and Courtney (1995) found that in the. Benguela region (off south-west Africa), the sea ..... Models: A Roughness Penalty Approach. Chapman and Hall, London.
Seasonal and Inter-Annual Variability of SST off the East Coast of the South Island, New Zealand A.G.P. Shaw Department of Marine Science and Department of Surveying, University of Otago, P.O Box 56, Dunedin, New Zealand
L. Kavalieris Department of Mathematics and Statistics, University of Otago, P.O Box 56, Dunedin, New Zealand
R. Vennell Department of Marine Science, University of Otago, P.O Box 56, Dunedin, New Zealand
Abstract This study examined the temporal variability of sea surface temperature (SST) of three water masses east of the South Island, New Zealand, where two of these water masses are part of the global Subtropical Front (STF). The STF is well defined and can be detected easily using remote sensing Advanced Very High Resolution Radiometer (AVHRR) SST data. Ten sites were used from a ten year AVHRR SST data set and they were compared with a manually sampled 45 year SST data set from Portobello Marine Laboratory, Dunedin. This investigation decomposed each SST series into a periodic seasonal component and an interannual trend. The latter component was further decomposed into low and mid-frequency bands using smoothing techniques. The results showed that the periodic component of offshore waters were in phase and reached their maximum temperature 10 days after the inshore neritic waters. The amplitudes of seasonal variations of Subantarctic and Subtropical water masses were similar while the neritic waters showed considerably higher seasonal variation. El Niño has a clear effect and lowers the temperatures of all water masses by 1.4ºC at Portobello and 2.2-3.4ºC for all offshore waters.
Introduction The Subtropical Convergence or Subtropical Front (STF) is a global front extending around the Southern Ocean and is the boundary where the subantarctic surface water mass (SAW) converges with the subtropical surface water mass (STW). The STF is located at approximately 40ºS, but is deflected south around New Zealand which lies across its path (Garner, 1954). The STF passes close to the southern east coast of the South Island, New Zealand, where it is named locally as the Southland Front (SF). Close inshore is the Neritic water mass (NW) which is influenced by freshwater runoff. A warm water mass (WW) has also been described further offshore that was of SAW origin with different thermal properties (Shaw, 1998). This feature can also be seen in the eof analysis in Chiswell (1994). This study investigates the seasonal and inter-annual variability in sea surface temperature (SST) of SAW, STW and NW at ten sites off the Otago and Canterbury coasts of the South Island, New Zealand. The SST measurements were taken from satellite Advanced Very High Resolution Radiometer (AVHRR) data. These data were compared Geocarto International, Vol. 14, No. 3, September 1999 Published by Geocarto International Centre, G.P.O. Box 4122, Hong Kong.
with direct temperature measurements taken in Otago Harbour at Portobello, near Dunedin. This study also examines the association between El Niño and SST at these sites. Difference in SST between the east and west tropical South Pacific drive the Walker circulation, which is the air system located near the equatorial South Pacific (Sturman and Tapper, 1996). The fluctuation in intensity of the Walker circulation is known as the Southern Oscillation or El Niño Southern Oscillation (ENSO). The atmospheric pressure difference between Darwin (Australia) and Tahiti is known as the Southern Oscillation Index (SOI) and is used as an indicator of the strength of the Walker circulation. When the atmospheric pressure at Tahiti is high compared to the pressure at Darwin, the SOI is positive (La Niña). Then the Walker circulation is considered strong, with strong south-easterlies prevailing over the tropical South Pacific region and enhanced cold ocean upwelling off the Peruvian coast. Alternatively, when the SOI is strongly negative, (E1 Niño period), the Walker circulation weakens and this causes an equatorial Kelvin wave to propagate from west to east across the Pacific in approximately 60 days. The energy of 29
the Kelvin wave is supplied by the accumulation of warm water in the western Pacific (Wyrtki, 1975,1982). Bjerknes (1973) noted that data from 1963-67 indicate that teleconnections along the equator were well formed having opposite pressure anomalies at sea level in Indonesia and the central Pacific. Teleconnections were described as “links between fluctuations in large-scale atmospheric and oceanic circulation systems and anomalous regional weather.” Sturman and Tapper (1996) and Wyrtki (1973) found that periods of exceptionally high transport by the oceanic countercurrent in the western Pacific coincided with E1 Niño events occurring thousands of kilometres downstream. This indicated that there were teleconnections between the atmosphere and the Pacific Ocean. Fluctuations in the Southern Oscillation influence the wind patterns in New Zealand. During La Niña periods, the dominant airflow is northeasterly, whereas south-southwesterly winds dominate during El Niño events (Gordon, 1985, 1986; Salinger, 1991). El Niño events have resulted in cooler coastal waters around New Zealand (Greig et al., 1988). The SST anomalies in the monthly Climate Diagnostics Bulletin of June, 1992 (an El Niño event) showed that the waters around New Zealand had some of the largest anomalies in the world, being cooler by more than 1ºC. Cooling of the sea surface in other areas in the southern hemisphere during El Niño events has also been recorded. For example, Jury and Courtney (1995) found that in the Benguela region (off south-west Africa), the sea surface had cooled during both the 1983 and 1987 El Niño periods.
1998). At both latitudes, two of the sites corresponding to STW and SAW were centered approximately 8 km on either side of the Southland Front. The positions of the AVHRR SST sites and the locations of the three water masses are displayed in Figure 1 and Table 1. The thermal channels of the NOAA satellite bands 4 and 5 (11µm and 12µm, respectively) were calibrated by the technique outlined by Lauritson et al. (1979). A non-linearity correction (Brown et al. 1985) that improved the absolute accuracy by 0.2ºC was included from April, 1989 (Barnes, Landcare Research New Zealand, pers. comm.). A “split window” algorithm (Barton, 1982) using bands 4 and 5 which corrects for Table 1
Location of AVHRR SST sites depicted in Figure 1.
Map Id
Water source
Latitude ºS
Longitude ºE
1 2 3 4 5 6 7 8 9 10
SAW SAW SAW SAW SAW STW STW STW Neritic Neritic
45 46 46 46 45 46 45 45 45 l 44º 20
174 174 172 171 172 171 172 l 171º30 l 170º30 l 171º30
Number of observations 1983Apr 89- Apr 89Sept 87 Mar 92 Apr 93 17 16 19 28 29 28 27 27 26 26
187 141 148 183 235 189 240 237 186 256
10 9 13 13 13 13 14 14 11 12
Data collection Remote sensing AVHRR SST data were used to observe ten sites covering three different water masses NW, STW and SAW near the Southland Front. Time series of thermal variability of each water mass were collected at several sites at latitudes 45ºS and 46ºS. These latitudes were chosen to cover a region where the SF was very well defined. The boundary between the STW and SAW masses is quite stable along the 500 m isobath (Shaw, 30
Figure 1
Location of study sites (1 - 10) and Subantarctic (SAW), Subtropical (STW) and Neritic (NW) water masses. The Southland Front is marked by the bold dashed line.
atmospheric water vapour was applied to the entire 10 year AVHRR data to calculate sea surface temperatures. The AVHRR SST data were then rectified to the New Zealand 1949 geodetic datum at a one minute of arc sampling distance and archived (Pairman et al., 1993). This allows the calculation of unbiased temperature differences that are the basis for this study. A window of 5 x 5 pixels (approximately 6.5 x 9 km) centred on each site was extracted from the satellite data. The median value of the 25 data points for each position was recorded. Cloud detection was not automated as data from the visible spectrum was not archived, but instead, cloud free images were selected manually. Selection techniques were confirmed when visual AVHRR channels became available in 1991. The AVHRR SST data set spans ten years from December 1982 to March 1993. There is a 19 month gap (September, 1987 - March 1989). Data is sparse in the 4 years prior to September 1987. Satellite images were available for each pass over the study area in the three year period spanning April 1989 - March 1992 (about 12 hr intervals), but only about 11% of the images were of sufficient quality to extract reliable temperature information. The remaining 89% suffered from excessive cloud cover. Further data at approximately monthly intervals was also available for a year after March, 1992. Lengths of the data records are displayed in Table 1. SST data collected from Portobello in Otago Harbour were also available as an independent measure of surface temperature. These data were collected by the Portobello Marine Laboratory on a daily basis since 1953 using a mercury-in-glass thermometer. The accuracy in reading the thermometer was estimated as 0.1ºC (Greig et al., 1988).
Statistical Data Analysis It is usual to analyse low frequency components of time series using a series of low pass filters in the frequency domain. For this kind of data a spectral analysis is possible in principle, but it is of limited value because observations are not available at almost 89% of potential observation times. Instead of Fourier methods, a time domain approach was taken where least squares regression was used to efficiently estimate the periodic aspects of the SST time series while smoothing methods were used to extract low frequency components. Periodic components The periodic component is essentially the annual seasonal cycle thus it has a period of T = 365.25 days. It is therefore represented as h h st = αk cos(ωkt+ φk)= β1,k cos(ωkt)+ β2,k sin(ωkt) (1) k=1 k=1
Σ
Σ
where the angular frequencies ωk = 2πk/T are harmonics of
the 365.25 day seasonal cycle and t is the observation time in days. The fundamental frequency ω1 captures in excess of 98.9% of the power of the periodic component, but the first and second harmonics ω2, ω3 did have a discernible effect in some series. A periodic model with h = 3 was judged adequate for all series. The presence of a slowly varying trend biases the parameter estimates slightly and it was found worthwhile to smooth the residuals from the regression to obtain a first estimate of the trend. Subtracting the trend estimate from the SST series gave a trend-free series which was again regressed on the sine and cosine series to obtain unbiased estimates of the periodic component. The smoothing procedures used are described in the next paragraphs. In table 2 the minima and maxima of the periodic components of all series are reported together with the phase φ1 (in days from January, 1) of the annual cycle. The residuals from the regression procedures are the “seasonally adjusted” series. Aperiodic components The non-periodic aspects of the data include level shifts of one or two degrees that drift on a time scale of years, as well as modulation of the periodic component on a time scale of months. For convenience we call this component a “trend”. These were extracted by smoothing methods. Higher frequency variation was not of interest and therefore deemed to be noise and modelled as a stationary stochastic process. Of course long range positive correlation in a stationary process manifests itself as low frequency variation, so a seasonally adjusted SST time series may be thought of as a positively correlated stationary process. Indeed a short segment of a stationary random process with long-range postive correlation may appear to contain a deterministic trend. Thus there is ambiguity between non-periodic variation and noise that cannot be easily resolved. While there are many smoothing algorithms available, they produce similar results and the choice is largely governed by the available software. Smoothing splines as implemented in the statistical system S-Plus (1996) were used in this study. An interpolating spline is a mathematical idealization of the draftsman’s mechanical spline and gives a smooth curve g(t) passing through all data points (ti, yi), i = 1,...,n. The mechanical spline takes its shape to minimize its energy, which is approximated by the total curvature ∫[g (t)]2dt. The smoothing spline no longer passes through all of the data points but provides a smooth curve g(t) of best fit (in the least squares sense) subject to a prescribed upper bound on the energy ∫[g (t)]2dt. If there is no upper bound, the smoothing spline reverts to an interpolating spline. The solution of the constrained minimisation problem can also be written as the curve g(t) that minimizes the penalized sum of squares ll
ll
Σ[xi - g(ti)]
2
∫
+ λ [g (t)]2dt. ll
(2)
The parameter λ is known as the smoothing parameter, and 31
determines the degree of smoothing. When λ is very small, the second term of (2) can be ignored and the first term is minimized by an interpolating spline. Alternatively, when λ is very large, the first term is largely irrelevant and the second term is minimized by fitting a straight line. In general large values of λ lead to very smooth functions g(t), while small values of λ lead to erratic functions that come close to interpolating the data. By choosing the amount of smoothing, features of the data that arise in different time scales can be explored. The most appropriate level for λ may be obtained by subjective choice, though so called “cross validation” procedures allow the degree of smoothing to be automatically determined from the data. The connections with classical regression motivate an alternative measure of smoothness, the “equivalent degrees of freedom” (df) that give an indication of the effective number of parameters - in some sense - that are fitted by a particular value of the smoothing parameter. In parametric regression, degrees of freedom are the number of parameters in the regression model, therefore few degrees of freedom implies a smooth fit, while many degrees of freedom suggests an erratic one. When smoothing data over different ranges, similar degrees of freedom per unit interval give comparable levels of smoothing. In particular, we find that 1- 2 degrees of freedom per year of data gives a trend that varies on a time scale of about a year, while 4 - 6 degrees of freedom per year implies variation on a scale of several months. A small change in degrees of freedom does not perceptibly alter the fitted curve. Further details can be found in Green and Silverman (1994) where a detailed mathematical development and notes on application may be found. Two stages of smoothing were used for each series. First a low frequency component using 10 degrees of freedom was estimated for each AVHRR SST data set. The smoothed data for the combined SAW (Sites 1 - 5) and STW (Sites 6 - 9) are displayed in Figure 3. The corresponding level of smoothing for the Portobello SST series and the SOI used 45 degrees of freedom. These two series are depicted in Figure 2. From these series, segments covering the temporal extent of the AVHRR data sets were superimposed on Figure 3. The second stage smoothes the residuals from the first stage to extract variation on a time scale of months. The results of this smoothing for Portobello data for the period of this study are displayed in Figure 4. The average of the open ocean sites 1 - 9 is superimposed.
Results Periodic components The average maxima and mimima temperature values within each of the three water masses were consistent (Table 2). Site 9 was initially classified as Neritic (NW) as it was close inshore having a water depth was approximately 40 m, however the thermal properties indicated that this site had STW characteristics. The average phase lag from
32
Table 2
Average minima (Min), average maxima (Max) and phase (Days from the beginning of January) for AVHRR sites (1 - 10) and Otago Harbour (Portobello).
Site
Min ºC
Max ºC
Phase
SAW
1 2 3 4 5
7.62 7.42 7.06 7.54 7.52
14.16 14.21 13.43 12.57 12.99
39.35 43.82 42.09 45.62 42.58
STW
6 7 8 9
9.31 9.27 9.33 9.30
14.47 14.48 15.17 14.86
42.29 42.58 40.01 43.44
10 Portobello
7.88 7.07
17.47 16.13
30.26 32.62
Neritic
sites 1-9 was approximately 42 days, i.e. 11 February is the warmest day of the year. The two other sites, Site 10 and Portobello had an average phase lag of 31 days. The similar phase lag of the offshore waters indicates that they may have similar mixed layer depths, assuming that they had the same amount of solar radiation. Site 10 and Portobello had different phase lags, possibly resulting from their limited depth of water, as Site l0 had a depth of 25 m and Portobello had a depth 2-4 m with strong tidal flows. The processes that occur during winter and summer are affected by limited water depth. In winter a convective cell occurs as the cool surface water sinks down the water column and is replaced by warmer water from below. However, a limited water depth interrupts this convective cell, and as a consequence the surface waters are much cooler. The second process (which occurs during summer) utilizes solar radiation to heat the surface water. The heat is distributed within the mixed layer due to turbulence, however a region of shallow water where the mixed layer occupies the entire water column will reach a higher temperature than deeper water. The similar phase lag of the Site 9 and the waters further offshore also suggests an STW influence, despite its depth of 40 m. However, this site may also be affected by vertical mixing from the large volume of water discharged from the Clutha River (Figure 1) which travels northwards. The Clutha River has an average flow rate of 600 m3s-1, peaking at times at 1200 m3s-1 (Murdoch, et al. 1990). Chiswell (1994) suggested that near the Southland Front the STW showed less annual variability than the SAW. However, this study did not show this, as there was no significant difference in amplitude when comparing the SAW (Sites 4 and 5) and STW (Sites 6 and 7). Subantarctic water further from the coast (Sites 1 and 2 and, to a lesser extent, Site 3) do show higher summer temperatures. Site 8 had significantly different amplitude compared with Site 7. The higher amplitude and slightly lower phase
lag of Site 8 are most probably influenced by its limited depth of 80 m, as its characteristics are similar to, but not as extreme as, Site l0 and Portobello.
Figure 2
Smooth trend estimates of the seasonally adjusted Prtobello SST (upper panel) and smoothed SOI (lower panel). df = 45 in both cases.
Figure 3
Smooth trend estimates of the seasonally adjusted SST for Portobello, SAW and STW. The smoothed SOI is included for reference.
Figure 4
Smooth trend estimates of the seasonally adjusted SST for Portobello (df = 60) and the open ocean (df = 20).
Smoothed aperiodic variation Figure 2 shows a persuasive relationship between the smoothed seasonally adjusted Portobello SST series and the smoothed SOI. Dominant peaks and dips in the SST series are associated with corresponding events in the SOI series that occur at similar times. Figure 3 depicts the low frequency components of the seasonally adjusted SST for Portobello and the AVHRR SST sites 1 - 9. Due to the paucity of data, the estimated trends for the AVHRR series are imprecise prior to April, 1989. After this date, Sites 1 - 5 (the SAW sites) were consistent and a common trend is plotted. For the same reason a common trend is plotted for the STW sites 6 - 9. Site 10 (Neritic water) shows slightly different behaviour. The Portobello data showed lower SST’s in the 1982-83, 1987 and 1991-5 El Niño events, while the open ocean sites show a similar response for the last two events. Although there was little AVHRR SST data prior to April 1989 to be able to link cooler SST’s to previous El Niño events (i.e., 1982 and 1987), there was some indication that this occurred. This agrees with Greig’s et al. (1988) observations of the cooling of SST around New Zealand due to El Niño events. The cooling of the inshore waters of Portobello was 1.47ºC between 1990 and mid 1992, while the offshore waters showed even greater variation. The SAW cooled by 2.27ºC in this period and the STW cooled by 2.87ºC. The Neritic water cooled even more by 3.44ºC. The reason for the reduced cooling at the Portobello site compared to the open ocean sites was not clear but may be a result of several physical factors which include, the effect of the surrounding land masses, the shallowness of the basin, and the local terrain affecting the atmospheric conditions. The long range trends depicted in Figures 2 and 3 are subtracted from the seasonally adjusted SST series. The result was then smoothed to extract variation on a time scale of several months which is plotted in Figure 4. The level of smoothing is selected to display variation on a time scale of months rather than years that was the case in the previous figures. These variations may be thought of as modulations of the seasonal cycle in response to aperiodic factors. All open ocean sites (sites 1 - 10) showed a similar response, and the average response is plotted. Smoothing at this level can only be usefully carried out when there is adequate data density, thus we exclude 33
data prior to April, 1989 from this plot. The open ocean trends match those from Portobello to a significant degree. The presence of volcanic aerosols after the Pinatubo and Cerro, Hudson eruptions in 1991 have been shown to depress AVHRR SST measurements An increase in aerosol levels from SAGE II and balloon-borne backscattersonde data (Rosen, et al. 1994) coincide with depressed SST measurements (Sutton and Chiswell, 1996). These eruptions coincide with the onset of the 1991-1995 El Niño event; thus it is difficult to distinguish the effect of bias in AVHRR SST data and the real cooling effect in the open ocean.
Conclusion The sinusoidal components of the seasonal changes in SST were in phase in all SAW and STW sites but the inshore neritic waters reached their peak temperature 10 days earlier. The amplitude was greatest in the neritic waters, which was probably due the the limited water depth at this site. The amplitude of both SAW and STW near the Southland Front was similar (about 2.6ºC) though the SAW was, on average, 1.8ºC cooler. Further offshore (the Warm Water mass) the amplitude and average temperature increase. There was a strong association between the SOI and the 45 year Portobello data set. SST at all sites peaked in 1990 and reached its mimimum during the 1992-93 El Niño. During this period, Portobello cooled by 1.4ºC while the open ocean sites cooled 2.2 - 2.9ºC and the Neritic waters cooled by 3.4ºC. Changes in temperature associated with the SOI appear to be in phase for all water masses including Portobello.
Acknowledgements Thanks to Ted Barnes, formerly of Landcare Research New Zealand for the AVHRR SST images, to Jane Hill and Daryl Coup of the Marine Science Department, University of Otago for writing the software to view the images to extract SST data and also to John Jillett from the Department of Marine Science, University of Otago for providing the Portobello SST record. This study was funded by an Otago Research Grant from the University of Otago.
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