Climate trends in the South‐west Pacific

Salinger, Jim; Basher, Reid E.; Fitzharris, B. B.; Hay, John E.; Jones, Phil; Macveigh, J. P.; Schmidely‐Leleu, I.

doi:10.1002/joc.3370150305

Cited by 121 publications

(90 citation statements)

References 31 publications

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“…Observational studies also provide evidence of temperature increases over Australia (Jones et al, 1990;Plummer, 1991 (Plummer et al, 1995), matching trends in DTR found in other parts of the world (Karl et al, 1993). Some of these trends in Australian climate over recent decades have also been identified in climate regions of the south-west Pacific (Salinger et al, 1995).…”

Section: Introductionsupporting

confidence: 61%

Time Series Analysis of Rainfall and Temperature Interactions in Coastal Catchments

Tularam¹

2010

Journal of Mathematics and Statistics

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Problem statement: As a result of climate change more work is now being done on climate indices such as SOI, rainfall, temperature and so on. The research concerning rainfall and temperature variations in coastal tropical and subtropical catchments as noted by the dated references in this study shows a lack of attention. The authors examined long term data from coastal areas of Queensland. Australian climate is highly variable making the identification of trends or interactions between rainfall and temperature difficult to discern from background variation. Due to the lack of attention in the past, this study examined the relationships between rainfall, temperature, minimum temperature and maximum temperature as well as over time using time series methods. In particular, the study examined whether a cyclic nature existed in the data sets and whether a simple trend relationship between rainfall and temperature existed. To examine autocorrelation and seasonality ARIMA models were also investigated. Approach: A large data set involving more that 50 years of rainfall and temperature data were examined using spectral analysis, time series analysis-ARIMA methodology to analyse climatic trends and interactions. Fourier analysis, linear regression and ARIMA based time series models were used to analyze the large data sets using Matlab, SPSS and SAS programs. Results: The rainfall data was variable and appeared seasonal while the temperature data appeared stationary. Interestingly, spectral analysis showed variations in rainfall and temperature over 50-60 years but the results showed that rainfall and temperature varied coherently, with a cycle of about 2-3 years. An inverse relationship in trend was noted between rainfall and daily temperature range using linear regression among the variables. The ARIMA models showed autocorrelation and seasonality providing time series models. Conclusion/Recommendations: There is a cyclic pattern noted in both the rainfall and temperature time series and a cycle of about 3 years in the rainfall and temperature data sets suggesting a coherent variance in the relationship. This is an interesting finding suggesting a cyclic nature of large rainfall events over time and has been confirmed by the recent large rainfalls events in 2009-10. Linear regression showed an inverse relationship in trend between rainfall and temperature range only even though the r value was around 0.27. The autocorrelation in the data appears to have caused the low r and ARIMA methods was used giving time series models for each series allowing for autocorrelation and seasonality. This study on rainfall and temperature is valuable contribution to the lack of research noted particularly in Queensland as noted in the dated references found; also contributing to the climate change debate forcing it on the cautious side. Further work involving multivariate and dynamic conditional correlation methods may provide further insights regarding the relationships between rainfall and temperature. More climatic indices ...

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Section: Introductionsupporting

confidence: 61%

Time Series Analysis of Rainfall and Temperature Interactions in Coastal Catchments

Tularam¹

2010

Journal of Mathematics and Statistics

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“…For temporal change detection in climatology, the moving average procedure is a conventional procedure (Sneyers, 1990). This procedure allows filtering the year-to-year variations to reveal more persistent trends (Wheeler and Martín-Vide, 1992;Salinger et al, 1995;De Luis et al, 2000). For this reason, prior to trend analysis, the different variables were smoothed by means of a moving average of 9 years, following De Luis et al (2000).…”

Section: Analysis Of Drought Spatial and Temporal Patternsmentioning

confidence: 99%

The influence of atmospheric circulation at different spatial scales on winter drought variability through a semi‐arid climatic gradient in Northeast Spain

Vicente‐Serrano

López‐Moreno

2006

Intl Journal of Climatology

127

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This paper analyses the spatial and temporal variability of winter droughts in a semi-arid geographic gradient in Northeast Spain, from the Pyrenees in the north to the Mediterranean coastland in the south. Droughts that occurred between 1952 and 1999 were analysed by means of the Standardised Precipitation Index (SPI). The influence of the weather-type frequency and of the general North Atlantic atmospheric circulation patterns was analysed. The results indicate that winter droughts show an important spatial variability in the study area, differentiating three well-defined patterns. These correspond to the Pyrenees, the centre of the Ebro Valley, and the Mediterranean coastland. General negative trends in winter SPI have been found, which are indicative of the increase in winter drought conditions in the study area. Nevertheless, important spatial differences have also been recorded. Dominant north-south gradients in the influence of weather types are shown. Moreover, the negative trends in winter-SPI values agree with the negative trend in the frequency of the weather types prone to cause precipitation, such as the C, SW and W weather types and the increase in the frequency of A weather types. Nevertheless, in the Mediterranean coastland, the positive trend in SPI values agrees with the increase in the frequency of weather types of the east (E, SE), which are prone to cause precipitation in this area. Interannual variations in the frequency of the different weather types have been highly determined by different general atmospheric circulation patterns, mainly the North Atlantic Oscillation (NAO). Nevertheless, the correlation between the time series of weather-type frequency and the winter SPI is higher than that found between the SPI and the NAO. Thus, although the interannual NAO variability explains a high percentage of the interannual differences in the frequency of different weather types, it is not sufficient to explain the spatial and temporal variability of droughts, which respond better to atmospheric variability at more detailed (synoptic) spatial scales.

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“…We also look at decadal variability of station rainfall with the NINO3.4 index. Decadal variability has been well documented in the Australian (Power et al 2006;Meyers et al 2007) and Pacific (Salinger et al 1995(Salinger et al , 2001Yeh and Kirtman, 2004;Wang et al 2009;Choi et al 2013) regions, with an increase in the central Pacific El Niño Modoki noted in recent decades by Ashok et al (2007).…”

Section: Introductionmentioning

confidence: 95%

An assessment of rainfall seasonal forecasting skill from the statistical model SCOPIC using four predictors

Cottrill¹,

Kulesho²

2014

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The statistical model SCOPIC (Seasonal Climate Outlook for Pacific Island Countries) has been used to produce seasonal forecasts in ten Pacific Island nations since mid-2007 to improve their seasonal forecasting capacity and to provide timely warnings to changes in rainfall. However, to date there has been no detailed hindcast validation study to compare the forecast skill from the different predictors used to produce the seasonal forecasts at different stations from across the Pacific region. Here, we compare the rainfall forecasts created by the linear discriminant analysis model within SCOPIC using the four predictors: the Southern Oscillation Index (SOI); empirical orthogonal functions of sea surface temperature anomalies (SST1&9) and the NINO3.4 and the 5VAR index. This indicates that skill varies from season to season across the Pacific, with the highest skill in the austral summer and lowest skill in the austral winter. This study using tercile hit rates and LEPS percentage scores shows the 5VAR index has slightly superior skill compared to the NINO3.4, SOI and the SST1&9 indices, but results will vary depending on the station location, analysis period and the number of months used to calculate the predictor value.

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Climate trends in the South‐west Pacific

Cited by 121 publications

References 31 publications

Time Series Analysis of Rainfall and Temperature Interactions in Coastal Catchments

Time Series Analysis of Rainfall and Temperature Interactions in Coastal Catchments

The influence of atmospheric circulation at different spatial scales on winter drought variability through a semi‐arid climatic gradient in Northeast Spain

An assessment of rainfall seasonal forecasting skill from the statistical model SCOPIC using four predictors

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