(CDOM) in the Arctic Ocean

Marine Chemistry 124 (2011) 108–118

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Marine Chemistry j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / m a r c h e m

The supply and characteristics of colored dissolved organic matter (CDOM) in the Arctic Ocean: Pan Arctic trends and differences C.A. Stedmon a,⁎, R.M.W. Amon b,c, A.J. Rinehart b,1, S.A. Walker b a b c

Department of Marine Ecology, National Environmental Research Institute, Aarhus University, Frederiksborgvej 399, Roskilde, 4000, Denmark Department of Marine Sciences, Texas A&M University at Galveston, Galveston, USA Department of Oceanography, Texas A&M University, College Station, USA

a r t i c l e

i n f o

Article history: Received 10 September 2010 Received in revised form 6 December 2010 Accepted 11 December 2010 Available online 13 January 2011 Keywords: Arctic Ocean River Colored dissolved organic matter (CDOM) Dissolved organic matter (DOM) Carbon Optics

a b s t r a c t A comprehensive data set of dissolved organic carbon (DOC) and colored dissolved organic matter (CDOM) absorption measurements is analysed in light of tracing the supply and distribution of dissolved organic matter in the Arctic Ocean. Two years of river data from six major Arctic rivers (Kolyma, Lena, Ob, Mackenzie, Yenisei, and Yukon) and measurements from a transect across the Arctic Ocean are presented. The results show that although the Lena River currently dominates the supply of DOC and CDOM, climate change induced increases in base flow discharge will likely increase the contribution of the Yenisei River. Seasonal variations in the spectral characteristics of CDOM in the rivers reflected the shift in the dominant source of organic matter from modern plant litter in the spring freshet to older more degraded material during winter low flow periods. Strong correlations were found between the river loading of CDOM and DOC across the systems studied indicating that in situ CDOM sensors could be used in the future to improve estimates of riverine DOC loading. CDOM in the surface waters of the Eurasian Basin was largely characterised as riverine material although extrapolations to riverine end member concentrations suggested that approximately half the riverine CDOM is removed during its transport across the shelf. In contrast the Canadian Basin surface waters were characterised by a much greater proportion of autochthonous CDOM. These differences in DOM quality in the surface waters of the two basins are hypothesised to also influence the extent to which material is remineralised during its passage through the Arctic Ocean. © 2011 Elsevier B.V. All rights reserved.

1. Introduction The Arctic is currently greatly affected by climate change. Increasing surface air temperatures are altering the region's hydrological cycle (Peterson et al., 2002) and influencing the integrity of the permafrost (Osterkamp and Romanovsky, 1999; Guo et al., 2007; Walvoord and Striegl, 2007). These changes have a global significance with regard to climate and carbon cycling. Increased freshwater discharge will have consequences for water column structure and circulation in the Arctic Ocean and impact sea ice extent (Rudels et al., 2004). The subsequent export of freshwater to the North Atlantic will also influence the formation of North Atlantic Deep Water, which drives Atlantic meridional overturning circulation (Rahmstorf, 1995). Climate warming in the Arctic will also have considerable effects on carbon cycling in the region. Soils in the northern circumpolar region represent a very large reservoir of organic carbon. They are estimated to contain 1672 Pg organic carbon which corresponds to approximately ⁎ Corresponding author. E-mail address: [email protected] (C.A. Stedmon). 1 Current address: University of Alaska Fairbanks, Institute of Arctic Biology, Fairbanks, AK, USA. 0304-4203/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.marchem.2010.12.007

50% of the global subterranean carbon pool (Tarnocai et al., 2009). The majority of this pool (88%) is estimated to be stored in perennially frozen soils (Tarnocai et al., 2009). Mobilisation of just a small portion of carbon stored in Arctic soils will have considerable impacts on the flux of organic carbon from land to the Arctic Ocean. For comparison, recent estimates of riverine flux of dissolved organic carbon are on the order of 25–36 Tg C yr− 1 (Raymond et al., 2007). Gruber et al. (2004) estimated that climate induced permafrost disintegration during the next 100 yr, will release as much as 25% of carbon stored in Arctic soils. This has the potential to greatly influence riverine supply of DOC to the Arctic Ocean and subsequent export to the North Atlantic. An increase in the riverine dissolved organic matter loading to the Arctic Ocean will have a series of effects on the physical, biological and chemical environment. A fraction of DOM is colored (CDOM) and absorbs ultra violet and visible light. Increased supply of CDOM by rivers will reduce the photic depth in the shelf regions in particular, resulting in continued light limitation even after sea ice retreat. In addition, reduced sea ice and higher concentrations of CDOM will lead to an increase in the importance of photochemistry (Osburn et al., 2009) for both direct remineralisation of terrestrial organic matter and production of labile organic material which can be utilized by microbes. The increased supply of labile organic carbon either directly via rivers (Holmes et al., 2008) or

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indirectly by photochemical degradation of refractory organic matter (e.g. Moran and Zepp, 1997) may also have a negative impact on primary productivity in the region, as heterotrophic bacteria out-compete phytoplankton for mineral nutrients (Thingstad et al., 2008). Increases in CDOM supply by Arctic rivers can potentially alter the heat budget for the ice free coastal waters and hereby stratification and water column structure (Granskog et al., 2007). Currently it has been estimated that surface waters of the Arctic Ocean absorb as much as 30% more solar energy per unit area compared to other oceans, as a result of the high CDOM concentrations (Pegau, 2002). CDOM also greatly influences the remotely sensed water color measured by satellites and used extensively to estimate phytoplankton productivity (e.g. Arrigo et al., 2008). Before this can be taken into account the properties and distribution of CDOM in the Arctic need to be described. Currently there is a limited amount of data available on this. Here we present a study describing the seasonal variability in characteristics of CDOM in six major Arctic rivers and trace the mixing of CDOM across the Arctic Ocean. Our aim is to provide insight on sources of CDOM in surface waters (terrestrial/riverine or local autochthonous production) and hereby improve efforts to predict its distribution and refine remote sensing techniques. 2. Methods River sampling was carried out in 2004 and 2005 as part of the PARTNERS project on six major Arctic rivers: Kolyma, Lena, Ob, Mackenzie, Yenisei, and Yukon (Fig. 1). Each river was sampled seven

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times per year. Additional details on sampling sites, dates, and methods can be found in Raymond et al. (2007) and Cooper et al. (2008). Briefly, water samples were taken using a depth integrated sampler (US D-96) at gauging stations according to USGS guidelines (McClelland et al., 2008). After collection, samples were filtered through pre-cleaned 0.45 μm capsule filters and frozen in polycarbonate bottles. All samples were stored frozen and shipped to the Department of Marine Sciences, Texas A&M University at Galveston, where the measurements for DOC and optical properties were made. Samples from the Arctic Ocean were obtained during the 2005 Arctic Ocean Section Expedition with the Swedish icebreaker Oden. The cruise was in summer and sampled 53 stations along a transect from waters north of Barrow, Alaska across the Canada Basin to the Nansen Basin (Jones et al., 2008). A total of 289 samples were collected at depths ranging between 2 and 4354 m. Samples were collected from Niskin bottles mounted on a CTD rosette, filtered trough precombusted glass fiber filters (Whatman) into combusted glass ampoules and frozen until analyses. CDOM absorbance was measured on a Shimadzu UV-2401PC/ 2501PC using a 5 cm quartz cuvette and Milli-Q water as a reference. Spectra were measured from 200 to 800 nm every 0.5 nm in triplicate then averaged. Absorbance measurements were transformed to absorption coefficients by multiplying by 2.303 and dividing by the path length (0.05 m). The shape of the absorption spectra between 300 and 650 nm was characterised by determining the exponential spectral slope coefficient (S) according to the approach by Stedmon et al. (2000). This wavelength range was chosen so that the data could

Fig. 1. a) Map of the Arctic Ocean and its river catchment (red line). Black line show the catchments of the individual rivers studied in the PARTNERS project. The black dots across the ocean represent the stations sampled during the AOS 2005 cruise. b) Catchment size and, c) annual discharge for the six rivers studied. Map was generated by Ocean Data View.

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be compared to results from earlier work in the Greenland Sea (Stedmon and Markager, 2001) and also because this wavelength range is most relevant for bio-optical and remote sensing applications. An alternative approach recently introduced by Helms et al. (2008) was also tested. The approach derives a ratio of slopes (Sr) from two different wavelength ranges of the absorption spectrum, 275–295 nm and 350–400 nm. The Sr data were linearly correlated to the S values and therefore, for simplicity, only S values will be discussed in any detail here. Sr values are, however, reported in Table 1 to facilitate comparisons with future studies. DOC concentrations were measured on a MQ-1001 TOC analyzer according to the protocol of Qian and Mopper (1996) and Peterson et al. (2003). Potassium hydrogen phthalate was used for standards and a daily calibration curve was measured ranging from 200 to 2000 μM C. Deep sea reference material supplied by D. Hansell (University of Miami) was run daily to assure proper instrument performance. The residual standard deviation on this instrument averaged 2.5% for the river samples, and milliQ water blanks averaged 10 μM C. The DSR values varied between 40 and 55 μM C based on the “out of range” calibration curve. DOC values agree well with the values reported by Raymond et al. (2007) measured on the same set of “PARTNERS” samples. Discharge data from the different rivers are from the USGS (Yukon), Water Survey of Canada (Mackenzie), and the Russian Federal Service of Hydrometeorology and Environment Monitoring (Ob, Yenisei, Lena, and Kolyma). Based on these discharge data we calculated annual and monthly flow-weighted discharge estimates. The loadings of DOC and CDOM were calculated by deriving a concentration vs. flow relationship for each river using a nonparametric regression technique (LOESS) available in SAS/STAT (SAS Institute). The relationship was then used to estimate daily concentrations based on discharge data. This data was subsequently summed to calculate monthly and annual loadings for each river. 3. Results 3.1. DOM characteristics between rivers and seasons Highest concentrations of DOC and CDOM were associated with the peak discharge rates in all rivers (Fig. 2). Maximum DOC concentrations (N1200 μM) were measured in the Lena and Yukon rivers during the spring freshet. Concentrations during summer and winter were generally lower. The seasonal trend in DOC concentrations in the Ob and Mackenzie was less pronounced compared to the other rivers. On average across the year DOC concentrations were lowest in the Mackenzie River (Table 1). In general, CDOM concentrations (depicted as a375) followed a similar seasonal and between river trend to that observed for DOC. Peak values were recorded in spring, intermediate levels during summer and low values during winter (Fig. 2). The Mackenzie had, on average, the lowest CDOM concentrations, and the Lena, Ob and Yenisei had the greatest CDOM concentrations (Table 1). Seasonal trends in CDOM characteristics were less obvious (Fig. 2), however, the CDOM exported during the spring freshet consistently had lower S values. This pattern was tested for each river individually by grouping the data into different flow regimes (spring freshet, winter and summer) and carrying out an analysis of variance (ANOVA) test. Significant seasonal trends were found in all rivers except the Kolyma. CDOM's S values during winter and summer for each river were not significantly different. Comparing CDOM's characteristics between rivers, winter S values were the same, except for the Mackenzie River data, which had significantly higher S values than the other five rivers. S values during the spring freshet were similar between all rivers. CDOM's characteristics can also be described by the UV specific absorbance at 254 nm (SUVA, Weishaar et al., 2003). The ANOVA

analysis did not identify any clear significant trends between SUVA and flow regime, however, highest SUVA values were often observed in association with spring freshet, in particular for Mackenzie, Ob and Yenisei rivers (Table 1). 3.2. Riverine DOM loadings Monthly loadings of water, DOC and CDOM were calculated based on averaging the data from the two years measured, and provide insight on the relative importance of each river at different times of the year to the combined loading (Fig. 3, Table 2). During the winter months (November to April) the Yenisei dominates the freshwater discharge, with 46% of the combined discharge from all rivers emerging from the Yenisei (Fig. 3). Peak discharge rates for the Eurasian rivers are in June while the spring freshet starts in May and extends into June for the Mackenzie and Yukon rivers (Fig. 3). During the month of June the Lena and Yenisei combined are responsible for 62% of the freshwater discharge from all six rivers, which equates to 17% of the combined annual discharge from these rivers. Due to the comparatively high winter flow rates in the Yenisei, a large part of the winter DOC and CDOM loading (48 and 46% respectively) originates from this river (Fig. 3). Throughout the year, DOC and CDOM loadings largely follow the trends seen in discharge and the months of May and June are particularly important with 63% and 69% respectively, of the annual loading exported during these two months alone. From June to October the Lena River dominates the supply of DOC and CDOM. Annual DOC and CDOM loads to the Arctic Ocean are dominated by Lena and Yenisei rivers, together contributing 64% and 70%, respectively (Fig. 4). Despite the fact that the Ob has a catchment of a similar size to the Lena and Yenisei, annual discharge is considerably lower (Fig. 1b and c) and therefore supplies less DOM. Together, the three largest Eurasian rivers contribute 82% and 87% of the combined annual river DOC and CDOM load to the Arctic Ocean from these rivers studied (Fig. 4). Normalising the loading to the size of the catchment results in a DOC yield which takes into account all river specific factors such as catchment size, river discharge and DOC concentrations, and allows one to better compare the flux of carbon from different rivers (Fig. 4c). Carbon flux from the Lena River dominates, yielding approximately 3 g C m− 2 yr− 1. The Yukon and Yenisei rivers have similar yields of approximately 2 and the Mackenzie River has the lowest DOC yield. 3.3. CDOM in the Arctic Ocean a375 in the Arctic Ocean samples ranged between 0 and 0.73 m− 1 with the greatest values recorded in the surface waters (Fig. 5a). At depths deeper than 300 m a375 generally remained below 0.2 m− 1 (average a375 0.10 m− 1). In the surface 100 m there were clear differences between the Canadian and Eurasian Basins (Fig. 5b). The average a375 in the surface 100 m of the Eurasian Basin was 0.41 m− 1 (S.D. 0.13), as opposed to 0.21 m− 1 (S.D. 0.09) in the Canada Basin. S values ranged between 8.2 and 47.1 μm− 1 and there were no clear systematic trends with depth (Fig. 5c), except that S was generally less variable in the upper 500 m (Fig. 5d). However, when a375 is plotted against S some trends became apparent with regard to the mixing of CDOM from different sources (Fig. 6). Three mixing lines and three pools of CDOM with different characteristics are identifiable. Firstly, at low a375 values (b0.1 m− 1) and with a high S value (N25 μm− 1) there is a low intensity CDOM pool predominantly found in deep waters (N300 m). This will be referred to as the oceanic CDOM (labelled O in Fig. 6). A second pool is characterised by low S values (~10 μm− 1) and a375 values between 0.5 and 0.6 m− 1 and is found mainly in the Canada Basin at depths between 50 and 300 m (Fig. 6). On the basis of earlier work (Stedmon and Markager, 2001) and the following discussion here, this will be

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Table 1 Dissolved organic carbon (DOC) concentrations and the characteristics of colored dissolved organic matter (CDOM) in the river samples. a375 is the absorption coefficient at 375 nm, S is the spectral slope derived from 300 to 600 nm, Sr is the slope ratio according to Helms et al. (2008) and SUVA is the specific UV absorbance at 254 nm normalised to DOC concentrations. River

Date

DOC (μM C)

a375 (m− 1)

S (μm− 1)

Sr

SUVA (m2 g C− 1)

Kolyma Kolyma Kolyma Kolyma Kolyma Kolyma Kolyma Kolyma Kolyma Kolyma Kolyma Kolyma Kolyma

11-Jun-04 15-Jun-04 25-Jun-04 15-Jul-04 10-Aug-04 23-Sep-04 22-Apr-05 30-Jun-05 19-Jul-05 14-Aug-05 27-Aug-05 12-Sep-05 29-Sep-05 Average 09-Apr-04 05-Jun-04 07-Jun-04 19-Aug-04 24-Aug-04 07-Oct-04 10-Oct-04 24-Mar-05 27-May-05 04-Jun-05 06-Aug-05 14-Aug-05 09-Oct-05 10-Oct-05 Average 24-Mar-04 17-Jun-04 22-Jun-04 13-Jul-04 04-Aug-04 25-Aug-04 08-Sep-04 16-Mar-05 14-Jun-05 29-Jun-05 14-Jul-05 09-Aug-05 30-Aug-05 13-Sep-05 Average 05-Apr-04 15-Jun-04 17-Jun-04 28-Jul-04 11-Aug-04 11-Oct-04 14-Oct-04 15-Mar-05 04-Jun-05 06-Jun-05 28-Jun-05 14-Jul-05 05-Sep-05 17-Sep-05 Average 19-Mar-04 14-Jun-04 16-Jun-04 18-Jun-04 25-Aug-04 01-Oct-04 02-Oct-04 26-Mar-05 11-Jun-05 16-Jun-05 17-Jun-05 16-Aug-05 21-Aug-05 21-Sep-05 Average

1025 917 600 558 392 400 250 500 467 517 942 533 617 594 550 1433 1517 650 608 583 708 825 1600 1458 1108 900 692 633 948 333 408 358 317 308 283 250 275 458 475 450 367 408 392 363 375 717 858 1008 1025 800 783 708 933 1058 950 1058 1033 942 875 208 1250 1192 1108 475 650 683 250 1025 983 1092 692 508 433 754

14.4 10.8 6.9 6.3 5.2 3.6 2.6 4.1 4.1 5.4 9.3 4.9 5.4 6.4 7.5 23.3 26.0 8.6 7.1 7.3 9.7 9.3 21.3 22.3 10.8 10.1 8.1 7.5 12.8 1.3 3.9 3.0 1.9 1.8 1.4 1.2 1.3 4.4 3.9 3.5 3.0 3.0 2.8 2.6 3.0 11.3 11.3 13.7 11.2 6.5 8.1 4.9 12.6 14.0 14.2 14.5 11.3 10.9 10.5 1.5 22.2 20.9 19.4 4.2 8.4 8.8 1.4 17.3 15.6 14.6 5.4 5.5 5.4 10.8

15.7 17.1 16.3 16.5 16.4 17.4 17.7 17.8 17.2 17.7 16.8 17.7 17.6 17.1 16.1 16.3 15.8 16.1 17.1 16.7 16.1 17.4 16.4 15.1 16.5 16.1 16.6 16.9 16.4 21.2 17.4 18.5 20.0 19.1 20.7 20.4 21.3 17.4 17.8 18.4 18.7 18.9 18.8 19.2 18.0 16.2 16.6 16.4 16.9 18.7 17.6 18.2 16.3 15.6 16.5 16.7 17.4 17.1 17.0 18.8 15.6 16.0 15.8 17.6 16.2 16.2 17.0 15.4 15.5 15.9 17.2 17.6 17.1 16.6

0.85 0.83 0.86 0.88 0.89 0.89 0.85 0.92 0.91 0.85 0.82 0.89 0.83 0.87 0.89 0.82 0.84 0.88 0.86 0.84 0.82 0.86 0.81 0.83 0.88 0.87 0.85 0.84 0.85 1.09 0.96 0.92 0.96 1.01 1.01 1.11 1.01 0.93 0.97 0.99 0.97 0.93 1.00 0.99 0.92 0.84 0.83 0.87 0.88 0.85 0.83 0.85 0.84 0.83 0.84 0.85 0.87 0.86 0.85 0.98 0.80 0.79 0.79 0.93 0.84 0.83 0.99 0.79 0.81 0.79 0.92 0.91 0.89 0.86

2.90 2.74 2.52 2.56 2.96 2.27 2.71 2.22 2.17 2.68 2.23 2.43 2.19 2.51 2.97 3.58 3.67 2.82 2.74 2.82 2.87 2.77 2.91 2.90 2.21 2.42 2.62 2.73 2.86 1.59 2.45 2.44 2.08 1.88 1.95 1.87 1.99 2.46 2.25 2.30 2.50 2.25 2.20 2.16 2.21 3.43 2.93 3.05 2.58 2.34 2.56 1.86 2.91 2.67 3.31 3.14 2.71 2.75 2.75 2.20 3.51 3.64 3.50 2.28 2.74 2.71 1.41 3.24 3.10 2.71 1.95 2.80 2.97 2.77

Lena Lena Lena Lena Lena Lena Lena Lena Lena Lena Lena Lena Lena Lena Mackenzie Mackenzie Mackenzie Mackenzie Mackenzie Mackenzie Mackenzie Mackenzie Mackenzie Mackenzie Mackenzie Mackenzie Mackenzie Mackenzie Ob Ob Ob Ob Ob Ob Ob Ob Ob Ob Ob Ob Ob Ob Yenisei Yenisei Yenisei Yenisei Yenisei Yenisei Yenisei Yenisei Yenisei Yenisei Yenisei Yenisei Yenisei Yenisei

(continued on next page)

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Table 1 (continued) River

Date

DOC (μM C)

a375 (m− 1)

S (μm− 1)

Sr

SUVA (m2 g C− 1)

Yukon Yukon Yukon Yukon Yukon Yukon Yukon Yukon Yukon Yukon Yukon Yukon Yukon Yukon

03-Apr-04 26-May-04 15-Jun-04 29-Jun-04 19-Jul-04 18-Aug-04 22-Sep-04 17-Mar-05 17-May-05 01-Jun-05 14-Jun-05 12-Jul-05 16-Aug-05 27-Aug-05 Average

208 1450 758 483 242 225 200 250 1617 1367 1033 517 375 717 674

1.5 18.5 9.9 5.7 2.6 1.9 1.5 1.1 15.8 12.8 8.1 5.3 3.1 7.3 6.8

18.4 15.9 16.7 17.3 17.9 18.3 19.4 19.5 16.7 16.2 16.8 17.6 17.7 17.0 17.5

1.01 0.81 0.82 0.86 0.89 0.91 0.92 0.95 0.81 0.83 0.85 0.87 0.90 0.85 0.88

2.12 2.66 2.95 2.92 2.85 2.30 2.31 1.55 2.25 2.02 1.80 2.62 2.27 2.51 2.37

referred to as autochthonous CDOM (labelled A in Fig. 6). The third CDOM pool is found only in Eurasian Basin surface waters and has S values within the range of values of Arctic river CDOM (15.1–21.3 μm− 1, Table 1) and a375 values N0.6 m− 1. We will refer to this pool as terrestrial CDOM (labelled T in Fig. 6). From Fig. 6 three mixing lines between the different CDOM pools are apparent; i) mixing between autochthonous CDOM and oceanic CDOM only, ii) mixing between terrestrial CDOM and ocean CDOM only, and iii) an intermediate mixing line resembling the mixing of a combination of the three. In order to further test the hypothesis that the terrestrial CDOM pool in the surface waters did actually originate from Arctic rivers, a375 was plotted against the estimated fraction of freshwater originating from river water in these samples. This was calculated by Jones et al. (2008) on the basis of salinity, alkalinity and nutrient ratios. There is no relationship between the fraction of river water and

Lena

4. Discussion 4.1. Characteristics and quantity of organic matter exported by Arctic rivers The concentrations of DOC measured and loadings calculated agree well with earlier reports for these rivers (Cooper et al., 2008; Raymond et al., 2007; Spencer et al., 2008, 2009). It is clear that the increased sampling frequency results in greater estimates of the DOC loading from these rivers, compared to the early estimates based on summer concentrations alone (Köhler et al., 2003). Despite the fact

Mackenzie

Ob

Yenisei

Yukon

S (µm-1)

a375(m-1)

Discharge 109 m3 d-1

DOC (µM)

Kolyma

a375 in the Canada Basin (Fig. 7) while a strong correlation is present in the Eurasian Basin, suggesting that this material is of terrestrial origin.

2004

2005

2004

2005

2004

2005

2004

2005

2004

2005

2004

2005

Fig. 2. Discharge, dissolved organic carbon (DOC), colored dissolved organic matter (CDOM) absorption coefficients (375 nm) and CDOM spectral slopes in the six rivers studied for samples in 2004 and 2005.

C.A. Stedmon et al. / Marine Chemistry 124 (2011) 108–118

Fig. 3. Average (2004, 2005) monthly loadings of freshwater (discharge), dissolved organic carbon (DOC) and colored dissolved organic matter (CDOM, a375) for the six rivers studied.

that some of the highest concentrations of CDOM and DOC were measured in the Yukon, the Lena, Yenisei, and to some extent the Ob have larger freshwater discharge making them the three dominant rivers. The Mackenzie and Kolyma supplied much less DOM. This is expected for the Kolyma as it has a relatively small catchment, however, even when this is taken into account by calculating yields (Fig. 4c) these two rivers still supply the lowest amount of DOM. It is clear that these catchments have some other fundamental difference. The catchments of the Lena, Ob and Yenisei rivers differ geologically and climatologically to those in the North American and East Siberian Arctic. Rivers in the latter are characterised by having a greater suspended sediment load and greater relative proportion of particulate organic matter relative to DOM (Gordeev et al., 1996; Holmes et al., 2002). This partitioning could be one explanation why the Kolyma and Mackenzie have considerably lower DOM yields. The SUVA values for these rivers were within the range reported in earlier studies (Neff et al., 2006; Spencer et al., 2008, 2009) although they did not reach the maximum values reported. For example Neff et al. (2006) reported SUVAs as high as 4 m2 g C− 1 during the spring freshet where DOC concentrations were also higher than those reported in this study for 2004–2005. In general SUVA increased with discharge (Table 1) indicating a change in the characteristics of the DOM across seasons. There was also a clear seasonal trend in the

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spectral characteristics of CDOM, represented here by S, for the majority of the rivers S values were negatively correlated to flow rates (Fig. 2). The high flow period of maximum CDOM export was characterised by CDOM with lower S values. The S values in the Kolyma and Lena rivers, however, had a limited range and showed no significant variation with respect to flow conditions. Values from the Mackenzie River were significantly higher than those from the other rivers (Fig. 2), but very similar to those reported by Osburn et al. (2009). The characteristics of CDOM (SUVA and S) vary depending on the composition of the CDOM pool. This in turn is dependent on the predominant source (or combination of sources) and exposure to degradation processes. For humic substances, it has been shown that fulvic acids have a relatively greater S value than humic acids and that S is inversely proportional to molecular weight (Carder et al., 1989; Blough and Green, 1995). This has also been reported more recently for water samples (as opposed to humic extracts) (Gueguen et al., 2006; Helms et al., 2008). Similarly, SUVA has been shown to be positively correlated to molecular weight (Chin et al., 1994). The results here therefore indicate that higher molecular weight material is exported during peak flow in these rivers. Earlier work on the same river systems has shown that the majority of DOM exported during peak and mid flow conditions is relatively young (decadal) (Amon and Meon, 2004, Benner et al., 2004; Guo and Macdonald, 2006; Raymond et al., 2007) and represents recently fixed carbon. Additionally, this material is characterised by having a higher molecular weight (Gueguen et al., 2006; Guo and Macdonald, 2006) and greater aromaticity (Neff et al., 2006). The seasonal changes in the characteristics of CDOM in these rivers are therefore likely due to the seasonal shift in dominant source from modern plant litter during the spring melt to older, more degraded soil organic matter originating from lower soil horizons during low flow conditions. The fact that the Mackenzie had notably higher S values than other rivers may be due to either preferential sorption of the high molecular weight hydrophobic DOM components to the abundant suspended sediments or a more extensive photodegradation of the high molecular weight fraction in the Mackenzie watershed (Osburn et al., 2009) which is characterised by a higher abundance of lakes compared to the other watersheds. For example, 7% of the Mackenzie catchment consists of open water whereas values for the Yenisei, Ob and Lena are 1.2, 0.44 and 0.40% respectively. The annual discharge by Arctic rivers is increasing as a result of climate change (Peterson et al., 2002). In depth studies of long time series for river discharge have shown that much of the increase in freshwater discharge to the Arctic is originating from Eurasian rivers (White et al., 2007). These rivers supply the majority of freshwater, carbon and CDOM to the Arctic Ocean (Figs. 1c, 4). Climate change will work to increase this dominance with subsequent effects on ocean stratification, carbon fluxes and light attenuation. Smith et al. (2007) have shown that the discharge increase is largely due to increases in daily minimum flow rates. The increase was interpreted as being due to increased ground water inputs as a result of deeper penetration of precipitation through soils. A shift in the relative importance of different river discharge conditions will impact not only the total loadings but also the relative distribution between rivers. For example we can compare the two dominant rivers studied here, the Yenisei and the Lena. At present the Lena dominates the annual DOC and CDOM loadings, being responsible for 36 and 40% of the combined loading from all six rivers (Fig. 4). However if we calculate the loading for the base flow period only (November to May) this changes (Fig. 3). For this period the Yenisei dominates, supplying 48% of the DOC and 46% of CDOM. The relatively higher base flow discharge in the Yenisei (Table 2) is responsible for this, despite the low DOM concentrations in the rivers during this period. So it is possible that climate change will induce alterations in the quantity and distribution of the freshwater loading with subsequent impacts on the timing and relative importance of riverine loading of DOM to the Arctic Ocean.

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Table 2 Average monthly and total annual discharge, dissolved organic carbon (DOC) loading and colored dissolved organic matter (CDOM) loading from each of the rivers. River

Month

Discharge (109 m3)

DOC (Tg C)

a375 (1012 m2 yr− 1)

Kolyma Kolyma Kolyma Kolyma Kolyma Kolyma Kolyma Kolyma Kolyma Kolyma Kolyma Kolyma

1 2 3 4 5 6 7 8 9 10 11 12 Total 1 2 3 4 5 6 7 8 9 10 11 12 Total 1 2 3 4 5 6 7 8 9 10 11 12 Total 1 2 3 4 5 6 7 8 9 10 11 12 Total 1 2 3 4 5 6 7 8 9 10 11 12 Total 1 2 3 4 5 6 7 8 9 10

0.64 0.48 0.55 0.53 8.19 42.04 16.73 13.72 9.52 3.79 0.84 0.64 97.7 11.43 10.16 9.40 7.31 33.68 178.69 114.71 88.44 78.80 53.81 15.18 13.47 615.1 12.34 10.06 10.23 10.02 42.74 53.48 38.38 31.70 27.49 25.47 10.91 9.19 282.0 11.76 9.95 10.55 10.77 37.23 86.13 71.88 37.46 24.58 24.26 14.32 13.95 352.8 33.85 24.56 33.62 26.89 72.82 192.11 56.25 42.52 44.26 41.91 41.95 35.54 646.3 4.19 3.32 3.47 4.09 46.64 47.57 31.41 25.20 21.10 19.91

0.0038 0.0030 0.0033 0.0031 0.0335 0.3165 0.1224 0.0905 0.0605 0.0230 0.0050 0.0038 0.668 0.0904 0.0804 0.0743 0.0611 0.2894 2.8202 1.4857 0.9073 0.7927 0.4379 0.1203 0.1066 7.266 0.0418 0.0335 0.0338 0.0334 0.1338 0.2742 0.1750 0.1345 0.1127 0.1013 0.0411 0.0384 1.154 0.0898 0.0744 0.0779 0.0809 0.2817 0.9309 0.8392 0.4190 0.2458 0.2389 0.1167 0.1115 3.507 0.2024 0.1835 0.1963 0.1904 0.9899 2.0238 0.4044 0.2655 0.2869 0.2816 0.2639 0.2249 5.514 0.0051 0.0037 0.0037 0.0076 0.6226 0.5688 0.2361 0.1496 0.1146 0.1101

0.0030 0.0024 0.0026 0.0025 0.0306 0.3249 0.1110 0.0771 0.0500 0.0185 0.0040 0.0030 0.629 0.0933 0.0830 0.0767 0.0632 0.3366 3.4933 1.6574 0.9439 0.8260 0.4460 0.1240 0.1100 8.254 0.0140 0.0106 0.0102 0.0102 0.0875 0.2062 0.1118 0.0776 0.0615 0.0522 0.0139 0.0123 0.668 0.0625 0.0507 0.0525 0.0554 0.2565 1.0766 0.9327 0.3912 0.2065 0.1991 0.0856 0.0805 3.450 0.1307 0.1191 0.1220 0.1186 1.2538 2.7699 0.4355 0.2285 0.2678 0.2539 0.2318 0.1641 6.096 0.0015 0.0008 0.0007 0.0036 0.5136 0.4836 0.2016 0.1250 0.0941 0.0903

Lena Lena Lena Lena Lena Lena Lena Lena Lena Lena Lena Lena Mackenzie Mackenzie Mackenzie Mackenzie Mackenzie Mackenzie Mackenzie Mackenzie Mackenzie Mackenzie Mackenzie Mackenzie Ob Ob Ob Ob Ob Ob Ob Ob Ob Ob Ob Ob Yenisei Yenisei Yenisei Yenisei Yenisei Yenisei Yenisei Yenisei Yenisei Yenisei Yenisei Yenisei Yukon Yukon Yukon Yukon Yukon Yukon Yukon Yukon Yukon Yukon

Table 2 (continued) River

Month

Discharge (109 m3)

DOC (Tg C)

a375 (1012 m2 yr− 1)

Yukon Yukon

11 12 Total

8.37 4.98 220.3

0.0188 0.0070 1.848

0.0113 0.0026 1.529

Discrete sampling programs have their limitations. Short-term events are frequently under-sampled or missed completely which can impact export estimates. The data from this study indicates that a robust CDOM–DOC yield relationship exists for these remote rivers (Fig. 4d). This is advantageous, as in situ sensors are available for monitoring CDOM concentrations and if deployed on gauged rivers may greatly improve estimates of riverine organic carbon loading (Stedmon et al., 2006; Spencer et al., 2009). The fact that the Arctic data overlaps well with earlier results from a temperate agricultural catchment in Denmark (Stedmon et al., 2006) suggests that there is a strong correlation between the yields which appears to be robust across a broad range of systems despite their very different catchment and climate characteristics. This is promising for the development and application of CDOM in situ sensors to trace CDOM in the Arctic Ocean. Holmes et al. (2008) investigated the bioavailability of DOC exported by three Alaskan rivers and found that 20–40% of the carbon exported during the spring freshet was remineralised by bacteria during three month incubations. In contrast, for the material exported during lower flow conditions in summer, less than 10% was remineralised. No incubations were carried out on DOM exported during winter base flow conditions, which from the calculations above appears to be an important period, in light of the increasing daily minimum flow rates reported (Smith et al., 2007). So for now we can only extrapolate by combining Holmes et al. (2008) with the (C)DOM quality results in this and earlier studies. The data imply that DOM exported in these rivers decreases in molecular weight and bioavailability and increases in age, across the hydrograph from the spring freshet, to summer to subsequently to winter base flow conditions. This implies that climate change induced increases in the winter export of DOM to the Arctic Ocean will result in an increase in the supply of essentially bio-refractory DOM. Additionally the fact that much of this transport occurs under ice in winter and therefore shielded from photodegradation, suggests that this material should survive transport far out into the Arctic Ocean. 4.2. Characteristics and origins of DOM in the surface waters of the Arctic Ocean The results of this study support the findings of Stedmon and Markager (2001) that recent autochthonous CDOM differs from oceanic CDOM by having greater a375 and low S values. The mixing curve derived for the Greenland Sea (Stedmon and Markager, 2001) appears to also apply in the Arctic Ocean in deeper waters where there is little terrestrial CDOM input (Fig. 6). These waters generally correspond to depths greater than 300 m. In surface waters (0–300 m) substantial differences are apparent in the amount and types of CDOM present between the Eurasian and Canada Basins, whereas the deepwater characteristics are similar. Surface waters (0–200 m) of the Canada Basin have slightly higher concentrations of CDOM than the deeper waters and S values are variable. This variability in S is in part due to the fact that these measurements are at the detection limit of the approach (Stedmon and Markager, 2001) but also likely an indicator of mixing between a low intensity autochthonous source of CDOM (with low S values) with a background oceanic CDOM pool with higher S values. The surface waters of the Canada Basin are stratified with the surface 50 m consisting of the polar mixed layer (PML) which is greatly influenced by pelagic and ice

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% of total: 3

36

6

18 28

9

3

a

17 30 7

b

5

DOC yield (gC m-2 yr-1)

c

40 3

d

4 3 2 1 0

Arctic 0

2

4

DK 6

CDOM yield (yr-1) Fig. 4. Total annual loading of a) dissolved organic carbon (DOC) and b) colored dissolved organic matter (CDOM, a375) for each river calculated on the basis of two year data (2004– 2005). The numbers at the top of the graphs represent the percent that each river represents of the combined total. c) The DOC yield from each catchment (annual loading normalised to catchment size). d) The relationship between the DOC and CDOM yields from each river. Also plotted are the results from an earlier study by Stedmon et al (2006) on Danish catchments (DK).

productivity (Honjo et al., 2010) as well as sea ice formation and melt, and inflow from the Pacific Ocean and rivers (Rudels et al., 2004; Jones et al., 2008). Below the PML, from 50 to 300 m, is the halocline layer with waters originating from different sources including the Bering Strait, the Alaskan coastal current and the Eurasian Basin (Rudels et al., 2004). During its passage from the Bering Strait this layer gains nutrients and organic matter from passing over the sediments of the extensive and productive Chuckchi Shelf (Jones et al., 2008). The CDOM results from the surface 300 m of the Canada Basin agree with other studies suggesting that these waters gain autochthonous DOM from being in contact with the sediments on the Chuckchi Shelf. Riverine input can also be expected to supply DOM to these waters but this is not clearly apparent from the CDOM measurements (Fig. 7). Although studies tracing freshwater inputs indicate that the surface water of the Canada and Eurasian Basins has similar proportions of riverine water (Jones et al., 2008), there is little apparent evidence for it in the CDOM concentrations and characteristics when comparing to the trends observed in the Eurasian Basin (Fig. 7). Recent results suggest that North American rivers play a relatively minor role in the surface waters of the Canada Basin and that the river water present in the PML originates from Eurasian rivers (Yamamoto-Kawai et al., 2005; Guay et al., 2009). Based on the results of Guay et al. (2009) we would expect a stronger correlation between CDOM and fraction of river water in the Canada Basin, as seen for the Eurasian Basin data. However, it is clear that in the Canada Basin surface waters there is no single dominant CDOM source (Fig. 7). The presence of a near linear mixing line apparent in Fig. 6 also implies that there are at least three CDOM end members that are mixing in these waters. In order to obtain a linear relationship between a375 and S, at least three different CDOM pools have to be present (Stedmon and Markager,

2003; Stedmon et al., 2010). In the Canada Basin PML riverine and autochthonous CDOM are both important resulting in the near linear mixing line apparent in Fig. 6. The CDOM measurements from the surface waters of the Eurasian Basin show clear influences of the supply of material from rivers (Figs. 5–7). As in the Canada Basin, the surface waters of the Eurasian Basin are also highly stratified. Below the surface PML (0–50 m), which is greatly influenced by riverine input and sea ice formation, is the Atlantic halocline layer, which has its origins in water flowing northwards from the Fram Strait and Barents Sea (Rudels et al., 2004). This water moves together with the underlying Atlantic water but is cooled and made less saline by sea ice melt and winter mixing, eventually forming an insulating layer above the warm Atlantic water which allows the surface waters to be cooled enough to sustain sea ice (Rudels et al., 2004). The CDOM signal in both the PML and the Atlantic halocline layer resembles that supplied by the major rivers (Fig. 6), indicating that this is the dominant CDOM source and that riverine CDOM is entrained into the halocline layer. S values overlap with the range of values reported for riverine CDOM. Extrapolating the relationship in Fig. 7 results in a riverine end member estimate for a375 of 4.5 m− 1. This is considerably lower (approximately half) than the average values reported in Table 1 for the three dominant CDOM sources to the Eurasian Basin (Ob, Lena and Yenisei), suggesting that riverine CDOM is removed during its transit across the shelf. A recent tracer study using an ocean circulation model to understand the distribution of riverine DOC in the surface waters of the Arctic arrived at a similar result (Manizza et al., 2009). Without the presence of a sink for a fraction of riverine DOM, the model over predicts DOC values, indicating that conservative mixing alone is insufficient. There are four major CDOM removal processes that are likely to be responsible; flocculation, photochemical degradation, microbial

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a375 (m-1)

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a

c

Fig. 7. Relationship between the fraction of riverine freshwater (fr) and CDOM a375 in surface 500 m for the Canada Basin (squares) and the Eurasian Basin (dots). No significant relationship was detected for the Canada Basin. Eurasian Basin a375 = 0.11 + 4.43fr, r2 = 0.73. fr was calculated according to the approach in Jones et al (2008).

d

b a375 (m-1)

S (µm-1)

Fig. 5. Depth profiles of CDOM properties in the Arctic Ocean. Data from the Eurasian Basin is plotted as diamonds with a solid black line representing a smoothed mean profile. The Canada Basin data is plotted as grey dots with dashed lines as the smoothed profile. a) a375 for all data, b) a375 surface 500 m only, c) CDOM spectral slope for all data, and d) S for the surface 500 m only.

degradation and fractionation during sea ice formation. During estuarine mixing there is a rapid increase in salinity, turbidity and water residence times, which can result in considerable dissolved-

S (µm-1)

O

T

A

a375 (m-1) Fig. 6. CDOM spectral slope plotted against a375. Symbols: black dots (polar mixed layer 0–50 m), squares (halocline waters, 50–300 m), grey dots (Atlantic layer 300–900 m) and diamonds (bottom waters N 900 m). The grey horizontal line represents the range of S values measured in the river data (Table 1). The black curve is the model for mixing of autochthonous CDOM with oceanic CDOM derived by Stedmon and Markager (2001). The labels T, A and O refer to the three dominant CDOM end members identified in Section 3.3, terrestrial, autochthonous and oceanic.

particulate phase interactions (Cauwet, 2002). Depending on local conditions, such as suspended sediment size and type, iron concentrations, and temperature, the net effect ranges from none to a slight removal of DOM (Sholkovitz et al., 1978; Droppo et al., 1998; Søndergaard et al., 2003; Amon and Meon, 2004). UV induced photochemical degradation of CDOM is also known to be a widespread phenomenon which can both bleach CDOM and directly remineralise organic carbon (Moran and Zepp, 1997). Several studies have shown that relatively fresh riverine CDOM is highly susceptible to photodegradation (Johannessen and Miller, 2001; Amon and Meon, 2004, Bélanger et al., 2006) and the process becomes biogeochemically important in shelf seas where light penetration and mixing depth increases (Del Vecchio and Blough, 2002; Osburn et al., 2009). However, due to conditions specific to the Arctic (ice cover, low sun angle, limited period with daylight), photochemical degradation of CDOM, although detectable, is deemed to have limited importance as a CDOM sink (Bélanger et al., 2006; Osburn et al., 2009). The extent to which microbial degradation influences DOM distributions is less clear. Some studies consider it to be a major sink for DOM supplied by Arctic rivers (Anderson, 2002; Hansell et al., 2004). Results from surface water of the Canada Basin have suggested that 68–79% of terrestrial DOM in the polar mixed layer is remineralised during its retention in the Beaufort Gyre (Hansell et al., 2004). Other studies examining the bacterial degradation of Arctic river DOM have found only a 5% degradation over 2 yr (Köhler et al., 2003). In the absence of noteworthy flocculation and photodegradation, the distribution of CDOM in the surface and halocline waters of the Arctic Ocean is controlled by the rate of supply from rivers, mixing between basins, and a combination of the rate of microbial remineralisation and water residence time (Manizza et al., 2009). Although the supply from rivers differs greatly between the basins (Fig. 4) the susceptibility of DOM to microbial degradation is assumed to be similar and the longer residence time of the surface waters of the Canada Basin is thought to be responsible for the lower DOM concentrations there (Hansell et al., 2004; Manizza et al., 2009). Our results, however, show that CDOM in the surface waters of the two basins have clearly different characteristics with the Canada Basin being more influenced by autochthonous DOM, which in general is assumed to be more labile over these mixing time scales. The influence of sea ice formation and subsequent ice melt on DOM and CDOM distribution has not been considered on a pan-Arctic scale but the removal of DOM from the sea ice matrix has been shown

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in freezing experiments (Amon, 2003, Walker, unpublished data). Because much of the Arctic sea ice is formed on the Eurasian shelf a considerable amount of river water can be trapped in sea ice without trapping the DOM component of river water. When this ice (modified river water) melts it adds river water back to the surface layer without adding the DOM and CDOM components. How important this process is for the overall distribution of CDOM in the Arctic Ocean needs to be investigated but depends on the geographical locations of sea ice formation and sea ice melt which are not the same e.g. ice formed on the Eurasian shelf might melt in the central Arctic Ocean adding a river water signal but no river DOM. 5. Conclusion The results of this study indicate that climate driven changes in river discharge are likely to alter the relative balance of DOC and CDOM supply to the Arctic Ocean from these six rivers. The increase in winter discharge rates will increase the importance of the Yenisei River. The quality of the organic matter exported by these rivers varies seasonally with relatively old and refractory material being exported during winter. This suggests that in the future we can expect terrestrial DOM, supplied by rivers during the winter months, to be more prevalent in the surface waters of the Arctic Ocean. Comparison of the oceanic data with the riverine loading data reveals that the CDOM in the surface waters of the Canada and Eurasian Basins differs considerably in character, with autochthonous material dominating in the former and riverine material in the latter. This can be expected to result in considerable differences in the remineralisation rates of DOM in these two basins. Approximately half of the riverine CDOM in the Eurasian sector seems to disappear during its transport across the shelf to the central Arctic Ocean. More work is, however, required to identify the relative importance of sea ice formation, flocculation and microbial degradation on this removal if we are to understand how the Arctic Ocean will respond to increasing riverine loading and the subsequent effect on the export of organic carbon to the North Atlantic. Acknowledgements This work was funded by grants from the National Science Foundation to R.M.W.A. (ARC 0425582, ARC 0713991, OPP 0229302-subcontract) and the Carlsberg Foundation, and Greenland Climate Research Centre to C.A.S. Special thanks goes to the PARTNERS project members, especially Bruce Peterson, Jim McClelland, and Max Holmes for coordinating the project and collecting a truly unique set of samples. We are also grateful to the captain, scientists and crew from the ice breaker Oden during AOS 2005 for their help with sampling and fruitful discussions. References Amon, R.M.W., 2003. The role of dissolved organic matter for the organic carbon cycle in the Arctic Ocean. In: Stein, R., MacDonald, R. (Eds.), The Organic Carbon Cycle in the Arctic Ocean. Springer, Berlin, pp. 83–99. Amon, R.M.W., Meon, B., 2004. The biogeochemistry of dissolved organic matter and nutrients in two large Arctic estuaries and potential implications for our understanding of the Arctic Ocean system. Mar. Chem. 92, 311–330. Anderson, L.G., 2002. DOC in the Arctic Ocean. In: Hansell, D.A., Carlson, C.A. (Eds.), Biogeochemistry of Marine Dissolved Organic Matter. Academic Press, San Diego, California, pp. 665–684. Arrigo, K.R., van Dijken, G., Pabi, S., 2008. Impact of a shrinking Arctic ice cover on marine primary production. Geophys. Res. Lett. 35, L19603. Bélanger, S., Xie, H., Krotkov, N., Larouche, P., Vincent, W.F., Babin, M., 2006. Photomineralization of terrigenous dissolved organic matter in Arctic coastal waters from 1979 to 2003: interannual variability and implications of climate change. Glob. Biogeochem. Cycles 20, GB4005. doi:10.1029/2006GB002708. Benner, R., Benitez-Nelson, B., Kaiser, K., Amon, R.M.W., 2004. Export of young terrigenous dissolved organic carbon from rivers to the Arctic Ocean. Geophys. Res. Lett. 31, L05305. doi:10.1029/2003GL019251.

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