Rainfall trends in Fiji

Transcription

1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 34: (214) Published online 16 July 213 in Wiley Online Library (wileyonlinelibrary.com) DOI: 1.12/joc.3779 Rainfall trends in Fiji Ravind Kumar, a Mark Stephens b and Tony Weir c * a Fiji Meteorological Service, Nadi, Fiji b School of Geography Earth Science and Environment, University of the South Pacific, Suva, Fiji c Consultant, Suva, Fiji ABSTRACT: In this article we examine trends in rainfall in the Fiji, using records from 14 stations in Fiji. These records cover more stations and significantly longer periods (>9 years for several stations) than those used in any previous studies. We find that over a period of nearly 1 years there is high interannual variability but no significant long-term trend in annual rainfall in Fiji in either wet-side or dry-side stations. This result is consistent with the more restricted results of almost all previous studies. We also find no significant trends in wet season and dry season rainfall, considered separately. Thus unlike temperature data, rainfall data from Fiji provides little evidence of long-term climate change. There is an indication that the few dry seasons with rainfall more than one standard deviation below the mean have occurred more frequently in the most recent 5 years than in the previous 5. Our results confirm that there is a significant influence oftheelniño Southern Oscillation on rainfall in Fiji, especially on the dry side of the larger islands. However, we find that the Interdecadal Pacific Oscillation (IPO) does not modulate the correlation between rainfall in Fiji and the Southern Oscillation Index; contrary to the case in eastern Australia, this correlation is strongly positive for all phases of the IPO. KEY WORDS rainfall; climate change; observations; Fiji; Pacific Islands; trends; ENSO Received 13 March 212; Revised 31 May 213; Accepted 8 June Introduction The Pacific Climate Change Science Program (PCCSP), funded by Australian Aid, aimed to help 14 developing island countries of the South Pacific gain a better understanding of how climate has changed in the past and may change in the future. A general description of the program is given on its website ( or by Power et al. (211). Its principal scientific results to date are set out in a regional overview (PCCSP, 211a) and a volume of country reports (PCCSP, 211b). Each country report, including that for Fiji, includes a suary of observations over the past 6 years, but only from one or two stations. The main purpose of this article is to extend the PCCSP results on Fiji rainfall to a longer period and to a greater range of stations. The longest rainfall records we use are from the four sugar mills and from the outlying island of Rotuma, all of which extend for nearly 1 years which is nearly twice as long as previously published data. Both PCCSP (211b) and Mataki et al. (26) considered rainfall data only from Nadi and Suva; their results are consistent with each other but not with those of Deo (211). Both Mataki et al. (26) and PCCSP (211b) also examined temperature records over the same period and found a significant upward trend of.7 C in mean * Correspondence to: T. Weir, Consultant, Suva, Fiji. weirtoabs@yahoo.co.uk annual temperature at both Nadi and Suva over the past 5 years; temperature data from eight other Fiji stations analysed by Kumar et al. (213a) show a similar trend. This is clear evidence of climate change in Fiji, unlike their results for rainfall. Fiji is a nation comprising some 3 islands in the South West Pacific Ocean (see Figures 1 and 2). The Fiji Meteorological Service suary (FMS, 1995) describes Fiji as having a tropical maritime climate without great extremes of heat or cold. Rainfall in Fiji is highly variable and mainly orographic (influenced by the island topography and the prevailing south-east trade winds). Witness the mean annual rainfall figures shown on the map at Figure 1. Fiji experiences a distinct wet season (conventionally taken, as here, to be November to April, though May is often wetter than November) and a dry season (taken here as May to October). This seasonal difference is largely associated with the South Pacific Convergence Zone (SPCZ), the main rainfall producing system for the region, which typically lies over Fiji in the wet season (see Figure 2) but in the dry season (southern winter) moves a few hundred kilometres north east and weakens. In this article we therefore briefly examine the trends in rainfall for the wet and dry seasons separately. Interannual variation of rainfall in the southern Pacific (including Fiji) is strongly associated with the El Niño Southern Oscillation (ENSO) (Salinger et al., 1995; McGregor and Nieuwolt, 1998; McGree, 27). Using the long-run data available to us, we test the extent to which these correlations extend over time. A tropical 213 Royal Meteorological Society

2 152 R. KUMAR et al. 178 E W Rotuma 16 S (3548 ) 16 S 177 E 12 3'S Labasa Mill (2229 ) Penang Mill Rarawai Mill(2257 ) (2154 ) Lautoka Mill (198 ) Nadi AP (1861 ) Nausori AP 18 S Lakeba (2934 ) 18 S (1919 ) Nacocolevu (1817 ) Nabouwalu (2458 ) Navua Laucala Bay (3573 ) (2984 ) Vunisea (21 ) Ono-i-Lau (1676 ) 2 3'S 178 E 'W 178 W Figure 1. Map of Fiji, showing location and mean annual rainfall of meteorological stations. The two large islands are Viti Levu (left of map) and Vanua Levu (top of map). Note that Ono-i-Lau and Rotuma are located well to the South and North, respectively, of the other stations, as indicated in Table 1 (Courtesy of C. Pene). Table 1. List of stations analysed in this article. Station a Start year b Latitude Longitude % years with data available Mean annual rain () Vunisea % 21 Penang Mill % 2257 Labasa Mill % 2229 Lautoka Mill % 198 Lakeba % 1919 Nadi AP c % 1861 Rarawai Mill (Ba) % 2154 Rotuma % 3548 Nacocolevu % 1817 Laucala Bay (Suva) % 2984 Nausori AP % 2934 Navua d % 3573 Nabouwalu % 2456 Ono-i-Lau % 1676 a Stations arranged in the order of rainfall trend to match Table 2. b Latest date analysed is 28 for all stations. c Nadi airport data used here is the revised homogenised data, as used by PCCSP (211b). d Navua data is merger of those for two nearby stations (Tamanoa and Tokotoko). cyclone can dramatically increase rainfall at a particular station in the wet season (which is more or less the same as the cyclone season ), thus further contributing to interannual variability. On average, for some 1 15 cyclones per decade the eye passes through some part of Fiji (FMS, 26). In the South West Pacific region generally, the long-term variability of rainfall is large compared with the magnitude of long-term rainfall trends. Thus trends in rainfall across the region are less uniform than those for temperature. PCCSP (211a) indicate a general increase in rainfall totals north-east of the SPCZ over the past 5 years, with mainly declines to the south-west of the SPCZ, and declines also north of the ITCZ just west of the International Date Line. This pattern of change, which generally applies in both wet and dry seasons, is associated with the fact that the SPCZ moved north-eastward around However, 213 Royal Meteorological Society Int. J. Climatol. 34: (214)

3 RAINFALL TRENDS IN FIJI N 1 N 1 S 2 S 3 S 11 E 12 E 13 E 14 E 15 E 16 E 17 E W 16 W 15 W 14 W Figure 2. Position of Fiji (near 18 E, 18 S) in relation to the average positions of the main climate features of the region in November to April. The pale arrows show near-surface winds, the shading represents the bands of rainfall (convergence zones with relatively low pressure) and the dashed oval indicates the West Pacific Warm Pool. H represents the typical positions of moving high pressure systems (Source: Australian Bureau of Meteorology and CSIRO (211) Climate Change in the Pacific: Scientific and Assessment and New Research, vol.1,p.37). PCCSP (211a) found that the pattern of trends has changed markedly in the south-west Pacific over the past two decades consistent with a shift since about 199 of the SPCZ back to the position shown in Figure 2. One motivation of the current article is to see to what extent these locational and temporal variations show up across the Fiji islands, which spread across the edge of the SPCZ. 2. Data sources and quality Site locations are indicated on Figure 1, but note that Rotuma and Ono-i-Lau lie some distance from the other islands. Rotuma (about 6 km north of Nadi) is the northernmost island of Fiji, while Ono-i-Lau (about 27 km south of Lakeba) is the southernmost. Rainfall data in the form of monthly totals were obtained directly from FMS. The monthly data used in this study have been derived from daily rainfall records. Only the 14 rainfall sites listed in Table 1 satisfied data integrity criteria based on the length of record (>5 years), percent of missing data (<2%), quality control and homogeneity tests. We would not expect quality or homogeneity to be major issues for daily or monthly rainfall data, so long as the gauge (or the whole observing station) was not relocated and the records were conscientiously maintained. This is because rain gauges have been essentially unchanged for at least 1 years, with the few minor changes in design detail carefully calibrated to ensure homogeneity of long-term records, and rain is mostly unaffected by urbanisation. All stations in the Fiji network have been regularly inspected by FMS since the s to ensure compliance with WMO standards and procedures. We performed statistical homogeneity tests in on all the data analysed here. These used the RHtestV3 test software [an updated version of that described by WMO (29) and Wang (28)] on untransformed monthly rainfall records, as did the tests on Nadi, Suva and numerous stations in other countries of the region done by the Australian Bureau of Meteorology as part of the Pacific Climate Change Science Program. These tests, which essentially look for jumps in the autocorrelation, showed no indications of non-homogeneity in the monthly rainfall records analysed here, except at Nadi Airport. Consequently the data for Nadi Airport was carefully rechecked, to tighten its quality; both PCCSP (211a, 211b) and this article use this revised data. The differences from the raw data used by Mataki et al. (26) are small (<1 for all except 3 years). At Navua, changes in land use forced FMS to move its station about 1.5 km within Tamanoa in 1985, and a further 1 km to Tokotoko in 23 (FMS unpublished records). However all three sites and Navua town lie on the same extensive flood plain, and are about the same distance from both the sea and the mountains inland, so we have merged all three into a single composite long-term record. The statistical tests indicate that this composite record satisfies WMO criteria for homogeneity. Suva (on a hilly peninsula) has had a rain gauge somewhere in the town since it became the capital of Fiji in the 188s, but the station location has moved several times until fixed at Laucala Bay in Only this last record is homogeneous enough for standard climatological analysis. For further details on these 213 Royal Meteorological Society Int. J. Climatol. 34: (214)

4 154 R. KUMAR et al. three stations and of the quality procedures generally, see Kumar et al. (213b). Even 1 day of data missing in a month can potentially significantly affect the monthly total and annual totals. Consequently some of the years for which we have rated annual totals as unavailable may actually have only 1-day s data missing. As an extreme example, a major cyclone in 1931 dropped 155 at Lautoka in February and the record flood associated with it rendered the nearby station at Rarawai Mill inoperable for that month. By far the longest climatological records available in Fiji are those for the four sugar mills, for which the mill owners have kept rainfall records for around 1 years (CSR Ltd. of Australia until the s, Fiji Sugar Corporation subsequently). These stations have been subject to FMS inspections since the s. The older records from these mills were originally maintained only as hand-written logbooks, but these have recently been digitised by FMS to enable easier analysis. This article is the first to publish a substantial analysis of this older data. 3. Statistical methods All correlations in this article have been calculated using standard single-variable linear regression analysis (Kumar et al., 213b). The trends over time in Table 2 are presented in the form m ± 2s where m is the slope of the least-squares regression line and s is the standard error in m. As suggested by Cuing (211), this presentation shows not only the statistical significance of the results but also gives numerical confidence limits on the calculated trends. Formulae given by Montgomery et al. (26) show that statistically there is only a 5% chance that the true trend lies outside the range (m 2s, m + 2s) according to Student s t-test (for samples of the size used here, with N > 15). In particular only if that range does not include zero, can we have 95% confidence that the trend differs significantly from zero. 4. Long-term trends The charts in Figure 3 show all the annual rainfall figures used in our analysis. For each station it also shows the line of best fit (least squares) for the entire sequence. These charts show that even in the drier years and drier locations in Fiji, annual rainfall rarely falls below 1, contrary to the corresponding charts of Deo (211). Our charts for Nadi and Suva agree with those of PCCSP (211b, p. 82), which were compiled in close co-operation with FMS. We therefore consider it likely that Deo s analysis has systematic errors which render unreliable his finding of significant decreasing trends in rainfall. See Kumar et al. (213b) for a more detailed critique of Deo s findings. It is clear from the charts that, for all stations, yearto-year variations in rainfall far exceed the variation due to the long-term trend. Year-to-year departures from the Table 2. Trends in annual rainfall in decade 1 over all years and 5 years ( ). Station Mean annual rainfall (all years) () Trend (all years) ( decade 1 ) Trend ( ) ( decade 1 ) Vunisea 21 9 ± ± 1 Penang Mill ± ± 124 Labasa Mill ± ± 11 Lautoka Mill ± ± 12 Lakeba ± 48 2 ± 88 Nadi AP ± ± 98 Rarawai Mill ± 44 4 ± 12 Rotuma ± ± 96 Nacocolevu ± ± 92 Laucala Bay ± 7 67 ± 14 Nausori AP ± ± 9 Navua ± ± 122 Nabouwalu ± 5 67 ± 116 Ono-i-Lau ± 6 7 ± 86 All years refers to all years for which data is available for that station. Trend here means slope m of the least-squares regression line; values are given as m ± 2s where s is the standard error in m. Stations are listed in decreasing order of m (all years). mean often exceed 5 (or even 1 ) up or down, whereas the notional long-term trends are typically 5 per century, i.e. 1 times smaller. The calculated long-term trends for all stations are tabulated in Table 2. The calculated slopes of the trend lines would suggest a slight tendency towards decreasing rain in the wetter sites (e.g. Rotuma, Suva, Nausori) and a slight tendency for increasing rain in the drier sites (e.g. Nadi, Labasa, Penang Mill). But all these apparent trends are small (a 2 change in a decade is only about 1% of the annual rainfall at even the drier stations in Fiji), and not statistically reliable or physically significant. For example for Lautoka Mill, Table 2 shows the long-term trend as 8 ± 38 decade 1. As indicated in Section 3, this implies that there is a 95% chance that the true trend could be anywhere between 3 and +46 decade 1 (m ± 2s), and a 67% chance that it lies between 11 and +27 decade 1 (m ± s), i.e. it could well be zero. On this basis, Table 2 shows that none of the trends over the past 5 years is statistically significantly different from zero, and only a dubious two of the long-term trends (see below). This is essentially a mathematical expression of the high ratio of year-to-year variation to trend variation. So too is Pearson s correlation coefficient, which is less than 2% for all the calculated trends (A correlation coefficient r =.2 implies r 2 =.4, which means that only 4% of the variance in annual rainfall over the period is accounted for by the linear trend.). The F-test applied to the regressions confirms the lack of statistical significance in the tabulated trends. The two largest apparent long-term trends are for Vunisea (+9 decade 1 ) and Ono-i-Lau ( 78 decade 1 ). But both these stations have substantial blocks of missing values in their earlier years (see Figure 3(c)). 213 Royal Meteorological Society Int. J. Climatol. 34: (214)

5 RAINFALL TRENDS IN FIJI 155 (a) 5 Penang mill annual () 5 Rotuma annual Labasa annual 5 4 Laucala Bay annual Navua annual 5 Nausori annual () Figure 3. Annual rainfall for various Fiji stations. Year-to-year variation and missing values are clearly visible. Each chart also shows the longterm trend (regression line for all years for which data is available), though extrapolation to earlier years than the available data has no physical significance. Horizontal scale (19 21) is same for all charts; vertical scale is as marked, but is fairly similar for all charts. (a) Wetter stations (scale: 5 ): Penang Mill (Rakiraki), Labasa Mill, Navua, Rotuma, Laucala Bay (Suva), Nausori. (b) Drier stations (scale: 4 ): Lautoka Mill, Rarawai Mill (Ba), Nadi, Nabouwalu, Nacocolevu, Lakeba. (c) Stations with more missing values: Vunisea, Ono-i-Lau. Dashed line (almost obscured for Ono-i-Lau) indicates trend , for which fewer values are missing. For those stations, in particular, the trend over the past 5 years (also shown in Table 2 and Figure 3(c)) is probably of more physical significance. For Vunisea, the 5-year trend is indeed much smaller than the all years trend, and for Ono-i-Lau, missing data in some recent years may have distorted the result. Another reason for calculating the 5-year trends is to see if there is any effect comparable to that found by IPCC (27) for temperature, namely that the linear warming trend for the last 5 years is nearly twice that for the last 1 years. However our results indicate that for rainfall in Fiji, the 5-year trends are not significantly different from those for longer terms (up to 1 years). That is, for each station (except Vunisea), the confidence limits of the all years trend overlap with those for the 5-year trend. PCCSP (211b) and Mataki et al. (26) both report trends only for Nadi and Suva. For both those stations, the 6-year trends found by PCCSP (211b) and the 5- year trends found by Mataki et al. (26) lie within the confidence limits of our 5-year trends. 5. Recent trends (2 years) The regional analysis of PCCSP (211a) suggests that rainfall trends in the SW Pacific are very different over 213 Royal Meteorological Society Int. J. Climatol. 34: (214)

6 156 R. KUMAR et al. (b) 4 3 Lautoka annual 5 4 Nabouwalu annual Rarawai (Ba) annual 4 Nacocolevu annual Nadi annual 4 Lakeba annual (c) Vunisea 3 Ono-i-Lau Figure 3. (Continued) the last 2 years from those of the longer-term (5-year) trends. Trend lines over a short period can be unduly sensitive to the end-points. Therefore, as an alternative measure of recent change we have calculated, instead, the difference between the average annual rainfall over and , as suggested by a referee. However the t-test shows that none of these differences between means are statistically significant (Kumar et al., 213b). It would be fair to conclude that a tendency for trends in rainfall to become stronger in recent years is not yet statistically demonstrable. 6. Effect of season There are distinct wet (suer) and dry (winter) seasons in Fiji (especially in the drier zones), as shown in the average seasonal rainfalls shown in Table 3. Given this seasonality, it is worth examining if there are distinct trends in rainfall in these two seasons. Mataki et al. (26) detected no significant increase or decrease in (number of) rain days in winter or suer season at either Nadi or Suva over Our results, shown in Figure 4 and Table 3, confirm that there is likewise no significant trend in the separate 213 Royal Meteorological Society Int. J. Climatol. 34: (214)

7 RAINFALL TRENDS IN FIJI 157 Table 3. Comparison of wet and dry season rainfalls for selected stations in Fiji. Station Season a Period Mean () Max. () Min. () Trend b ( decade 1 ) Nadi Dry ± 22 Nadi Wet ± 6 Lautoka Dry ± 12 Lautoka Wet ± 36 Suva Dry ± 46 Suva Wet ± 54 Navua Dry ± 4 Navua Wet ± 48 a Wet season for 195 is defined as November 195 April 1951, etc. b Shown as m ± 2s where s is the standard error in m. 4 Lautoka dry 4 Navua dry Lautoka wet 4 Navua wet Figure 4. Annual dry and wet season rainfalls () and trend lines for Lautoka (dry side) and Navua (wet side). seasonal rainfalls at either of these stations, and that the longer-run data from Lautoka Mill (near to Nadi) and from Navua (near to Suva) have similar null trends. There is some indication of a downward trend in wet season rainfall at Navua and Suva, i.e. on the wet side of Viti Levu, but like the long-term trends in annual rainfall (Table 2) it is barely significant, with the standard error in slope of the best fit line being of the same magnitude as the calculated slope. As with annual rainfall, both wet-season and dryseason rainfall have considerable year-to-year variation, as indicated in Figure 4. Extremely wet seasons usually cause floods and thus significant social damage (McGree et al., 21). Extremely dry seasons in Fiji are frequently associated with severe agricultural drought, especially if they are followed or preceded by a relatively dry wet season; such droughts can also have severe social consequences (FMS, 23). Here we simply observe from Figure 4 that there are only 2 years (1987 and 1998) in which the dry season rain at Lautoka is less than 15 (= 33% of the mean, which is 449 ) and a further 4 years (1966, 1969, 1977 and 1995) in which it is below 2 (= 45% of mean). For comparison, the standard deviation of dry seasons at Lautoka is 175. We note that all of these very dry seasons have occurred in the most recent 52 years of the 96 years available for analysis. 7. Effect of ENSO 7.1. Southern Oscillation Index and rainfall Mataki et al. (26) examined the impact of ENSO on annual rainfall at Nadi and Suva between 1961 and 23 and concluded that a moderate to strong El Niño event has a significant effect on the rainfall at Nadi (on the leeward side of Viti Levu) leading to serious to severe drought conditions. These effects on rainfall 213 Royal Meteorological Society Int. J. Climatol. 34: (214)

8 158 R. KUMAR et al. at Nadi are broadly confirmed by a chart in PCCSP (211b, p. 82). Physically this happens because in an El Niño event, the SPCZ moves a few hundred kilometres towards the north east, which mimics its movement in a normal dry season. Thus what would be a wet season at Nadi becomes drier (PCCSP, 211b, p. 8). This movement of the SPCZ simultaneously increases the rainfall at places a few hundred kilometres north of Nadi, as can be seen in the rainfall record of Funafuti in Tuvalu which is about 1 km NNE of Nadi (PCCSP, 211b, p. 235) and to a lesser extent in the rainfall at Rotuma, about 6 km north of Nadi (Figures 3 and 5). Conversely, La Niña events tend to increase rainfall at Nadi. The very strong La Niña events of and both entailed several days when Nadi airport recorded rainfall exceeding 2 and nearby Nadi town was flooded. Conversely Tuvalu experienced severe drought in both those seasons (Sinclair et al., 212). For Suva (on the windward side of Viti Levu) Mataki et al. (26) found a similar pattern but with a much milder impact, which is most likely because the predominantly orographic rainfall there is less sensitive to the precise position of the SPCZ. Figure 5 adds to these findings by plotting directly the Southern Oscillation Index (SOI) alongside anomalies in annual rainfall for longer runs of data from Labasa Mill, Rarawai Mill and Navua, which show a broadly similar pattern to those from Nadi and Suva, respectively, and for Rotuma where the pattern is different to the rest of Fiji. The SOI is a normalised measure of the barometric pressure difference between Tahiti and Darwin (Troup, 1965; Power and Kociuba, 21) and is a coon measure of the strength of the Southern Oscillation. Large negative values of the SOI (< 1) correspond to El Niño events, and large positive values (>+1) correspond to La Niña events. The SOI values plotted in Figure 5 are annual averages of the monthly values obtained from the website of the Australian Bureau of Meteorology. Correlation coefficients for all 14 stations are shown in Table 4. Those for all years refer to all available years for that station, ranging from 15 years for Labasa Mill (where the record covers 19 years but four are missing ) to 52 years at Nausori. These correlations are quite high: exceeding 6% for four stations (all on the dry side of Viti Levu or Vanua Levu) and exceeding 5% for a further five stations. At all stations except Rotuma, the correlation over all years is positive and statistically significant at the 99% level (F-test). The correlation at Rotuma is negative but statistically insignificant for the reason given above. The relation between SOI (and other measures of ENSO) and seasonal rainfall is complex but strong enough to be used as the principal basis for the seasonal rainfall outlooks compiled by a Pacific-wide group of meteorological services (including those of United States, Australia and New Zealand) and published regularly in the series of Island Climate Updates (on Table 4. Effect of Interdecadal Pacific Oscillation (IPO) on correlation coefficient between SOI and annual rainfall. Period a All years IPO b Positive Negative Positive Varies Station Labasa Mill Nabouwalu Lautoka Mill Rarawai Mill Nadi AP Nacocolevu Ono-i-Lau.54 n/a.535 Penang Mill Lakeba Nausori AP Navua Vunisea Laucala Bay Rotuma Bold type indicates r.6. a No values available for for several stations because rainfall record there is too short. b Index of PDO/IPO taken from Figure 3.28 of (Solomon et al., 27), which is based on (Mantua et al., 1997). and of Fiji Climate Outlook published by FMS (on (Walsh et al., 21). Part of this complexity arises from the different way in which individual El Niño or La Niña events evolve over months. To analyse how such evolution relates to the data we have on monthly rainfall at multiple stations in Fiji requires considerable further work, which we have begun, but is beyond the scope of this article Modulation by Pacific Decadal Oscillation? As another complication, Power et al. (1999) reported that the effect of ENSO on rainfall in Australia is strongly modulated by the Interdecadal Pacific Oscillation (IPO), with periods for which the IPO index is positive (e.g ) having a strong correlation between rainfall and SOI and periods for which the IPO Index is negative having almost no correlation. In a comprehensive analysis of the link between rainfall in New Caledonia and ENSO between 195 and 21, Barbero and Moron (211) similarly found that this relationship is stronger in recent decades ( 21) than in previous decades ( ), particularly for the Central Pacific mode of ENSO. The annual index of the Pacific Decadal Oscillation (which is very closely related to the IPO) has been positive for most of the period , negative for almost all of and positive for almost all of (Solomon et al., 27, p. 289). We therefore examined the correlation between annual rainfall and SOI separately for each of these periods. (The gaps between these periods, e.g , are periods of transition.) The full data set is shown in Table 4, while Figure 6 shows the results for the seven stations where the correlation for all years exceeds For six of 213 Royal Meteorological Society Int. J. Climatol. 34: (214)

9 RAINFALL TRENDS IN FIJI 159 Figure 5. Plots of annual average SOI (line, right axis) and annual rainfall anomaly in (columns, left axis) for four stations. Anomalies are relative to 3-year averages for Horizontal scale (years) is for Labasa, Rarawai and Rotuma, for Navua. Penang Mill Nacocolevu Nadi AP Rarawai Mill Lautoka Mill Nabouwalu Labasa Mill Correlation: annual rain and SOI Figure 6. Correlation coefficient r between annual rainfall and SOI for selected stations, as a function of period. Top bar for each station is r for , middle is for , lower is for These periods represent successive positive and negative phases of the Pacific Decadal Oscillation. these seven stations, the correlation in the period of negative PDO is even more strongly positive than for the two periods of positive PDO. We therefore conclude that rainfall in Fiji is positively correlated with SOI, but this correlation is not subject to the PDO/IPO modulation found in eastern Australia by Power et al. (1999). This may be because the rainfall in Fiji is more strongly influenced than that in Australia by the precise position of the SPCZ, and the SPCZ position has been shown by Folland et al. (22) to be influenced by both ENSO and IPO but independently rather than jointly. 8. Conclusions Conclusions about long-term trends in rainfall require caution about the time frame of study. We find that over a period of nearly 1 years there is no significant longterm trend in rainfall in Fiji. Nor are there significant trends in wet season and dry season rainfall, considered separately. Thus unlike temperature data, rainfall data from Fiji provide little evidence of long-term climate change. There is an indication that the few dry seasons with rainfall more than one standard deviation below the mean have occurred more frequently in the most recent 5 years than in the previous 5. Our results confirm that there is a significant influence of the El Niño Southern Oscillation on rainfall, especially on the dry side of the larger islands. However, we find that the Inter-decadal Pacific Oscillation does not modulate the correlation between rainfall in Fiji and the SOI; contrary to the case in eastern Australia, this correlation is strongly positive for all phases of the IPO. Acknowledgements This article is based on research carried out while R. K. was a postgraduate student and T.W. was based at PACE-SD at the University of the South Pacific (USP). It is based on raw data kindly supplied by the FMS and is published with the permission of the Director of FMS. 213 Royal Meteorological Society Int. J. Climatol. 34: (214)

10 151 R. KUMAR et al. However the opinions expressed are those of the authors and not necessarily those of FMS. This work benefitted from advice from Dr Ajal Kumar and Dr Zhenquan Li, and the cartographic skills of Conway Pene. We also thank Dr M. G. M. Khan for advice on statistical methods, Professor C. Folland for advice about IPO/PDO, and three anonymous referees for constructive coents on earlier drafts. R. K. acknowledges support from an AusAID postgraduate scholarship. References Barbero R, Moron V Seasonal to decadal modulation of the impact of El Nino Southern Oscillation on New Caledonia (SW Pacific) rainfall (195 21). Journal of Geophysical Research D: Atmospheres 116. DOI: 1.129/211JD Cuing G Understanding The New Statistics: Effect Sizes, Confidence Intervals, and Meta-Analysis. Routledge: London, UK. Deo RC On meteorological droughts in tropical Pacific Islands: time-series analysis of observed rainfall using Fiji as a case study. 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