Abstract
This study was motivated by two main concerns including (a) prediction of the Persian Gulf Sea surface temperature (PGSST) anomalies using an autoregressive integrated moving average (ARIMA) model and (b) detection of the climate change signatures in the considered SST data. An ARIMA model was, therefore, developed to predict the SST anomalies having lead times from 1 to 3 months. While the SST time series for the period of 1950–2006 used to fit the model, corresponding records for January 2007 to June 2011 were applied as the test data. The developed model had a minimum value of Akaike information criterion, and its parameters were significantly different from zero. The correlation coefficients between the observed and simulated data for the lead times of 1, 2, and 3 months were found to be significant and equal to 0.72, 0.69, and 0.65, respectively. The corresponding hit rates were estimated as 79, 75, and 72 %, indicating a reasonable forecasting capability of the model. The Heidke’s forecast scores were 0.59, 0.52, and 0.48 for the prediction schemes having 1, 2, and 3 months of lead time, respectively. It is shown that the Persian Gulf skin temperatures have warmed up about 0.57 °C during the 732 successive months of the period 1950–2010 noted as an upward significant trend. Although a significant trend was not evident for the 1950–1969 and 1970–1989 period, the PGSST has abruptly increased during the recent two decades. Almost all of the observed warming in the PGSST data is related to this period.
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References
Akaike H (1974) Markovian representation of stochastic processes and its application to the analysis of autoregressive moving average processes. Ann Inst Stat Math 26:363–387
Baker-Austin C, Trinanes JA, Taylor NGH, Hartnell R, Siitonen A, Martinez-Urtaza J (2012) Emerging Vibrio risk at high latitudes in response to ocean warming. Nat Clim Chang. doi:10.1038/nclimate1628
Barnston AG, Ropelewiski CF (1992) Prediction of ENSO episodes using canonical correlation analysis. J Clim 5:1316–1345
Bauman AG, Pratchett MS, Baird AH, Riegl B, Heron SF, Feary DA (2012) Variation in the size structure of corals is related to environmental extremes in the Persian Gulf. Mar Environ Res. doi:10.1016/j.marenvres.2012.11.007
Bloomfield DS, Higgins PA, McAteer RTJ, Gallagher PT (2012) Toward reliable benchmarking of solar flare forecasting methods, The Astrophysical Journal Letters, 747(2), doi:10.1088/2041-8205/747/2/L41
Box GEP, Pierce DA (1970) Distributions of residuals autocorrelations in autoregressive integrated moving average time series models. J Am Stat Assoc 59:1509–1525
Box GEP, Jenkins GM (1976) Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day
Box GEP, Jenkins GM, Reinsel GC (1994) Time Series Analysis: Forecasting and Control, 3rd edn. Prentice Hall, Englewood Cliffs
Chu PS, Kats RW (1987) Measures of predictability with applications to the Southern Oscillation. Mon Weather Rev 115(8):1542–1549
Chu PS, Kats RW (1985) Modeling of forecasting the Southern Oscillation: a time domain approach. Mon Weather Rev 113:1876–1888
Copsey D, Sutton R, Knight JR (2006) Recent trends in sea level pressure in the Indian Ocean region. Geophys Res Lett 33(19):L19712
Haklander AJ, van Delden A (2003) Thunderstorm predictors and their forecast skill for the Netherlands. Atmos Res 67–68:273–299
Johns W E, Yao F, Olson DB, Josey SA, Grist JP, Smeed DA (2003) Observations of seasonal exchange through the straits of Hormuz and the inferred heat and freshwater budgets of the Persian Gulf. Journal of Geophysical Research: Oceans (1978–2012), 108(C12)
Kämpf J, Sadrinasab M (2006) The circulation of the Persian Gulf: a numerical study. Ocean Sci 2:27–41
Krishnamurti TN, Chakraborty A, Krishnamurti R, Dewar WK, Clayson CA (2006) Seasonal prediction of sea surface temperature anomalies using a suite of 13 coupled atmosphere–ocean models. J Clim 19:6069–6088
Landman WA, Mason SJ (2001) Forecasts of near-global sea surface temperature using canonical correlation analysis. Am Meteorol Soc 15:3819–3833
Ljung GM, Box GEP (1987) On a measure of lack of fit in time series models. Biometrika 65:297–303
Martin M, et al. (2012) Group for high resolution sea surface temperature (GHRSST) analysis fields inter-comparisons: Part 1. A GHRSST multi-product ensemble (GMPE). Deep Sea Research Part II: Topical Studies in Oceanography 77-80: 21–30
Martinez SA, Hsieh WW (2009) Forecasts of tropical Pacific sea surface temperatures by neural networks and support vector regression. Int J Oceanogr 2009:1–13
Mazaris AD, Kornaraki E, Matsinos YG, Margaritoulis D (2004) Modeling the effect of sea surface temperature on sea turtle nesting activities by investigating seasonal trends. Nat Resour Model 17:445–465
Nazemosadat SMJ (1998) The Persian Gulf sea surface temperature as a drought diagnostic for southern parts of Iran. Drought News Netw 10:12–14
Nazemosadat SMJ, Samani N, Barry DA, Molaii Nikoo M (2006) ENSO forcing on climate change in Iran: precipitation analysis. Iran J Sci Technol 30(B4):555–565
Nazemosadat SMJ, Shirvani A (2006) The application of canonical correlation analysis for investigating the influence of Persian Gulf SSTs on winter rainfall of southern Iran. J Agr Sci 29(2):65–77 (in Persian)
Nazemosadat SMJ, Ghasemi AR, Amin SA, Soltani AR (2008) The simultaneous impacts of ENSO and the Persian Gulf SST on the occurrence of drought and wet periods in northwestern Iran, Tabriz University. J Agr Sci 18(3):1–17, in Persian
Neelin JD (1990) A hybrid coupled general circulation model for El-Niño studies. J Agr Sci 47:674–693
Penland C, Magorain P (1993) Prediction of Niño 3 sea surface temperature using linear inverse modeling. Am Meteorol Soc 6:1067–1076
Penland C, Matrosova L (1999) Forecast of tropical Atlantic sea surface temperature using linear inverse modeling. J Clim 11:483–496
Reynolds RW, Smith TM, Liu C, Chelton DB, Casey KS, Schlax MG (2007) Daily high-resolution blended analyses for sea surface temperature. J Clim 20:5473–5496
Shumway RH, Stoffer DS (2006) Time series analysis and its applications with R examples. Springer Science and Business Media, LLC
Smith TM, Reynolds RW, Peterson TC, Lawrimore J (2008) Improvements to NOAA’s historical merged land-ocean surface temperature analysis (1880-2006). J Clim 21:2283–2296
Sugiura N (1987) Further analysis of the data by Akaike’s information criterion and the finite corrections. Comm Stat Theor Meth 7:13–26
Syu HH, Neelin JD, Gutzler D (1995) Seasonal and interannual variability in a hybrid coupled GCM. J Clim 8:2121–2143
Wilks DS (2011) Statistical methods in the atmospheric sciences, 3rd edn. Academic, San Diego
Xue Y, Leetmaa A, Ji M (2000) ENSO prediction with Markov models: the impact of sea level. J Clim 13:849–871
Xue Y, Smith TM, Reynolds RW (2003) Interdecadal changes of 30-yr SST normals during 1871–2000. J Clim 16:1601–1612
Yao F, Johns WE (2010) A HYCOM modeling study of the Persian Gulf: 2. Formation and export of Persian Gulf Water. J Geophys Res 115(C11):C11017
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The authors would like to thank Thomas M. Smith for his valuable suggestions.
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Shirvani, A., Nazemosadat, S.M.J. & Kahya, E. Analyses of the Persian Gulf sea surface temperature: prediction and detection of climate change signals. Arab J Geosci 8, 2121–2130 (2015). https://doi.org/10.1007/s12517-014-1278-1
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DOI: https://doi.org/10.1007/s12517-014-1278-1