and wave
action, sea-ice conditions further offshore, and net heat transport
associated with the advection of Atlantic water masses . In northern Svalbard, oceanic pack ice can prevent sea ice from
being exported out of the fjord, thus extending the sea-ice season
. This is expected to lead to a significant delay of the
phytoplankton spring bloom. The presence of sea ice in the previous 8 d
period in the summer months in this region is thus an indication of
hydrological spring conditions. This interpretation of a late spring bloom is
supported by a negative association of CHL with changes in SIF,
meaning that chlorophyll a is increasing when sea-ice coverage is
decreasing (Fig. f). The latter association is, however,
not restricted to northern Svalbard but is significant also for other regions in
Svalbard.
Advection of water masses of Atlantic origin
Similar as for the sea-ice variables, we found delayed associations of
CHL with SST and with changes in SST. A delayed positive
association with SST is revealed along the entire west coast of
Spitsbergen (Fig. h). This may indicate the influence of
the WSC, flowing along the West Spitsbergen Shelf and spilling onto the
shelf. Note that the 50 km offshore distance aligns approximately with
the shelf edge along the western and northern side of Spitsbergen, indicating
that variations in overflow of the West Spitsbergen Current may affect the
outer region (20–50 km). High SST points at the advection of
warm Atlantic Water, which is also characterized by high salinity and nutrient
content, thus being capable of enhancing primary production and hence
CHL. The importance of warm saline Atlantic Water for fjord and shelf
water masses and the marine ecosystem was previously reported by
and . Variations in the correlation
between CHL and SST for different fjord systems may at least
partly be explained by the presence and depth of entry sills which regulate
the exchange of water masses between the shelf and the fjords
.
Around Edgeøya, a strong negative association of CHL with a change
in SST coincides with the positive association of CHL with
RUNOFF (Fig. i and c). Cooling SST may be
associated with meltwater spreading out on the surface away from the coast,
meaning that the association of CHL with this variable and
RUNOFF may reflect the same process. The negative association of
CHL with change in SST might also be caused by increased
stratification due to solar heating, leading to nutrient limitation in surface
waters.
Vertical mixing is closely linked with the mixed-layer depth (MLD). The
generally positive associations between MLD and CHL along the
west coast are possibly caused by advection of Atlantic Water onto the shelf,
leading to increased vertical mixing as evident in a deepening of the
MLD. Vertical mixing increases the supply of essential nutrients to
surface water layers, thereby increasing primary production as indicated by
high CHL (Fig. j). A deepening of the MLD
caused by winds could have the same effect when nutrients in the euphotic
zone have been depleted in summer. In a spring situation when nutrients are
plentiful, deep vertical mixing and high MLD are, however, likely to
reduce the build-up of CHL as the phytoplankton multiply more slowly
because they get access to less light . Deepening of
MLD can also have a dilution effect on near-surface phytoplankton
biomass e.g.,. These phenomena could explain the
negative associations between MLD and CHL in some northern
regions.
Phytoplankton dynamics during the productive season
Our time series of chlorophyll a, glacier runoff, and physical
ocean and sea-ice variables allows us to put the summer bloom into a larger
temporal context. We discuss phytoplankton dynamics in Svalbard over the
entire productive season, which lasts from about April to September, and
compare our findings to those from other regions. Investigating primary
production in a tidewater-glacier fjord in southwest Greenland,
were able to divide the productive season into three
distinct phases: the spring bloom (April–May; phase 1), a transition period
with low primary production (June; phase 2) and the summer bloom
(July–August; phase 3).
To investigate whether these three phases can be identified in Svalbard, we
average monthly means of all relevant variables over the period 2003–2013
(Fig. ). The spring bloom typically occurs in May
(Fig. a), coincident with increased solar
insulation, sea-ice breakup (Fig. c) and
initialization of a weak stratification, in line with phase 1 of
. Stratification (shallow MLD;
Fig. e) seems to be dominated by solar heating
(increasing SST; Fig. d). Significant
runoff starts in June when stratification is already established
(Fig. b and e), but CHL has declined from
its spring-bloom value, indicative of nutrients depletion (phase 2 in
). Runoff during the later summer, i.e., July and
August, coincides with a second period of high CHL (phase 3;
Fig. a and b), in some cases exceeding the monthly
mean values during spring bloom. Note that the spring bloom typically only
lasts for a short time, i.e., one 8 d period, during which
concentrations can be several times larger than what is reflected in the
monthly mean. Peak values of CHL during summer may be lower but more
persistent, resulting in monthly mean values similar or larger than those
during spring time. For regions that show a positive association between
CHL and RUNOFF (e.g., regions 11, 12, 13, 14, 16), monthly mean
CHL during summer (July–August) typically matches or exceeds that during
spring bloom (May), with a minimum in June, in line with phytoplankton
dynamics described by . Few studies have focused on
primary production in glacier fjords dominated by land-terminating
glaciers. found low but persistent primary productivity
in a northeast Greenland fjord throughout the ice-free season and well into
autumn. The relatively low productivity was attributed to glacier runoff
causing low light availability and a strong stratification, thereby limiting
the nutrient supply to the photic zone. showed that
plankton communities had adapted to the low-light regime in glacier-influenced
waters, similar to findings from northern Svalbard .
Average evolution of monthly variables for all primary hydrological regions and associated marine areas within 10 km distance from the coast: (a) chlorophyll a concentration, CHL; (b) specific glacier runoff, RUNOFF, per marine area; (c) sea-ice fraction, SIF; (d) sea-surface temperature, SST; and (e) mixed-layer depth, MLD. Solid lines represent regions that exhibit a significant positive correlation between RUNOFF and CHL (a), whereas dashed lines represent regions where no significant correlation was found.
In northeast Svalbard and Nordaustlandet (regions 17, 21–24), the 10-year monthly
mean SIF is around 40 %–50 % in June and
20 % in July. Several regions in northern Svalbard showed a delayed
association of CHL with SIF (regions 15, 16, 23;
Fig. e) that indicates a delayed spring bloom. In this
case, two separate production phases cannot be distinguished, at least at
monthly temporal resolution. Instead, CHL during spring is low and
steadily increases towards a maximum in July (e.g., regions 17, 21,
25). MLD during springtime (April) varies from up to 150 m in
western Spitsbergen to around 30 m in northeast Svalbard and typically
shallows in late spring to early summer (May–June). The shallowing MLD
coincides with rising SST, suggesting that solar heating plays an
important role in initiating stratification. Stable stratification of surface
waters, as indicated by a shallow MLD, is already established when
significant glacier runoff starts in July. Generally lower CHL in June
than May suggests that phytoplankton may be nutrient limited when glacial
melting sets in. The peak meltwater discharge coincides with elevated
CHL during summer (July–August). Glacier runoff terminates in
September, which may lead to the observed increase in the MLD, along
with the recession of solar insulation and, possibly, initiation of
wind-induced autumn mixing. Vertical mixing, as evident in a deepening of the
MLD, may supply the photic zone with limiting nutrients, which could
explain sustained CHL well into autumn, as observed in a northeast
Greenland fjord .
Challenges and uncertainties of satellite-based surface chlorophyll a products
Although remotely sensed chlorophyll a is a commonly used proxy of
phytoplankton biomass, there are several limitations to this
approach. Firstly, data sampling relies on sufficient daylight, clear skies
and largely sea-ice free conditions as ocean color sensors cannot detect
ice algae or phytoplankton cells beneath sea ice . For
Svalbard, chlorophyll a observations are typically limited to late
March to early September. In the beginning and end of the acquisition period,
spatial sampling is generally poor due to the persistence of sea ice and
limited daylight (low sun angles). Spatial sampling is also poor under cloudy
conditions, typical for Svalbard during summertime. The variable sampling
intensity was accounted for in the statistical analysis as 8 d
periods and regions with many satellite observations of CHL were given
more weight in the analysis than periods and regions with few
observations. Secondly, although the algorithm used to estimate CHL
from surface reflectance accounts for the possible presence of inorganic
particles, bias from inorganic particles originating from glacial meltwater
cannot be ruled out. Some fjords of Svalbard are heavily influenced by
suspended sediments from terrestrial or subglacial runoff which influences
ocean color significantly e.g.,. Thirdly, subsurface maxima of chlorophyll a, as may
occur in summer situations, are easily missed by satellite sensors because
data retrieval is restricted to the upper layer of the water column down to
the 1 % photosynthetically available radiation . It
should therefore be kept in mind that our results show what happens in
near-surface layers and not the entire water column. Subsurface
chlorophyll a maxima are common in the Arctic Ocean
and have also been reported for Svalbard
. Furthermore, phytoplankton can rapidly respond to reduced
light availability, for example due to suspended matter, by increasing the
chlorophyll a concentrations in their cells . It is therefore uncertain whether possible increased
chlorophyll a concentrations at high meltwater runoff also reflect
increased phytoplankton biomass. Further verification of remotely sensed
chlorophyll a as a proxy of phytoplankton biomass in complex Arctic
waters is required to gain more confidence in the results from our statistical
analysis. This can only be achieved by in situ observations, extensive in both
space and time, including simultaneous measurements of phytoplankton biomass,
glacier runoff and nutrient concentrations in different water masses.
Conclusions
We investigated the effect of glacier runoff on regional-scale phytoplankton
dynamics in Svalbard by combining chlorophyll a from satellite ocean
color with glacier mass-balance modeling. Statistical analysis of regional
time series revealed significant positive associations of CHL and
RUNOFF for 7 out of 14 primary hydrological regions. The association of
regional-scale CHL with RUNOFF is typically restricted to the
major fjord systems and within 10 km distance from the coast. For
regions characterized by open coastal conditions and beyond 10 km
distance from the coast, the relationship between glacier runoff and marine
primary production generally vanishes. Our results suggest that the overall
effect of glacier runoff on marine primary production in these regions is
positive despite counteracting effects of glacier runoff on the availability
of light and essential nutrients, both of which are required for an increase
in phytoplankton biomass.
We find that regions that display significant positive associations between
CHL and RUNOFF have a 26 % higher mean summertime
chlorophyll a and a 19 % higher mean annual maximum
chlorophyll a compared to regions without such associations. Our
analysis suggests that an increase in specific runoff of
10 mmw.e.8d-1 raises regional summertime
chlorophyll a concentrations by 5.2 % to 20.0 %, or
9.3 % on average, with a standard deviation of 4.6 %. During
the annual peak discharge the effect is even larger, when glacier runoff is
associated with 13.1 % to 50.2 % increase in
chlorophyll a or 28.4±13.5% on average. Glacier
runoff thus facilitates a secondary phytoplankton bloom in July to August,
typically following a spring bloom in May and a minimum in June, in line with
in situ observations from Greenland e.g.,. In
terms of monthly mean CHL, the magnitude of the summer bloom is similar
or may even exceed that of the spring bloom.
A common characteristic of regions which display significant positive
associations between CHL and RUNOFF, i.e., the major
fjord systems in Svalbard, is that they receive high volumes of glacier runoff
per marine area. Mean specific runoff rates and specific mean annual peak
runoff rates exceed those in open coastal regions by 46 % and
69 %, respectively. The primary hydrological regions associated with
the fjord systems are also characterized by a highly variable glacier
coverage, ranging from 35.5 % to 81.2 %, as well as glacier
area drained through tidewater glaciers, ranging from 40.2 % to
87.4 %. This indicates that upwelling effects of nutrients from
subglacial discharge plumes at a few tidewater glaciers may be sufficient to
fuel regional-scale primary production. Alternatively, other mechanisms, such
as enhanced estuarine circulation, driven by runoff from both land- and
marine-terminating glaciers and down-fjord winds, may play a role.
To our knowledge, this study is the first to link large-scale
chlorophyll a from satellite ocean color, an indicator of
phytoplankton biomass, with glacier runoff from glacier mass balance
modeling. Statistical analysis allowed us to identify and quantify significant
associations between glacier runoff and regional chlorophyll a. We
empirically show that glacier-runoff effects on primary production in Svalbard
are mainly restricted to the major fjord systems and do not extend far
outside the mouth of the fjords and onto the shelves. As we also consider
physical-ocean and sea-ice variables in our statistical analysis, we are able
to identify other environmental factors controlling regional summertime
chlorophyll a dynamics in Svalbard. These factors include sea-ice
conditions, especially in northern Svalbard, pointing at the influence of
persistent sea ice and late sea-ice breakup. Furthermore, associations of
CHL with SST and MLD along the West Spitsbergen Shelf
indicate the role of the West Spitsbergen Current, i.e., the advection of warm
saline and nutrient-rich water masses of Atlantic origin. Our method can be
applied on a regional to pan-Arctic scale, thereby complementing valuable
in situ observations which are only available from a few sites and often of
short duration, thus not capturing inter-seasonal to interannual variability.
Partial effects of environmental variables on chlorophyll a
Partial effects of environmental variables on chlorophyll a, CHL, within 10 km from the coast. Each row shows the model (Eq. 1) for one hydrological region (Table 1). Each panel shows the relationship between a predictor variable (x axes) and CHL (y axes), with lines showing estimated partial effects and points showing partial residuals. Blank panels imply that the variable was not selected. Asterisks show statistical significance at levels 5 % (∗), 1 % (∗∗) or 0.1 % (∗∗∗).
Partial effects of environmental variables on chlorophyll a, CHL, within 10 to 20 km from the coast. Each row shows the model (Eq. 1) for one hydrological region (Table 1). Each panel shows the relationship between a predictor variable (x axes) and CHL (y axes), with lines showing estimated partial effects and points showing partial residuals. Blank panels imply that the variable was not selected. Asterisks show statistical significance at levels 5 % (∗), 1 % (∗∗) or 0.1 % (∗∗∗).
Partial effects of environmental variables on chlorophyll a, CHL, within 20 to 50 km from the coast. Each row shows the model (Eq. 1) for one hydrological region (Table 1). Each panel shows the relationship between a predictor variable (x axes) and CHL (y axes), with lines showing estimated partial effects and points showing partial residuals. Blank panels imply that the variable was not selected. Asterisks show statistical significance at levels 5 % (∗), 1 % (∗∗) or 0.1 % (∗∗∗).
Summary of models for regions within 0 to 10 km from the coast. The model equations give parameter estimates with standard errors and statistical significance in brackets (∗P<0.05;∗∗P<0.01;∗∗∗P<0.001). N is the sample size, and R2 is the proportion of variance explained. ΔAIC0 is the difference in the Akaike information criterion corrected for small sample size between the selected model and a null model with CHL in the previous time step, log(CHLt-1), as the only predictor. ΔAICE is the difference in the Akaike information criterion corrected for small sample size between the selected model and an environmental model with RUNOFF excluded from the predictor variables.
Summary of models for regions within 10 to 20 km from the coast. The model equations give parameter estimates with standard errors and statistical significance in brackets (∗P<0.05;∗∗P<0.01;∗∗∗P<0.001). N is the sample size, and R2 is the proportion of variance explained. ΔAIC0 is the difference in the Akaike information criterion corrected for small sample size between the selected model and a null model with CHL in the previous time step, log(CHLt-1), as the only predictor. ΔAICE is the difference in the Akaike information criterion corrected for small sample size between the selected model and an environmental model with RUNOFF excluded from the predictor variables.
Summary of models for regions within 20 to 50 km from the coast. The model equations give parameter estimates with standard errors and statistical significance in brackets (∗P<0.05;∗∗P<0.01;∗∗∗P<0.001). N is the sample size, and R2 is the proportion of variance explained. ΔAIC0 is the difference in the Akaike information criterion corrected for small sample size between the selected model and a null model with CHL in the previous time step, log(CHLt-1), as the only predictor. ΔAICE is the difference in the Akaike information criterion corrected for small sample size between the selected model and an environmental model with RUNOFF excluded from the predictor variables.
Chlorophyll a products and physical ocean variables, including sea-surface temperature (SST) and sea-ice fraction (SIF), are available through the Copernicus Marine Service (CMEMS) at https://doi.org/10.48670/moi-00066 (last access: 13 November 2017, ) and https://doi.org/10.48670/moi-00007 (last access: 13 November 2017, ). Note that the most recent release of the Chlorophyll a product is available at daily or monthly temporal resolution, replacing the 8 d product used in this study (https://doi.org/10.48670/moi-00066, last access: 21 December 2021, ). Time series of simulated glacier meltwater runoff for primary hydrological regions in Svalbard are available at 10.5281/zenodo.5115647.
Author contributions
TD, LCS and KD designed the study. TD extracted regional time series of glacier runoff from CMB simulations for Svalbard ran by KSA. KD analyzed datasets of chlorophyll a and physical ocean and sea-ice variables. LCS ran the statistical model. TD designed the main figures, and LCS designed the Appendix figures. TD wrote the initial manuscript with contributions from co-authors on their respective methods. All authors discussed the results and commented on or edited the manuscript.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We thank the Copernicus Marine Environment Monitoring Service for satellite and reanalysis data provided through their website. This study benefitted from discussions during two cross-cutting activities on “Glacier-ocean interactions and their impact on Arctic marine ecosystems” organized by the IASC Network on Arctic Glaciology in 2019 and 2020. Finally, we would like to express our gratitude to three anonymous reviewers whose constructive feedback helped to improve the clarity and content of the final paper.
Financial support
This work was supported by the Nordforsk-funded GreenMAR project. Leif
Christian Stige thanks the Research Council of Norway for support through the
project The Nansen Legacy (RCN no. 276730), and Kaixing Dong thanks the China Scholarship Council.
Review statement
This paper was edited by Jean-Pierre Gattuso and reviewed by three anonymous referees.
ReferencesAas, K. S., Berntsen, T. K., Boike, J., Etzelmuller, B., Kristjansson, J. E., Maturilli, M., Schuler, T. V., Stordal, F., and Westermann, S.: A Comparison between Simulated and Observed Surface Energy Balance at the Svalbard Archipelago, J. Appl. Meteorol. Clim., 54, 1102–1119, 10.1175/JAMC-D-14-0080.1, 2015.Aas, K. S., Dunse, T., Collier, E., Schuler, T. V., Berntsen, T. K., Kohler, J., and Luks, B.: The climatic mass balance of Svalbard glaciers: a 10-year simulation with a coupled atmosphere–glacier mass balance model, The Cryosphere, 10, 1089–1104, 10.5194/tc-10-1089-2016, 2016.Dunse, T. and Aas, K. S.: Timeseries of simulated glacier meltwater runoff for primary hydrological regions in Svalbard, Zenodo [data set], 10.5281/zenodo.5115647, 2021.
AMAP: Snow, Water, Ice and Permafrost in the Arctic (SWIPA) 2017, Tech. rep., Arctic Monitoring and Assessment Programme (AMAP), 2017.Ardyna, M., Babin, M., Gosselin, M., Devred, E., Bélanger, S., Matsuoka, A., and Tremblay, J.-É.: Parameterization of vertical chlorophyll a in the Arctic Ocean: impact of the subsurface chlorophyll maximum on regional, seasonal, and annual primary production estimates, Biogeosciences, 10, 4383–4404, 10.5194/bg-10-4383-2013, 2013.Arendt, K. E., Agersted, M. D., Sejr, M. K., and Juul-Pedersen, T.: Glacial meltwater influences on plankton community structure and the importance of top-down control (of primary production) in a NE Greenland fjord, Estuar. Coast. Shelf S., 183, 123–135, 10.1016/j.ecss.2016.08.026, 2016.Arrigo, K. R.: Sea Ice Ecosystems, Annu. Rev. Mar. Sci., 6, 439–467, 10.1146/annurev-marine-010213-135103, 2014.Arrigo, K. R. and van Dijken, G. L.: Continued increases in Arctic Ocean primary production, Prog. Oceanogr., 136, 60–70, 10.1016/j.pocean.2015.05.002, 2015.Arrigo, K. R., Matrai, P. A., and van Dijken, G. L.: Primary productivity in the Arctic Ocean: Impacts of complex optical properties and subsurface chlorophyll maxima on large-scale estimates, J. Geophys. Res.-Oceans, 116, C11022, 10.1029/2011JC007273, 2011.Arrigo, K. R., van Dijken, G. L., Castelao, R. M., Luo, H., Rennermalm, A. K., Tedesco, M., Mote, T. L., Oliver, H., and Yager, P. L.: Melting glaciers stimulate large summer phytoplankton blooms in southwest Greenland waters, Geophys. Res. Lett., 44, 6278–6285, 10.1002/2017GL073583, 2017.Bamber, J. L., Tedstone, A. J., King, M. D., Howat, I. M., Enderlin, E. M.,
van den Broeke, M. R., and Noel, B.: Land Ice Freshwater Budget of the Arctic
and North Atlantic Oceans: 1. Data, Methods, and Results,
J. Geophys. Res.-Oceans, 123, 1827–1837,
10.1002/2017JC013605, 2018.Behrenfeld, M. J. and Boss, E. S.: Resurrecting the Ecological Underpinnings of Ocean Plankton Blooms, Annu. Rev. Mar. Sci., 6, 167–194, 10.1146/annurev-marine-052913-021325, 2014.Bhatia, M. P., Kujawinski, E. B., Das, S. B., Breier, C. F., Henderson, P. B., and Charette, M. A.: Greenland meltwater as a significant and potentially bioavailable source of iron to the ocean, Nat. Geosci., 6, 274–278, 10.1038/NGEO1746, 2013.Calbet, A., Riisgaard, K., Saiz, E., Zamora, S., Stedmon, C., and Nielsen, T. G.: Phytoplankton growth and microzooplankton grazing along a sub-Arctic fjord (Godthabsfjord, west Greenland), Mar. Ecol.-Prog. Ser., 442, 11–22, 10.3354/meps09343, 2011.Calleja, M. L., Kerherve, P., Bourgeois, S., Kedra, M., Leynaert, A., Devred, E., Babin, M., and Morata, N.: Effects of increase glacier discharge on phytoplankton bloom dynamics and pelagic geochemistry in a high Arctic fjord, Prog. Oceanogr., 159, 195–210, 10.1016/j.pocean.2017.07.005, 2017.Cantoni, C., Hopwood, M. J., Clarke, J. S., Chiggiato, J., Achterberg, E. P., and Cozzi, S.: Glacial Drivers of Marine Biogeochemistry Indicate a Future Shift to More Corrosive Conditions in an Arctic Fjord, J. Geophys. Res.-Biogeo., 125, e2020JG005633, 10.1029/2020JG005633, 2020.Carroll, D., Sutherland, D. A., Shroyer, E. L., Nash, J. D., Catania, G. A., and Stearns, L. A.: Subglacial discharge-driven renewal of tidewater glacier fjords, J. Geophys. Res.-Oceans, 122, 6611–6629, 10.1002/2017JC012962, 2017.Claremar, B., Obleitner, F., Reijmer, C., Pohjola, V., Waxegard, A.,
Karner, F., and Rutgersson, A.: Applying a Mesoscale Atmospheric Model to
Svalbard Glaciers, Adv. Meteorol., 2012, 321649, 10.1155/2012/321649,
2012.
Colella, S., Volpe, G., Santoleri, R., Forneris, V., Brando, V.E., Garnesson, P., Taylor, B., and Grant, M.: Arctic Chlorophyll Concentration from Satellite observations (8-day average; Issue 1.4) Reprocessed L4 (ESA-CCI), Copernicus Marine Service [data set], https://doi.org/10.48670/moi-00066, 2017.Colella, S., Garnesson, P., Netting, J., Calton, B., Cesarini, C., and Böhm, E.: Arctic Chlorophyll Concentration from Satellite observations (Issue 8.0) Reprocessed L4 (ESA-CCI), Copernicus Marine Service [data set], 10.48670/moi-00066, 2021.Cottier, F., Tverberg, V., Inall, M., Svendsen, H., Nilsen, F., and Griffiths, C.: Water mass modification in an Arctic fjord through cross-shelf exchange: The seasonal hydrography of Kongsfjorden, Svalbard, J. Geophys. Res.-Oceans, 110, C12005, 10.1029/2004JC002757, 2005.Cottier, F. R., Nilsen, F., Inall, M. E., Gerland, S., Tverberg, V., and Svendsen, H.: Wintertime warming of an Arctic shelf in response to large-scale atmospheric circulation, Geophys. Res. Lett., 34, l10607, 10.1029/2007GL029948, 2007.Cottier, F. R., Nilsen, F., Skogseth, R., Tverberg, V., Skarðhamar, J., and Svendsen, H.: Arctic fjords: a review of the oceanographic environment and dominant physical processes, Geol. Soc. Spec. Publ., 344, 35–50, 10.1144/SP344.4, 2010.Dowdeswell, J. A., Hogan, K. A., Arnold, N. S., Mugford, R. I., Wells, M., Hirst, J. P. P., and Decalf, C.: Sediment-rich meltwater plumes and ice-proximal fans at the margins of modern and ancient tidewater glaciers: Observations and modelling, Sedimentology, 62, 1665–1692, 10.1111/sed.12198, 2015.Dubnick, A., Kazemi, S., Sharp, M., Wadham, J., Hawkings, J., Beaton, A., and
Lanoil, B.: Hydrological controls on glacially exported microbial assemblages,
J. Geophys. Res.-Biogeo., 122, 1049–1061, 10.1002/2016JG003685, 2017.
Etherington, L. L., Hooge, P. N., Hooge, E. R., and Hill, D. F.: Oceanography of Glacier Bay, Alaska: Implications for biological patterns in a glacial fjord estuary, Estuar. Coast., 30, 927–944, 2007.Finkel, Z. V.: Light absorption and size scaling of light-limited metabolism in marine diatoms, Limnol. Oceanogr., 46, 86–94, 10.4319/lo.2001.46.1.0086, 2001.Finkel, Z. V., Irwin, A. J., and Schofield, O.: Resource limitation alters the 3/4 size scaling of metabolic rates in phytoplankton, Mar. Ecol.-Prog. Ser., 273, 269–279, 10.3354/meps273269, 2004.Fransson, A., Chierici, M., Nomura, D., Granskog, M. A., Kristiansen, S.,
Martma, T., and Nehrke, G.: Effect of glacial drainage water on the
CO2 system and ocean acidification state in an Arctic
tidewater-glacier fjord during two contrasting years, J. Geophys. Res.-Oceans, 120, 2413–2429,
10.1002/2014JC010320, 2015.Gohin, F., Saulquin, B., Oger-Jeanneret, H., Lozac'h, L., Lampert, L., Lefebvre, A., Riou, P., and Bruchon, F.: Towards a better assessment of the ecological status of coastal waters using satellite-derived chlorophyll-a concentrations, Remote Sens. Environ., 112, 3329–3340, 10.1016/j.rse.2008.02.014, 2008.
Hackett, B., Bertino, L., Ali, A., Burud, A., Williams, T., Xie, J., Yumruktepe, C., and Wakamatsu, T.: Arctic Ocean Physics Reanalysis (Issue 5.4), Copernicus Marine Service [data set], https://doi.org/10.48670/moi-00007, 2017.
Hagen, J. O., Liestøl, O., Roland, E., and Jørgensen, T.: Glacier Atlas of Svalbard and Jan Mayen, Norsk Polarinstitutt, Oslo and Norway, 1993.Halbach, L., Vihtakari, M., Duarte, P., Everett, A., Granskog, M. A., Hop, H., Kauko, H. M., Kristiansen, S., Myhre, P. I., Pavlov, A. K., Pramanik, A., Tatarek, A., Torsvik, T., Wiktor, J. M., Wold, A., Wulff, A., Steen, H., and Assmy, P.: Tidewater Glaciers and Bedrock Characteristics Control the Phytoplankton Growth Environment in a Fjord in the Arctic, Frontiers in Marine Science, 6, UNSP 254, 10.3389/fmars.2019.00254, 2019.
Hanssen-Bauer, I., Førland, E. J., Hisdal, H., Mayer, S. Sandø, A., and
Sorteberg, A.: Climate in Svalbard 2100, Tech. rep., The Norwegian Centre for
Climate Services (NCCS), 2019.
Hawkings, J., Wadham, J., Tranter, M., Lawson, E., Sole, A., Cowton, T.,
Tedstone, A., Bartholomew, I., Nienow, P., Chandler, D., and Telling, J.: The
effect of warming climate on nutrient and solute export from the Greenland Ice
Sheet, Geochem. Perspect. Lett., 1, 94–104,
2015.Hegseth, E. N. and Tverberg, V.: Effect of Atlantic water inflow on timing of the phytoplankton spring bloom in a high Arctic fjord (Kongsfjorden, Svalbard), J. Marine Syst., 113, 94–105, 10.1016/j.jmarsys.2013.01.003, 2013.Hegseth, E. N., Assmy, P., Wiktor, J. M., Wiktor, J., Kristiansen, S., Leu, E., Tverberg, V., Gabrielsen, T. M., Skogseth, R., and Cottier, F.: Phytoplankton Seasonal Dynamics in Kongsfjorden, Svalbard and the Adjacent Shelf, Springer International Publishing, Cham, 173–227, 10.1007/978-3-319-46425-1_6, 2019.Hodson, A., Nowak, A., and Christiansen, H.: Glacial and periglacial floodplain sediments regulate hydrologic transfer of reactive iron to a high arctic fjord, Hydrol. Process., 30, 1219–1229, 10.1002/hyp.10701, 2016.Hodson, A. J., Mumford, P. N., Kohler, J., and Wynn, P. M.: The High Arctic glacial ecosystem: new insights from nutrient budgets, Biogeochemistry, 72, 233–256, 10.1007/s10533-004-0362-0, 2005.Holding, J. M., Markager, S., Juul-Pedersen, T., Paulsen, M. L., Møller, E. F., Meire, L., and Sejr, M. K.: Seasonal and spatial patterns of primary production in a high-latitude fjord affected by Greenland Ice Sheet run-off, Biogeosciences, 16, 3777–3792, 10.5194/bg-16-3777-2019, 2019.Hop, H., Assmy, P., Wold, A., Sundfjord, A., Daase, M., Duarte, P., Kwasniewski, S., Gluchowska, M., Wiktor, J. M., Tatarek, A., Wiktor, J., Kristiansen, S., Fransson, A., Chierici, M., and Vihtakari, M.: Pelagic Ecosystem Characteristics Across the Atlantic Water Boundary Current From Rijpfjorden, Svalbard, to the Arctic Ocean During Summer (2010–2014), Frontiers in Marine Science, 6, UNSP 181, 10.3389/fmars.2019.00181, 2019.Hopwood, M. J., Connelly, D. P., Arendt, K. E., Juul-Pedersen, T., Stinchcombe, M. C., Meire, L., Esposito, M., and Krishna, R.: Seasonal Changes in Fe along a Glaciated Greenlandic Fjord, Front. Earth Sci., 4, UNSP 15, 10.3389/feart.2016.00015, 2016.Hopwood, M. J., Carroll, D., Browning, T. J., Meire, L., Mortensen, J., Krisch, S., and Achterberg, E. P.: Non-linear response of summertime marine productivity to increased meltwater discharge around Greenland, Nat. Commun., 9, 3256, 10.1038/s41467-018-05488-8, 2018.Hopwood, M. J., Carroll, D., Dunse, T., Hodson, A., Holding, J. M., Iriarte, J. L., Ribeiro, S., Achterberg, E. P., Cantoni, C., Carlson, D. F., Chierici, M., Clarke, J. S., Cozzi, S., Fransson, A., Juul-Pedersen, T., Winding, M. H. S., and Meire, L.: Review article: How does glacier discharge affect marine biogeochemistry and primary production in the Arctic?, The Cryosphere, 14, 1347–1383, 10.5194/tc-14-1347-2020, 2020.How, P., Benn, D. I., Hulton, N. R. J., Hubbard, B., Luckman, A., Sevestre, H., van Pelt, W. J. J., Lindbäck, K., Kohler, J., and Boot, W.: Rapidly changing subglacial hydrological pathways at a tidewater glacier revealed through simultaneous observations of water pressure, supraglacial lakes, meltwater plumes and surface velocities, The Cryosphere, 11, 2691–2710, 10.5194/tc-11-2691-2017, 2017.Hu, C., Lee, Z., and Franz, B.: Chlorophyll aalgorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference, J. Geophys. Res.-Oceans, 117, C01011, 10.1029/2011JC007395, c01011, 2012.Hugonnet, R., McNabb, R., Berthier, E., Menounos, B., Nuth, C., Girod, L., Farinotti, D., Huss, M., Dussaillant, I., Brun, F., and Kääb, A.: Accelerated global glacier mass loss in the early twenty-first century, Nature, 592, 726–731, 10.1038/s41586-021-03436-z, 2021.Hurvich, C. and Tsai, C.: Regression and Time-series Model Selection in Small Samples, Biometrika, 76, 297–307, 10.2307/2336663, 1989.IMBIE Team: Mass balance of the Greenland Ice Sheet from 1992 to 2018, Nature, 117, 233–239,
10.1038/s41586-019-1855-2, 2019.
IPCC Special Report on the Ocean and Cryosphere in a Changing Climate [H.- O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N.M. Weyer (eds.)], 2019.Isaksen, K., Nordli, O., Forland, E. J., Lupikasza, E., Eastwood, S., and Niedzwiedz, T.: Recent warming on SpitsbergenInfluence of atmospheric circulation and sea ice cover, J. Geophys. Res.-Atmos., 121, 11913–11931, 10.1002/2016JD025606, 2016.Juul-Pedersen, T., Arendt, K. E., Mortensen, J., Blicher, M. E., Sogaard, D. H., and Rysgaard, S.: Seasonal and interannual phytoplankton production in a sub-Arctic tidewater outlet glacier fjord, SW Greenland, Mar. Ecol. Prog. Ser., 524, 27–38, 10.3354/meps11174, 2015.Kahru, M., Kudela, R. M., Anderson, C. R., Manzano-Sarabia, M., and Mitchell, B. G.: Evaluation of Satellite Retrievals of Ocean Chlorophyll-a in the California Current, Remote Sens.-Basel, 6, 8524–8540, 10.3390/rs6098524, 2014.Kanna, N., Sugiyama, S., Ohashi, Y., Sakakibara, D., Fukamachi, Y., and Nomura, D.: Upwelling of Macronutrients and Dissolved Inorganic Carbon by a Subglacial Freshwater Driven Plume in Bowdoin Fjord, Northwestern Greenland, J. Geophys. Res.-Biogeo., 123, 1666–1682, 10.1029/2017JG004248, 2018.Kilpelainen, T., Vihma, T., and Olafsson, H.: Modelling of spatial variability and topographic effects over Arctic fjords in Svalbard, Tellus A, 63, 223–237, 10.1111/j.1600-0870.2010.00481.x, 2011.Kilpelainen, T., Vihma, T., Manninen, M., Sjoblom, A., Jakobson, E., Palo, T., and Maturilli, M.: Modelling the vertical structure of the atmospheric boundary layer over Arctic fjords in Svalbard, Q. J. Roy. Meteor. Soc., 138, 1867–1883, 10.1002/qj.1914, 2012.König, M., Nuth, C., Kohler, J., Moholdt, G., and Pettersen, R.: A digital glacier database for svalbard, Springer Berlin Heidelberg, Berlin, Heidelberg, 229–239, 10.1007/978-3-540-79818-7_10, 2014.Lee, Y., Matrai, P. A., Friedrichs, M. A. M., Saba, V. S., Antoine, D., Ardyna, M., Asanuma, I., Babin, M., Belanger, S., Benoit-Gagne, M., Devred, E., Fernandez-Mendez, M., Gentili, B., Hirawake, T., Kang, S. H., Kameda, T., Katlein, C., Lee, S. H., Lee, Z. P., Melin, F., Scardi, M., Smyth, T. J., Tang, S., Turpie, K. R., Waters, K. J., and Westberry, T. K.: An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/chlorophyll-a based models, J. Geophys. Res.-Oceans, 120, 6508–6541, 10.1002/2015JC011018, 2015.Lee, Z., Weidemann, A., Kindle, J., Arnone, R., Carder, K. L., and Davis, C.: Euphotic zone depth: Its derivation and implication to ocean-color remote sensing, J. Geophys. Res.-Oceans, 112, C03009, 10.1029/2006JC003802, 2007.
Loeng, H.: Features of the Physical Oceanographic Conditions of the Barents Sea, Polar Res., 10, 5–18, 1991.Lydersen, C., Assmy, P., Falk-Petersen, S., Kohler, J., Kovacs, K. M., Reigstad, M., Steen, H., Strom, H., Sundfjord, A., Varpe, O., Walczowski, W., Weslawski, J. M., and Zajaczkowski, M.: The importance of tidewater glaciers for marine mammals and seabirds in Svalbard, Norway, J. Marine Syst., 129, 452–471, 10.1016/j.jmarsys.2013.09.006, 2014.Matrai, P. A., Olson, E., Suttles, S., Hill, V., Codispoti, L. A., Light, B., and Steele, M.: Synthesis of primary production in the Arctic Ocean: I. Surface waters, 1954–2007, Prog. Oceanogr., 110, 93–106, 10.1016/j.pocean.2012.11.004, 2013.McGovern, M., Pavlov, A. K., Deininger, A., Granskog, M. A., Leu, E., Søreide, J. E., and Poste, A. E.: Terrestrial Inputs Drive Seasonality in Organic Matter and Nutrient Biogeochemistry in a High Arctic Fjord System (Isfjorden, Svalbard), Frontiers in Marine Science, 7, 747, 10.3389/fmars.2020.542563, 2020.Meire, L., Mortensen, J., Rysgaard, S., Bendtsen, J., Boone, W., Meire, P., and Meysman, F. J. R.: Spring bloom dynamics in a subarctic fjord influenced by tidewater outlet glaciers (Godthåbsfjord, SW Greenland), J. Geophys. Res.-Biogeo., 121, 1581–1592, 10.1002/2015JG003240, 2015JG003240, 2016.Meire, L., Mortensen, J., Meire, P., Juul-Pedersen, T., Sejr, M. K., Rysgaard, S., Nygaard, R., Huybrechts, P., and Meysman, F. J. R.: Marine-terminating glaciers sustain high productivity in Greenland fjords, Glob. Change Biol., 23, 5344–5357, 10.1111/gcb.13801, 2017.Milner, A. M., Khamis, K., Battin, T. J., Brittain, J. E., Barrand, N. E., Füreder, L., Cauvy-Fraunié, S., Gíslason, G. M., Jacobsen, D., Hannah, D. M., Hodson, A. J., Hood, E., Lencioni, V., Ólafsson, J. S., Robinson, C. T., Tranter, M., and Brown, L. E.: Glacier shrinkage driving global changes in downstream systems, P. Natl. Acad. Sci. USA, 114, 9770–9778, 10.1073/pnas.1619807114, 2017.Mölg, T., Cullen, N. J., Hardy, D. R., Kaser, G., and Klok, L.: Mass balance of a slope glacier on Kilimanjaro and its sensitivity to climate, Int. J. Climatol., 28, 881–892, 10.1002/joc.1589, 2008.Mölg, T., Cullen, N. J., Hardy, D. R., Winkler, M., and Kaser, G.: Quantifying Climate Change in the Tropical Midtroposphere over East Africa from Glacier Shrinkage on Kilimanjaro, J. Climate, 22, 4162–4181, 10.1175/2009JCLI2954.1, 2009.Moses, W. J., Gitelson, A. A., Berdnikov, S., and Povazhnyy, V.: Estimation of chlorophyll-a concentration in case II waters using MODIS and MERIS data-successes and challenges, Environ. Res. Lett., 4, 045005, 10.1088/1748-9326/4/4/045005, 2009.Nilsen, F., Skogseth, R., Vaardal-Lunde, J., and Inall, M.: A Simple Shelf Circulation Model: Intrusion of Atlantic Water on the West Spitsbergen Shelf, J. Phys. Oceanogr., 46, 1209–1230, 10.1175/JPO-D-15-0058.1, 2016.Nordli, O., Przybylak, R., Ogilvie, A. E. J., and Isaksen, K.: Long-term temperature trends and variability on Spitsbergen: the extended Svalbard Airport temperature series, 1898–2012, Polar Res., 33, 21349, 10.3402/polar.v33.21349, 2014.Nuth, C., Kohler, J., König, M., von Deschwanden, A., Hagen, J. O., Kääb, A., Moholdt, G., and Pettersson, R.: Decadal changes from a multi-temporal glacier inventory of Svalbard, The Cryosphere, 7, 1603–1621, 10.5194/tc-7-1603-2013, 2013.Piquet, A. M.-T., van de Poll, W. H., Visser, R. J. W., Wiencke, C., Bolhuis, H., and Buma, A. G. J.: Springtime phytoplankton dynamics in Arctic Krossfjorden and Kongsfjorden (Spitsbergen) as a function of glacier proximity, Biogeosciences, 11, 2263–2279, 10.5194/bg-11-2263-2014, 2014.Popova, E. E., Yool, A., Coward, A. C., Aksenov, Y. K., Alderson, S. G., de Cuevas, B. A., and Anderson, T. R.: Control of primary production in the Arctic by nutrients and light: insights from a high resolution ocean general circulation model, Biogeosciences, 7, 3569–3591, 10.5194/bg-7-3569-2010, 2010.Pramanik, A., Van Pelt, W., Kohler, J., and Schuler, T. V.: Simulating climatic mass balance, seasonal snow development and associated freshwater runoff in the Kongsfjord basin, Svalbard (1980–2016), J. Glaciol., 64, 943–956, 10.1017/jog.2018.80, 2018.Prospero, J. M., Bullard, J. E., and Hodgkins, R.: High-Latitude Dust Over the North Atlantic: Inputs from Icelandic Proglacial Dust Storms, Science, 335, 1078–1082, 10.1126/science.1217447, 2012.R Core Team R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, available at: https://www.R-project.org/ (last access: 13 November 2017), 2016.Rysgaard, S. and Nielsen, T. G.: Carbon cycling in a high-arctic marine ecosystem – Young Sound, NE Greenland, Prog. Oceanogr., 71, 426–445, 10.1016/j.pocean.2006.09.004, 2006.
Rysgaard, S., Nielsen, T. G., and Hansen, B. W.: Seasonal variation in
nutrients, pelagic primary production and grazing in a high-Arctic coastal
marine ecosystem, Young Sound, Northeast Greenland, Mar. Ecol.-Prog. Ser.,
179, 13–25, 1999.Rysgaard, S., Vang, T., Stjernholm, M., Rasmussen, B., Windelin, A., and Kiilsholm, S.: Physical conditions, carbon transport, and climate change impacts in a northeast Greenland fjord, Arct. Antarct. Alp. Res., 35, 301–312, 10.1657/1523-0430(2003)035[0301:PCCTAC]2.0.CO;2, 2003.Sakov, P., Counillon, F., Bertino, L., Lisæter, K. A., Oke, P. R., and Korablev, A.: TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic, Ocean Sci., 8, 633–656, 10.5194/os-8-633-2012, 2012.Sakshaug, E.: Primary and Secondary Production in the Arctic Seas, in The Organic Carbon Cycle in the Arctic Ocean, Springer, Berlin, Heidelberg, 57–81, 10.1007/978-3-642-18912-8_3, 2004.
Sakshaug, E., Johnsen, G., and Kovacs, K.: Ecosystem Barents Sea, Tapir
Academic, edited by: Sakshaug, E., Johnsen, G., and Kovacs, K., Tapir Academic Press, 2009.Sathyendranath, S., Brewin, B., Mueller, D., Doerffer, R., Krasemann, H.,
Melin, F., Brockmann, C., Fomferra, N., Peters, M., Grant, M., Steinmetz, F.,
Deschamps, P. Y., Swinton, J., Smyth, T., Werdell, J., Franz, B.,
Maritorena, S., Devred, E., Lee, Z. P., Hu, C. M., and Regner, P.: Ocean
Colour Climate Change Initiative – Approach and Initial Results,
Int. Geosci. Remote Se., 2024–2027, 10.1109/IGARSS.2012.6350979,
2012.Schild, K. M., Renshaw, C. E., Benn, D. I., Luckman, A., Hawley, R. L., How, P., Trusel, L., Cottier, F. R., Pramanik, A., and Hulton, N. R. J.: Glacier Calving Rates Due to Subglacial Discharge, Fjord Circulation, and Free Convection, J. Geophys. Res.-Earth, 123, 2189–2204, 10.1029/2017JF004520, 2018.Schuler, T. V., Kohler, J., Elagina, N., Hagen, J. O. M., Hodson, A. J., Jania, J. A., Kääb, A. M., Luks, B., Malecki, J., Moholdt, G., Pohjola, V. A., Sobota, I., and Van Pelt, W. J. J.: Reconciling Svalbard Glacier Mass Balance, Front. Earth Sci., 8, 156, 10.3389/feart.2020.00156, 2020.Skamarock, W. C. and Klemp, J. B.: A time-split nonhydrostatic atmospheric model for weather research and forecasting applications, J. Comput. Phys., 227, 3465–3485, 10.1016/j.jcp.2007.01.037, 2008.Slater, D., Nienow, P., Sole, A., Cowton, T., Mottram, R., Langen, P., and Mair, D.: Spatially distributed runoff at the grounding line of a large Greenlandic tidewater glacier inferred from plume modelling, J. Glaciol., 63, 309–323, 10.1017/jog.2016.139, 2017.Song, H., Ji, R., Jin, M., Li, Y., Feng, Z., Varpe, Y., and Davis, C. S.: Strong and regionally distinct links between ice-retreat timing and phytoplankton production in the Arctic Ocean, Limnol. Oceanogr., 66, 2498–2508, 10.1002/lno.11768, 2021.Spall, M. A., Jackson, R. H., and Straneo, F.: Katabatic Wind-Driven Exchange in Fjords, J. Geophys. Res.-Oceans, 122, 8246–8262, 10.1002/2017JC013026, 2017.Straneo, F. and Cenedese, C.: The Dynamics of Greenland's Glacial Fjords and Their Role in Climate, Annu. Rev. Mar. Sci., 7, 89–112, 10.1146/annurev-marine-010213-135133, 2015.Straneo, F., Sutherland, D. A., Stearns, L., Catania, G., Heimbach, P., Moon, T., Cape, M. R., Laidre, K. L., Barber, D., Rysgaard, S., Mottram, R., Olsen, S., Hopwood, M. J., and Meire, L.: The Case for a Sustained Greenland Ice Sheet-Ocean Observing System (GrIOOS), Front. Mar. Sci., 6, 138, 10.3389/fmars.2019.00138, 2019.Sundfjord, A., Albretsen, J., Kasajima, Y., Skogseth, R., Kohler, J., Nuth, C., Skarohamar, J., Cottier, F., Nilsen, F., Asplin, L., Gerland, S., and Torsvik, T.: Effects of glacier runoff and wind on surface layer dynamics and Atlantic Water exchange in Kongsfjorden, Svalbard; a model study, Estuar. Coast. Shelf S., 187, 260–272, 10.1016/j.ecss.2017.01.015, 2017.Svendsen, H., Beszczynska-Moller, A., Hagen, J. O., Lefauconnier, B.,
Tverberg, V., Gerland, S., Orbaek, J. B., Bischof, K., Papucci, C.,
Zajaczkowski, M., Azzolini, R., Bruland, O., Wiencke, C., Winther, J. G., and
Dallmann, W.: The physical environment of Kongsfjorden-Krossfjorden, an Arctic
fjord system in Svalbard, Polar Res., 21, 133–166,
10.3402/polar.v21i1.6479, 2002.Terhaar, J., Lauerwald, R., Regnier, P., Gruber, N., and Bopp, L.: Around one third of current Arctic Ocean primary production sustained by rivers and coastal erosion, Nat. Commun., 12, 169, 10.1038/s41467-020-20470-z, 2021.Torsvik, T., Albretsen, J., Sundfjord, A., Kohler, J., Sandvik, A. D., Skarohamar, J., Lindback, K., and Everett, A.: Impact of tidewater glacier retreat on the fjord system: Modeling present and future circulation in Kongsfjorden, Svalbard, Estuar. Coast. Shelf S., 220, 152–165, 10.1016/j.ecss.2019.02.005, 2019.Tremblay, J. E., Michel, C., Hobson, K. A., Gosselin, M., and Price, N. M.: Bloom dynamics in early opening waters of the Arctic Ocean, Limnol. Oceanogr., 51, 900–912, 10.4319/lo.2006.51.2.0900, 2006.Tremblay, J. E., Simpson, K. G., Martin, J., Miller, L., Gratton, Y., Barber, D., and Price, N. M.: Vertical stability and the annual dynamics of nutrients and chlorophyll fluorescence in the coastal, southeast Beaufort Sea, J. Geophys. Res.-Oceans, 113, C07S90, 10.1029/2007JC004547, 2008.van de Poll, W. H., Kulk, G., Rozema, P. D., Brussaard, C. P. D.,
Visser, R. J. W., and Buma, A. G. J.: Contrasting glacial meltwater effects on
post-bloom phytoplankton on temporal and spatial scales in Kongsfjorden,
Spitsbergen, Elementa – Science of the Anthropocene, 6, 50, 10.1525/elementa.307, 2018.
van Pelt, W., Pohjola, V., Pettersson, R., Marchenko, S., Kohler, J., Luks, B., Hagen, J. O., Schuler, T. V., Dunse, T., Noël, B., and Reijmer, C.: A long-term dataset of climatic mass balance, snow conditions, and runoff in Svalbard (1957–2018), The Cryosphere, 13, 2259–2280, 10.5194/tc-13-2259-2019, 2019.Wadham, J. L., De'ath, R., Monteiro, F. M., Tranter, M., Ridgwell, A., Raiswell, R., and Tulaczyk, S.: The potential role of the Antarctic Ice Sheet in global biogeochemical cycles, Earth Env. Sci. T. R. So., 104, 55–67, 10.1017/S1755691013000108, 2013.Walczowski, W. and Piechura, J.: Influence of the West Spitsbergen Current on the local climate, Int. J. Climatol., 31, 1088–1093, 10.1002/joc.2338, 2011.Wassmann, P., Carmack, E., Bluhm, B., Duarte, C., Berge, J., Brown, K., Grebmeier, J., Holding, J., Kosobokova, K., Kwok, R., Matrai, P., Agusti, S., Babin, M., Bhatt, U., Eicken, H., Polyakov, I., Rysgaard, S., and Huntington, H.: Towards a unifying pan-arctic perspective: A conceptual modelling toolkit, Prog. Oceanogr., 189, 102455, 10.1016/j.pocean.2020.102455, 2020.Xie, J., Bertino, L., Counillon, F., Lisæter, K. A., and Sakov, P.: Quality assessment of the TOPAZ4 reanalysis in the Arctic over the period 1991–2013, Ocean Sci., 13, 123–144, 10.5194/os-13-123-2017, 2017.Zajaczkowski, M. and Wlodarska-Kowalczuk, M.: Dynamic sedimentary environments of an Arctic glacier-fed river estuary (Adventfjorden, Svalbard). I. Flux, deposition, and sediment dynamics, Estuar. Coast. Shelf S., 74, 285–296, 10.1016/j.ecss.2007.04.015, 2007.