A Required Data Span to Detect Sea Level Rise

• Autorzy: Niedzielski T. , Kosek W.
• Języki publikacji: EN

Abstrakty

EN

Altimetric measurements indicate that the global sea level rises about 3 mm/year, however, in various papers different data spans are adopted to estimate this value. The minimum time span of TOPEX/Poseidon (T/P) and Jason-1 (J-1) global sea level anomalies (SLA) data required to detect a statisti-cally significant trend in sea level change was estimated. Seeking the trend in the global SLA data was per-formed by means of the Cox-Stuart statistical test. This test was supported by the stepwise procedure to make the results independent of the starting data epoch. The probabilities of detecting a statistically significant trend within SLA data were computed in the relation with data spans and significance levels of the above-mentioned test. It is shown that for the standard significance level of 0.05 approximately 5.5 years of the SLA data are required to detect a trend with the probability close to 1. If the seasonal oscillations are removed from the combined T/P and J-1 SLA data, 4.3 years are required to detect a statistically significant trend with a probability close to 1. The estimated minimum time spans required to detect a trend in sea level rise are ad-dressed to the problem of SLA data predictions. In what follows, the above-mentioned estimate is assumed to be minimum data span to compute the representative sample of SLA data predictions. The forecasts of global mean SLA data are shown and their mean prediction errors are discussed.

Słowa kluczowe

EN

Sea Level; Meteorological Aspects; Oceanography; Sea Level Anomalies (SLA); Seasonal Oscillations; Climate Theory; Climate Changes; Prediction Technique

• Wydawca: Faculty of Navigation, Gdynia Maritime University
• Czasopismo: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
• Rocznik: 2008
• Tom: Vol. 2, no. 2
• Strony: 143--147
• Opis fizyczny: Bibliogr. 11 poz., rys., tab.

Twórcy

autor

Niedzielski T.

• Space Research Centre, Polish Academy of Sciences, Warsaw

autor

Kosek W.

• Space Research Centre, Polish Academy of Sciences, Warsaw, Poland

Bibliografia

• 1. Brockwell, P.J. & Davis, R.A., 1996. Introduction to time se-ries and forecasting. New York: Springer.
• 2. Dobrovolski, S.G., 2000. Stochastic Climate Theory: Models and Applications. Berlin – Heidelberg: Springer.
• 3. Douglas, B.C., 1991. Global sea level rise. Journal of Geo-physical Research 96: 6981-6992.
• 4. Kosek, W., 2001. Long-term and short period global sea level changes from TOPEX/Poseidon altimetry. Artificial Satel-lites 36: 71-84.
• 5. Leuliette, E.W., Nerem, R.S. & Mitchum, G.T., 2004. Calibra-tion of TOPEX/Poseidon and Jason Altimeter Data to Con-struct a Continuous Record of Mean Sea Level Change. Marine Geodesy 27: 79-94.
• 6. McCuen, R.H., 2003. Modeling Hydrologic Change: Statistical Methods. Boca Raton, London, New York, Washington, D.C.: Lewis Publishers.
• 7. Neumaier, A. & Schneider, T., 2001. Estimation of parameters and eigenmodes of multivariate autoregressive models, ACM Transactions on Mathematical Software 27: 27-57.
• 8. Niedzielski, T. & Kosek, W., 2005. Multivariate stochastic pre-diction of the global mean sea level anomalies based on TOPEX/Poseidon satellite altimetry. Artificial Satellites 40: 185-198.
• 9. Niedzielski, T. & Kosek, W., 2006. A minimum time span of TOPEX/Poseidon and Jason-1 global sea level anomalies data required for trend determination and the multivariate autoregressive forecast of these data. Geophysical Research Abstracts 8, 2006, European Geosciences Union, abstract EGU-06-A-04198.
• 10. Niedzielski, T. & Kosek, W. Minimum time span of TOPEX/Poseidon and Jason-1 global altimeter data to de-tect the significant trends in sea level change. Submitted to Advances in Space Research, under review.
• 11. Reinsel, G.C., 1997. Elements of multivariate time series analysis. Berlin, Heidelberg, New York: Springer.