Freshwater distribution in the Arctic Ocean: Simulation with a highresolution model and model-data comparison

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 113,, doi: /2007jc004111, 2008 Freshwater distribution in the Arctic Ocean: Simulation with a highresolution model and model-data comparison Robert Newton, 1 Peter Schlosser, 1,2 Douglas G. Martinson, 1 and Wieslaw Maslowski 3 Received 19 January 2007; revised 31 December 2007; accepted 5 February 2008; published 21 May [1] A high-resolution numerical simulation of the Arctic Ocean is analyzed in order to study the fate of river runoff and freshwater fluxes in the Arctic Ocean. The model is driven by realistic winds and thermodynamic forcing from the European Centre for Medium-range Weather Forecasting (ECMWF) Reanalysis data set. Dye tracers have been added to visualize the pathways followed by low-salinity water from the major Arctic rivers and Bering Strait Inflow. The model is spun up using repeated forcing with the 1979 annual cycle for 20 years; then the 1979 through 1998 atmospheric forcing is applied. Under the influence of the 1979 through early 1980s winds, a large plume of river runoff exits the broad Eurasian shelf seas along the Lomonosov Ridge. Starting in about 1985, the locus of shelf-to-basin transport shifts eastward to the Alpha-Mendeleyev ridge complex. This shift in the model output is related to changes in the sea-surface height (SSH) fields, which we attribute primarily to shifts in surface wind stresses. Model resolution, runoff inputs, and relaxation terms in the Lena River delta region are analyzed in detail to expose issues with model performance at boundaries with freshwater inflow. Suggestions are made for improving future simulations of river runoff in basin-scale models. Citation: Newton, R., P. Schlosser, D. G. Martinson, and W. Maslowski (2008), Freshwater distribution in the Arctic Ocean: Simulation with a high-resolution model and model-data comparison, J. Geophys. Res., 113,, doi: /2007jc Introduction: Freshwater Fluxes in the Arctic Ocean [2] In this study we use a basin-scale general circulation model (GCM) and hydrographic and tracer data to study the distribution of river runoff over the Arctic Ocean, changes in that distribution, and its flux from the Arctic to the North Atlantic. Runoff is the largest contributor of freshwater to the Polar Water cap over the Arctic Ocean [Serreze et al., 2006[, playing a major role in setting both the vertical and horizontal density gradients in the Arctic Ocean. It is an excellent tracer for the fate of material entering the Arctic Ocean in coastal regions, including pollutants, nutrients and plankton. In addition, the flux of Polar Waters from the Arctic to the Nordic and Labrador seas constitutes a positive buoyancy flux to areas of deep and intermediate water formation. On glacial timescales, freshwater has long been implicated in shutting down deep convection [e.g., Stocker and Wright, 1991]. More recently, it has been suggested that smaller changes in buoyancy flux could modulate the strength of the meridional overturning circulation (MOC). Hall et al. [2006] show MOC slowdowns before the last glacial termination, while Mauritzen and Hakkinen [1997] show MOC sensitivity on the order of 5 Sv to changes in 1 Lamont-Doherty Earth Observatory, Palisades, New York, USA. 2 Columbia University, New York, New York, USA. 3 Naval Postgraduate School, Monterey, California, USA. Copyright 2008 by the American Geophysical Union /08/2007JC sea-ice flux of about 800 km 3 per year in an ice-ocean GCM, and Koenigk et al. [2006] show that in the Echam5/ MPI-OM coupled model freshwater flux anomalies through Fram Strait modulate convection in the Labrador Sea and affect regional climate. Both data and model studies indicate that the MOC is sensitive to stability of the water column in the Nordic and Labrador seas, and an understanding of buoyancy fluxes in these regions is required to predict the fate of the MOC in the future [Latif et al., 2006]. [3] Arctic surface waters contain a freshwater anomaly, relative to the average salinity of the Arctic Ocean, of about 74,000 km 3 (Figure 1) sea-ice contains approximately another 10,000 km 3 (annual average). Serreze et al. [2006] have recently reviewed and summarized the current literature on freshwater sources and sinks to and from the Arctic Ocean. They find that the largest contributors to freshwater in the surface Arctic are: runoff from rivers (38%), Bering Strait inflow (BSI), which carries relatively fresh North Pacific surface water (30%), and in situ precipitation (24%). Freshwater flows out primarily through Fram Strait and the Canadian Arctic Archipelago (CAA), and sporadically through the Barents Sea and Bering Strait. The major, gauged, rivers inject about 2500 km 3 annually, which is about 78% of total runoff. The Bering Strait contributes approximately an additional 2,500 km 3, relative to the average salinity of the Arctic Ocean [Serreze et al., 2006, and references therein]. In the long run, the average freshwater flux into the Arctic Basin is balanced by outflow through Fram Strait and the Canadian Arctic Archipelago (CAA). However, there is no reason for this balance to be in 1of15

2 Figure 1. Hydrography from the 1994 Arctic Ocean Sciences cruise. equilibrium on decadal or shorter timescales, and in fact Arctic liquid and frozen reservoirs of fresh water vary on all timescales relevant to the current study. Together, sea-ice and the surface freshwater anomaly represent about 10 years of freshwater influx from all sources, which gives a rough time-scale for the residence time of these waters. However, as is clear from our model results as well as from transient tracer data, the time for freshwater to transit the Arctic Ocean and exit to the North Atlantic varies significantly, according to both the location and the year in which the source reaches the Ocean. In particular, between the late 1970s and the early 1990s, remarkable shifts were observed in the Arctic Ocean circulation. Hydrographic fronts, previously believed to be quasi-stationary, moved by hundreds of kilometers [Schauer et al., 2002; Morison et al., 1998; Steele and Boyd, 1998; Zhang et al., 1998; Schlosser et al., 2002; Maslowski et al., 2000], sea ice extent and concentrations shrank [Kwok and Rothrock, 1999; Zhang et al., 2000; Rigor et al., 2002], temperatures rose [Rigor et al., 2000; Gerdes et al., 2003], storm frequency increased [Serreze et al., 2000; McCabe et al., 2001], and average sea level pressure (SLP) dropped [Thompson and Wallace, 1998; Rigor et al., 2002]. [4] Over most of the Arctic Ocean, the density field is dominated by salinity changes, with temperature playing a secondary role in setting both vertical stratification and horizontal density gradients. Coldest waters are at the surface where, except for continental shelf seas in summer, the water temperature is near the freezing point and sea ice is usually present (Figure 1). In May and June a seasonal layer of fresher water, generally less than 10 m thick, is formed as sea ice melts and river runoff mixes into the surface. In the fall, ice formation results in the creation of dense brines and overturning that homogenizes the upper ca. 30 m into a winter mixed layer. Below the mixed layer, density increases with salinity through a halocline about 125 to 200 m thick. Over most of the basin, the upper halocline is near the freezing point (the cold halocline ), whereas in the lower halocline, both temperature and salinity increase. Below the halocline, lies a relatively warm Atlantic Layer, fed by inflow from the North Atlantic through Fram Strait and the Barents Sea [Rudels et al., 1999; Schauer et al., 2002]. Below the Atlantic layer, temperature decreases gently and salinity increases to a cold (t < 0), salty (S > 34.8) layer of deep water. Below the Atlantic Layer, the horizontal structure of the hydrography is dominated by peaks and valleys aligned with the main bathymetric 2of15

3 features (the Lomonosov, Gakkel, and Alpha-Mendeleyev ridges); and by small differences in properties between deep basins segregated by those ridges. Density gradients between the relatively fresh surface waters and the base of the halocline modulate the vertical flux of heat from the Atlantic layer to the surface, and changes in water column stability have been implicated as one factor in retreat of the summer sea-ice minimum [Martinson and Steele, 2001]. [5] Halocline water is formed mainly during sea ice formation: from above as dense brines sink down through the mixed layer, and from the sides, as high salinity waters work their way off the shelves and mix into the basin at a variety of density horizons [e.g., Rudels et al., 1996]. Pacific water entering through Bering Strait feeds a more or less discrete layer in the halocline, which is present over the Canadian Basin, thins northward, and disappears before the Lomonosov Ridge. The freshwater content above the base of the halocline is relatively low over the Eurasian Basin, whereas in the southern part of the Canadian Basin, anti-cyclonic winds cause a downward bowing of the isopycnals and convergence of fresh water in the Beaufort Gyre [Ekwurzel et al., 2001]. The Beaufort Gyre and its freshwater reservoir expand and contract in response to changes in wind [Hunkins and Whitehead, 1992; Proshutinsky and Johnson, 1997; Proshutinsky et al., 2002; Newton et al., 2006]. 2. Model Description 2.1. Basic Architecture [6] We used the Naval Postgraduate School (NPS) Arctic regional Model (NAME), ( NAME/name.html) [Maslowski and Semtner, 1995; Maslowski et al., 2000, 2001; Maslowski and Lipscomb, 2003; Maslowski et al., 2004], a coupled ice-ocean general circulation model (GCM), to simulate the distribution of runoff from the major Arctic rivers and the Bering Strait Inflow. NAME runs on a latitude-longitude-depth grid in rotated spherical coordinates. It is based on the Parallel Ocean Program (POP), developed at the Los Alamos National Laboratory [Maltrud et al., 1998] which has been optimized for distributed-memory, massively parallel, computers. It has been run by the NPS using Department of Defense computer resources at the Arctic Region Supercomputing Center (ARSC). The combination of a freesurface model, high resolution and very large computing power facilitates a more realistic representation of bathymetry and coastlines. In the version described here NAME runs with about 18 km resolution in the horizontal and 30 levels in the vertical. (A version with 9 km horizontal resolution and 45 levels is currently running [e.g., Maslowski et al., 2004].) The domain includes the Arctic Ocean with its peripheral seas, the Nordic and Labrador seas, Baffin Bay and the North Atlantic down to approximately 50 North. The major straits of the Canadian Arctic Archipelago are open, though the Bering Strait is not. The ETopo-5 bathymetry has been used with no additional smoothing. In coastal seas, including near islands and in straits, significant improvements were made to the bathymetry from navigational charts. The model uses a free surface, so that barotropic pressure gradients are calculated as a difference of the total water column pressure, which includes sea surface elevation, facilitating the inclusion of islands, straights and complex coastlines around the Arctic shelves. We used Laplacian diffusivity (tracers) and viscosity (momentum) in the vertical and bi-harmonic functions in the horizontal. In each case, the coefficient of viscosity is approximately times that of diffusivity. At the southern boundary of the model domain a sponge layer is implemented, over the southern 10 grid points, that relaxes toward climatological values at the wall. The ocean is coupled to a Hibler-type sea-ice model with a plastic-viscous rheology and updated numerics [Zhang and Hibler, 1997]. In the configuration reported on here, running on the ARSC super-computer, integration time was approximately 2 days per model year Addition of Freshwater Sources [7] To simulate runoff into the Arctic, we added 10 individual freshwater sources to the NAME model, representing the Mackenzie, Severnaya Dvina, Pechora, Ob, Yenisey, Khatanga, Lena, Indigirka and Kolyma rivers, and the freshwater content of the BSI. BSI freshwater was calculated as the freshwater anomaly of the Inflow relative to a reference salinity of Some authors [e.g., Aagaard and Carmack, 1989] have used a reference of 34.8, which is the average salinity of the Arctic Basin. Our choice reflects our focus on the interchange between the Arctic Ocean and the North Atlantic. The freshwater content of the BSI was treated identically to the river inflows, as a source of freshwater, but not heat or momentum. The rivers included in this simulation account for approximately 90 percent of the gauged runoff. For each river and the BSI an annual cycle of freshwater flow was constructed from available data. The Russian rivers were monitored beginning in the 1930s. However, with the end of the Soviet Union, it has become impractical to obtain data for most rivers after The Canadian river flows are available from Environment Canada ongoing from 1979, through its Hydat product ( BSI freshwater content in the simulation was based on mass flux and salinity data from moorings in the Bering Strait area in the early 1990s [Roach et al., 1995]. Since the times spanned by the data were so different, we used an annual climatology for each inflow, based on an average annual cycle from all available data. For the Khatanga River, there were only data available for several summer seasons in the 1960s. In that case, we used data from neighboring rivers (Yenisey and Lena) to create an annual cycle, scaled by the average Khatanga summer peak. Figure 2a shows the annual climatology of river runoff for the major rivers; Figure 2b compares the total of the major rivers to freshwater influx through the Bering Strait. We performed two simultaneous experiments in this run: We added freshwater flux equivalent to the climatological runoff by decrementing the salinity in an input patch at each river mouth and the boundary at the Bering Strait. In addition, we added a passive dye tracer to the model domain in volumes equivalent to the observed annual cycle of freshwater inputs. The input patches, shown in Figure 3d, varied in size from 20 to 50 grid cells, depending on the geometry of the river front. The Severnaya Dvina and Pechora rivers, which empty into the Barents Sea were combined as a single dye tracer. The tracer input was distributed evenly 3of15

4 Figure 2. Climatological freshwater inputs used in the model run. across the patches and between the upper two layers of the water column (45 m) Forcing [8] The model was forced from above by parameterizations of the heat balance at the surface, momentum fluxes from wind stresses, and a relaxation to the Levitus 1994 climatologies [Levitus and Boyer, 1994; Levitus et al., 1994] for sea-surface temperature and salinity. The heat balance includes incoming shortwave radiation and albedo, incoming and outgoing long wave radiation, and sensible and latent heat transfers. Sensible and latent heat fluxes were calculated using the European Center for Medium- Range Weather Forecasts (ECMWF) Reanalysis 2-m dew point temperature, surface air temperature and cloud fraction. The wind stresses were computed from the ECMWF 10-m geostrophic winds, using a turning angle of 25 degrees and a drag coefficient of As the growth or melting of sea ice was calculated, the salinity of the surface ocean was adjusted to account for the implied freshwater flux. The ECMWF Reanalysis data set was used for forcing from 1979 through 1993; the operational forecast (ECMWF-OF) was used from 1994 through The model was initialized with temperature, salinity, and velocity fields from the end of a previous 200-year integration with ECMWF forcing from the 1990s. A spinup was conducted with 20 years of repeated 1979 forcing, after which the time series of forcing was applied. [9] The relaxation to the Levitus climatology was performed in the surface layer, the upper 20 m, using a 365-d timescale for the sea surface temperature and a 120-d timescale for sea surface salinity. Neither diffuse runoff nor precipitation was included explicitly in the model fluxes since at the time of the experiments these two fields were insufficiently understood to make reasonable forcing data sets. The 120-d relaxation in the salinity forcing was implemented in lieu of complete freshwater forcing. 3. Results and Discussion 3.1. Modeled Distribution of Freshwater [10] The sum of the 9 individual freshwater tracer concentrations at the end of the model spinup is displayed in Figure 3a. Each individual tracer has been averaged over the upper four model levels (the upper 100 m) which contain most of the dye. High runoff dye concentrations are visible over the Siberian shelf seas, with much of the model s shelf area covered by water that is over 50% riverine input. A well-defined plume of runoff has exited the shelf north of the Laptev Sea, crossing the Eurasian Basin approximately parallel to the Lomonosov Ridge. North of Greenland the plume splits, with some of the tracer recirculating anticyclonically in the Canadian Basin, and some exiting the central Arctic in the East Greenland current. Maximum total runoff dye concentrations in the plume crossing the central Arctic are approximately 25%. Individual dye maps (not shown) indicate that all the Eurasian dye plumes flow to the right, eastward, along the coast. On the Canadian 4of15

5 Figure 3. Distribution of the sum of all dye tracers representing freshwater in the model runs: (a) 1979 (after spinup), (b) 1993, (c) , and (d) tracer input patches. side, the Mackenzie and Bering Strait plumes flow both eastward and westward. Interaction between the buoyancy gradient and the bottom topography forces the plumes eastward, as with the Eurasian rivers [Garvine, 1995; Yankovsky and 1999]. However, is the Beaufort regional current 5 of 15 Chapman, 1997; Weingartner et al., the prevailing wind system in the area High pressure system; and the main is the Beaufort Gyre, established by

6 Figure 4. Sum of concentrations of all model freshwater dye tracers in the surface level at the end of the experiment: Orange line, model I = 100 coordinate; green crosses, Polarstern Ark XII (1996) cruise; red circles, Oden 1991 cruise. the contrast between the Beaufort High and low-pressure systems over the Eurasian Basin and the Nordic Seas [Hunkins and Whitehead, 1992; Newton et al., 2006]. Thus the buoyancy and mean surface stress forcing act oppositely on the plume and it will trend east or west depending on the (highly variable) winds. The average result is a splitting of the plume into a westward branch that joins the Eurasian runoff plume and an eastward branch, much of which exits the Arctic through the CAA. [11] By 1993, the modeled freshwater plumes have shifted eastward over both the shelves and the deep basin (Figure 3b). The change in the surface ocean reflects a strong increase in positive vorticity input at the surface, as seen in the sea-ice motion of the model [Maslowski et al., 2000, 2001]. The dyes still leave the shelf in a single, wellorganized plume, but the plume has shifted from the Laptev Sea, where the Lomonosov Ridge joins the shelf, to the Chukchi Cap, about 1,500 km to the East. There, the plume flows over the Chukchi Cap and then approximately follows the Alpha and Mendeleyev ridge systems across the Canadian Basin. As in 1979, the plume splits in the Lincoln Sea, north of Greenland. Some of the recirculating fraction exits through Nares Strait into Baffin Bay, while the rest makes its way along the northern edge of the Canadian Arctic Archipelago. The plume s connection with the Laptev Sea along the Lomonosov Ridge no longer exists. Figure 3c shows the differences between the concentrations in 1993 and Generally, concentrations in the Eurasian Basin have dropped whereas those over the Canadian Basin have increased. The outflow along both sides of Greenland has increased. [12] Figure 4 shows the total freshwater tracer in Near the dye sources, where tracer peaks had shifted eastward between 1979 and 1993, they have now shifted back toward the west. In the critical saddle region, where the Lomonosov Ridge meets the Siberian continental shelf, the runoff concentrations have risen from less than 5 percent to about 15 percent, while over the Chukchi Cap the opposite change has taken place. We interpret this as a return of the cross-shelf runoff plume to a position similar to the one occupied in the early 1980s. At the same time, over the central Arctic Ocean the shift of the dye plume toward Canada has continued. The plume has become much more heterogeneous, and there is some indication that part of the fresh water has been stored in the Beaufort Gyre Comparison With Observed Changes in Meteoric Water Distributions [13] The direction and magnitude of shifts in the model dye concentrations between 1979 and the mid-1990s are consistent with observed shifts in hydrographic fronts in the Arctic Ocean. Data from the SCICEX cruises reveal an eastward shift in a salinity front from approximately the Lomonosov Ridge to approximately the Alpha-Mendeleyev Ridge complex between the early and mid-1990s [Steele and Boyd, 1998]. This shift has also been observed in sections from cruises in 1991 (Oden-91, green crosses in Figure 4) and 1996 (Polarstern Ark-XII, red circles in Figure 4). Figure 5, adapted from Schlosser et al. [2002], shows the salinity (Figures 5a and 5b) and meteoric water fraction (Figures 5c and 5d) along two transects crossing the Eurasian Basin in 1991 (Figures 5a and 5c) and 1996 (Figures 5b and 5d). Between 1991 and 1996, the reservoir of low-salinity water overlaying the warm, salty Atlantic Layer has eroded over the Amundsen Basin. Using oxygen isotope ratios, in combination with salinity of the seawater samples, it can be determined that the missing freshwater component is mainly meteoric water (runoff plus local precipitation) [Schlosser et al., 2002]. For example: the 6of15

7 a. b. c. d. e. f. Figure 5. Comparison of salinity and meteoric water fraction crossing the Eurasian basin. (a, c) From the Oden 1991 cruise. (b, d) From the ARCSYS 1996 cruise. (e, f) From the NAME model, years 1990 and approximately 6% drop in meteoric water content of the surface waters over the Lomonosov Ridge accounts for the full magnitude of the increase in salinity there from 31.7 to 33.7 psu. We can compare the shift in meteoric water between the two cruises with that of river runoff dye from the NAME model. Figures 5e and 5f show the distribution of runoff dyes in 1990 and 1996, respectively, from the model run. [14] The modeled distribution of runoff in 1990 has a horizontal extent that is similar to the 1991 distribution of meteoric water from ocean sampling, with the maximum of the horizontal gradient at the surface, just south of the Gakkel Ridge, being within 50 km of each other. The maximum of the meteoric water in the observations is 13% versus 16% runoff at the peak of the plume in the model. Both plumes are surface trapped, with the model plume being restricted nearly entirely to the upper 45 m and the observed plume being largely limited to the upper 100 m. The main difference between the observations and the model is that the observations show a more diffuse distribution than the model fields. [15] By 1996 the observations and the model both demonstrate increased salinity over the Amundsen Basin and the northern Nansen Basin. There are insufficient data to determine with certainty what the source of the meteoric water was or what caused the shift. Schlosser et al. [2002], hypothesize that the shift was caused by a reorganization of freshwater plumes originating on the shelves. The model confirms that suggestion, indicating that the river runoff plumes shifted to the Canadian side of the Lomonosov ridge, and that this shift was responsible for the reduction in salinity over the Amundsen and northern Nansen basins. [16] Since the model run spans the time of the property shift, we can use its output to investigate the timing of the shift in meteoric water distribution. Figures 6a and 6b show the time evolution of the concentration of Mackenzie and Yenisey river runoff, respectively, along the model I coordinate 100, corresponding to the orange line across the model domain in Figure 4, which crosses the model domain from the Beaufort Sea, north of Alaska (at the bottom of Figure 6) to the Kara Sea, north of Russia (top of Figure 6). The Lomonosov Ridge lies between points 120 and 125 in the vertical axis. Time proceeds from left to 7of15

8 Figure 6. Evolution of the average concentration of river runoff in the upper 70 m of the model ocean along a transect extending from about Barrow, Alaska, to about the St. Anna Trough in the Kara Sea (i.e., along the i = 100 coordinate of the model domain). right, from 1979 to Tracer concentrations in the upper 70 m (3 levels) of the model have been averaged. The Bering Strait Inflow behaves similarly to the Mackenzie runoff; the Eurasian river plumes (Lena, Ob, etc.) follow the evolution pattern of the Yenisey. By the end of the spinup, there is a strong plume of runoff dyes, from both sides of the Arctic, over the Lomonosov Ridge. As the experiment continues into the 1980s, the plume intensifies, and spreads over both sides of the Ridge. Then, between about 1985 and 1989, the surface circulation undergoes a dramatic, change. The tracer migrates away from the Eurasian Basin; first to the Canadian side of the Lomonosov Ridge over the Makarov Basin, and then even further, until the highest concentrations are over the Beaufort Sea, north of the Canadian continental slope. The model indicates that the observed shift in salinity was probably caused by a shift in forcing that affected all of the shelf-sea sources of freshwater together. The shift appears to have been underway by 1986, consistent with observations of shifting sea ice drift, that show a change beginning at about [Pfirman et al., 2004], and shifts in nutrient concentrations analyzed by Swift et al. [2005]. As the freshwater distribution in the Arctic Ocean has changed, the salinity of the surface waters of the Eurasian Basin have increased and the halocline has been weakened (Figure 5b versus Figure 5a) Freshwater Exports to the Nordic and Labrador Seas [17] Fram Strait and the CAA are the exit routes for the dye tracers, to the Greenland Sea and Baffin Bay, respectively. While driven by the spinup forcing, the minimum transit time for Eurasian river tracers to reach the southern side of Fram Strait was between 7 and 14 years. The Barents Sea dyes arrived ahead of the Siberian ones, a pattern also seen in observations of sea ice export from those regions [Pfirman et al., 2004]. Eurasian runoff dyes first reach Baffin Bay through the CAA after about 17 years, when driven by the spinup forcing. Mackenzie River tracer reaches both Baffin Bay and the GIN seas after about 6 to 7 years. During the spinup, the BSI tracer is entrained in the Beaufort Gyre and does not exit the Arctic in significant volume. [18] Once the tracers begin flowing through the CAA, there is a significant annual cycle in outflow: strong in the spring, and stagnating in the fall. This cycle appears to be driven by a corresponding cycle in the sea surface height (SSH) over the Arctic shelves. In the fall, the modeled SSH over the shelves north of the CAA is low, the pressure gradient between the Arctic and Baffin Bay is small, and so is the outflow. The outflow picks up in winter, when SSH begins climbing, but temperatures are still low, and the icepack is still solidly in place. Thus (at least in the model) liquid freshwater outflow through the CAA is modulated primarily by SSH gradients. [19] After the spinup, under the influence of wind fields, freshwater fluxes through both the CAA and Fram Strait grow dramatically. Fluxes along the east and west sides of Greenland are comparable, although the CAA flux is somewhat larger. Mackenzie runoff reaches the CAA before any other freshwater signals. As the model spins up, Laptev and Kara Sea runoff join in approximately equal fractions. On the east side of Greenland, the Eurasian rivers and BSI both contribute significantly to the outflow. [20] For those dyes which have come to a quasi-steady volume (i.e., which are no longer spinning up ) during the run, we can estimate the mean transit time by dividing the influx into the volume of the dye in the Arctic part of the model domain. For the Mackenzie and the Yenisey the estimates are 19 and 24 years, respectively. The volume of the other dyes in the Arctic had not leveled off by the end of the simulation, indicating longer mean residence times. By the end of the run, the total volume of dye in the Arctic 8of15

9 Figure 7. Sea level pressure difference in hpa, average field minus average field. Ocean from river sources and from the Bering Strait Inflow would represent a combined freshwater content of about 110,000 km 3, larger than Aagard and Carmack s [1989] estimate of 80,000 km 3. Although the freshwater dyes in the model have leveled off, it is not known from this simulation what their steady state volumes would be. However, most of the excess, relative to observations, is accounted for by the very high concentrations of dye near the input regions Wind Stress and Sea Surface Height Changes [21] In this experiment, the interannual variability of the surface buoyancy forcing is damped as the surface salinity and temperature fields are relaxed to their climatological values. As a result, the interannual changes were primarily driven by interannual variations in the wind field Changes in the Winds [22] To assess the trend in winds associated with the major shift in model ocean currents in the late 1980s, we considered the difference in the 3-year averaged wind fields between the beginning of the run and the early 1990s. Figure 7 is the difference field between the average SLP and the average SLP. The pattern entails a meridional shift of sea level pressure (SLP): toward high SLP at midlatitudes and lower SLP over the Arctic. This increase in the pressure gradient between the middle and high latitudes corresponds to an eastward acceleration of the surface winds. The strength of the pressure anomaly between the mid- and high latitudes is between 5 and 7 mb, which would correspond roughly to a 3 m/s eastward increase in the average zonal winds over the Arctic. Comparing the difference pattern with the averaged field, one can see that spatial SLP gradients in the difference are of the same order of magnitude as the spatial gradients in the 3-year average at the beginning of the experiment. The change in average wind stress during the model run is comparable to the average stress fields themselves. It is thus not surprising that the surface currents and the distribution of dyes underwent a major reorganization. The pattern of the difference is similar to the anomaly pattern associated with the North Atlantic Oscillation (NAO) as well as to the first EOF pattern of Northern Hemisphere (NH) SLP, the Northern Annular Mode (NAM) [Thompson and Wallace, 1998; Thompson et al., 2000; Hurrell et al., 2003; Newton et al., 2006] SSH Changes [23] Low SLP and increased westerlies corresponds to an increase in the input of positive vorticity to the Arctic Ocean. The expected response of the surface ocean is increased divergence and a shift in mass from the central Arctic to the peripheral seas [Newton et al., 2006]. Divergence at the center leads to convergence along the coasts, and should be visible in the SSH field there. Proshutinsky et al. [2001, 2004] have looked for such a response in the tidegauge data from the Eurasian side of the Arctic, and have found a general trend toward higher sea level. However, it is not simple to disentangle SSH contributions from convergence, density and the inverse barometer effect (elevation of sea level as the weight of the overlying atmospheric drops). In the model, interannual SSH changes could be definitively identified as a result of divergence or convergence driven by changes in wind stress (the model does not include the inverse barometer effect and interannual density changes in the simulation were too small to account for the simulated changes in SSH gradients). [24] Figure 8a shows the model s annual average SSH field at the end of the spinup; Figure 8b shows the difference between that and the 1993 annual average. SSH has dropped throughout the Central Arctic, by up to 10 cm in a region straddling the northern Lomonosov Ridge. Figure 8c shows the time series of SSH over the center of action (the dark blue region in Figure 8b). There is an annual cycle of about 2 cm, and a drop of about 11 cm between 1980 and Over the shelves, the SSH rose by an average of about 2.5 cm. The rise is uneven, and in some small shelf areas in the European sector, it even drops, while in broad areas of the Siberian and Canadian coastal seas it reaches 5 cm. At the northern end of McClure Strait, an entrance to the CAA, the rise is over 10 cm. These changes in SSH along the Siberian coasts are in the same direction, and of the same order of magnitude, as the observations from the Proshutinsky et al. [2004] study. They also agree well with an analytic model by Newton et al. [2006] that links changes in SSH to annular mode anomalies in the atmosphere and changes in freshwater export from the Arctic Ocean to the Nordic Seas. [25] The outward shift of water from the Central Arctic increased the annually averaged sea-surface tilt within the shelf seas as well. Figure 9 shows the annual cycle of the modeled SSH rise from the outer edge of the Laptev Sea to the Lena River delta, a distance of about 400 km. The SSH slope is shown for the whole run (black), for (blue) and for (red). The shape of the annual cycle has not changed noticeably, but the annual mean tilt has risen, as water was driven from the central Arctic toward the coastline. The increased tilt accelerates currents toward the east, which is important in increasing the export of river runoff from the Laptev Sea to the East Siberian, and shifting runoff plumes from the Lomonosov Ridge to the Canadian Basin SSH at the Shelf Break [26] To investigate the behavior of the SSH across the continental slope region, we averaged the model s monthly 9of15

10 Figure 8. Model sea-surface heights, in centimeters. (a) Average sea-surface height (SSH) field for 1979, at the end of the model spinup. (b) Difference between the annual-average SSH fields for 1993 and (c) Time series of SSH averaged over the SSH low in the difference field (white box in Figure 8b). average SSH over the upper slope (between the 180 m and 700 m bathymetric contours.) In Figures 10a and 10b the horizontal axis is simulation time, and the vertical axis is longitude. The color contours show the average SSH over the upper continental slope going around the Arctic basin. Figure 10a shows the annual cycle and Figure 10b shows the monthly time series with the mean and annual cycle removed. [27] Between 1983 and 1995 SSH over the upper slope rises with a trend of about 0.5 cm per year. In late 1994 and into 1995, the model exhibited an extreme anomaly of water mass impinging on the upper part of the continental slope. In the atmospheric data, this year was notable for the highest NAM index of the past century. Detailed monitoring of ice export between 1990 and 1997 indicates that in the export was 1.9 times the average of the other years in that period. Data for other decades is less precise, but it appears that the season may have been the largest ice-export year since 1933 [Vinje et al., 1998]. The model indicates that the same winds driving ice from the Arctic also set up sea-surface tilt that forced the export of large volumes of freshwater from the Arctic into the Nordic Seas and Baffin Bay. (See, also, Karcher et al. [2005] and Newton et al. [2006] for discussion of this mechanism.) After 1995, the SSH levels off, and begins to decline at all longitudes. This decline may signal a return 10 of 15

11 Figure 9. Annual cycle of the tilt of the sea-surface height between the Arctic Ocean and the coastline of the Laptev Sea, from the model run. Red, average monthly values over 3 years, ; black, average monthly values for the entire run: ; blue, average monthly values for to less cyclonic conditions in the Arctic, which would be consistent with the observed drop in the NAM and with published assertions that the Arctic Oscillation was returning toward an anticyclonic regime [Johnson et al., 1999; Maslowski et al., 2001]. [28] The area around 130 E to 150 E, north of the New Siberian Islands, is a topographic saddle where the Lomonosov Ridge joins the continental shelf. The saddle geometry creates the most extreme annual cycle of SSH around the Arctic continental slope (see Figure 10a). In a previous article [Maslowski et al., 2000] we showed that the modeled increase in SSH tilt at this location drove increased transport of Atlantic water across the ridge, strongly enhancing the Atlantic influence in the Makarov Basin. The increased tilt is also a factor in shifting river runoff eastward at the surface by changing the focus of shelf-basin exchange. Figure 10b shows that in the early part of the run there is a relative highstand at this longitude, which implies strong on- and off-shelf transport near the eastern and western sides of the saddle, respectively. By the end of the run, the pattern in this region has changed to onshore forcing from about 110 E to 130 E, and offshore forcing at about 140 E, corresponding to an eastward shift in the outflow of Laptev Seawaters. [29] From 130 W to about 90 W is the entrance to the Northwest Passage, a primary route for freshwater leaving the Central Arctic for Baffin Bay and the Labrador Sea. Here, in the early years of the run, sea-surface height generally slopes upward to the east. At about 1982, however, the tilt changes, and through the remainder of the run SSH slopes generally downward toward the East. This shift implies a change in geostrophic balance from northward to southward flow. As a result, during the spinup and early years of the experiment the tracer north of the CAA is driven away from the Passage and recirculates in the Arctic. After about 1982, it is drawn onto the shelf and into the CAA. These changes in sea-surface tilt, the associated changes in alongshore and offshore currents, and the resulting Figure 10. Sea-surface height averaged over the Arctic shelf break (between 180-m and 700-m isobaths). Vertical coordinate is longitude around the Arctic basin; horizontal coordinate is time. SSH values are in centimeters. (a) Annual cycle, averaged for each longitude throughout the model run. (b) Time series for each longitude with the model annual cycle and mean removed. 11 of 15

12 Figure 11. Salinity within 200 km of the Lena River Delta. Blue, Levitus climatology; black, NAME level 1 (upper 25 m); red circles, observations. redistribution of freshwater are driven by changes in the large-scale wind stresses applied to the model River Inflow Regions [30] Near the river mouths dye concentration in the model sometimes exceeded 100%. Apparently, the dye, which is delivered in volumes equal to the observed river runoff, was not swept out of the input patches as quickly as the runoff is in the real ocean. Once the model was spun up, the flux of runoff dye away from the input regions was realistic and the distribution pattern on the shelves was similar too, albeit at higher concentrations than, the observed runoff plumes over the Arctic shelves [e.g., Weingartner et al., 1999]. Consistent with observations, the runoff plumes exit the shelf at a few discrete locations, particularly at topographic features such as the Lomonosov Ridge or the North Wind Rise, and the models ability to model this behavior is good (see Figure 3). In addition, over the deep Arctic basin the range of runoff concentrations (from several to approximately 25 percent) agrees well with the observed range of meteoric waters there. Thus, we believe that the large-scale distributions and variability in the model, described in sections above, were not greatly affected by the high concentrations near the rivers. Nonetheless, the model behavior raises some issues with respect to modeling river inflows to large-scale GCMs that bear discussion. [31] GCM experiments that do not resolve the Rossby radius face a practical question in setting the boundary condition for freshwater flux at river fronts. Accurate representation of coastal plumes requires replication of the observed salinity near the coast, whereas to study residence times and reservoirs of freshwater, it is important to add a realistic volume of freshwater at the source. In the real world, these two goals converge, but for large domains the grid scales of the model and the forcing fields create a conflict between these two goals. In the Arctic, where peak runoff volumes can be several tens of thousands of cubic meters per second, the SSH tilts can reach tens of centimeters over an offshore distance of several km, driving geostrophic flow over 2 m s 1. To explicitly model these plumes would require horizontal resolution of the Rossby radius (about 4 km in the Arctic) and vertical resolution vertical resolution of the freshwater lens, typically less than 10 m thick at the peak of the runoff season. [MacDonald et al., 1989; Carmack et al., 1989; MacDonald and Carmack, 1991; Garvine, 1995; Yankovsky and Chapman, 1997; Garvine, 1999; Golovin et al., 1999; Chandrasekher and Garvine, 2002]. [32] Smoothing the freshwater gradient diminishes the SSH slope, leading to broader, more sluggish coastal currents. Plume geometry near the inflow has been shown to depend on two non-dimensional dynamical parameters [Garvine, 1995; Yankovsky and Chapman, 1997; Garvine, 1999; Chandrasekher and Garvine, 2002]: The inflow Rossby number, R i =v i /fl = M i /fl 2 H 0 ; and the Burger number S = (L D /L) 2 =g(dr/r)h 0 /(fl) 2 ; where L is the width of the inflow, L D the first internal Rossby radius, v i the velocity of the inflow normal to the boundary, M i the mass influx, dr the density difference between the plume and the shelf waters, r the shelf-water density, H 0 the depth of the inflow, and f the Coriolis parameter. In the real Arctic, 0.14 <R i < 2.2 and 0.75 < S < 4.5. In the model, on the other hand, R I < and S < The implication is that neither the momentum flux nor the buoyancy gradient established by the plumes is dynamically significant near the modeled river mouths. For the inflow Rossby number to be realistic, the model grid would have to resolve the Rossby radius. For the Burger number to be realistic, the inflow patches would have to be significantly smaller, and the salinity gradient between the inflow plume and the ambient waters would have to be between 12 and 24 psu. [33] We consider the Lena River delta and surrounding Laptev Sea as an example. Observations show that within about 40 km of the Lena delta, the surface salinity drops from about 10 psu in May to less than 1 psu after the spring flood in June [Golovin et al., 1999]. At about 200 km from the delta, the salinities are higher, but the differences between early spring (March April) and late summer (August September) salinity at 5 m are also about 10 psu (red dots in Figure 11) ( ANWAP/LaptevSalinity5seasons.html). [34] Along the model Lena delta, salinities drop by about 5 psu between their peak values in spring and their lowest in summer. Over the whole Laptev Sea, the average difference between model winter and summer salinities is less than 2 psu. In part, the model-data difference is caused by the thickness of the upper layer of the model, 20 m, whereas near the real Lena River the summer mixed layer is about 10 m thick [Golovin et al., 1999]. Spreading the water instantly over a layer 2 times as thick as the observed surface mixed layer, will lead to a salinity anomaly only 40 percent as large. In the case of the model river runoff, which was injected into the top two layers, the impact of the fresh water flux on salinity would be about 22 percent of what it would be on a layer 10 m thick. If the summer mixed layer is not resolved, one can structure the model boundary conditions to achieve either realistic freshwater fluxes, or realistic salinity cycles, but not both. (In the current experiment, both the active freshwater and passive dye tracer fluxes to the input patches were scaled to the observed runoff.) [35] Coarse vertical resolution also interacted with the relaxation term: ds/dt = (L-s)/t, where L is the climatological value, s the model salinity, and t the time constant in 12 of 15

13 [39] Taking w = 1 cycle/year and S 0 = 0 (ignoring the transient part of the solution) we have St ðþ¼a cosðþ= t 1 þ t 2 þ tsinðþ= t 1 þ t 2 ; where A is the amplitude of the annual cycle. The solution is both damped and lagged as a function of t. Figure 12 shows the Levitus annual cycle (averaged over the Laptev) and the salinity resulting from relaxation to Levitus with a 120-d time constant. The lag and damping are similar to the model-levitus contrast in the NAME model run. Figure 12. Solution for a simple relaxation to Levitus with a 120-d relaxation timescale. seconds. The freshwater flux calculated for a given difference in salinity in a 20-m surface layer would be twice that required for the same change, ds/dt, in a 10-m summer mixed layer. [36] Near the Lena River, the Levitus salinity is higher than the model salinity for all seasons, so the relaxation term mimics evaporation there. The Levitus data also appears to be higher than observations along the coast for all seasons (Figure 11), which is due to the coarse Levitus grid and under-sampling in the region. The model salinities fall within the observations, though the model response is muted relative to Levitus, which itself is more muted than the observations. [37] Translating the relaxation term, ds, into freshwater flux: F = (ds*v)/(s + ds), where V is the volume of a model surface grid cell, shows that within about 200 km of the Lena delta, the relaxation term was always positive (evaporation) ranging from 30,000 to 90,000 m 3 /s of freshwater, with an average of slightly over 60,000 m 3 /s for the year. Interannual variability was noticeable, but small relative to the annual cycle. The annual cycle amplitude (60,000 m 3 /s) is comparable to that of the Lena River input (about 75,000 m 3 /s). [38] There is about a 2-month phase lag between the Levitus surface salinity and the model annual cycles over the Laptev Sea; Levitus values are high in February March, and low in July August, while the model values peak in May June and are lowest in August September. We hypothesize that the lag is a result of forcing by the annual cycle in the relaxation term. If we idealize the Levitus annual cycle as a sinusoid, the relaxation equation can be integrated analytically, ds=dt ¼ ðlðþ St t Þ=t; ds=dt ¼ ðacosðwtþ St ðþþ=t; St ðþ¼s 0 expð t=tþþ A= 1=t þ w 2 t ðcosðwtþ=t þ wsinðwtþþ : 4. Summary and Conclusion [40] Modeled dye plumes, representing the distribution of river runoff and Bering Strait inflow, tend to follow topographic features. They remain trapped over the shelves, and tend to exit from the shelves along saddles where the Lomonosov or Mendeleyev Ridges meet the continental shelf. Under repeated 1979 forcing, the plumes follow the Lomonosov Ridge north, but in response to changing wind patterns, associated with a trend toward positive values of the NAO, the plumes shift into the Canadian Basin. The change in location of the plumes is traced to changes in seasurface height (SSH) as a result of Ekman transport associated with shifts in the large-scale wind patterns. As the NAO moves to a more positive state, the SSH tilt from the central Arctic toward the coasts becomes steeper, creating a pressure gradient that drives the freshwater further east along the continental shelf. Over the Arctic Ocean, a major reorganization takes place between about 1986 and 1989 with the freshwater plumes shifting from the Lomonosov Ridge to the Mendeleyev Ridge. The model conforms to observations of changing ice flow, based on satellite data [Maslowski et al., 2001] and isotope-based provenance studies [Pfirman et al., 2004]. It also agrees with observed eastward shifts in water properties, and indicates that these shifts likely began several years earlier than they were observed. [41] Early in the model run, relatively little runoff or Bering Strait inflow exits the Arctic through Fram Strait or the Canadian Arctic Archipelago. As the large-scale ocean circulation shifts in the mid-1980s, the freshwater export to the North Atlantic increases. The changes in export also result from changes in the SSH driven by wind stress changes. In late 1994 and 1995 both the modeled SSH tilt from basin to the coast and the modeled freshwater export to the North Atlantic peak. This implies that BSI and river runoff residence times in the Arctic depend on the largescale wind stress regime [see also Karcher et al., 2005]. [42] Dynamics near the river inflows are sluggish as a result of the coarse resolution, vertical as well as horizontal. The salinity cycle there is also damped and phase lagged, probably as a result of relaxation to the climatology, which itself underestimates the annual cycle. The problem is inherent in coarse resolution models driven by relaxation, and will require higher resolution and/or improved forcing data sets to be corrected. [43] Several of the changes in freshwater distribution and fluxes during the model run (the SSH tilts, the eastward shift of concentrations along the shelves, the redistribution of the plume crossing the central Arctic, the shifting pattern 13 of 15