modelling hydrographic changes in the labrador sea over the past five decades

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ModellinghydrographicchangesintheLabradorSeaoverthepastfivedecades

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Progress in Oceanography 73 (2007) 406–426

www.elsevier.com/locate/pocean

Progress inOceanography

Modelling hydrographic changes in the Labrador sea overthe past five decades

Youyu Lu *, Daniel G. Wright, Igor Yashayaev

Ocean Sciences Division, Department of Fisheries and Oceans, Bedford Institute of Oceanography, Dartmouth,

Nova Scotia, Canada B2Y 4A2

Received 1 April 2005; accepted 28 February 2007Available online 4 May 2007

Abstract

Inter-annual to inter-decadal changes of hydrographic structure and circulation in the subpolar North Atlantic arestudied using a coarse resolution ocean circulation model. The study covers 1949 through 2001, inclusive. A ‘‘time-meanstate nudging’’ method is applied to assimilate the observed hydrographic climatology into the model. The method signif-icantly reduces model biases in the long-term mean distribution of temperature and salinity, which commonly exist incoarse-resolution ocean models. By reducing the time-mean biases we also significantly improve the model’s representationof inter-annual to inter-decadal variations. In the central Labrador Sea, the model broadly reproduces the heat and saltvariations of the Labrador Sea Water (LSW) as revealed by hydrographic observations. Model sensitivity experiments con-firm that the low-frequency hydrographic changes in the central Labrador Sea are closely related to changes in the intensityand depth of deep convection. Changes in surface heat flux associated with the winter North Atlantic Oscillation (NAO)index play a major role in driving the changes in T–S and sea surface height (SSH). Changes in wind stress play a secondaryrole in driving these changes but are important in driving the changes in the depth-integrated circulation. The total changesin both SSH and depth-integrated circulation are almost a linear combination of the separate influences of variable buoy-ancy and momentum fluxes.Crown Copyright � 2007 Published by Elsevier Ltd. All rights reserved.

1. Introduction

The subpolar North Atlantic (NA) plays important roles in global ocean climate dynamics. This is a regionof intense atmosphere–ocean interaction that ultimately results in deep convective mixing, particularly in theLabrador Sea. Strong boundary flows redistribute heat, salt and fresh water within the region and contributeto the global transports of these properties. Surface waters that are mixed downward in the Labrador Seacombine with deep overflows from the Nordic Seas and contribute to the lower limb of the global ocean merid-ional overturning circulation (MOC). Thus, changes in water mass properties within this region can haveimpacts far beyond the subpolar basins of the NA.

doi:10.1016/j.pocean.2007.02.007

* Corresponding author.E-mail address: LuY@mar.dfo-mpo.gc.ca (Y. Lu).

Y. Lu et al. / Progress in Oceanography 73 (2007) 406–426 407

Hydrographic observations have revealed that the subpolar NA undergoes prominent changes at inter-annual to inter-decadal time scales (Dickson et al., 2002, 2003; Yashayaev et al., 2003; Bersch, 2002). Theselow-frequency variations in hydrography are believed to be strongly influenced by large-scale changes in atmo-spheric forcing that are significantly correlated with the North Atlantic Oscillation (NAO) index (e.g., Visbecket al., 2003). The changes must also be influenced by ocean dynamics and internal ocean variability, but theexact nature of these influences is not well established.

In this study, we examine the hydrographic changes in the subpolar NA based on numerical ocean modelsimulations. The simulations cover more than five decades from 1949 to 2001, inclusive. During this period ofmodel simulation, surface forcing fields are available from atmospheric reanalysis and hydrographic changesare well documented in ocean observations. Our primary objective is to gain further insight into some rela-tively straightforward questions that would be difficult, if not impossible, to answer from ocean observationsalone. First, we acknowledge the likely influence of long-term changes in Arctic conditions on the subpolarAtlantic south of the high-latitude ridge systems, but we ask ‘‘how much of the variability observed in the Lab-rador Sea can be reproduced without allowing for this influence?’’ We then ask a series of related questions tofurther define the processes controlling the variability observed in the Labrador Sea. How sensitive are themodel results to realistic changes in the surface salinity? How much of the observed variability in the LabradorSea can be reproduced if all variability outside of the Labrador Sea is damped? And what are the relative con-tributions of changes in surface momentum and buoyancy fluxes to the observed variability?

For this investigation, we use a well-tested ocean model that is driven by a widely used surface forcing prod-uct. The model resolution is coarse and the effects of meso-scale eddies have to be parameterized. We achievesubstantial progress in modelling natural variability by using a technique in which the observed long-termmean hydrography is used to constrain the time-mean state of the model solution through a specialized nudg-ing method. This simple data assimilation technique significantly reduces the long-term mean model biasestypically present in coarse-resolution ocean model simulations. Our attention is thus focused on the time-var-iability in the model solution which is also improved through removing the time-mean biases. The analyseswill show that the model is able to reproduce a significant portion of the observed hydrographic variability,and the unexplained portion will be less ambiguously related to certain aspects that are misrepresented by themodel. After discussing the model-simulated changes in hydrography, we shall extend the analysis to the cor-responding changes in the circulation and sea surface heights, again focusing on the subpolar NA.

2. Model setup

Version 2.0.1 of the Parallel Ocean Program (POP; Maltrued et al., 1998; Smith et al., 2000) is configuredfor the Atlantic Ocean from 30�S to 70�N. The horizontal resolution is 1� in longitude and 1�cos/ in latitude,where / is the latitude. The vertical dimension is discretized using 23 geopotential levels, with level thicknessesincreasing from 10 m at the surface to 500 m near the bottom. Horizontal mixing is parameterized by bihar-monic diffusion. Maximum momentum and tracer mixing occur at the southern boundary of the model wherethe mixing coefficients are set at �2.5 · 1022 m4 s�1 and �2.5 · 1020 m4 s�1, respectively, and they decreaseaway from the equator with the fourth power of the horizontal grid spacing so that the CFL number associ-ated with horizontal diffusion is approximately uniform over the model domain. The effects of meso-scaleeddies are parameterized by a variation of the scheme proposed by Gent and McWilliams (1990), with theeddy-induced advection velocity implemented in the momentum rather than in tracer equations. We usethe KPP mixing scheme of Large et al. (1994), which is a standard option in POP, to parameterize unresolvedvertical mixing processes. All the lateral boundaries are closed for velocity. ‘‘Sponge layers’’, where the modeltemperature and salinity are strongly restored to monthly climatology, are used near the artificially closed lat-eral boundaries to crudely account for watermass transformations that occur outside of the model domain andalso to avoid unrealistic T–S properties associated with the anomalous local circulation induced by the closedboundaries. This approach is used near the northern and southern boundaries, where the inverse of the restor-ing time decreases from 1/(6 days) right at the boundary to zero over 5� of latitude away from the boundary.Note that the strong restoring to climatology in the sponge layers suppresses the local hydrographic changesthat are generated within the model domain, and also neglects the influence of low-frequency variability gen-erated outside of the model domain on model solutions.

408 Y. Lu et al. / Progress in Oceanography 73 (2007) 406–426

Surface momentum, heat and freshwater fluxes are obtained from the NCAR/NCEP reanalysis (Kalnayet al., 1996). The monthly surface fluxes for 1949–2001 drive all simulations except the spin-up, which is drivenby the monthly climatology derived from the 52-year monthly data. All model simulations are initiated after acommon 50-year spin-up, which is initiated with zero flow and the climatology values of temperature andsalinity in January. A time-mean state nudging approach (explained below) is applied to reduce time-meanbiases in temperature and salinity that are generally found in this type of model. The surface heat flux is for-mulated according to Barnier et al. (1995),

Fig. 1.showbenchm

Q ¼ Qnet þ oQ=oT sðT smod � T s

obsÞ; ð1Þ

mod is the modelled surface temperature and T sobs is

the observed surface temperature included in the NCAR/NCEP reanalysis product. The surface salinityboundary condition is the virtual salt flux calculated from the rates of evaporation minus precipitation(E � P), plus a restoring to observational estimates of the surface salinity using a restoring time of 30 days.The observed surface salinities used in the benchmark run discussed in the following sections is based onmonthly climatological values, plus observational estimates of annual anomalies. All observed surface salin-ities are derived from the hydrography datasets archived at the Bedford Institute of Oceanography.

First, we consider the results of a conventional prognostic run. The thin solid lines in Fig. 1 show verticalprofiles of the model’s potential temperature and salinity in the central Labrador Sea averaged over 52 years ofmodel integration. For comparison, the dashed lines show the profiles of the annual mean climatological val-ues. Clearly, the simulated properties are much too warm and saline in the upper 2 km of the water column.Indeed, the salinity bias is such that the vertical gradient below 100 m is actually of the opposite sign to whatthe annual mean profile has ever been observed to be. Treguier et al. (2005) report that such biases in thewatermass properties of the Labrador Sea, especially salinity, exist even in the solutions of models with highresolution. They list a number of reasons for this ‘‘salinization’’ bias. These include the lack of fresh waterinput from sea ice melting and river runoff, unrealistic representation of the East Greenland Coastal Currentor its hydrographic condition, and unrealistic transport of salty water above or around Reykjanes Ridge.Although the temperature and salinity biases tend to be density compensating so that the vertical profile ofpotential density is not unreasonable, it must be admitted that such biases are a concern for studies ofinter-annual and inter-decadal variability, particularly in a region where convective mixing is expected to playan important role in determining the variability. The bias in salinity will be particularly critical when consid-ering the variability associated with vertical mixing; any increase in entrainment of the deep waters into theintermediate or shallow waters will result in a slight decrease in salinity of the upper waters, whereas in thereal ocean the salinity tendency is larger and of the opposite sign. The prognostic simulation obtains qualita-tively reasonable inter-annual and longer time scale variations in T–S but with very substantially reduced mag-nitudes compared with T–S observations in the central Labrador Sea, especially for salinity (Section 3).

Vertical distribution of potential temperature (left panel) and salinity (right panel) in the central Labrador Sea. The dashed curvesthe annual mean climatology determined from the observations. Thicker solid curves are the long-term mean solution of the

ark nudged run and thinner solid curves are from the prognostic run.

The biases in the model’s long-term mean temperature and salinity fields can be substantially reduced byusing a ‘‘time-mean state nudging’’ method to assimilate available information on the observed T–S climatol-ogy (Fig. 1). The method, a simplification of the ‘‘spectral nudging’’ method introduced by Thompson et al.(2006), is formulated as

xt ¼ Dþ ð�c� �xÞ=s; ð2Þ

�xt ¼ ðx� �xÞ=sf : ð3Þ

Note that through the above approach, time-independent nudging terms are determined and these do notdirectly force time variations other than acting to counter-balance the unrealistic long-term drift in the prog-nostic simulation. The resulting improved mean state of temperature and salinity leads to more accurate mod-elling of the tracer fluxes due to advection, diffusion and convection, which may further improve thesimulation of time-variability. In the following section, we show that temporal variations in salinity are sig-nificantly improved by applying the time-mean state nudging method. The improvement is not obvious forthe variability of temperature. Thus, it seems that the major deficiency of the prognostic model, comparedwith the nudged model, is the bias in time-mean salinity due to the errors in the freshwater fluxes as pointedout by Treguier et al. (2005). If errors in freshwater fluxes are corrected, the prognostic model might possesssimilar skill to the nudged model in simulating the time-variability of T–S in the Labrador Sea. We anticipatethat constraining the time-mean state will lead to some improvements in the variability of surface freshwaterflux (Section 4), but we emphasize that realistic variability in the tracer fluxes through the lateral open bound-aries is not presented. Indeed, any variability induced by changes in the fluxes through open boundaries is sup-pressed by our use of sponge layers with restoring to climatological conditions. This effect will be discussedfurther in Section 3 with referenced to the underestimation of deep T–S changes by the model.

Finally, it is of interest to note that the model biases shown in Fig. 1 for temperature at depths between 1.5and 2 km are actually increased when nudging is applied. Although the region below 1500 m where this occurswas not nudged in the present case, it is of interest that forcing the model to agree more closely with obser-vations in one region can result in increased discrepancies in other locations. Increased disagreement can evenoccur within a region that is nudged. This is undoubtedly in part due to model limitations, but it should alsobe noted that observation errors can play a role. As a relevant example, we note that the climatology used here(as well as all other known climatologies) was based on recent and historical hydrographic data covering atleast five decades. During different time periods, different regions or lines are more heavily observed and whenthese data are combined as if they are contemporaneous, the result is that temporal variations can be misin-terpreted as spatial variations (a method to reduce this problem will be considered in future work). With this inmind, it is easy to see how nudging towards a specified climatology can result in unrealistic density gradients.These in turn can result in artificial currents that advect water masses along a path that is inconsistent witheither the model or real world dynamics. Thus, errors in advection velocity may subsequently induce errorsin both the time-mean and the time-variability of the tracers. The degree of improvement achieved by nudgingdepends critically on the quality of the climatology.

Note that we have nudged on a time scale that varies between 30 days near surface and 270 days at 1500 m sothat the model is not forced to agree with discrepancies that can be removed over shorter time intervals. This can

be particularly important for the removal of small scale inconsistencies due to observational errors or inconsis-tencies between the real topography and its coarsely discretized version used in the model. Also, some spatialsmoothing was applied to the nudging terms before using them. This reduces the nudging of the smallest scaleswhere the largest uncertainties occur. In our case, we have applied two passes of a 2D spatial filter with weights(1,2,1; 2,4,2; 1,2,1) to suppress nudging on very small spatial scales (land points were, of course, excluded).

In summary, in applying the POP model to the North Atlantic we include three different restoring or nudg-ing techniques to constrain the modelled T–S fields. Conventional nudging is applied in the sponge layers toaccount for watermass transformations that occur outside of the model domain. Without strong restoring nearthe lateral boundaries that are artificially closed for velocity, the T–S fields can become very different fromclimatology due to unrealistic vertical and horizontal velocities. Conventional restoring is also applied inthe formulation of surface heat and virtual salt fluxes. This surface restoring acts to account for errors in sur-face fluxes (Section 4, Figs. 9 and 10). Finally, the time-mean state nudging method is applied within the mod-el’s interior. This modified nudging method significantly reduces the biases in the time-mean state of the modeland can also improve the simulation of time variations in T–S and circulation.

3. Hydrographic changes in the Labrador Sea

Fig. 2 compares the model-simulated and observed changes in potential temperature and salinity (T–S) inthe central Labrador Sea during 1949–2001. The observed T–S (panels b, d) were composed from all available

Fig. 2. Time series of the vertical distribution of potential temperature (a, b) and salinity (c, d) in the central Labrador Sea, obtained fromthe benchmark nudged simulation (a, c) and compiled from hydrographic observations (b, d). The model results are averaged for June–July of each year. In panels (b, d) the dashed lines denote isopycnic surfaces and the white blocks at the bottom indicate a period wheredata are not available.

data collected for water depths greater than 3400 m within 200 km of 57�40 0N; 51�30 0W (representative ofocean weather station Bravo). The model results (panels a, c) are obtained from the benchmark nudgedrun discussed in the previous section. For comparison with observational data, which are biased to late springand early summer, the model-simulated T–S are averaged over June–July of each year. Fig. 2 reveals reason-able agreement between the variability in the model results and observations. The agreement is remarkableconsidering that the model solution does not include the influence of changes in the Arctic except throughthe surface boundary conditions. The model reproduces many of the observed changes in the upper 2000 mof the water column, which is strongly affected by formation and subsequent departure/dissipation of the Lab-rador Sea Water (LSW). Both observations and model results show relatively warm LSW from the late 1950sto early 1970s and cooling and freshening events in the 1970s, in the mid-1980s, and over the first half of the1990s. The coldest LSW occurs in the mid-1990s and warming occurs in the late 1990s. Note that there is adata gap during the period 1982–1986 so the observational estimates during this period have large uncertain-ties associated with them.

There are, however, some notable discrepancies between the model solution and observations. Most evi-dently, the model fails to reproduce the variations of temperature and salinity below 2000 m, i.e., in the layeroccupied by the Northeast Atlantic Deep Water (NEADW) and the Denmark Strait Overflow Water(DSOW). Clearly, the mechanisms that cause the deep changes in temperature and salinity are not includedin the model. The poor representation of source waters in the model is a likely contributor to this discrepancybecause it seems almost certain that the Arctic influence is critical to this deep variability (Dickson et al.,2002). Also the model grid may be too coarse to properly resolve the processes that control the formationand propagation of the waters in the DWBC (e.g., deep mixing and advection processes involve layers thatare only about 100 m thick, whereas the deep layers in our model are 500 m thick).

Fig. 3 provides another comparison between observed and simulated results that focuses on the remarkabledifference in observed conditions between the mid-1960s and the mid-1990s. These plots show observationsand model results along a section across the Labrador Sea close to the WOCE (World Ocean CirculationExperiment) hydrography section AR7W for 1966 and 1994. Conditions in 1966 are typical of the periodof warm and salty LSW and conditions in 1994 are typical of the colder and fresher LSW period. With regardto the vertical extent of LSW, the model solution agrees quite well with observations. Even with low horizontaland vertical resolutions, the model has captured the major temperature and salinity changes of LSW thatoccurred between 1966 and 1994. The agreement with observations is better for 1966 than for 1994. Observa-tions suggest that in 1994, the cold and fresh LSW spreads extensively across the AR7W section. By contrast,in the model solution the cold and fresh LSW mainly occupies the central and south-western part of the sec-tion. Apparently, the model’s coarse-resolution and possibly the nudging result in a much broader IrmingerCurrent which results in a broader region of warmer and saltier water imported from the Irminger Sea into theLabrador Sea.

With regard to the deep and bottom water masses in Fig. 3, it is obvious that the model does not resolve thedistribution and variability of temperature and salinity in the NEADW and DSOW. One deficiency is that themodel does not reproduce the upward tilting of the deep isotherms and isohalines (and isopycnals) around theouter rim of the Labrador Sea. This indicates that the model has difficulty reproducing the vertical shear in thedeep western boundary current as it passes through the Labrador Sea. This shortcoming of the model is likelydue to inadequate representation of the source waters by the sponge layers used at the northern and southernboundaries, as well as the model’s relatively coarse horizontal and vertical resolutions. The missing upward tilthas a horizontal extent of no more than 200 km and a vertical extent of a few hundred meters, which are notwell-resolved by the model grid.

Another limitation of the model is the fact that long-term changes in temperature and salinity are clearlyunderestimated. Fig. 4 compares the observed time variations of temperature and salinity at 222 m and 1542 mat Bravo with those from three model simulations: the prognostic run, the benchmark nudged run, and a mod-ified nudged run that tests the sensitivity to the surface salinity condition (Section 4). From the 1960s to themid-1990s, observations suggest that salinity at 1542 m decreased by 0.07 psu, whereas the nudged run onlyreproduces about half of this change. There is a similar underestimation in the magnitudes of the temperaturechanges. The large discrepancy between the deep salinity observations and the nudged run occurred after themid-1980s, whereas the discrepancy in the deep temperature occurred during the mid-1960s to mid-1970s. It is

Fig. 3. The distributions of potential temperature (a–d) and salinity (e–h) across the Labrador Sea along the AR7W section. The leftpanels show the solutions of the benchmark nudged model simulation for 1966 (a, e) and 1994 (c, g), averaged for June–July of each year.The right panels show corresponding observations.

Fig. 4. Time variations of T and S at 222 m and 1542 m in the central Labrador Sea derived from observations (open circles connectedwith thinner solid lines) and from three model simulations: the unnudged (prognostic) run (thinner solid lines), the benchmark nudged runincluding the annual anomalies of surface salinity in the restoring part of the surface condition (thicker solid lines), and the nudged runexcluding annual SSS anomalies (dashed lines, almost overlap with the thicker solid lines). The model results are averaged for June–July ofeach year.

not clear whether these discrepancies are due to a model deficiency in estimating the convection strength, orerrors in surface heat and freshwater fluxes or upstream T–S properties. Despite underestimating the variabil-ity at depth, the nudged simulation obtains the correct magnitudes of T–S changes at 222 m in the upper layer.

Fig. 5 shows the steric height (referenced to 2000 dbar) computed from temperature and salinity in the cen-tral Labrador Sea, from both the observations and the benchmark nudged simulation. The figure also showsthe separate steric height contributions associated with temperature and salinity variability. The model under-estimates the variations associated with salinity and temperature by about 50% and 30%, respectively. Due toweaker density compensation in the model than in the observations, the model slightly overestimates thechanges in the total steric height even though it underestimates the changes in both temperature and salinity.Despite this slight overestimation, the inter-annual to inter-decadal changes in the steric height from the modelare very similar to those observed. This suggests that despite the inability of our model to account for allobserved changes in fresh water content, the model quite accurately reproduces the major changes in dynam-ical characteristics (density, steric height) caused by atmospheric forcing (expressed via momentum and heatflux). Even if unable to bring sufficient amounts of fresh water to fully compensate the steric height changeassociated with cooling of the water column, the general effect of salinity on the steric height seen in the modeloutput resembles the observed signal fairly well. We anticipate a further improvement in understanding of thesea level change with inclusion of variable fresh and cold water inflow from the Arctic.

Fig. 6 shows the depth-time variations of potential density at Bravo obtained from the benchmark nudgedsimulation. This figure shows the monthly data hence it well depicts the annual occurrence and intensity of latewinter deep convection as well as the long-term changes. Overlaid on the density variations is the depth ofconvection computed from the monthly averaged density at the grid cell corresponding to the location ofBravo. The maximum value over all 12 months (typically in March) is shown for each year. Note that Bravowas located at the edge rather than at the center of the strongest convection. The time variations of the con-

Fig. 5. Time variations of the steric height anomalies in the central Labrador Sea. The darker solid curves depict the total steric height(referenced to 2000 dbar); the broken and lighter solid curves denote contributions from temperature and salinity, respectively. Top panelshows observations and bottom panel shows the solution of the benchmark nudged run. The model results are averaged for June–July ofeach year.

vection depths are correlated with the winter NAO index (lower panel). That is, convection penetrates deeperduring periods with positive NAO index and shallower during periods of negative NAO index.

Although the model results presented here underestimate the magnitude of long-term T–S changes in LSWas well as the changes in NEADW and DSOW, they do reproduce much of the variability in the hydrographiccharacteristics observed in the Labrador Sea over the past five decades. Since this is achieved without allowingfor any variability in the exchanges with the Arctic or the region south of 30�S, it is clear that the model var-iability is forced entirely by the specified surface fluxes over the model domain. The time-mean state nudging iseffective in correcting errors in the simulation of time-variability due to biases in the time-mean state (partic-ularly in the case of salinity). However, variability in the lateral fluxes from the Arctic are not accounted for inour model results. Improved representation of the exchanges across the northern boundary of the model areclearly called for and improved horizontal and vertical resolution would improve the simulation of transportand mixing processes.

4. Sensitivity studies focused on the Labrador Sea

In this section, we consider several additional model simulations designed to better determine the factorsresponsible for the simulated variability in water mass properties in the Labrador Sea. Our tests includesensitivity to the sea surface salinity used in the restoring component of the surface boundary condition,

Fig. 6. Changes in the potential density and the depth of deep convection at Bravo obtained from the benchmark nudged simulation. Thebottom panel shows the winter NAO index defined as the principle component time series of the leading empirical orthogonal function ofthe Atlantic-sector sea level pressure, based on the NCEP reanalysis product.

low-frequency variations in the water mass properties outside of the Labrador Sea, low-frequency variabilityin the NA circulation, and the relative importance of changes in wind stress versus surface buoyancy forcing.

4.1. Surface salinity

Our first sensitivity test relates to the underestimation of the salinity variations in the model in comparisonwith those observed near Bravo. We wish to determine the sensitivity of the salinity variations produced by themodel at depth to changes in the salinity used in the surface boundary condition. The test run is identical tothe benchmark nudged run discussed in the previous sections except that the restoring term for surface salinityuses the monthly climatological values with the annual anomalies excluded. (Note that inter-annual variationsof the freshwater flux are still contained in the surface salinity boundary condition but nudging will suppressinter-annual variations in surface salinity.) Fig. 4 illustrates the sensitivity of the model-simulated T–S varia-tions at 222 m and 1542 m to the changes in the surface salinity boundary condition. The comparison shows

that including surface salinity anomalies has only a slight impact on salinity changes in the upper layer. Thereis even less influence on salinity variations at depth. And the influence on the temperature variability is evensmaller than the influence on salinity variability. Clearly, the influence is small compared to the discrepancywith observations. The influence of including surface salinity variability can be quantified in terms of the asso-ciated changes in the surface heat and freshwater fluxes, which as shown later (Figs. 9 and 10) are small.

4.2. External preconditioning

The second experiment is designed to investigate how the LSW variability is influenced by low-frequencyT–S variations within the model domain but outside of the Labrador Sea. We refer to this influence as ‘‘exter-nal preconditioning’’ to distinguish it from the ‘‘local’’ preconditioning that represents the memory of whatoccurred locally (i.e., within the Labrador Sea) in previous years. In this experiment the inter-annual varia-tions in water mass properties outside of the Labrador Sea are strongly suppressed. To do this, we have simplyreduced the filter time scale, sf, to 60 days outside of the region containing the Labrador Sea that is boundedby 48�N and 45�W. There is essentially no low-frequency variability in the ‘‘upstream’’ region of the IrmingerSea and the amplitude of the variability ‘‘downstream’’ in the Newfoundland basin is reduced by more than afactor of 5.

The temperature and salinity variations at 1542 m depth at Bravo, obtained with the variability in the prop-erties entering the Labrador Sea suppressed, are shown as broken lines in Fig. 7. It is interesting to see that thevariability in both temperature and salinity are almost unaffected by the suppression of variability in theinflow properties. This result indicates that the model-simulated T–S changes in the Labrador Sea are mostlylocally forced. The rest of the subpolar gyre has little influence probably due to the absence of deep convectionthere. However, a significant influence from the Arctic, which is not included in the present model, cannot beruled out by this experiment. This effect is most likely to be seen at depth, consistent with the fact that ourmodel does a poor job of simulating the observed variability below 2000 m.

4.3. Low-frequency circulation changes

To consider the influence of low-frequency variations in the circulation, we make use of the semi-prognosticmethod introduced by Sheng et al. (2001) to control the circulation while allowing the temperature and salinityto evolve according to the same model dynamics as the simulations discussed above. First, we introduce twoauxiliary tracer variables T* and S* that evolve according to exactly the same advection–diffusion equations asT and S but with modified restoring terms. The form of the nudging terms is the same as described earlier, butthe filter time scale, sf, is reduced to 60 days so that T* and S* are constrained to remain close to the observed

Fig. 7. Time variations of T and S at 1542 m in the central Labrador Sea derived from observations (open circles connected by thinnersolid lines), from the nudged run excluding annual SSS anomalies (thicker solid lines), and from two model sensitivity simulationsdiscussed in Section 4: the run with external T–S variations damped (dashed lines) and the run with variation of circulation damped(thinner solid lines). The model results are averaged for June–July of each year.

climatology. We now use T* and S* in the semi-prognostic approach as if they were observations. That is, wereplace the horizontal component of the pressure gradient in the momentum equations by a weighted mean ofthe gradients of the hydrostatic pressure p (based on T and S) and p* (based on T* and S*):

Fig. 8.and th

rp! rp þ arðp� � pÞ;

The thin lines in Fig. 7 show that strongly damping the circulation variability has little effect on the low-frequency variations of temperature and salinity at Bravo. However, the seasonal and the mean circulationstill play roles in T–S variations because advection determines the rate of drainage of the convectively formedLSW and is an important mechanism in the re-stratification process after winter time convection. The pointthat we wish to make is that convection and advection jointly determine the seasonal variation of watermassproperties in the Labrador Sea, and the low-frequency variations of the LSW are mainly caused by thechanges in the intensity and extent of deep convection. Inter-annual changes in circulation do not have amajor impact on the low-frequency T–S changes in the Labrador Sea, at least to the extent that the modelworks. This result does not exclude the possibility of circulation variations associated with changes in water-mass properties, a point to be discussed in Section 5.

4.4. Wind stress vs buoyancy forcing

Two test runs are conducted to get some indication of the relative importance of the surface momentumflux and the surface buoyancy flux in causing inter-annual variations in water mass properties. First a runis done with NCEP wind stress replaced by the monthly climatological values while the inter-annual variationsin the surface heat and salt fluxes are retained. In the second run, we retain the inter-annual variations in thewind stress, but the formulation of the surface buoyancy fluxes is based on the monthly climatology of heatand freshwater fluxes, sea surface temperature and salinity. We emphasise that we are deliberately making adistinction between variations in winds and variations in wind stress (i.e., surface momentum flux). Of course,inter-annual variations in winds affect the variations in surface heat flux, but we wish to distinguish betweenthe influence that wind variations have through momentum exchanges and through heat exchanges.

Temperature and salinity variations at 1542 m in the central Labrador Sea obtained from both test runs areshown in Fig. 8. The run with monthly wind stress replaced by climatological wind stress gives similar T–Svariations in the Labrador Sea to the results obtained from the run forced by wind stress and heat flux thatinclude inter-annual variations. The direct effect of the low-frequency changes in momentum flux is to cause

Same as Fig. 7 except that the two model sensitivity simulations are the run forced by the climatological wind stress (dashed lines)e run forced by the climatological surface buoyancy flux (thinner solid lines).

changes in circulation. Hence, this test result is consistent with our earlier finding that the T–S variability nearBravo is not sensitive to damping of the low-frequency variations in the circulation.

When inter-annual variations in wind stress are included but with no inter-annual variations in the specifiedsurface heat and salt fluxes or restoring temperature and salinity, the model produces greatly reduced long-term T–S variations in the central Labrador Sea. This result suggests that a dominant role is played by theinter-annual variations in the surface buoyancy flux in causing the low-frequency changes in T–S propertiesin the Labrador Sea. The relative contributions of the surface heat flux versus the freshwater (or salt) fluxto the inter-annual variability of the surface buoyancy flux are addressed in the following subsection.

4.5. A closer look at surface buoyancy flux

The formulation of surface buoyancy fluxes includes a restoring component which is based on the differencebetween the model-simulated and observed surface temperature and salinity. Thus, the actual buoyancy fluxesthrough the air–sea interface must be different among different runs. To some extent, the changes in the buoy-ancy flux due to restoring counteract the impacts of the changes in circulation, preconditioning, wind stresses,etc. However, if those impacts were important in the benchmark run but were erroneously compensated by therestoring term in the sensitivity runs, unrealistic changes would occur in the effective buoyancy fluxes. A closerexamination of the effective buoyancy fluxes is thus necessary to confirm the robustness of the sensitivityexperiments.

Fig. 9 shows the variation of sea surface heat fluxes averaged in winter months (January–March, the periodwhen the strongest convection occurs) and over an area of 400 · 400 km with its center at Bravo. The surfaceheat flux from the benchmark nudged run is very close to that obtained from the NCEP reanalysis. The sur-face restoring only results in a modest contribution (within the uncertainty) to the changes in the net surfaceheat flux experienced by the model. Drastic changes in surface heat flux (and also in surface salt flux, Fig. 10)are only found in the run that includes inter-annual variations of wind stresses but excludes inter-annual vari-ations in the formulation of surface buoyancy fluxes. This run confirms that without the low-frequencychanges in the surface heat flux the model cannot simulate T–S changes in the Labrador Sea. In all of the other

Fig. 9. Time variations of surface heat flux over a 400 · 400 km square surrounding Bravo averaged for the winter months (January–March). In each panel the heat flux obtained from the NCEP reanalysis (thick solid line) is compared with the net heat fluxes from twomodel simulations discussed in Section 4. Negative heat flux indicates a loss of heat from the ocean.

Fig. 10. Time variations of surface freshwater flux over a 400 · 400 km square surrounding Bravo averaged for the winter months(January–March). In each panel the freshwater flux obtained from the NCEP reanalysis (thick solid line) is compared with the netfreshwater fluxes from two model simulations discussed in Section 4.

sensitivity experiments, differences in the effective heat flux compared with the benchmark nudged run amountto �20 to 20 W m�2 at the maximum. The magnitudes of the differences are about 10% of the range of thetotal heat flux changes. Similar T–S changes in the Labrador Sea are obtained for all of these runs with similartime variations in the net surface heat flux.

We have also compared T smod and T s

obs defined in Eq. (1), i.e., the sea surface temperature obtained from thebenchmark simulation and the ‘‘observed’’ values included in the NCEP reanalysis product. We find that thelow-frequency variations of T s

mod are weaker compared with the variations of T sobs during winter, while the two

quantities are comparable during other seasons. Hence, despite the fact that the surface heat flux is modifiedby the restoring, the model still underestimates the low-frequency SST changes in winter. This conclusionholds at Bravo as well as to its west where the most intensive deep convection occurs. The discrepancy in win-ter may be related to the effective restoring time used in Barnier et al.’s formulation (Eq. (1)). The typical valueof 20–30 days in the Labrador Sea allows significant deviation of T s

mod from T sobs in winter when rapid changes

in surface temperature can occur especially associated with deep convection. This discrepancy clearly points tothe need for further improvement of the surface heat fluxes used in the models of the Labrador Sea.

Fig. 10 shows a similar comparison for the surface freshwater fluxes, converted from the virtual salt fluxes.The benchmark nudged run (including annual anomalies of surface salinity in restoring) results in significantchanges in the winter freshwater flux, compared with the P–E obtained from the NCEP reanalysis. Excludingsurface salinity anomalies in surface restoring introduces noticeable changes. Although similarity exists in theinter-annual variations of the fluxes, there is an obvious difference in the time-mean freshwater fluxes. Surfacerestoring leads to increases in the effective surface freshwater flux compared with that obtained from theNCEP reanalysis. During winter the model generally requires adding freshwater at the surface. This modelrequirement of excess precipitation over evaporation is consistent with the finding of Sathiyamoorthy andMoore (2002) from the analysis of meteorological observations at Bravo. On the other hand, the NCEPreanalysis flux generally indicates that precipitation is less than evaporation during winter. This may bedue to the underestimation of the precipitation rate by the NCEP reanalysis. In fact, the analysis of Sathiya-moorthy and Moore (2002) obtains twice the amount of precipitation during the winter months comparedwith the NCEP reanalysis.

A question to ask is whether the changes in the freshwater fluxes due to surface restoring in various runsmake a significant contribution to the changes in the total buoyancy flux. A simple calculation shows that apeak-to-peak change of 1 m yr�1 in freshwater flux only amounts to 13% of the changes in buoyancy fluxcaused by a peak-to-peak change of 300 W m�2 in surface heat flux. Hence, variability in the surface heat fluxdominates the changes in surface buoyancy flux. And, as long as the inter-annual variability in surface heatflux over the Labrador Sea is included, the model is able to reproduce a significant portion of the observedT–S variability. Impacts of the changes in the local surface freshwater flux and the large-scale circulation, pre-conditioning and wind stresses indeed play less significant roles in determining the changes in watermass prop-erties within the Labrador Sea.

5. Barotropic circulation and sea surface height

The sensitivity experiments presented in the previous section reveal that changes in circulation do not havea significant impact on T–S variations in the Labrador Sea, but we do not expect the reverse to hold. In fact,Fig. 5 has already shown that the steric height at Bravo undergoes significant low-frequency changes corre-sponding to changes in hydrography. In this section, we examine changes in subpolar NA circulation andthe sea surface height.

We first show in Fig. 11 the model-simulated pattern of the average subpolar and subtropical gyre circu-lation over 1949–2001, denoted by contours of the barotropic streamfunction (w). The maximum subpolargyre strength reaches 45 Sv. The subpolar gyre circulation exhibits considerable time-variability over this per-iod. Fig. 12 shows the time variations of w at the location of Bravo that represents the volume transport inte-grated from the Labrador coast to the central Labrador Sea. Spectral analysis of the monthly time seriesreveals a red spectrum with energy peaks at seasonal and decadal time scales. The low-frequency variationsare indicated in Fig. 12 by a time series of the annually averaged w. The root-mean-square (rms) value ofthe variability in the annually averaged transport is 2.2 Sv. If the annual means are removed from the unfil-tered results, the rms value of w (representing the seasonal variations) at Bravo is 3.3 Sv. Evidently, the con-tributions of the seasonal and lower-frequency variability to the total variance are close in magnitude.

Fig. 13 shows the model-simulated sea surface height (SSH) variations at Bravo and on the Labrador coastat the south-western end of the WOCE-AR7W section. Changes in SSH at the two locations differ in both

Fig. 11. Time-mean barotropic streamfunction (w) obtained from the benchmark nudged simulation. Contour interval is 5 Sv. Solidcontours depict anti-clockwise circulation (w < 0) and dashed contours denote clockwise circulation (w > 0). Along the land boundary anddotted contours w = 0. The solid circles indicate the locations of Bermuda and Ocean Weather Station Bravo.

Fig. 12. Benchmark simulation of the changes in the volume transport through the section extending from Bravo (central Labrador Sea)to the coast of Labrador. The thinner line shows the monthly results and the thicker line shows the annual means.

Fig. 13. Benchmark simulation of the sea level variations at the location of OWS Bravo and at the western end of the WOCE-AR7W lineon the Labrador coast. The thinner lines are monthly results and the thicker lines are annual averages.

magnitude and frequency distribution. At Bravo, the anomalies of the annual mean SSH have an rms value of3.6 cm and a peak-to-peak change of 15 cm. There are clear indications of decadal to inter-decadal changes inthe SSH at Bravo. At the Labrador coast, the anomalies of the annual mean SSH have an rms value of just

1.7 cm and peak-to-peak range of only 5 cm. At shorter time scales, SSH changes in the central Labrador Seaare weaker compared with those at the coast; after the annual mean values are subtracted, the rms value ofSSH at Bravo is 2.4 cm while that at the Labrador coast is 4.5 cm. Lazier and Wright (1993) discuss the sea-sonal variations in SSH and steric height on the Labrador Shelf and at Bravo. Here, we focus on the lowerfrequencies.

Fig. 14 shows that the inter-annual changes in the model’s steric height relative to 2000 dbar almost fullyaccount for the corresponding SSH changes at Bravo. Combined with the fact that inter-annual variations incoastal SSH are substantially smaller than those at Bravo we conclude that the sea level difference betweenBravo and the coast of Labrador is dominated by steric height variations at Bravo for inter-annual time scales.As a result, a good correlation is found between the SSH difference between Bravo and the Labrador coast andsimple steric height variations at Bravo at these low frequencies. If we now assume that the vertical structureof the currents normal to this line does not vary greatly (or varies in a manner that is correlated with the vari-ations in geostrophic surface current), then we might anticipate a correlation between the steric height atBravo and the volume transport integrated from the Labrador coast to Bravo. For the annual mean modelresults, the correlation between w and steric height at Bravo is 0.72 at zero-lag, and reaches a maximum of0.82 when the steric height leads w by 1 year. This simple result points to the possibility of estimating thelow-frequency variations in transport through the Labrador Sea from steric height variations at Bravo.

The sensitivity experiments described in the previous section also reveal the relative importance of windstress and buoyancy forcing in driving the low-frequency variations in circulation and sea surface height.Fig. 15 shows the annually averaged volume transport integrated from the Labrador coast to Bravo, obtainedfrom the three runs used to create Fig. 8. It is clear that the inter-annually varying buoyancy forcing and windstress are both important in driving the changes in the depth-integrated circulation, and the total changes incirculation are almost a linear combination of the separate changes in buoyancy forcing and winds. Fig. 16shows the analogous comparison for the sea level changes at Bravo. The inter-annual changes in sea surfaceheight are, again, almost a linear combination of the separate changes in buoyancy forcing and winds. How-ever, wind stress plays only a secondary role compared with buoyancy forcing.

Hakkinen and Rhines (2004) examine the changes in the subpolar NA based on various available datasets.They conclude that during the mid-to-late 1990s, the subpolar NA gyre circulation declined in its strength,

Fig. 14. Benchmark simulation of the steric height (referenced to 2000 dbar) and the difference between sea surface height and the stericheight at the location of Ocean Weather Station Bravo. Top panel shows the monthly data and bottom panel shows the annual means.

Fig. 15. Model-simulated annual anomalies of the barotropic volume transport through a section extending from the Labrador coast toOWS Bravo. The thick solid curves are obtained from a reference run that excludes SSS anomalies in the surface restoring. In the toppanel, the thinner solid and dashed curves are obtained from two test runs that exclude the inter-annual changes of wind stress and surfacebuoyancy flux, respectively. The lower panel compares the result from the reference run and the combined changes of the two test runs.

Fig. 16. Same as Fig. 15 except for the surface height anomalies at Bravo.

coincident with decreases in surface buoyancy forcing and altered hydrographic conditions. Our model solu-tion reproduces this latest change, and further presents this change in the context of long-term variability. Weshow in Fig. 12 that the subpolar circulation at the end of the 1990s is of similar strength to the circulation

from the 1950s to the early 1980s. The model results suggest that the subpolar circulation generally gainedstrength (note negative axis) from the early 1970s to the mid-1990s while it experienced variations at quasi-decadal time scales.

6. Discussion and conclusions

In this study, the inter-annual to inter-decadal variations in the North Atlantic are simulated using acoarse-resolution ocean circulation model driven by surface forcing obtained from the NCAR/NCEP reanal-ysis. Our analysis has focused on the subpolar gyre region and the central Labrador Sea near OWS Bravo inparticular. A time-mean state nudging method is implemented. The method reduces the model biases in thelong-term mean distribution of temperature and salinity which commonly exist in coarse-resolution oceanmodels. Most importantly, by reducing the time-mean biases we significantly improve the model’s represen-tation of low-frequency variations in the subpolar gyre. The simulation covers over five decades from 1949to 2001. In the central Labrador Sea, the model broadly reproduces the variations in the temperature andsalinity of LSW as revealed by hydrographic observations. In our model, the changes in LSW properties(thickness, density, temperature and salinity) are primarily associated with changes in the strength of deepconvection, which correlates well with the winter NAO index.

Model sensitivity experiments are carried out to determine what effects are responsible for the T–S changesin the Labrador Sea. The conclusions from these experiments are that the model-simulated changes in LSWproperties are primarily due to changes in the local surface heat flux over the Labrador Sea. Changes in sur-face freshwater fluxes make a secondary contribution to the changes in surface buoyancy flux, a point that hasbeen previously pointed out by Lazier et al. (2002). Including surface salinity anomalies in the surface restor-ing condition results in obvious changes in the virtual surface salt flux, but the surface buoyancy flux is notaltered significantly. Inter-annual variability of wind stress has little influence on the evolution of LSW prop-erties, although it plays an important role in causing changes in the circulation of the subpolar gyre. Weemphasize that we have deliberately separated the influences of wind variations on the momentum exchangefrom those on the surface heat flux. Obviously the two forcing terms are strongly correlated in reality, but ourmodel results indicate that it is the surface heat flux that dominates in the determination of LSW propertyvariations.

Changes in circulation are found to play little role in causing T–S changes of LSW. This conclusion isdrawn from an experiment in which the circulation variability is strongly suppressed using the semi-prognosticmethod (Sheng et al., 2001). However, the role played by the time-mean circulation cannot be neglectedbecause advection must determine the time scale on which newly formed LSW is drained out of the LabradorSea. Another important hypothesis tested is the role played by T–S variations outside of the Labrador Sea.Our numerical tests show that the suppression of inter-annual variations in the inflow to the Labrador Seahas little influence on the model’s inter-annual variations of LSW. Note, however, that this result on ‘‘externalpreconditioning’’ is limited to what occurred in the model domain. The potential influence of the Arctic on thesubploar NA is not addressed by our experiments. Further, the importance of local preconditioning associatedwith the accumulative effects of local forcing over previous years is not contradicted by our results.

Excluding the influence of the variability produced by forcing at higher latitudes is a significant deficiencyof the model setup in this study. Such influences are of central importance, for example, in the ‘‘advective’’mechanism of the ‘‘Great Salinity Anomaly’’ (e.g., Dickson et al., 1988). Inflow of anomalously fresh waterfrom the Arctic on both the western and eastern side of Greenland is an important source of freshwater var-iability in the subpolar NA and this source of variability is not included in the surface freshwater flux from theNCEP reanalysis. Surface restoring increases the effective freshwater flux seen by the model (Fig. 10) and thismay be taken as a correction to either the E � P fields or the near surface freshwater influx from the Arctic.However, the inflow from higher latitudes is not confined to the surface layer. Consequently, the model’sunderestimation of the magnitudes of the T–S variations in the LSW calls for further improvement of the lat-eral open boundary condition. Another imperfect aspect is that the model fails to simulate the changes in theLabrador Sea at the depths of NEADW and DSOW. The source of these deep changes is undoubtedly dom-inated by the changes that have occurred in the Nordic Seas (e.g., Dickson et al., 2003). Although the modeldomain includes part of the ocean north of the Greenland–Iceland–Scotland Ridges, the variability in this

region is suppressed because of the lack of influence from the Arctic and the strong restoring to climatologicalconditions applied in the ‘‘sponge layer’’ near the northern boundary of the model. Another limiting factor isthe coarse-resolution of the model which degrades the simulation of overflows over the sills. Hence, to repro-duce the deep T–S variations at depth we will likely have to both improve the northern boundary forcing andaccount for the processes controlling the overflows.

The present study is focused on the Labrador Sea although the model domain covers the whole NorthAtlantic. Various parts of the North Atlantic undergo coordinated changes in watermass properties underthe forcing of the North Atlantic Oscillation (e.g., Dickson et al., 1997; Sy et al., 1997; Curry et al., 2003).Observations also reveal that the signal of hydrographic changes in the subpolar NA propagates to the sub-tropical regions and influences the changes in watermass properties there (e.g., Curry et al., 1998). It is of inter-est to examine how the model simulates the long-term changes in the whole North Atlantic. Such anexamination will be undertaken elsewhere. A preliminary analysis indicates that the present model simulationsof low-frequency changes in the subtropical NA are sensitive to the wind stress fields used to force the model.This result contrasts with our results for the Labrador Sea region, but is consistent with previous studies bySturges and Hong (1995), Sturges et al. (1998) and Ezer (1999).

Finally, we note that the response of the North Atlantic to variations in surface forcing (dominated by theNAO) has been the subject of previous modelling studies (for a recent review, see Visbeck et al., 2003). Modelsof similar complexity to the present one were used by, e.g., Eden and Willebrand (2001), Eden and Jung(2001), Ezer (1999) and Hakkinen (1999). Each of these studies examines certain aspects of the large-scalechanges in the North Atlantic extending from seasonal to inter-decadal time scales. The present study com-plements the previous work by focusing on the changes in the subpolar NA with the guidance of long-termhydrographic observations. Substantial, but still only partial, success is achieved in simulating the long-termchanges. The study points to the need to further improve surface and lateral boundary forcing, and dataassimilation schemes, if such models of intermediate complexity are to be used to more realistically simulateocean climate changes.

Acknowledgements

We are pleased to acknowledge the support received from the Canadian Foundation of Innovation forcomputer resources at the Center for Marine Environmental Prediction, Dalhousie University, as well as fund-ing from the Department of Fisheries and Oceans’ Strategic Science Fund and the Offshore Environmentalsubprogram of the Canadian Program for Energy Research and Development. We thank Allyn Clarke andtwo anonymous reviewers for constructive comments that helped to improve the discussion and interpretationof results.

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