Influence of May Atlantic Ocean initial conditions on the subsequent North Atlantic winter climate

Transcription

1 Q. J. R. Meteorol. Soc. (26), 132, pp doi: /qj.5.62 Influence of May Atlantic Ocean initial conditions on the subsequent North Atlantic winter climate By A. M. IWI 1,R.T.SUTTON 2 and W. A. NORTON 2 1 Rutherford Appleton Laboratory, Didcot, UK 2 University of Reading, UK (Received April 25; revised March 26) SUMMARY Analyses of observational data have suggested a link between late spring/summer conditions in the North Atlantic Ocean and atmospheric conditions over the same region in the subsequent winter. This link, and its potential value for seasonal forecasting, is investigated using a coupled climate model, HadCM3. It is found that there is memory of ocean conditions from May through to the following winter, but that the model s sea-surfacetemperature (SST) anomalies are too weak in the tropics in winter, partly because they are damped too strongly by comparison with observations and partly because of errors in the model SST anomalies in May, particularly in the east Atlantic where the mixed layer is found to be too deep. It is hypothesized that poor agreement between the winter atmospheric signals in the model and in observations is a consequence of the insufficient tropical SST anomalies. Nevertheless, it is demonstrated that, for suitable initial conditions, seasonal forecasts from 1 May could provide useful information about North Atlantic climate the following winter. KEYWORDS: Climate variability Ocean atmosphere interaction Seasonal prediction 1. INTRODUCTION Seasonal climate forecasting relies on the impact of slowly changing boundary conditions primarily the state of the oceans on the evolving state of the atmosphere. Because of the pre-eminent importance of the El Niño Southern Oscillation (ENSO), developments in seasonal forecasting have focused mainly on the role of the tropical Pacific Ocean. However, anomalous conditions in other ocean basins can also have significant effects on climate. These effects must be understood and quantified if the full potential of seasonal forecasting is to be realized. There is evidence from observational studies that the wintertime circulation over the North Atlantic region is sensitive to conditions in the Atlantic Ocean in previous seasons. Results from maximum-covariance analysis indicate a significant association between sea-surface-temperature (SST) anomalies in late spring, summer or early autumn and geopotential-height anomalies in the subsequent winter (Czaja and Frankignoul 1999, 22; Rodwell and Folland 22; Drevillon et al. 23; Frankignoul and Kestenare 25). Rodwell and Folland (22) showed, for example, that the North Atlantic SST in May could be used as a predictor of the subsequent winter (December February) North Atlantic Oscillation (NAO) index. One hypothesis is that conditions present in the North Atlantic Ocean in late spring persist (probably with some evolution) through the summer to influence the atmosphere in the following autumn and winter. During summer the ocean anomalies may be capped by the shallow mixed layer, subsequently to emerge as the mixed layer deepens in early winter; this mechanism is known as re-emergence (Deser et al. 23; Cassou et al. 24). Motivated in part by the observational results, there have been a number of modelling studies into the influence of Atlantic Ocean conditions on the wintertime atmospheric circulation over the North Atlantic. The usual approach has been to force an atmosphere model with prescribed patterns of SST. Sutton et al. (21), for example, Corresponding author: Rutherford Appleton Laboratory, Chilton, Didcot, Oxfordshire OX11 QX, UK. a.m.iwi@rl.ac.uk c Royal Meteorological Society,

2 2978 A. M. IWI et al. showed that a tripole pattern of SST anomalies could induce a weak NAO-like atmospheric response. Further experiments indicated that the tropical part of the SST pattern (approximately 2 N) played the most important role, but that the extratropical part also made a significant contribution. Other studies (e.g. Terray and Cassou 22) have also emphasized the importance of SST anomalies in the tropical Atlantic. Recently Cassou et al. (24) suggested that SST anomalies in the tropical Atlantic are important for the persistence of an oceanic signal from spring into summer. Peng et al. (25), using an atmosphere model coupled to an extratropical oceanic mixed layer, suggested that SST anomalies in the tropical Atlantic are also important for the persistence of a signal from summer into autumn and winter. The atmosphere model studies have provided valuable insights, but they suffer from some limitations. First they give information about the instantaneous response of the atmosphere to SST anomalies, and so cannot provide a full account of how ocean conditions at a particular time influence atmospheric conditions many months later. Assumptions about the persistence or damped persistence of SST anomalies are necessarily an approximation to the full effects of ocean atmosphere coupling (including phenomena such as re-emergence). Secondly, even if the evolution of SST is represented perfectly, it is not necessarily the case that the full effects of the ocean on the atmosphere can be captured by forcing an atmosphere model with SST anomalies. The reason is that there is no unique relationship between SST anomalies and air sea flux anomalies (see Sutton and Mathieu 22, for further discussion). The aim of this study is, therefore, to investigate the impact of Atlantic Ocean anomalies on the subsequent winter climate in the North Atlantic region using a full coupled ocean atmosphere model rather than an atmosphere model alone. Specifically we aim to explore the relationships between Atlantic Ocean conditions in May and ocean and atmosphere states in the subsequent autumn and winter. With the coupled model we are able to examine the detailed evolution of both the ocean and atmosphere, and therefore to identify the mechanisms that provide memory in the coupled system and hence the potential for skillful seasonal forecasts. The structure of the paper is as follows: the methodology is described in section 2, the results in sections 3 5 and the conclusions in section METHODOLOGY The methodology has two parts: composite analysis and initial-condition ensembles. The composite analysis is based on oceanic conditions in May. SSTs from a control simulation of a coupled ocean atmosphere model, or from observations, were projected onto a chosen SST pattern (to be described shortly). Years that gave a large positive or negative projection onto this pattern (as expressed as an anomaly from the period mean) were selected to form composites of ocean and atmosphere variables both for May and for subsequent months through the following summer, autumn and winter. For the initial condition ensembles, oceanic states for 1 May were selected from the control integration by identifying years with particularly large projection onto the chosen SST pattern. For each ocean state, a set of atmospheric states was generated (see details below) in order to sample atmospheric internal variability. The ensemble forecasts were integrated from 1 May through to the end of March of the following year. We investigated several alternative criteria for the composite analysis. First, we used the May SST pattern identified by Rodwell and Folland (22) (their Fig. 5(a)). Secondly we used an SST pattern derived from empirical-orthogonal-function (EOF) analysis, similar to that used by Sutton et al. (21) but focused on the tropical and

3 ATLANTIC OCEAN INFLUENCE ON SUBSEQUENT WINTER CLIMATE 2979 Figure 1. (a) Subtropical North Atlantic sea-surface-temperature (K) dipole pattern. Shading denotes land and other grid points where values are identically zero. (In this and subsequent figures, an equal-area map projection is used, and positive, zero and negative contours are shown with solid, dotted and dashed lines, respectively). (b) Time series of the index obtained by projecting May sea surface temperatures from detrended HadISST data onto the pattern shown in (a) note that the index is normalized, but the pattern itself is shown here with magnitude (in K) as for a one-standard-deviation anomaly in this data. Years forming members of positive or negative composites are shown with large symbols (crosses for strong El Niño or La Niña years; stars for other years). See text for more details. subtropical North Atlantic region (specifically, the leading EOF of SSTs for January March between the equator and 4 N, and between 11 W and 48 W; this explains 46% of the variance for the years ). This pattern was chosen because of the evidence (discussed in the introduction) that SST anomalies in the tropical Atlantic region are particularly influential, and also because Sutton et al. (21) found evidence of sensitivity to the SST gradient between the tropics and subtropics. We refer to this pattern as the subtropical North Atlantic (SNA) dipole, and it is shown in Fig. 1(a). A third criterion we considered was the NAO index (as defined e.g. by van Loon and Rodgers 1978) for the previous winter (December April average). This criterion was motivated by the evidence (Cassou et al. 24) that the NAO in one winter can, via the development of anomalies in the ocean, influence the following winter. Although the three criteria we considered had very different origins, we found in practice that in many respects they gave very similar results. This is less surprising than it may seem, because the SNA dipole pattern has clear features in common with the Rodwell and Folland (22) pattern and, furthermore, the previous winter NAO has a significant role in forcing these patterns (Cassou et al. 24). Because the results are similar for the different criteria, we present composite analyses based only on the projection of May SSTs onto the SNA dipole pattern shown in Fig. 1(a). (a) Observational and model composites The SST observations used in this study were taken from the Hadley Centre SST dataset, HadISST (Rayner et al. 23), and the atmospheric fields from the NCAR/NCEP reanalysis (Kistler et al. 21), both for the period , and gridded observations of subsurface ocean temperature from expendable bathythermograph (XBT) measurements (White 1995) for the period To focus on interannual variability, all the datasets used were detrended using a least-squares fit to a quadratic function of time at each grid point. National Center for Atmospheric Research/National Centers for Environmental Prediction.

4 298 A. M. IWI et al. The coupled model used was the Hadley Centre climate model, HadCM3 (Gordon et al. 2), run using version 4.5 of the Met Office Unified Model on a cluster of personal computers. A 5-year integration was performed, forced using pre-industrial values of greenhouse-gas concentrations, and initialized from a well-equilibrated state of HadCM3 previously obtained by the Met Office. A separately conducted validation of this integration did not detect model drifts during the 5 years. For both the model and the observational datasets separately, SNA dipole indices for each year were obtained by projecting May monthly-mean SSTs onto the SNA dipole pattern as shown in Fig. 1(a), and were normalized to have mean zero and standard deviation unity. For each dataset, three composites were obtained: a SNA composite for years with normalized SNA dipole index 1, a +SNA composite for years with normalized SNA dipole index 1, and a control composite consisting of all remaining years. A time series of the dipole index for the observations is shown in Fig. 1(b). In order to separate out the effects of El Niño, similar composites, but consisting only of non-enso years, were also calculated as follows. Niño 3.4 indices were calculated by averaging SST anomalies in space over the box extending from 17 W 12 Wand5 S 5 N and in time from November to March for the winter following the May on which the composites were based. The ENSO-neutral composites were obtained by subsetting the +SNA and SNA composites so as to include only those years for which Niño 3.4 indices were of magnitude less than.65 K (although it was judged more consistent for these composites to be then compared against the same control composite, rather than a similarly subsetted control). When calculating the Niño 3.4 indices for observational data, the quadratic detrending was not used in order to ensure consistency with common practice (this being the only context in which non-detrended observations were used), and the cut-off criterion for ENSO-neutral years was relaxed to ±.73 K in order to avoid an excessively small composite size. Note that these ENSO-neutral composites are informative for discussion but, because they assume prior knowledge of forthcoming ENSO conditions, they have relevance for predictability only to the extent that ENSO can be forecast. For all composites (i.e. observed or modelled; +SNA, SNA or control; full or ENSO-neutral), certain averaged quantities of interest were calculated, generally twomonth means but also some monthly-mean SSTs with averaging also in latitude. From these, anomalies from the control were calculated, together with their associated local statistical significance according to a two-tailed t-test with 95% confidence level. The anomalies and significance are shown together in the figures. (For reasons of space, certain composites or periods are described but not shown; the methodology used in these cases is identical.) (b) Initial-condition ensembles The initial-condition ensemble integrations were performed as follows. Initial conditions from 1 May were used from eight years chosen from the 5-year HadCM3 integration: four years with a strongly positive and four with a strongly negative SNA signal. For robustness, these were not simply taken using the most extreme values of the SNA index described above, but were chosen (by inspection) as years when there is a strong signal of appropriate sign according to all three of the criteria previously discussed. For each of the years thus chosen, a 2-member ensemble was constructed by taking the ocean state for 1 May and then selecting atmospheric states from the successive 2 days (conceptually these atmospheric states are taken from the 5-year integration, but in fact from a 2-day repeat integration during which these daily atmospheric states

5 ATLANTIC OCEAN INFLUENCE ON SUBSEQUENT WINTER CLIMATE 2981 were written out). The total number of initial conditions was therefore 8 ocean states 2 atmospheric states = 16. For each initial condition an integration of 11 months duration was performed. The results were then composited into two 8-member grand ensembles, each derived from four ocean initial states (+SNA or SNA). These composites were then processed in the same way as the model composites taken directly from the 5-year control integration (specifically, they were differenced from the same control composite). 3. OBSERVATIONAL COMPOSITES This section describes the evolution of North Atlantic Ocean and atmosphere conditions, as diagnosed from observational data using the method of compositing based on the May SST signature (as described in section 2). This method yielded 1- member composites for each of +SNA and SNA and a 36-member control composite. (Subsurface data, shown later, are unavailable prior to 1955 and so contain one fewer SNA year and six fewer control years.) Figure 2 shows SST anomalies for various periods in the following winter for both the +SNA and SNA composites. Looking first at the +SNA composite, the SST anomalies show a tripole pattern in the latitudinal direction, which persists throughout this period. From October November to December January the SST anomalies strengthen a little (possibly suggesting re-emergence); they then weaken from December January to February March. This broad tripole pattern exists also in the May SSTs (not shown), i.e. although the composite was obtained by projecting onto a pattern consisting only of the equatorward dipole part, the analysis has identified a correlated maximum around 5 N 6 N as part of this mode of variability. In Fig. 2 there is a suggestion that this poleward part of the tripole pattern propagates north-eastwards, while in the tropical lobe there is a suggestion of westward propagation. Some associated atmospheric fields mean sea-level pressure (SLP) and 5 hpa geopotential height, chosen to be representative of the near-surface and upper-air dynamical signal are shown in Fig. 3. The main signal is seen in October November and December January. In October November, the significant atmospheric signal is mainly in the north-west Atlantic, with northward (slightly north-eastward) pressure gradients indicating a weakening of the mean westerlies. Comparison of the SLP and 5 hpa height suggests some baroclinicity in these flow anomalies. In Fig. 3(b) there is a suggestion of a Rossby wave-train extending north-eastward over northern Europe, although circulation anomalies over Europe may not be significant. It has been shown that Rossby wave-trains with a similar appearance can be generated in response to diabatic heating anomalies in the western tropical Atlantic region (Blackburn and Hoskins 21). The diabatic heating anomalies may, in turn, be a response to local SST anomalies. Note also that the reduced westerlies off the coast of Newfoundland would help to maintain the warm SST anomaly in this region (high-latitude part of the tripole) through reduced sea air fluxes. By December January, the circulation anomalies have moved further east, so that Europe is more clearly affected. The projection of these anomalies on the NAO pattern is negative, associated with weaker westerlies and consistent with the results of both Sutton et al. (21) and Rodwell and Folland (22). By February March, the composites suggest that this anomalous flow has changed sign; however, there is now less statistical significance in the atmospheric fields, and there is no significant signal in SST. Turning now to the SNA composite, the SST anomalies (Figs. 2(b), (d) and (f)) are approximately the negative of those seen in the +SNA composite. However, it is notable

6 2982 A. M. IWI et al. Figure 2. Bimonthly averages of sea-surface-temperature anomalies for the +SNA (left-hand panels) and SNA (right-hand panels) composites (see text) from observations, shown as composite-minus-mean with respect to the control composite, for the winter following the May on which the composites were based. Light shading indicates local statistical significance (95% two-tailed t-test).

7 ATLANTIC OCEAN INFLUENCE ON SUBSEQUENT WINTER CLIMATE 2983 Figure 3. Bimonthly anomalies of atmospheric pressure (hpa) at mean sea level (left-hand panels) and geopotential height (m) at 5 hpa (right-hand panels) for the +SNA composite. Light shading indicates local statistical significance (95% two-tailed t-test).

8 2984 A. M. IWI et al. that the warm mid-latitude part of the tripole is largely absent in October November and December January, although a significant warm anomaly appears in the Gulf-Stream Extension (GSE) region in February March; in earlier months (not shown), the warm anomaly is seen as a prominent feature during June July but the attenuation has already occurred by August September. The tropical cold anomaly is generally centred further west than the corresponding warm anomaly in +SNA. Its spatial extent increases from October November to December January and then decreases in February March. The atmospheric signal in SNA is shown in Fig. 4. In this case, there is very little evidence of a significant atmospheric signal during the autumn and early winter (except the small, but statistically significant, height anomaly in the deep tropics in October November). However, in February March there is a prominent pressure dipole anomaly, which resembles a positive NAO anomaly. This anomaly, which is again in line with the December February results of Sutton et al. (21) and Rodwell and Folland (22), could be a response to the significant positive SST anomalies that appear in the GSE region at this time. The anomalous air sea fluxes (which can be inferred from the pressure field) would tend to damp the SSTs in the GSE region while maintaining the negative anomalies to the north-east. A possible alternative forcing mechanism is a Rossby wave-train forced by anomalous convection in the eastern Atlantic part of the Intertropical Convergence Zone (in Fig. 2(f), the unlabelled contour off the coast of Sierra Leone is in fact.4 K, and peak values exceed.55 K). We now examine composites that were obtained similarly from these observations, but with the removal of ENSO years, as described in section 2. These are shown, for December January only, in Fig. 5. In the +SNA case, only three of the ten years that were present in the full composite were removed as being ENSO years, and the resulting composite-means show little difference to the original composite, both for December January (Figs. 5(a), (c), and (e)) and also for October November and February March (not shown); the SLP anomaly over Greenland in December January is somewhat enhanced, but the features remain qualitatively very similar. In the SNA case, five years of the original ten are classified as ENSO years, and are therefore removed; some larger differences emerge in this case. Most notably, the ENSO-neutral composite shows an atmospheric signal during in December January that is somewhat similar to that seen only in February March in Fig. 4. (Results for other bimonth periods, not shown in Fig. 5, are that the removal of ENSO years leaves the SNA signal in February March largely unchanged, and there is still no signal in October November.) This result could suggest that ocean conditions associated with SNA May SST anomalies tend to generate a similar response in December January and February March, but that the remote effects of ENSO, although relatively weak in the Atlantic, are enough to mask the signal during December January. The effect is less apparent in the Atlantic SSTs than in the atmospheric fields (compare Figs. 2(d) and 5(b)). However, it must be kept in mind that the size of the SNA composite is small (only five members), so sampling may well be an issue. This said, the results of the ENSO-neutral composites appear to confirm that the main signals seen in Figs. 2 4 cannot be readily explained as a remote response to the ENSO, consistent with the hypothesis that Atlantic Ocean conditions can influence the North Atlantic winter climate. 4. MODEL COMPOSITES This section describes the North Atlantic response to the ocean conditions discussed above, but as seen in output from the HadCM3 integration, diagnosed using the same

9 ATLANTIC OCEAN INFLUENCE ON SUBSEQUENT WINTER CLIMATE 2985 Figure 4. As Fig. 3, but for the SNA composite.

10 2986 A. M. IWI et al. Figure 5. Panels (a) to (f) are as for Figs. 2(c), 2(d), 3(c), 4(c), 3(d), and 4(d), respectively, except that the ENSO-neutral composites (see text) are used.

11 ATLANTIC OCEAN INFLUENCE ON SUBSEQUENT WINTER CLIMATE 2987 method of composites as was used in the results from observational data discussed in section 3. (The composite sizes obtained were 9 years for +SNA and 83 years for SNA leaving 327 in the control composite these reducing to 35 and 39 respectively on removal of ENSO years.) SST anomalies from the model composites (without removal of ENSO years) are shown in Fig. 6; these may be compared with the observations in Fig. 2. The anomalies in the model composites have captured the basic tripole structure seen in observations again, despite the composites being based only on a dipole pattern. However, it is clear that the tropical part of the tripole is generally too weak in the model during the winter months; the SST anomalies in this region are already too weak in October November and the observed strengthening in December January is not captured by the model. Anomalies in the extratropical parts of the tripole pattern are, by contrast, generally too strong, notably the warm anomaly region south-east of Newfoundland in the SNA composite. Comparison of the model and observational SST composites for May and the subsequent summer months (not shown) indicates that the differences in May are relatively small (except in the tropical east Atlantic discussed below), but divergence between model and observational results begins during the summer. In the extratropics, for example, the model +SNA composite already shows under-damping compared to observations in summer. For SNA the under-damping is more prominent in autumn. We now focus somewhat more attention on the evolution of tropical anomalies. For these, see the time series of SST anomalies in Figs. 7(a) (d) and subsurface temperature anomalies in Figs. 7(e) (h). The anomalies shown are averaged over 1 N 2 N, approximately the extent of the tropical part of the pattern. Compared with the extratropics, this region has somewhat larger discrepancies between the model and observational SSTs in May, with the observations showing a maximum SST anomaly in the eastern Atlantic for both +SNA and SNA composites, whereas the model does not capture this, but shows anomalies that are somewhat smaller and centred further west. We discuss later possible reasons for this initial discrepancy in the eastern Atlantic but, for now, note that this eastern-atlantic anomaly shows a fairly rapid decay in the observations and, by about August, the model and observations are showing similar SST anomalies. After this, however, observed values at most longitudes remain fairly constant into the autumn, and then increase again during the winter (around January in +SNA and December in SNA). This increase does not appear to be due to horizontal advection, and is most likely to be the result of re-emergence of anomalies previously isolated from the surface by the shallow summer mixed layer. There is some support for this idea in the subsurface signal. For +SNA, the XBT data show a strong signal persisting into the winter particularly in mid-basin (shown at 6 m in Fig. 7(e), as this is the level with the strongest signal); for SNA the XBT data show almost no statistically significant signals beyond the summer at any level (see, for example, Fig. 7(f) which also shows the 6 m level), although this does not rule out such a signal at depth for example, the SSTs from HadISST show significant winter signals not found in the near-surface ( m) XBT temperature anomalies. In the model, in contrast to the observations, monotonic decay of the initial SST anomalies continues throughout the autumn and winter in both the +SNA and SNA composites. Beneath the surface, larger temperature anomalies do persist in the model into winter (see Figs. 7(g) and (h); here again the level with the strongest signal is shown in this case the 96 m model level and persistent significant signals also exist at all levels from the surface to 139 m, and a short-lived signal exists also at 24 m). However, these anomalies are still smaller than the largest observed SST anomalies

12 2988 A. M. IWI et al. Figure 6. As Fig. 2, but for the model composites.

13 ATLANTIC OCEAN INFLUENCE ON SUBSEQUENT WINTER CLIMATE 2989 Figure 7. Longitude time Hovmöller plots of monthly-mean tropical North Atlantic Ocean temperature anomalies for the +SNA (left-hand panels) and SNA (right-hand panels) composites (see text) comparing the observational composites (the sea surface temperature (SST) from HadISST and the 6 m temperature from XBT soundings) and model composites (the SST and the 96 m temperature). The fields are latitudinally averaged over 1 N 2 N, for eleven months starting from the May on which the composites are based. Note that the plots are over the longitudes for which there are no model land points between 1 N and 2 N, and so do not extend into the Caribbean. Light shading indicates local statistical significance, as in Fig. 2.

14 299 A. M. IWI et al. Figure 8. (a) May-mean climatological ocean-temperature structure at 12 N from gridded XBT data; (b) Maymean ocean-temperature structure at 11.9 N from the HadCM3 integration (averaged over all years). (and the observed subsurface anomalies for +SNA), and they appear to have limited impact on the model SST; in particular, any such impact through re-emergence is insufficient to reverse the monotonic decay of the model SSTs. However, corresponding time series at mid-latitudes (not shown) do show wintertime re-emergence of these parts of the tripole signal in the model, and similar behaviour is also seen in the observations. Figure 7 clearly suggests that surface and subsurface temperature anomalies in the tropical Atlantic are damped too quickly in the HadCM3 model. This finding is consistent with the work of Frankignoul et al. (24), which showed HadCM3 (and other models) to over-damp tropical Atlantic SST anomalies (and also showed possible evidence of under-damping at higher latitudes). We now return to the point noted above, that the May east-atlantic tropical SST anomalies are too weak in the model. We hypothesize that this error may be a consequence of the over-deep mixed layer in HadCM3. Figure 8 shows the mean subsurface temperature structure in the tropical North Atlantic in May, in the XBT observations and in the model. Various differences are evident, but the feature of note here is the position of the thermocline in the east Atlantic. For most longitudes east of about 4 W, the XBT data show large vertical temperature gradients starting at about 2 m depth, whereas in HadCM3 these start at typically nearer to 5 m. The over-deep thermocline can be expected to reduce the amplitude of SST anomalies generated by coastal upwelling and, in addition, the over-deep mixed layer will reduce the impact of surface-flux anomalies on SST in this region. Note that errors in the winds may also contribute to the SST errors arising from both these mechanisms. The under-representation of May tropical North Atlantic SST anomalies in the east of the basin, together with their subsequent over-damping at all longitudes, leads to an under-representation of model SST anomalies in winter. These errors may be expected to have an effect on the wintertime atmospheric signal, which we now examine. For the SNA composite, the wintertime atmospheric anomalies in the model are shown in Fig. 9. The strongest signal is seen in October November: an equivalent barotropic anomaly pattern in the extratropics consisting of high pressure over Iceland with a band of low pressure at 4 N. In the tropical Atlantic there is a further region

15 ATLANTIC OCEAN INFLUENCE ON SUBSEQUENT WINTER CLIMATE 2991 a.4 Model composite: SNA, atmospheric fields Sea-level pressure (hpa) 5 hpa height (m) b October November c d December January e f -8 8 February March Figure 9. As Fig. 4, but for the corresponding model composite.

16 2992 A. M. IWI et al. Figure 1. As Figs. 3(c) and (d), but for the corresponding model composite. of high SLP, which may be a local response to the negative SST anomalies in this region (see Fig. 6(b) and Lindzen and Nigam 1987). In December January, the significant highpressure anomalies in this region persist, but there is no longer a significant signal over the extratropical Atlantic Ocean. In February March, the main atmospheric signal is a high-pressure region south-east of Newfoundland. This feature overlies, and might possibly be a response to, the anomalous SST gradient in this region (see Fig. 6(f)). Comparison of the SNA model results with observations (Fig. 4) shows very little correspondence. The observations show little signal during October November when the model signal is strongest, and in February March (when the observations suggest the signal is strongest) the pattern of anomalies in the model is quite different. For the corresponding model composite with ENSO removed (not shown), there was substantially better maintenance of the tropical SST anomaly through to February March on the eastern side of the basin near to the African continent, but the signal was still over-damped on the western side. The atmospheric anomalies were very similar to those seen when the ENSO years were included (although the mid-atlantic high seen in February March loses statistical significance), suggesting that the improved SST anomalies in the eastern tropical Atlantic have little impact. For the +SNA composite, the atmospheric signal is very weak in all the winter periods. The one possibly significant signal occurs during December January, and this is shown in Fig. 1. Again, correspondence with observations is poor; the observed signal (Figs. 3(c) and (d)) has the opposite sign. It is possible that, in the model, because the SST anomalies in the tropical Atlantic are too weak, the atmospheric signal is primarily a response to the extratropical SST anomalies, whereas the observed signal is more strongly influenced by the tropical SST anomalies. For +SNA, removing ENSO years from the composite has very little effect either on the SSTs or on the atmospheric fields during winter. In summary, the model composites have shown that HadCM3 captures the tripolar pattern of SST anomalies that is seen in the observational composites, but that the anomalies in the tropical part of the pattern are damped too quickly (and in the east

17 ATLANTIC OCEAN INFLUENCE ON SUBSEQUENT WINTER CLIMATE 2993 Atlantic are also too weak initially). There is no sign of the mid-winter strengthening of SST anomalies in this region that is suggested by the observations. The model atmospheric fields indicate that any response to the Atlantic Ocean anomalies that persist from May is weak (except perhaps for SNA in October November). Poor correspondence with the observations is likely to be a consequence of the excessive damping of SST anomalies in the tropical Atlantic. 5. INITIAL-CONDITION ENSEMBLES Notwithstanding the weaknesses of the HadCM3 model identified by our composite analysis, we pursued our investigations through the use of initial-condition ensembles (described in section 2). As was mentioned, the calculations performed are comparable with the model composites presented in section 4, but the ensembles were initialized only from a few of the strongest May SST anomaly patterns of each sign arising during the 5-year integration, and can therefore be expected to give a stronger signal. Prior to examining the results from the ensembles, we make the following remarks regarding their interpretation. First, note that there is no observational equivalent to compare them against directly, because a mere subsetting of the composites to those years with strongest initial ocean anomalies, but without the availability of atmospheric initial-condition ensembles to increase the composite size, would simply result in a loss of statistical significance. Second, recall that for each sign of the SNA pattern there are four ocean states and, for each ocean state, there are 2 atmospheric states. There are, therefore, 8 members in each grand ensemble. However, because there are only four independent ocean states, the grand ensemble mean for ocean fields is noisier than might be expected from the ensemble size of 8. We therefore apply some spatial smoothing when examining SST, although no such smoothing is required for atmospheric fields. Similarly, note that the statistical significance of SST anomalies may be overestimated as the t-test calculation assumes 8 independent states. The SST anomaly patterns arising from the initial-condition grand ensembles are shown in Fig. 11. They again show the basic tripole structure in SST for both signs of initial anomaly. Aside from the expected greater magnitude of signal than was seen in the composites discussed earlier, there is also slightly better persistence of tropical SSTs through into December January, although the anomalies are still weak in February March. Time series of the tropical SSTs (equivalent to Fig. 7, but not shown) again show a lack of evidence for wintertime re-emergence of tropical SSTs in the model; however, re-emergence is again seen at mid-latitudes. The atmospheric fields for +SNA are shown in Fig. 12. The pattern of anomalies is more persistent than in the previous composites. In December January and February March there is a tripolar pattern of SLP anomalies that roughly overlies the tripolar pattern of SST anomalies seen in Fig. 11. Most prominent is the centre of high SLP over the mid-latitude Atlantic Ocean. In October November a similar pattern of SLP anomalies is seen, but there is an additional centre of high pressure over the Greenland Sea. At 5 hpa the signal in geopotential height appears less consistent than at the surface, but significant positive height anomalies are seen in the tropical/subtropical Atlantic region in all three periods examined. The results in Fig. 12 agree with the model composites for December January (Fig. 1), but show almost no correspondence with observational composites (except possibly in February March). For the SNA ensemble (Fig. 13), in October November there is an extended region of positive SLP anomalies in the tropical Atlantic, overlying the negative SST anomalies in this region (Fig. 11), together with weaker negative pressure anomalies

18 2994 A. M. IWI et al. Figure 11. As Fig. 6, but shown for the grand ensembles composited from the initial-condition ensembles (all with +SNA ocean initial conditions, and likewise all with SNA) rather than for the composites used in Fig. 6. Additionally, moderate smoothing (Gaussian with 2 km e-folding distance and 4 km cut-off) has been applied to the anomaly fields (though significance regions are shown for the unsmoothed fields).

19 ATLANTIC OCEAN INFLUENCE ON SUBSEQUENT WINTER CLIMATE 2995 Figure 12. As Fig. 1, but shown for all months for the grand ensemble composited from all the members with +SNA ocean initial conditions.

20 2996 A. M. IWI et al. Figure 13. As Fig. 12, but for the SNA grand ensemble.

21 ATLANTIC OCEAN INFLUENCE ON SUBSEQUENT WINTER CLIMATE 2997 to the north overlying positive SST anomalies. This pattern was also seen in the model composites (Fig. 9(a)). By comparison, Fig. 12 (for the +SNA initial-condition ensembles) showed an opposite situation with the signs of SST and SLP anomalies reversed. Therefore, the October November atmospheric response in these regions appears, to a first approximation, linear. The tropical high-pressure anomaly that is prominent in October November in Fig. 13 is present throughout the summer (not shown), but by December January has almost disappeared. At the surface there is no significant response over the North Atlantic in December January, although there does appear to be some response at 5 hpa. In February March the surface response is also very weak over the North Atlantic, except for a low-pressure anomaly centred over Europe. Interestingly, this feature is also present (but not significant) in October November and December January; however, there is no corresponding feature in either the model or the observational composites. Consistency between the SNA ensemble results and the model composites is only seen in October November and in the sign of anomalies in the tropical Atlantic in December January and February March. There is no agreement (in regions of significance) with the observational composites. 6. CONCLUSIONS Using SST observations, atmospheric re-analyses, and the HadCM3 coupled climate model, we have investigated the evolution of North Atlantic Ocean and atmosphere conditions in the winters following Mays in which a particular pattern of SST anomalies (the SNA dipole pattern, Fig. 1(a)) was present. The choice of SST pattern was motivated by previous studies that have suggested an impact of late spring and summer Atlantic Ocean conditions on the subsequent winter climate (Czaja and Frankignoul 1999, 22; Rodwell and Folland 22; Drevillon et al. 23; Frankignoul and Kestenare 25). We first used composite analysis (based on projection of May SSTs onto the SNA pattern), and then carried out ensemble experiments integrated from 1 May through the following winter. In both cases we focused our analysis on the two-month periods October November, December January and February March. Our key findings are as follows: Observational SST composites showed a tripolar SST pattern (similar to that present in May). The SST anomalies appear to strengthen a little between October November and December January, possibly due to re-emergence, before weakening (in the case of +SNA) in February March. Observational composites of atmospheric fields (SLP and 5 hpa geopotential height) showed generally weak signals. The strongest signal found was for +SNA in October November. In addition, +SNA in December January and February March, and SNA in February March, showed anomaly patterns resembling the NAO pattern. The sign of the NAO projection was consistent with the December February results of Rodwell and Folland (22) for +SNA in December January and SNA in February March, but not for +SNA in February March. (Note that the Rodwell and Folland analysis was linear, so did not distinguish between +SNA and SNA.) Composites were also examined with the removal of years in which an ENSO event developed during the winter and, although the sample size was small, the signals were generally little changed from, or slightly stronger than, those including ENSO years, confirming at least that the signals were not dominated by possible remote ENSO effects. Model SST composites (computed from a 5-year control simulation) showed a tripolar pattern of anomalies in reasonable agreement with the observations but,

22 2998 A. M. IWI et al. with one notable exception, the anomalies in the tropical Atlantic region were too weak, especially in December January and February March. In the east Atlantic, the anomalies are too weak even in May; this is probably due to the mixed layer being too deep in the model, reducing the ability of the along-shore wind anomalies and surface flux anomalies to generate SST anomalies in this region. However, the underrepresentation of winter anomalies also affects the west Atlantic where May anomalies are more realistic; this appears to be because SST anomalies in the tropical Atlantic region are damped too quickly in the HadCM3 model (consistent with the findings of Frankignoul et al. 24). Possible causes for this are that the vertical resolution of the mixed layer is insufficient to capture the capping of subsurface temperature anomalies during summer, or that air-sea fluxes in the subtropics are too strong in the model due to overly strong trade winds. Model composites of atmospheric fields again showed weak signals. Where signals were significant (e.g. for SNA in October November) there was no agreement with the observations. We hypothesize that the failure of the model to reproduce the atmospheric signals present in the observational composites is largely a consequence of the excessive damping of SST anomalies in the tropical Atlantic region. Evidence in support of this hypothesis is that the atmospheric component of the coupled model, when forced with observed SST anomalies, can simulate some observed atmospheric anomalies (Mathieu et al. 24). Initial-condition ensembles were constructed in order to ensure higher signalto-noise in the results. In these, the SST fields showed the tripolar pattern with, as expected, larger anomalies than in the model composites computed from the control simulation. Nevertheless, the SST anomalies in the tropical Atlantic were still damped too rapidly, and were very weak by February March. For +SNA initial conditions a significant atmospheric signal, with a consistent pattern throughout October November, December January and February March, was found. In the tropical and mid-latitude Atlantic the SLP pattern was similar to the SST pattern, with positive (negative) SLP anomalies overlying negative (positive) SST anomalies. For SNA initial conditions the atmospheric signals were still weak. Overall, our results lend support to the suggestion from previous studies that Atlantic Ocean conditions can influence the wintertime climate over the North Atlantic region. They confirm that the influences are weak, but can be significant, and may, therefore, be exploitable for seasonal forecasting. In particular, our +SNA initialcondition ensemble is direct evidence that a seasonal forecast initialized on 1 May could provide useful information about the conditions the following winter. This is in spite of the fact that the model we used, HadCM3, appears to suffer from a significant error in damping too rapidly SST anomalies in the critical tropical Atlantic region. If this problem could be corrected, we would expect the signal-to-noise associated with the seasonal forecasts to increase significantly. Obtaining a better understanding of the causes of this overdamping is, therefore, an important priority for future research. ACKNOWLEDGEMENTS This research was supported by the UK Natural Environment Research Council s COAPEC programme. We acknowledge use of the Met Office s Portable Unified Model and HadISST data, the NCEP re-analysis and XBT datasets. Coupled Ocean Atmosphere Processes and European Climate.

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