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Received Date : 20-Jun-2014 Revised Date : 09-Dec-2014 Accepted Date : 15-Jan-2015 Article type

: Primary Research Articles

Corresponding author mail id : [email protected]

CLIMATE CHANGE ENHANCES PRIMARY PRODUCTION IN THE WESTERN ANTARCTIC PENINSULA

Running head: Climate change enhances PP in the WAP waters

Sébastien Moreau1,2,*; Behzad Mostajir3; Simon Bélanger4; Irene R. Schloss2,5,6; Martin Vancoppenolle7; Serge Demers2; Gustavo A. Ferreyra2;

1

Georges Lemaître Centre for Earth and Climate Research, Earth and Life Institute, Université

catholique de Louvain, Louvain-La-Neuve, Belgium. 2

Institut des sciences de la mer de Rimouski (ISMER), Université du Québec à Rimouski (UQAR),

310 allée des Ursulines, Rimouski, Québec, G5L 3A1 Canada 3

Laboratoire d’ECOlogie des SYstèmes Marins côtiers (ECOSYM), UMR 5119 (Universités

Montpellier 2 et 1– CNRS – IFREMER – IRD), Case 093 34095 Montpellier Cedex 05, France

4

Université du Québec à Rimouski (UQAR), groupe BORÉAS - Département de Biologie, Chimie

et Géographie,300 allée des Ursulines, Rimouski, Québec, G5L 3A1 Canada This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1111/gcb.12878 This article is protected by copyright. All rights reserved.

Instituto Antártico Argentino, Balcarce 290, C1064AAF Ciudad de Buenos Aires, Argentina

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5

6

CONICET, Av. Rivadavia 1917 (C1033AAV), Buenos Aires, Argentina

7

Sorbonne Universités (UPMC Paris 06), LOCEAN-IPSL, CNRS/IRD/MNHN, Paris, France.

* Corresponding author

KEYWORDS: Regional warming; sea ice; ozone hole; UltraViolet Radiation; seawater temperature; primary production; photoinhibition

Abstract Intense regional warming was observed in the Western Antarctic Peninsula (WAP) over the last 50 years. Here we investigate the impact of climate change on primary production (PP) in this highly productive region. This study is based on temporal data series of ozone thickness (1972-2010), sea ice concentration (1978-2010), sea surface temperature (1990-2010), incident irradiance (1988-2010) and satellite-derived chlorophyll-a concentration (Chl-a, 1997-2010) for the coastal WAP. In addition, we apply a photosynthesis/photoinhibition spectral model to satellite-derived data (19972010) to compute PP and examine the separate impacts of environmental forcings. Since 1978, sea ice retreat has been occurring earlier in the season (in March in 1978 and in late October during the 2000s) while the ozone hole is present in early spring (i.e. August to November) since the early 1990s, increasing the intensity of UltraViolet B Radiation (UVBR, 280-320 nm). The WAP waters have also warmed over 1990-2010. The modelled PP rates are in the lower range of previously reported PP rates in the WAP. The annual open water PP in the study area increased from 1997 to 2010 (from 0.73 to 1.03 Tg C yr-1) concomitantly with the increase in the production season length.

The coincidence between the earlier sea ice retreat and the presence of the ozone hole increased the exposure to incoming radiation (UVBR, UVAR and PAR) and, thus, increased photoinhibition during austral spring (September to November) in the study area (from 0.014 to 0.025 Tg C yr-1).

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This increase in photoinhibition was minor compared to the overall increase in PP, however. Climate change hence had an overall positive impact on PP in the WAP waters.

INTRODUCTION

The Western Antarctica Peninsula (WAP) is one the regions on Earth that has experienced the most rapid warming over the last 50 years (Marshall et al., 2002, Turner et al., 2005, Vaughan et al., 2003); this is probably also the case over the last 100 years (Thomas et al., 2009). The warming of the WAP has been demonstrated in air (Turner et al., 2005) and sea temperatures (Gille, 2002, Gille, 2008, Meredith & King, 2005, Whitehouse et al., 2008), and for ice-sheets (Steig et al., 2013). Additionally, climate variability in the WAP is linked to El Niño Southern Oscillation index (ENSO; Kwok & Comiso, 2002) and the Southern Annular Mode (SAM; Meredith & King, 2005), two climate patterns that have an effect on climate variability in the southern hemisphere.

In addition, a strong reduction in the concentration of the stratospheric ozone over Antarctica has been observed during spring over the last 30 years, a phenomenon known as the “Ozone Hole” (McKenzie et al., 2007). Stratospheric ozone concentrations commonly fall below 30% and sometimes < 50% of pre-ozone-hole concentrations in this season (Kerr, 1998). As a consequence, the intensities of UltraViolet B Radiation (UVBR, 280-320 nm) reaching the surface of the Southern Ocean increase (Arrigo, 1994, Frederick &

Lubin, 1994) and can potentially harm marine

organisms, including primary producers (Häder &

Sinha, 2005). Moreover, spring ozone

concentration over Antarctica is not expected to recover to the pre-1970s state for several decades (i.e. 2070, McKenzie et al., 2007) and will continue to be a threat.

Although a certain degree of stratospheric ozone depletion may persist throughout the period of greatest primary production (PP ; Frederick & Lubin, 1994, Jones & Shanklin, 1995), the lowest ozone concentrations usually occur during the period of greatest sea ice extent over the WAP (i.e.

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400-700 nm) which influence the marine habitats of the WAP and 2) to assess the potential response of marine PP to the observed change in the temporal dynamics of these physical properties. To do so, time series of satellite-derived Chlorophyll-a concentration (Chl-a, 13 years: 1997-2010) have been studied together with long-term data series of ozone thickness (38 years: 1972-2010), sea ice cover (32 years: 1978-2010), surface water temperature (18 years: 1992-2010) and UVBR, UVAR and PAR level (22 years: 1988-2010) in the coastal WAP around Anvers and Brabant Islands; climatic indices of ENSO 3.4 and SAM (13 years: 1997-2010) are also used. The potential impact on marine PP was assessed by using a photosynthesis and photoinhibition spectral model (Cullen et al., 1992), which was run for the 1997-2010 period for which ocean colour remote sensing data are available.

MATERIALS AND METHODS This section first describes how the time series of physical variables and biological parameters were obtained and treated. In a second part, the photosynthesis and photoinhibition spectral model used in this study is described. Finally, the model parameters and data inputs are given.

Time series data sets A 200 × 100 km grid around Anvers and Brabant Islands was defined as our study zone (Figure 1). The rectangle spans 200 km along the coast and extends 100 km offshore in order to include the most coastal part of the Coastal and Continental Shelf Zone (CCSZ) (Tréguer & Jacques, 1992) and to take account of the available time series from the different scientific stations: ground-based stratospheric ozone thickness from the Faraday/Vernadsky station and intensity of UVBR, UVAR and PAR from Palmer Station. In addition, satellite-derived Chl-a, sea-surface temperature (SST) and sea ice percent (%) cover within this grid were studied (see next paragraphs).

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400-700 nm) which influence the marine habitats of the WAP and 2) to assess the potential response of marine PP to the observed change in the temporal dynamics of these physical properties. To do so, time series of satellite-derived Chlorophyll-a concentration (Chl-a, 13 years: 1997-2010) have been studied together with long-term data series of ozone thickness (38 years: 1972-2010), sea ice cover (32 years: 1978-2010), surface water temperature (18 years: 1992-2010) and UVBR, UVAR and PAR level (22 years: 1988-2010) in the coastal WAP around Anvers and Brabant Islands; climatic indices of ENSO 3.4 and SAM (13 years: 1997-2010) are also used. The potential impact on marine PP was assessed by using a photosynthesis and photoinhibition spectral model (Cullen et al., 1992), which was run for the 1997-2010 period for which ocean colour remote sensing data are available.

MATERIALS AND METHODS This section first describes how the time series of physical variables and biological parameters were obtained and treated. In a second part, the photosynthesis and photoinhibition spectral model used in this study is described. Finally, the model parameters and data inputs are given.

Time series data sets A 200 × 100 km grid around Anvers and Brabant Islands was defined as our study zone (Figure 1). The rectangle spans 200 km along the coast and extends 100 km offshore in order to include the most coastal part of the Coastal and Continental Shelf Zone (CCSZ) (Tréguer & Jacques, 1992) and to take account of the available time series from the different scientific stations: ground-based stratospheric ozone thickness from the Faraday/Vernadsky station and intensity of UVBR, UVAR and PAR from Palmer Station. In addition, satellite-derived Chl-a, sea-surface temperature (SST) and sea ice percent (%) cover within this grid were studied (see next paragraphs).

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Daily mean ozone layer thickness above Faraday/Vernadsky (65°15’S, 64°16’W) was obtained for the

last

38

years

(1972-2010)

from

(http://www.antarctica.ac.uk/met/jds/ozone/). Data

the

British

Antarctic

Survey

were collected using a Dobson ozone

spectrophotometer. The ozone hole was defined for stratospheric ozone thickness < 220 Dobson Units (DU), according to the NASA definition (http://ozonewatch.gsfc.nasa.gov/), while normal ozone concentration over Antarctica was considered to be 344 DU (Smith et al., 1992).

Sea ice cover data were estimated from the NASA’s Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) and Defense Meteorological Satellite Program (DMSP) -F8, -F11 and -F13 Special Sensor Microwave/Imager (SSM/I). Daily, or two-day frequency (SMMR), sea ice concentration from 1978 to 2010 were obtained from the EOS Distributed Active Archive Center (DAAC) at the National Snow and Ice Data Center, University of Colorado in Boulder, Colorado (http://nsidc.org) as processed using the NASA team algorithms (Cavalieri et al., 2008). The sea ice cover data are mapped onto a 25×25 km grid on a polar stereographic projection. For the purpose of this study, the mean day of sea ice retreat was considered as the last day of the year for which sea ice cover was > 15% of the studied grid zone, as defined by Stammerjohn et al. (2008a).

Satellite derived sea surface temperature (SST) for the last two decades (1990-2010) was obtained from the National Oceanic and Atmospheric Administration (NOAA). SST is based on the Reynolds Optimally

Interpolated

SST

(OISST)

Version

2

product

(Reynolds

et

al.,

2002)

(http://www.emc.ncep.noaa.gov/research/cmb/sst_analysis/). Monthly averages for all the data were calculated and then classified according to seasons: winter (June to August), spring (September to November), summer (December to February) and fall (March to May). Model I linear regressions were computed for annual and seasonal data to detect temporal trends. It should be noted that SST is considered in this study only in relation to the physical environment, i.e., the link with sea ice retreat

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and its effect on water column stratification, which is discussed. No direct physiological/metabolic effect of temperature on PP was computed (see next sections). Incident irradiances at Palmer Station from 1988 to 2010 were provided by the National Science Foundation Polar Programs UV Monitoring Network (http://www.biospherical.com/nsf/). Incident irradiances were measured at Palmer station with a scanning SUV-100 spectroradiometer (Biospherical Instruments Inc.). To describe the effect of the ozone hole on the penetration of UVBR within the troposphere and, therefore, the potential daily effects of UVBR on phytoplankton, the daily dose of UVBR relative to the daily dose of UVAR was used (the UVBR/UVAR ratio). For the photosynthesis and photoinhibition model (see the next two sections), midday instantaneous surface irradiances from Palmer station are provided in the UVBR, UVAR and PAR ranges.

Satellite-derived Chl-a and remotely sensed normalized water-leaving radiance (Lwn(λ)) at 6 wavelengths (412, 443, 490, 510, 555 and 670 nm) were obtained from the GlobColour project at http://hermes.acri.fr/. We used the data from SeaWiFS, MODIS and MERIS, merged following the procedure described by Maritorena and Siegel (2005). For the present study, 8-day (9 km2 resolution)

data were processed from 1997 to 2010 for the period for which satellite data are available (i.e., from September 6th to March 30th). Because previous algorithms significantly underestimated chlorophyll concentrations at high latitudes, the time series of Chl-a (8-days periods) was estimated using the new regional algorithm developed by Johnson et al. (2013) applied to water-leaving radiance.

To relate climate variability in the WAP to global oscillations, ENSO and SAM indexes were used over the period 1997-2010. The ENSO index used is the El Niño 3.4 sea-surface temperature index, consisting of averaged eastern equatorial Pacific sea-surface temperature for 5°N–5°S, 170–120°W

and distributed by the Earth System Research Laboratory (ERSL) from the National Oceanic and Atmospheric Administration (NOAA; http://www.esrl.noaa.gov/psd/data/climateindices/). The SAM

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(a)

(b)

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open water primary production lost due to photoinhibition during austral spring in the study area (Gg C yr-1 = 109 g C yr-1)

26

26

y = - 0.06 x + 41 r = - 0.73**

24

24

22

22

20

20

18

18

16

16

14

14

12 240

260

280

300

320

340

360

380

y = 0.05x + 20.7 r = 0.7**

12

Previous winter sea ice retreat day

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-80

-60

-40

-20

0

20

40

Ozone hole exposure (days)

60

80

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just below the water surface at a wavelength λ and a time t. E*PUR, the PUR at the surface scaled to the saturation irradiance for photosynthesis (E0k,PUR) is given as: (eq. 2)

The water column transparency to PUR is estimated as: (eq. 3)

Kd(λ) is the diffuse attenuation coefficient at a wavelength λ, zs the depth scale set to 1 m, and

E0PAR(0-) the incident irradiance just below the water surface integrated from 400 to 700 nm. E*PUR

and T*PUR are dimensionless.

The spectral irradiance biologically weighted for photoinhibition is estimated as: (eq. 4)

The water column transparency to photosynthesis inhibiting radiation is estimated as (eq. 5)

ε(λ) is a biological weighting function (BWF) for the photoinhibition of phytoplankton ([µW cm-2]-

1

). E*PIR and TPIR are also dimensionless.

Empirical models for daily depth-integrated production Following Lehmann et al.’s approach, Cullen et al. (2012) derived empirical relationships between these independent variables (i.e. E*PUR, T*PUR, E*PIR and T*PIR) and the normalized potential and inhibited daily water column photosynthesis (respectively Ppot,z,t and Pz,t) and the percentage of daily

water column photosynthesis lost due to photoinhibition (ΔP%z,t) for a broad range of water types,

solar angles, stratospheric ozone layer thicknesses and biological properties of phytoplankton. These production estimates (i.e. Ppot,z,t, Pz,t and ΔP%z,t) had been previously calculated using a numerical model of inhibited PP (Cullen et al., 1992) within a spectral model of uninhibited photosynthesis

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(Neale et al., 1998) and for several thousand numerical simulations. In the present study, we only used the empirical relationships of Cullen et al. (2012) relating the daily depth integrated production estimates (i.e. Ppot,z,t, Pz,t and ΔP%z,t) to midday (or solar noon) E*m,PUR, T*m,PUR, E*m,PIR and T*m,PIR

because, in the study of Cullen et al. (2012), these estimates were closer to the results of conventional depth integrated PP models than when using instantaneous depth integrated production estimates.

(eq. 6)

(eq. 7)

(eq. 8)

where PsB is the maximum attainable rate of photosynthesis (mg C (mg Chl-a)-1 h-1), Chl-a is the satellite-derived Chl-a (mg m-3), zs the depth scale set to 1 m, D the day length (h) and D* is the

fractional day length (D* = D /24). The advantages of such an approach are that the dimensionless This article is protected by copyright. All rights reserved.

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variables used (E*m,PUR, T*m,PUR, E*m,PIR, T*m,PIR) have clear physical meanings and describe the effects of optical variability on PP well, and that simple equations can realistically reproduce the results of complex numerical models (Cullen et al., 2012). We also calculate the amount of photosynthesis that was inhibited as: (eq. 9)

Finally, to calculate the annual open water potential and inhibited PP for the region studied, as in Arrigo et al. (2008a), we integrate, over the entire phytoplankton production season (i.e. September to March), the daily depth integrated potential and inhibited PP multiplied by the study area which is not covered by sea ice: (eq. 10) (eq. 11) (eq. 12)

Where SA is the total study area and [ice] is the ice concentration. Thus, this calculation does not take into account PP that potentially takes place within sea ice.

Model parameters and inputs For this study, photosynthetic parameters from Fritz et al. (2008) in the WAP waters were used. The saturation irradiance for photosynthesis (E0k,PUR) was assumed constant at 40 µmol quanta m-2 s-1

based on Fritz et al. (2008)’s Es values after proper conversion from PAR to PUR and from W m-2 to µmol quanta m-2 s-1. In addition, the average maximum hourly rate of photosynthesis normalized to

Chl-a, PBs, was also assumed constant to 1.95 mg C (mg Chl-a)-1 h-1. In equation 4 and 5, the

biological weighting function (BWF, [µmol m-2 s-1]-1) determined by Fritz et al. (2008) for phytoplankton assemblages near Palmer Station was used to estimate photoinhibition. Following

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Fritz et al. (2008), this BWF also includes inhibition by PAR with an average weight of 1.25 10-4 (µmol quanta m-2 s-1)-1.

From satellite derived Chl-a, the spectral absorption of phytoplankton (aph(λ)) in equation 1 and 2

was modelled using a power function: aph(λ) = A(λ) × (Chl-a)B(λ), where A and B are statistical

coefficients published by Bricaud et al. (1998).

In equations 1 to 5, solar-noon averaged ±1h surface irradiances from Palmer station are used; these are provided in 6 broad spectral bands (in µW cm-2): 290-315, 320-340, 340-360, 360-400, 400-600 nm. The incident irradiance was first averaged for 8-day periods to correspond to ocean color time binning. Reference spectra at 1 nm resolution were then determined from 290 to 700 nm using precomputed clear-sky reference spectra of various ozone concentrations (100 to 550 DU) and solar zenith angle with an atmospheric radiative transfer model (SBDART; Ricchiazzi et al., 1998). Total ozone column concentration and solar zenith angle, as averaged for the period of interest, were used to select the reference Ed(λ) spectrum. Using measured Ed(λ) and the reference spectra, the full shape

of the incident irradiance spectra were determined. The irradiance just below the water surface (E0-) was further calculated by considering a constant value of 0.90 for the transmittance of light through the water surface (Moreau et al., 2010).

Spectral incident irradiance is then propagated through the water column (eq. 3) using satellitederived diffuse attenuation coefficients (Kd(λ)). Remotely sensed normalized water-leaving radiance (Lwn(λ)) at 6 wavelengths (412, 443, 490, 510, 555 and 670 nm) were used to compute Kd(λ). From

290 to 490 nm, Kd(λ) was determined using the SeaUV model developed by Fichot et al. (2008), which is an empirical model based on principal components of the remote sensing reflectance spectra. From 490 to 700 nm, we adopted the empirical Kd model of Morel and Maritorena (2001).

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For consistency between these two methods, Chl-a input to the Kd model of Morel and Maritorena (2001) was re-calculated from the SeaUV model’s Kd (490 nm) using Morel and Maritorena (2001)’s model:

(eq. 13)

where Kd(490) was obtained using Fichot et al. (2008). Finally, Major axis Model II linear

regressions were computed to detect relationships between independent variables.

RESULTS Climatic variability in the coastal WAP over the last 30 years The ozone hole, considered here as ozone thickness < 220 DU, has occurred over Faraday/Vernadsky during spring since the early 1980s (Figure 2a and 3a). Since 1980, the minimum daily ozone thickness measured was 118 DU, on September 20th, 2000. The duration of the ozone hole period,

defined here as the number of days between its formation and recovery, increased regularly from 1982 to 1996 (r = 0.86, p < 0.01) and stabilized after 1996 (Figure 3a). In addition, the timing of ozone hole development showed large fluctuations between 1982 and 1992, starting on average around day 257 ± 23 (September 14th) and lasting on average until day 305 ± 18 (November 1st, Figure 3b). The ozone hole onset timing was more stable from 1993 to 2010, starting on average around day 223 ± 9 (August 11th) and lasting on average until day 321 ± 16 (November 17th).

Over the last three decades (1978-2010), the day of sea ice retreat has clearly advanced in time (Figure 2b and 3b). Figure 3b does not include sea ice retreat for the years 1987 and 1989 because spring data is lacking for 1987 and because no ice was present in the region studied in 1989 (Jacobs & Comiso, 1993). In 1978, sea ice cover lasted all winter and spring and only retreated the following year at day 445 (i.e. march 21st of the following year, 1979, or day 365 + 80 days). From

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1978 to 1990, sea ice retreat timing was on average on day 374 ± 42 (January 9th of the following year). For the next two decades (1991-2000 and 2001-2010), the day of sea ice retreat was earlier than during the 1978-1990 period, retreating on average on days 321 ± 21 and 302 ± 34 (17th of November and 29th of October), respectively. The earliest sea ice retreat happened on day 262 (19th of September) of the year 2008. Figure 3c represents the numbers of days during which the study region was exposed to the ozone hole (i.e. the number of days from the retreat of sea ice to the end of the ozone hole). In 1980, the ozone hole ended 77 days before the retreat of sea ice. The exposure of the study region to the ozone hole increased clearly after 1988, when sea ice retreat happened during the presence of the ozone hole for the first time (Figure 3c). This was also the case in 1990, 1992, 1993, 1996, 1998, 2003, and from 2006 to 2010. More particularly, for the years 2007, 2008 and 2010 sea ice retreated respectively 61, 69 and 59 days before stratospheric ozone thickness had recovered, leaving the water column exposed to ozone hole-induced increased UVBR for a particularly long time.

Sea water temperature fluctuates seasonally in the WAP (Figure 2c). From 1990 to 2010, a clear increase of the summer sea surface temperature was observed (Table 1 and Figure 4). This warming was evident for the summer months (December to February) together with early autumn (i.e. March, Table 1). In addition, there was a significant negative correlation between the annual sea ice concentration anomaly and the annual sea surface temperature anomaly for the last two decades (i.e. 1990-2010; r = -0.68, p < 0.01).

In the WAP, the incident radiation (with monthly averages from 24 to 47 KJ m-2 d-1 for UVBR, from 586 to 1054 KJ m-2 d-1 for UVAR and from 4257 to 7557 KJ m-2 d-1 for PAR) is highest from October to February, which corresponds to the phytoplankton production season. As expected, from 1988 to 2010, UVBR was negatively correlated with stratospheric ozone concentration during austral

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spring and the early summer months (August to December; r ~ -0.5, p 1200 mg C m-2 d-1) reported by Vernet et al. (2008) were measured in the Southern WAP

(i.e. Marguerite Bay), outside of the present study area. In addition, the model estimates PP rates based on satellite derived Chl-a concentrations which may be underestimated in the Southern Ocean

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(Dierssen & Smith, 2000). However, to account for this possible Chl-a underestimation, we used the algorithm of Johnson et al. (2013). The annual open water column PP for the study area increased from 1997 to 2010 (Figure 6a). This long-term increase in PP did not coincide with a long-term increase in Chl-a, for which we observed no trend over the last decade. Thus, this increase in PP cannot be due to factors that would have resulted in higher phytoplankton biomasses: e.g. lower grazers’ impact, such as the decrease in Antarctic krill abundance which was observed in the WAP in the last decades (Atkinson et al., 2004), and higher nutrient concentrations, which may have resulted from more frequent upwellings of relatively warm UCDW in the WAP over the last decade due to regional climate change (Martinson et al., 2008). Instead, the increase in the annual open water column PP from 1997 to 2010

was related to the increase in the duration of the production season and, to a lesser extent, to the increase in the summer sea surface temperature observed in the WAP between 1997 and 2010.

First, the increase in PP was related to the decrease in sea ice concentration and the earlier retreat of sea ice (Figure 6b and c). This suggests that the lower the presence of sea ice during winter, the higher the annual integrated PP over the following growth season. This result brings new and challenging information to the generally accepted hypothesis that high sea ice extent and duration during the previous winter usually leads to a high Chl-a biomass and PP during the following summer in the WAP (Ducklow et al., 2013, Smith et al., 2001). For example, Vernet et al. (2008) found that the later the sea ice retreated, the higher the PP was in the following January. The effect Vernet et al. (2008) described was related to the shallower summer mixed layer that resulted from the late disappearance of sea ice (Steinberg et al., 2012). In contrast, in the present study, the decrease of sea ice spatial and temporal extent during the last decade increased the duration of the phytoplankton production season and, therefore, enhanced the annual PP. This is in apparent

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contradiction with the results of Vernet et al. (2008) but, while these authors described PP for January only, here we describe the annual integrated PP as estimated over the entire growth season.

These findings are similar to those of Arrigo et al. (2008a) who used a PP model parameterized for Arctic waters (Pabi et al., 2008) to determine the impact of decreasing sea ice cover on Arctic PP. They found that the annual PP in the Arctic Ocean increased by 122 Tg C yr-1 for every 106 km2

between 1998 and 2007. Of this increase, 30% was attributable to the decreased extent of sea ice and 70% to a longer growth season. Arrigo and van Dijken (2011) also simulated a significant 20% increase in the Arctic Ocean net PP from 1998 to 2009 due to the increase in open water extent and in the duration of the open water season. In contrast, Arrigo et al. (2008b) measured no impact of sea ice dynamics on marine PP in the Southern Ocean from 1997 to 2006. However, in their study, these authors considered the whole Southern Ocean, for which sea ice is not necessarily decreasing as for the WAP (Stammerjohn et al., 2008a, Stammerjohn et al., 2008b). In addition, Smith and Comiso (2008) observed an increase in the annual PP of the Southern Ocean between 1998 and 2004, although they could not conclude what was the cause of this increase. Considering the clear decrease in sea ice cover and duration observed in the WAP from 1978 to 2010 (Stammerjohn et al., 2008a and this study), the PP increase observed here may well be linked to changing sea ice conditions and, thus, to longer growth seasons.

However, it should be noted here that the annually integrated PP and the length of the growing season are not independent from each other (see eq. 10-11). This represents a limitation of the model: although the model considers PP as a function of the open water area, it does not estimate PP associated with sea ice. PP within sea ice may be responsible for an important part of the Southern

Ocean PP regionally (up to 12%) but only represents ~1% of the annual PP over the whole Southern Ocean (Saenz & Arrigo, 2014). With the exception of these limitations, the present analysis is

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consistent with the previously described modelling studies in the Arctic Ocean which used a similar formulation with respect to sea ice (Arrigo et al., 2008a, Pabi et al., 2008). In addition, the increase in the annual PP given by the present modelling study assumes that PP is not limited by nutrient concentrations; this should be considered as another limitation of this study.

Our analysis also showed that the observed increase in the annual open water column PP for the study area was related to the increase in the surface water temperature during the summer (Figure 6d), although the correlation was not as strong as with sea ice. This result may still suggest that, during the last decades in the WAP, higher water temperatures during the summer have promoted higher PP rates. This effect of temperature on PP could be due to a direct physiological effect of temperature on metabolic rates (Martin et al., 2012), but is most probably due to an indirect physical effect of temperature on the sea ice melting/retreat, influencing the length of the growing season and the sea water stratification. The exact role of sea surface temperature on PP in the WAP waters should, therefore, be further studied.

Finally, monthly anomalies of satellite derived Chl-a were negatively correlated with the El Niño 3.4 index (Figure 5b) similarly to the results from Smith et al. (2008) on a shorter time scale. El Niño/La Niña events are recognized to have an impact on the WAP climate: on water masses, generating anomalies in sea surface temperature (Martinson et al., 2008, Meredith et al., 2008) and air temperature, winds and ice cover (Schloss et al., 2012, Stammerjohn et al., 2008a). In fact, over the whole Southern Ocean, the interannual to subdecadal variability in the atmosphere-ice-ocean response seem to be related to long-term climate forcing by the Southern Annular Mode and the El Niño Southern Oscillation. In turn, this may have a forcing effect on biological processes as seen in the WAP waters (Murphy et al., 2007, Smith et al., 2008, Vernet et al., 2008). For example, Schloss et al. (2012) observed that Chl-a anomalies in the outer part of Potter Cove, in King George Island

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Accepted Article

(WAP), were associated with the ENSO index with a 1 year lag. The higher phytoplankton biomass that Schloss et al. (2012) measured coincided with enhanced water column stratification and reduced surface salinity, related to ENSO. In contrast to the results on Chl-a, we observed no correlation between ENSO and the annual PP. This result contrasts somewhat with the relationship that Vernet et al. (2008) observed between PP in January and ENSO, although their analysis is constrained to one month (January).

Climate change hence had an overall positive impact on PP in the WAP waters. In the future, changing climatic factors in the WAP may have additional impacts on the composition and structure of the microbial food webs that cannot be determined here. For example, Montes-Hugo et al. (2008) observed shifts in the size of the phytoplankton communities, from large to small cells, in the northern WAP from 1997 to 2006. Lastly, because of the reduction in ice cover, PP within sea ice will reduce, with possible consequences for the higher trophic levels’ organisms of this ecosystem (e.g. on krill, a key component of the Southern Ocean ecosystem and which heavily depend on ice algae; Flores et al., 2012).

ACKNOWLEDGEMENT We want to thank our funding sources and partners: the NSERC Special Research Opportunity Program grant nr. 334876-2005 conceded to S. Demers and a NSERC Discovery grant to G. Ferreyra., the Belgian Science Federal Policy Office (BIGSOUTH project), and BEPSII (Biogeochemical Exchange Processes at the Sea Ice Interfaces, SCOR Working Group 140). UV data from Palmer station was provided by the NSF UV Monitoring Network, operated by Biospherical Instruments Inc. under a contract from the United States National Science Foundation's Office of Polar Programs via Raytheon Polar Services Company. Data from the Palmer LTER data archive were supported by Office of Polar Programs, NSF Grants OPP-9011927, OPP-9632763 and

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Accepted Article

OPP-0217282. The 1978–2007 SMMR-SSM/I sea ice cover data were from the EOS Distributed Active Archive Center (DAAC) at the National Snow and Ice Data Center, University of Colorado in Boulder, Colorado (http://nsidc.org). The 1972-2010 ozone thickness above Faraday/Verdnasky was compiled by J. D. Shanklin, British Antarctic Survey, Madingley Road, Cambridge, England CB3 0ET. Gareth Marshall provided the SAM index data (http://www.nerc-bas.ac.uk/icd/gjma/sam.html). The Niño 3.4 index was provided by the Earth System Research Laboratory (ERSL) from the National

Oceanic

and

Atmospheric

Administration

(NOAA;

http://www.esrl.noaa.gov/psd/data/climateindices/). We thank the NASA for providing SeaWiFS data. We thank E. Aldana-Jague for his help with R and Sally Close for proof reading the article. This work is a contribution to the Institut des sciences de la mer de Rimouski (ISMER) and to the Université catholique of Louvain. S. M. is postdoctoral researcher with the F.R.S.-FNRS. Finally, we thank Patrick Neale and two anonymous reviewers for their very constructive comments that truly helped improved this study.

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Accepted Article

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Table 1: Slopes of the linear regressions for the surface sea temperature analyzed for the different seasons and months from 1990 to 2010. slope 0.009

r 0.515

June July August

0.000 -0.004 0.000 0.004

0.008 0.167 0.000 0.118

September October November

0.000 0.000 -0.003 0.004

0.001 0.000 0.134 0.118

December January February

0.030 0.016 0.034 0.042

0.758 0.392 0.631 0.673

March April May

0.008 0.029 0.005 -0.008

0.358 0.634 0.230 0.333

annual winter

spring

summer

fall

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Accepted Article

Table 2: September to March monthly averages ± SE of the normalized potential daily water column photosynthesis (Ppot,z,t, mg C m-2 d-1), the normalized inhibited daily water column photosynthesis (Pz,t, mg C m-2 d-1) and the daily photosynthesis lost due to photoinhibition (Pinh, mg C m-2 d-1) from 1997 to 2010. Month September October November December January February March

Ppot.z.t 166.4 ± 28.3 286.2 ± 54.6 543.6 ± 106 730.9 ± 69.2 663.5 ± 66.4 483.3 ± 56.2 335 ± 52.3

Pz.t 156.6 ± 27.5 267.5 ± 52.1 500.9 ± 99.2 667.3 ± 66.6 610.4 ± 62.2 449.6 ± 53.1 318.4 ± 50.6

Pinh 9.9 ± 1.3 18.7 ± 2.8 42.8 ± 7.3 63.6 ± 7.4 53.1 ± 6.6 33.7 ± 4.8 16.7 ± 2.6

Figure legends: Figure 1: The western Antarctic Peninsula (WAP, left) and the position of the data acquisition and sampling sites (right): A, Faraday/Vernadsky station (ozone thickness layer); B, Palmer station (PAR, UVBR and UVAR incident irradiance); and the black rectangle (region used to evaluate satellite derived Chl-a, sea surface temperature and sea ice cover)

Figure 2: a) Ozone layer thickness from 1972 to 2010. No data are available during the austral winter. The ozone hole is considered when ozone thickness (O3) is < 220 DU. b) Sea ice percent cover (%) from 1978 to 2010. Sea ice advance and retreat are considered when sea ice is > or < 15% respectively, and are indicated by a black line. c) Sea-surface temperature (°C) from 1990 to 2010

Figure 3: a) number of days per year with ozone thickness (O3) < 150 DU and 150 < O3 < 220 DU

from 1972 to 2010. b) Julian day for the beginning of the ozone hole, the end of the ozone hole and sea ice retreat from 1972 to 2010. The linear regression for sea ice retreat is plotted. c) Days of exposure to the ozone hole (i.e. number of days from the retreat of sea ice to the end of the ozone hole) from 1972 to 2010. The linear regression is plotted

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Accepted Article

Figure 4: Mean seasonal sea surface temperature from 1990 to 2010 for a) winter and spring and b) summer and fall. The linear regression for summer is plotted

Figure 5: a) Chl-a from 1997 to 2010 (in mg m-3). b) Monthly anomalies for Chl-a and the Niño 3.4 index. The anomalies are computed as the monthly values minus the average value for that month for the period shown. Chl-a and the Niño 3.4 index were negatively correlated (r = -0.38, p < 0.05) so the El Niño 3.4 index anomaly was plotted with a change of sign for ease of comparison. El Niño and La Niña events are indicated. This latter figure was inspired and extended from the Figure 5 of Smith et al. (2008) who described the co-variability between ENSO and satellite derived Chl-a for the WAP from 1997 to 2005

Figure 6: a) Annual open water inhibited PP for the study area (Tg C yr-1) and length of the

production season (days from sea ice retreat to the sea ice advance of the following year) from 1997 to 2010. Scatter plot between the annual open water inhibited PP for the study area (Tg C yr-1) and b)

the annual sea ice concentration anomaly, c) the previous winter day of sea ice retreat and d) the surface water temperature summer anomaly

Figure 7: Scatter plot between the open water PP lost due to photoinhibition during austral spring for the study area (Gg C yr-1) and a) the previous winter day of sea ice retreat and b) the days of exposure to the ozone hole

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Latitude S.

Accepted Article

61

63.5

62 63

64 Brabant Island

64 65

Anvers Island

64.5

B

66 67

A

65

68 69 72 70 68 66 64 62 60 58 56 Longitude W.

65.5

This article is protected by copyright. All rights reserved.

65

64

63

62

61

Ozone thickness (DU) 1975 1980 300

1985 1990

Year

Accepted Article

(a)

1995 220 2000 2005 2010 30

60

90

Year

(b)

100 120 150 180 210 240 270 300 330 360 Ice-cover (%)

1980 95 1985 75 1990 55

1995

35

2000

15

2005

2010 30

60

90

120

150

180

210

240

270

Year

(c)

300

330

360

Surface water temperature (o C) 1990 2.5 1.5

1995

1

2000

0.5 0

2005

-0.5 -1

2010

-1.5

30

60

90

120

150

180 210 Julian days

240

270

300

330

360

This article is protected by copyright. All rights reserved.

Days

Accepted Article

(a)

Julian days

(b)

Ozone hole exposure (days)

(c)

80 O3 < 150 DU 150 < O3 < 220 DU

60 40 20 0 450

1975

1980

1985

1990

1995

2000

2005

2010

y = -3.6 x + 396 r = -0.78

400 350 300 250 200 150 1975 120

Sea-ice retreat Ozone hole end Ozone hole start 1980

1985

1990

1995

2000

2005

2010

2005

2010

y = 3.8 x - 85 r = 0.72

80 40 0 -40 -80 1975

1980

1985

1990

1995

Year

This article is protected by copyright. All rights reserved.

2000

Temperature (o C)

Accepted Article

(a)

-0.4

Winter

Spring

-0.5 -0.6 -0.7 -0.8 -0.9 -1.0 -1.1 -1.2

1990

1995

2005

2010

2005

2010

Year

(b)

Temperature (o C)

2000

1.6 1.4

Summer Fall

y = 0.03 x + 0.48 r = 0.76

1.2 1.0 0.8 0.6 0.4 0.2 0.0

1990

1995

2000 Year

This article is protected by copyright. All rights reserved.

This article is protected by copyright. All rights reserved.

Chl-a anomaly El Niño 3.4 anomaly 10

20

09

20

08

20

07

20

06

20

05

20

04

20

03

El N iñ o

El N iñ o

-2

20

02

20

01

20

00

El N iñ o a

iñ

N

La N iñ a

La N iñ a

La N iñ a

La

2

20

99

19

98

19

El Niño standardized anomaly 3

0.5

1

0 0

-1 -0.5

-3 -1

Chl-a anomaly

10

20

09

20

08

20

07

20

06

20

05

20

04

20

03

20

02

20

01

20

00

20

99

19

98

19

Chl-a (mg m-3)

Accepted Article (a) 1.2

0.8

0.4

0.0

(b)

1

Annual open water primary production in the study area (Tg C yr-1 = 1012 g C yr-1)

1.1

1.0

0.7

0.6

1.0

0.6 240

y = - 0.003 x + 1.66 r = - 0.85**

0.9

0.8

0.7

260 280 300 320 340 360

Annual open water primary production in the study area (Tg C yr-1 = 1012 g C yr-1)

(c)

19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 20 09 20 10

Annual open water primary production in the study area (Tg C yr-1 = 1012 g C yr-1)

Accepted Article (a)

300

0.9 250

0.8

200

150

1.1

Annual open water primary production in the study area (Tg C yr-1 = 1012 g C yr-1)

Length of the production season (days)

350 (b)

(d)

380

Previous winter sea ice retreat day

This article is protected by copyright. All rights reserved.

1.1

1.0

y = -0.017 x + 0.88 r = - 0.8**

0.9

0.8

0.7 -8 -6

Annual sea ice concentration anomaly

-4

-0.2

-2

0.9

0.0

0 2

0.2

4 6

1.1

1.0

y = 0.24 x + 0.87 r = 0.47*

0.8

0.7

Surface water temperature summer anomaly

0.4

8

(a)

(b)

Accepted Article

open water primary production lost due to photoinhibition during austral spring in the study area (Gg C yr-1 = 109 g C yr-1)

26

26

y = - 0.06 x + 41 r = - 0.73**

24

24

22

22

20

20

18

18

16

16

14

14

12 240

260

280

300

320

340

360

380

y = 0.05x + 20.7 r = 0.7**

12

Previous winter sea ice retreat day

This article is protected by copyright. All rights reserved.

-80

-60

-40

-20

0

20

40

Ozone hole exposure (days)

60

80