A MID-SHELF, MEAN WAVE DIRECTION CLIMATOLOGY FOR SOUTHEASTERN AUSTRALIA, AND ITS RELATIONSHIP TO THE EL NIÑO SOUTHERN OSCILLATION SINCE 1878 A.D.

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 25: (25) Published online in Wiley InterScience ( DOI: 1.12/joc.127 A MID-SHELF, MEAN WAVE DIRECTION CLIMATOLOGY FOR SOUTHEASTERN AUSTRALIA, AND ITS RELATIONSHIP TO THE EL NIÑO SOUTHERN OSCILLATION SINCE 1878 A.D. IAN D. GOODWIN* Environmental and Climate Change Group, School of Environmental and Life Sciences, University of Newcastle, Callaghan NSW 238, Australia Received 4 October 24 Revised 4 April 25 Accepted 4 April 25 ABSTRACT Coastal systems behave on timescales from days to centuries. Shelf and coastal wave climatological data from the Tasman Sea are only available for the past few decades. Hence, the records are too short to investigate inter- and multidecadal variability and their impact on coastal systems. A method is presented to hindcast monthly mid-shelf mean wave direction (MWD) for southeastern Australia, based on the monthly, trans-tasman mean sea-level pressure (MSLP) difference between northern NSW (Yamba) and the north island of New Zealand (Auckland). The MSLP index is calibrated to instrumental (Waverider buoy) MWD data for the Sydney shelf and coast. Positive/negative trans-tasman MSLP difference is significantly correlated to southerly/easterly Sydney MWD, and to long/short mean wave periods. The 124-year Sydney annual (MWD) time series displays multidecadal variability, and identifies a significant period of more southerly annual MWD during 1884 to 1914 than in the period since The Sydney MWD is significantly correlated to the Southern Oscillation Index (SOI). The correlation with the SOI is enhanced during periods when the Interdecadal Pacific Oscillation (IPO) is in its negative state and warm SST anomalies occur in the southwest Pacific region. The Sydney MWD was found to be associated with Pacific basin-wide climate fluctuations associated with the El Niño-Southern Oscillation (ENSO). Southerly/easterly Sydney MWD is correlated with low/high MSLP anomalies over New Zealand and the central Pacific Ocean. Southerly/easterly Sydney MWD is also correlated with cool/warm SST anomalies in the southwest Pacific, particularly in the eastern Coral Sea and Tasman Sea. Copyright 25 Royal Meteorological Society. KEY WORDS: wave climate; mean wave direction; El Niño-southern oscillation; south-east Australia; mean sea-level pressure 1. INTRODUCTION Wave-driven currents are the principal mechanism for sand transport on the southeastern Australian inner continental shelf and surf zone. The direction of these currents is governed by the direction of the deepwater ocean waves and their subsequent refraction over the shoaling continental shelf. The southeastern Australian wave climate is dominated by low to moderate ocean swell waves generated in the Tasman Sea by midlatitude easterly tracking depressions (Short and Trenaman, 1992). These waves generally propagate towards the coast from the east southeast to south (76% frequency, M. Kulmar, Manly Hydraulics Laboratory (MHL), New South Wales Department of Commerce, personal communications, 25). This wave climate has resulted in a longshore sand transport system along the coast from southern New South Wales (NSW) to southeast Queensland. Coastline stability in southeastern Australia is intrinsically linked to the temporal variability in sand transported by longshore and cross-shore mechanisms, under modal and storm conditions (Thom, 1978; * Correspondence to: Ian D. Goodwin, Environmental and Climate Change Group, School of Environmental and Life Sciences, University of Newcastle, Callaghan NSW 238, Australia; Ian.Goodwin@newcastle.edu.au Copyright 25 Royal Meteorological Society

2 1716 I. D. GOODWIN Short, 1999). Hence, coastal engineering and planning studies require daily to decadal time series of wave direction data in conjunction with wave height and period statistics. In southeastern Australia, Waverider buoys have been deployed on the inner to middle continental shelf to collect wave data over the past few decades. Waverider buoys have been moored off Eden (37 17 S, 15 1 E) since February 1978, Batemans Bay (35 42 S, 15 2 E) since May 1986, Port Kembla (34 28 S, E) since February 1974, off Sydney (33 47 S, E) since 1971, Crowdy Head (31 49 S, E) since October 1985, Coffs Harbour (3 21 S, E) since May 1976, and Byron Bay (28 43 S, E) since October 1976 (Manly Hydraulics Laboratory, 1999). However, instrumental wave directional data have only been collected since the deployment of a directional Waverider buoy in March 1992 off Sydney. The buoy is moored approximately 1 km off Sydney s northern beaches (33 47 S, E) in 85 m water depth, on the middle shelf. Subsequently, directional Waverider buoys have been deployed in October 1999 off Byron Bay, and in 2 off Batemans Bay. Prior to these deployments, daily incident, coastal wave direction data have been collected by shore-based observers at manned lighthouses (data held by the Bureau of Meteorology BOM), port authorities, and specifically, at Narrabeen Beach on Sydney s northern beaches (1976 till present), by the Coastal Studies Unit, University of Sydney (Short and Trenaman, 1992; Short et al., 2; Ranasinghe et al., 24). In addition, proxy mean wave direction (MWD) data have been graphically determined by staff at the MHL using the Australian Bureau of Meteorology (BOM) daily synoptic sea-level pressure analysis charts (M. Kulmar, MHL, personal communications, 21). The hindcast MWD data span the duration of the wave height and period time series recorded by the NSW Waverider buoys. However, there has been no clear methodology available to verify the accuracy of the hindcast wave direction time series. While the spectral wave height and period data collected by the network of Waverider buoys along the NSW coast have led to the calculation of comprehensive monthly and annual wave climate summaries including wave exceedence statistics, the short duration of the record has prevented any statistical examination of inter- and multidecadal variability. Research by Short et al. (1995, 2) and Ranasinghe et al. (24) has demonstrated the statistical relationship between the Troup Southern Oscillation Index (SOI) and embayed beach alignment changes on Sydney s northern beaches. They found that incident wave direction at Sydney s beaches was predominantly south east during both phases of El Niño Southern Oscillation (ENSO), but became more southerly/northerly with decreasing/increasing SOI (El Niño/La Niña). Their research has focused on the impact of quasi-biennial (QB) (2 2.5 years) ENSO fluctuations particularly in the event. Phinn and Hastings (1992, 1995) have examined the relationship between the summer wave climate in southeastern Australia and the SOI, over an observational period spanning QB and low-frequency, inter-annual (2.5 7 years) variability. This paper describes a method for hindcasting mid-shelf, monthly MWD along the southeastern Australian coast, over the full period of instrumental meteorological observations. The temporal variability of MWD from QB, to interdecadal periods is examined. The Australian and southwest Pacific regional climatology associated with monthly and annual MWD in the southeastern Australian region is also investigated, together with the ENSO and Interdecadal Pacific Oscillation (IPO) (Power et al., 1999) phenomena. 2. METHODOLOGY Previous hindcasting of MWD has been based on the daily synoptic sea-level pressure analysis charts (M. Kulmar, MHL, personal communications, 21), and covers the periods of non-directional Waverider buoy deployment. Hence, a methodology was designed in this study, to hindcast mid-shelf MWD from the mean sea-level pressure (MSLP) difference spanning the Tasman Sea, between eastern Australia and New Zealand. The focus on the mid-shelf wave climate was determined by the location of the Sydney directional Waverider buoy. The trans-tasman Sea MSLP difference index was regressed against the instrumental MWD data (Sydney Waverider buoy), and the relationship applied after cross validation to hindcast MWD data, for the Sydney region.

3 WAVE DIRECTION CLIMATOLOGY MSLP data The main synoptic features of this region are the ubiquitous sub-tropical anticyclone (STAC) over Australia, and the transient mid-latitude cyclonic eddies and high-pressure ridges over the Tasman Sea and New Zealand (Sturman and Tapper, 1996). The STAC typically moves poleward between 3 to 4 S during the austral summer and equatorward to about 3 S during the austral winter (Pittock, 1973). The latitudinal position of the STAC is associated with the path of transient mid-latitude cyclonic eddies in the Tasman Sea, which are the dominant influence on the deepwater wave direction. Hence, the BOM archives were searched for longest instrumental MSLP records on the NSW coast that best described the synoptic pattern associated with the time-varying position of the STAC over the western Tasman Sea. Similarly, the National Institute of Water and Atmosphere Research (NIWA), New Zealand, Climate Database was searched for instrumental MSLP records on the west coast of New Zealand that described the synoptic pattern associated with the time-varying position of the STAC, and the path of mid-latitude cyclones over the eastern Tasman Sea. Two MSLP time series were selected from the BOM archive: a record from the NSW central coast at Sydney (Observatory Hill) (BOM 66 62, S, E) 1858 to present; and a record from the northern NSW coast at Yamba (Clarence Heads) Pilot Station (BOM 5812, S, E) 1877 to present. One MSLP time series was selected from the NIWA archive: the record from the north island of New Zealand at Auckland (A64871, S, E) 1853 to present. The analyses in this paper were conducted using monthly average MSLP data for the time period up to 22, as determined by data availability. The available metadata for the MSLP stations was analysed to ensure that the data sets were free from SLP data reduction errors. Barometer index, gravity, temperature, and altitude corrections were applied to the Yamba MSLP data by BOM and were checked by the author, to ensure internal data homogeneity. The Yamba MSLP data were also cross-correlated (R =.83) against the MSLP data for Brisbane (location in Figure 1) to ensure that barometer changes had not influenced the homogeneity of the time series, and that the Yamba data accurately represented the regional MSLP field, over the 124-year period. The Auckland MSLP data set had previously been checked for homogeneity and data reduction by NIWA and has been used in regional climate change studies (Jones et al., 1999). Hence, the monthly, mean MSLP difference data presented below are considered to be homogeneous Mid-shelf Waverider buoy wave direction data Monthly MWD data are time-averaged data calculated from hourly observations recorded by the directional Waverider buoy at Sydney, and were obtained from MHL. MWD is an integral wave parameter and represents the MWD for the peak of the wave energy spectrum, which corresponds to swell waves in the recorded spectrum. Hence, the MWD is not an average of all swell and sea states present over any hour, but the direction of the peak wave energy that is associated with swell waves rather than wind waves. Kulmar (1995) analysed Sydney wave direction data for , and showed that wave direction distribution was similar for both short-period (T z < 7 s) and long-period (T z > 7 s) waves, except that the short-period wave distribution included 1% occurrence from the northeast, which is absent from the long-period wave direction distribution. Hence, the MWD used in this study is indicative of both short- and long-period waves, except for the occurrence of short period north east waves, which are usually associated with sea breezes and anticyclonic conditions during summer months. The Sydney MWD time series available for this study covers the period from March 1992 to June REGRESSION ANALYSIS OF TRANS-TASMAN MSLP DIFFERENCE AND SYDNEY WAVERIDER BUOY MEAN WAVE DIRECTION DATA Two trans-tasman monthly mean MSLP difference indices were calculated: Yamba Auckland (Y A) ( ) and Sydney Auckland (S A) ( ). These are shown in Figures 2a and b, respectively. Both indices contain high inter-annual variability, while the Y A time series contains greater decadal to multidecadal variability. The Y A time series best captures the time-varying location of the STAC since

4 1718 I. D. GOODWIN Figure 1. Location map showing the southeastern Australian, Tasman Sea, and New Zealand region the two MSLP data sets were observed across the range of latitudinal movement of the STAC (Yamba 29 S, and Auckland 37 S). Both the Y A and S A monthly mean MSLP difference indices were regressed against the Sydney MWD time series for 1992 to 21, yielding respective correlation coefficient R values of.63 (p <.1) and.44(p<.1). The higher correlation for the Y A MSLP index indicates that the two sites best represent the time-varying synoptic climate pattern over the Tasman Sea. The Y A monthly MSLP index and the Sydney monthly MWD time series are shown in Figure 3. Both time series display a strong annual cycle, and the figure shows that the Y A index significantly captures the annual and inter-annual wave direction variability. Positive/negative Y A MSLP difference is correlated to southerly/easterly Sydney MWD. The Y A MSLP index is best correlated to southerly and southeasterly fluctuations in wave direction, and is less sensitive (albeit significant correlation) to summer fluctuations in easterly to northeasterly wave direction. The latter are produced by tropical cyclones and depressions in the Coral Sea, located to the north of the Y A transect. Consequently, this method of hindcasting wave direction best describes the fluctuations in the modal (south east) wave climate as defined by Short and Trenaman (1992), which is incident along the NSW shelf and coastline. Regression between the Y A MSLP difference and the Sydney monthly mean spectral-peak wave period (MTP) also reveals significant correlation (R =.46, p<.1), with the highest MTP of 8.5 to 12 s in the Waverider buoy dataset coincident with the winter maximum in Y A MSLP difference. The association reflects the typical winter synoptic pattern with a strengthened and/or equatorward STAC producing higher MSLP over eastern Australia and the passage of mid-latitude cyclones moving eastwards across the southern Tasman Sea and New Zealand, generating long-period swell from the southern Tasman Sea. Figure 4 shows the typical synoptic patterns described by a high positive and high negative Y A MSLP index, together with the Sydney MWD associated with these patterns. Since the data contain a strong annual cycle, the Y A monthly MSLP and Sydney MWD datasets were deseasonalised (converted to monthly anomalies) and regressed to determine whether the seasonality

5 WAVE DIRECTION CLIMATOLOGY Yamba Auckland MSLP difference index (hpa) (a) Sydney Auckland MSLP difference index (hpa) (b) Figure 2. The monthly mean (thin grey line) and annual mean (thick black line) sea-level pressure (MSLP) difference indices for (a) Yamba minus Auckland, and (b) Sydney minus Auckland significantly biases the correlation between the two series. The correlation coefficient is equivalent to R =.62 (p <.1) for the deseasonalised data. This indicates that the annual cycle is not significantly biasing the high correlation between the Y A and Sydney MWD time series. Whilst the overlapping period of the Y A and Sydney MWD time series is restricted to 1992 to 21, this is not considered to bias the use of the Y A time series as a reliable proxy for MWD variability over the past century. The short period from 1992 to 21 includes both extended El Niño (3 years) and La Niña events (3 years) and 4 neutral years. It is interpreted from Figure 3 that the Y A monthly MSLP index adequately captures MWD variability throughout the ENSO cycle.

6 172 I. D. GOODWIN Y-A MSLP monthly difference index (hpa) Sydney monthly mean wave direction ( T) Figure 3. The monthly mean Yamba minus Auckland (Y A) MSLP difference index (solid line) together with the Sydney monthly MWD (dashed line) measured by a directional Waverider buoy, for HINDCAST MONTHLY MID-SHELF MEAN WAVE DIRECTION TIME SERIES Since the Y A monthly MSLP difference index and Sydney MWD are significantly correlated, the deseasonalised data were used to determine whether a linear regression model was suitable to predict (hindcast) Sydney MWD. The observed monthly MWD and hindcast monthly MWD were cross-validated to determine the skill of the hindcasting method. The hindcast monthly MWD are predicted to be better than ±1 of the observed data for 74% of the 1992 to 21 period, and better than ±5 for 4% of the time series. Using annual mean hindcast and observed MWD data, the skill is improved with the predicted MWD within ±5 of the observed data for 8% of the time series. Low skill at predicting MWD occurs for some austral spring seasons (particularly October and November) when the Y A MSLP difference is small and easterly waves become dominant over the southeasterly waves (Short and Trenaman, 1992). The strong sub-tropical ridge over the northern Tasman Sea (as shown in Figure 7B) is typical of the October and November months. It is interpreted that the Y A MSLP difference technique is least sensitive at reliably predicting Sydney MWD during these months, and during strong La Niña years, such as On the basis of the cross-validation, the raw monthly Y A MSLP differences spanning 1878 to 21, were calibrated to raw monthly MWD using the regression equation MWD = 2.21(MSLPdiff) + (129.96), which was determined for all months during the 1992 to 21 period. The resulting monthly MWD time series is shown in Figure 5a, and the annual MWD time series for Sydney is shown in Figure 5b. Monthly MWD ranges from 98 (Jan, 1883) to 169 (July, 1939) and the mean monthly MWD is 133 over the 124 years. Spectral analysis of the monthly MWD data revealed significant periods of variability apart from the annual cycle, at multi-decadal (58 years, p>.1), and quasi-decadal (13 years, p>.1). Two distinct multi-decadal periods in 1894 to 1914 and 197 to 199 represent the extreme multi-decadal shifts in MWD. High inter-annual variability of 8 to 1 within a range from 127 to 14 occurs throughout the annual mean MWD record. The mean annual MWD during 1894 to 1914 is more southerly (136 ) when compared to the 124-year mean (133 ). Short and Trenaman (1992) showed that strong seasonality characterises the Sydney wave climate as a result of the seasonal dominance of the major wave generation sources: tropical cyclones from December to April; east coast cyclones from May to September, particularly May, June and July; and mid-latitude cyclones, throughout the year but particularly from March to September. North east sea breezes and east to

7 WAVE DIRECTION CLIMATOLOGY 1721 (a) (b) Figure 4. Typical daily MSLP analysis charts from the BOM showing the synoptic patterns associated with (a) strong positive Y A MSLP index (+23 hpa) on 17 June 21 and recorded southerly monthly MWD (145 ) for June 21 and (b) strong negative Y A MSLP index ( 11 hpa) on 6 March 21 and a recorded easterly monthly average MWD (117 ) for March 21 northeast storm waves produced by anticyclone intensification contribute to the wave climate in the summer months. Short and Trenaman (1992) summarised the monthly modal MWD for the 1976 to 1985 period, as: east for January to March and October to November; southeast for April to September, and December; and with subdominant waves from the northeast in all months. For comparison, the monthly MWD is shown in Figure 6 for The monthly mean statistic was used for this comparison rather than the monthly mode statistic since the hindcast MWD data are monthly mean data rather than daily mean data that were used in Short and Trenaman (1992) for the study. The MWD over the 1878 to 21 record shows a defined annual cycle from (south east) in the austral summer months (December to March) to (south south-east) in the austral winter months (May to September). Figure 6 also shows the annual MWD cycle for the two extreme multi-decadal periods from 1894 to 1914 and 197 to

8 1722 I. D. GOODWIN 1 Sydney monthly mean wave direction ( T) (a) Sydney annual mean wave direction ( T) East South (b) Figure 5. (a) The monthly (thin grey line) and, (b) annual (thick black line) hindcast MWD time series for 1878 to During the 1894 to 1914 period, the mean MWD for every month, except June, defines a more southerly wave direction than the remainder of the record. During this anomalous period, MWD ranges from 2 to 3 more southerly in January to May, increasing to 4 5 more southerly from September to December. This corresponds to an increase in the Y A monthly MSLP difference by 2 hpa. Analysis of the mean MWD for austral spring early summer months (October, November and December (OND)) over the whole record showed that the MWD was the most variable during these months, with a monthly MWD range of 116 to 147. Figure 6 shows that mean monthly MWD during 1894 to 1914 was up to 9 more southerly than the MWD during 197 to 199 for the spring months. The composite mean monthly MSLP field (NCEP/NCAR Reanalysis (NNR) data) for June, July and August (JJA), and OND 197 to 199, are shown in Figure 7,

9 WAVE DIRECTION CLIMATOLOGY Sydney monthly mean wave direction ( T) Figure 6. The annual cycle in MWD based on monthly MWD for all months between 1878 and 21. Also shown is the monthly MWD for the multi-decadal periods and together with the corresponding MWD. The 9 more southerly wave climate in JJA is associated with a stronger STAC over eastern Australia and the western Tasman Sea, and a weaker STAC over the southwest Pacific, when compared to the synoptic pattern for OND 197 to 199. The more southerly monthly MWD during OND in is the result of a dominance of the JJA type synoptic conditions, with either a greater frequency of mid-latitude cyclones or that the wave generation area of mid-latitude and east coast cyclones was located further south in the Tasman Sea. In order to understand the temporal pattern of Sydney MWD forcing further, the regional climatology is investigated in the next section. 5. REGIONAL CLIMATOLOGY ASSOCIATED WITH SOUTHEASTERN AUSTRALIAN MID-SHELF MEAN WAVE DIRECTION The previous work by Short et al. (1995, 2) and Ranasinghe et al. (24) has demonstrated a strong link between Sydney beach behaviour, MWD, storm frequency, and the Troup SOI (National Climate Centre, BOM) for the past few decades. Such a relationship has the potential to provide coastal managers with a simple forecasting tool. To test this association over the longer time series, the monthly MWD and SOI time series were correlated for each month of the 124-year time series. The highest correlations exist during the austral spring (October, November, R =.26, p<.4, R =.31, p<.4) and the austral autumn (March, April, R =.21, p<.19,.28, p<.2). The modal MWD during these months is easterly, with southeasterly subdominant (Short and Trenaman, 1992) and is associated with ocean waves generated by anticyclonic airflow, rather than mid-latitude cyclones. Figure 8 shows the annual mean MWD and SOI data for the 1878 to 21 period, which are significantly correlated (R =.5, n = 124, p>.1). A significant correlation between the MWD and the SOI was expected because both the MWD and the SOI are determined from meridional differences of the Indo Pacific pressure field, and the tropical and midlatitudes should be teleconnected through the Hadley circulation. However, there are noticeable departures between the MWD and SOI series in Figure 8. The two series are poorly correlated during some periods, e.g and Whilst the SOI is a measure of tropical atmospheric behaviour associated with ENSO, the dominant weather systems controlling MWD are located in the mid-latitudes. While there are strong teleconnections associated with ENSO between the tropics, the mid-latitudes, and the extratropics (Meehl, 1987), the MWD is also influenced by time-varying atmospheric circulation associated with the

10 1724 I. D. GOODWIN EQ 5S 1S 15S 2S 25S 3S 35S 4S 45S 5S 55S 6S 65S 7S (a) E NCEP/NCAR Reanalysis Sea level pressure (mb) composite mean E 14E 16E 18 16W 14W 12W 1E 8W Jun to Aug: 197 to 199 NOAA CIRES/Climate Diagnostics Center EQ 5S 1S 15S 2S 25S 3S 35S 4S 45S 5S 55S 6S 65S 7S (b) E NCEP/NCAR Reanalysis Sea level pressure (mb) composite mean E 14E 16E 18 16W 14W 12W 1E 8W Oct to Dec: 197 to 199 NOAA CIRES/Climate Diagnostics Center Figure 7. The composite mean MSLP (NCEP/NCAR Reanalysis data) for: (a) June, July and August 197 to 199; and (b) October, November and December 197 to 199. Also shown are the respective average hindcast MWD for the composite period, and correspond to a MWD difference of 9 which is equivalent to the difference in the Y A MSLP values (5.25 hpa) between the composite seasons Southern Annular Mode (SAM) of climate variability. The SAM describes the see-saw of atmospheric mass between the mid-latitudes and Antarctica (Rogers and van Loon, 1982; Kidson, 1988). When the SAM index is positive, the mid-latitude westerlies are latitudinally displaced poleward (towards 6 S) and when the index is negative, the westerlies are displaced equatorward (towards 45 S). During the positive index phase of the SAM, cyclone frequency to the east of New Zealand is reduced and the frequency of anticyclonic blocking increased (Sinclair et al., 1997). Under these conditions mid-latitude cyclones are often deflected northwards over the Tasman Sea and lead to a more easterly MWD. Goodwin et al. (24) reconstructed the winter SAM

11 WAVE DIRECTION CLIMATOLOGY Sydney annual mean wave direction ( T) SOI annual average Figure 8. The hindcast annual MWD (solid line) plotted against the annual mean SOI (dashed line) for 1878 to 21. Also shown in the bar at the bottom of the plot are the positive (grey) and negative (black) phases of the Interdecadal Pacific Oscillation (IPO) index and mid-latitude climate variability over the past 7 years using Antarctic ice core chemical time series. Comparison with the reconstructed winter mid-latitude climate (not shown) suggests that the periods when the mean annual MWD and SOI are poorly correlated occur during positive SAM index climates. It is suggested that the SAM may modulate MWD fluctuations during these positive SAM index climate periods. Alternatively, negative SAM index climate reinforces a southerly MWD because of the higher frequency of mid-latitude cyclones in the southern Tasman Sea (Goodwin et al., 24). Using the NNR data set (Kalnay et al., 1996; Kistler et al., 21), the mean monthly and mean annual MWD time series were correlated against the MSLP and sea surface temperature (SST) fields to examine the associated broadscale climate patterns in the Indo Pacific. Figure 9 shows the cross-correlation between the mean annual MWD and the corresponding NNR MSLP for the period (NNR dataset commences in 1948). The spatial pattern is similar to that between the SOI and the MSLP fields, except that the regions of greatest cross-correlation are located in the subtropics and the circum-antarctic. This is consistent with the latitudinal range of the Y A MSLP index. While high correlation exists in tropical Australia and the subtropical to circum-antarctic Pacific and New Zealand areas for all months, analyses shows that high correlation between MWD and MSLP over southeastern Australia exists only for February, March, May, September, and December. Southerly/easterly MWD is significantly correlated with low/high MSLP anomalies over New Zealand and the central Pacific Ocean. Recent work on Australian and South Pacific climate has identified that inter-decadal climate variability described by the IPO modulates the inter-annual fluctuations described by ENSO with a 15 3 year period (Power et al., 1999; Salinger et al., 21). The IPO has been defined by the third empirical orthogonal function (EOF) of the low frequency SST variability across the Pacific basin (Folland et al., 1998; Power et al., 1999). IPO SST anomalies in the central tropical Pacific are anti-correlated to SST in the sub-tropical and mid-latitude southwest Pacific (Power et al., 1999; Folland et al., 22). The cross-correlation between the monthly MWD and the corresponding NNR SST for the is shown in Figure 1. The pattern is similar to that for the SOI and SST, except for the stronger correlation with the south west Pacific node of IPO variability. The MWD is negatively correlated (R =.6, p<.5) to this node and in particular to the SST along a crescent shaped belt from the northern Coral Sea to the southern Tasman Sea and over New Caledonia and New Zealand. Southerly/easterly MWD is correlated with cool/warm SST anomalies in the southwest Pacific.

12 1726 I. D. GOODWIN 4N 3N 2N 1N EQ 1S.6.2 2S 3S 4S 5S 6S 7S 8S 9S E 12E 14E 16E 18 16W 14W 12W 1W 8W 6W Jan to Dec: 1978 to 2: surface sea level pressure Seasonal correlation w / Jan to Dec Sydney wave direction NCEP/NCAR reanalysis NOAA CIRES/Climate Diagnostics Center Figure 9. The spatial pattern of cross-correlation between monthly MWD and NCEP/NCAR Reanalysis MSLP in the South Pacific region, for The correlation between MWD and SST in the eastern Tasman Sea was investigated further to determine whether the association was robust. The most suitable long-time series of monthly SST for comparison with the MWD was the extended reconstruction of the Comprehensive Ocean Atmosphere Dataset SST (COADS) known as the NOAA NCDC ERSST version 2 data (Smith and Reynolds, 24). The 5 5 grid cell centred on 37.5 S and E contained the greatest number of ship-based observations in the earlier part of the 1854 to 24 record, since it covered the shipping routes between Australia and New Zealand. Both sets of monthly data were filtered using a 12-month running mean and are shown in Figure 11. The two serially correlated records display high correlation (R =.47,p <.1) and highlights the robust association between the two parameters. The anomalous period of more southerly wave direction between 1894 and 1914 is correlated to cool SST anomalies in the Tasman Sea, and to extended El Niño events (Figure 8). Power et al. (1999) established that the association between ENSO and Australian climate is reinforced during periods when the IPO raised SST s in the south west Pacific node (IPO negative phase). This is supported by the time-series plot in Figure 8, which shows that the best coherence between the inter-annual MWD variability and the SOI occurs during the IPO negative phases. The R value for IPO negative phases is -.53, while for IPO positive phases, it is reduced to During IPO negative phases, the warm SST decadal anomalies in the south west Pacific directly modulate the sea-level pressure field and the wave climate in

13 WAVE DIRECTION CLIMATOLOGY N 3N.4.2 2N 1N EQ 1S 2S 3S 4S 5S 6S 7S 8S S 1E 12E 14E 16E 18 16W 14W 12W 1W 8W 6W Jan to Dec: 1948 to 2: surface SST Seasonal correlation w / Jan to Dec Sydney wave direction NCEP/NCAR reanalysis NOAA CIRES/Climate Diagnostics Center Figure 1. The spatial pattern of cross-correlation between monthly MWD and NCEP/NCAR Reanalysis SST in the South Pacific region for the Coral Sea and Tasman Sea. In contrast, during IPO positive phases, the wave climate is modulated by the behaviour of the STAC over eastern Australia, the Tasman Sea, and New Zealand. It is possible that when the behaviour of the STAC is coupled to the SAM during IPO positive phases, the SAM exerts a modulating influence on MWD. 6. CONCLUSIONS The method described for hindcasting and forecasting MWD based on the trans-tasman MSLP difference between Yamba and Auckland is temporally robust when compared to instrumental MWD data over short periods ( ), incident MWD observations at Sydney s northern beaches ( ), and to the SOI and Tasman Sea SST over 124 years. The method is most reliable during the austral autumn and winter months and during El Niño years. While the MWD data have been calibrated to the Sydney region, these data are representative of wave direction variability not only along the NSW central coast but also along the entire NSW coast, since the method captures the variability of the modal southeasterly wave climate defined by Short and Trenaman (1992). The MWD varies with a strong annual cycle, coupled to mean, spectral-peak wave period. Accordingly, months and years where a more southerly MWD occurs are accompanied by an increase in the spectral-peak wave period. The most significant multi-decadal fluctuation in the time series is from 1894 to 1914, when

14 1728 I. D. GOODWIN ERSST 37.5S 167.5E 12-month mean ( C) Sydney mean wave direction 12-month mean ( T) Figure 11. The temporal SST variability (dashed line) in the eastern Tasman Sea, centred on 37.5 S and E, together with MWD (solid line). The monthly mean data have been filtered with a 12 month moving mean Tasman Sea SST were C cooler, monthly and annual wave direction was up to 4 5 more southerly, and by inference, spectral-peak wave period was longer when compared to the series since The sustained shift in wave direction would have had a significant influence on beach and coastal compartment alignment along the NSW coast, if the more recent results of Ranasinghe et al. (24) are applied to the data. The model of Ranasinghe et al. (24) would indicate coastal recession at the southern ends, and strong onshore sand transport and coastal accretion at northern ends of embayed beaches along the NSW central coast. Accordingly, these fluctuations in mid-shelf wave direction have important consequences for the understanding of longshore sediment transport variability along the central and north coasts of NSW and its impact on coastal stability. These impacts of wave climate variability are the subject of an additional paper and are not discussed further here. The Sydney MWD time series and its relationship to Pacific climate variability presented in this paper allow greater prospects for predicting the wave climate and its impact on coastal behaviour. Prospects for forecasting MWD fluctuations are greatest for periods when the IPO is in its negative phase and MWD is more closely coupled to ENSO. However, the significant correlation between the annual MWD and the SOI for the entire 124 years indicates that mid-shelf wave direction behaviour can be interpreted in association with ENSO. The quasi- and multidecadal variability in the MWD data are the combined influence of both the IPO ( 15- to 3-year periods) and the SAM (1.5- to 23-year periods, Goodwin et al., 24). The MWD fluctuations are significantly correlated to both MSLP and SST anomalies in the southwest Pacific region. Southerly/easterly MWD is correlated with low/high MSLP anomalies over New Zealand and the central Pacific Ocean. Southerly/easterly MWD is also correlated with cool/warm SST anomalies in the southwest Pacific, particularly in the eastern Coral Sea and Tasman Sea. ACKNOWLEDGEMENTS I thank staff at the National Climate Centre, Bureau of Meteorology, Melbourne, Australia, in particular, Mr John Flannery for overseeing the digitisation and correction of the Yamba MSLP data from the original pressure observations. Mr Mark Kulmar at the Manly Hydraulics Laboratory, NSW Department of Public Works and Services provided the monthly wave climate data for the Sydney Waverider buoys. Dr Jim Salinger at the

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