Analysis of Climate Variability: Applications of Statistical by Antonio Navarra (auth.), Hans von Storch, Antonio Navarra

By Antonio Navarra (auth.), Hans von Storch, Antonio Navarra (eds.)

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Bottom: corresponding sea-surface temperature spectrum. The continuous curve is the stochastic model prediction. (From Frankignoul, 1979). LATENT + SENSIBLE HEAT FLUX (OWS P, 50 o N, 145°W) 1958-1967 f" N E 210 3 ~ t ~t t t ct a: ~ u LU a. 15 ~ ct Ir ~ t:: a. 3: Normalized spectra, with approximately 19 degrees offreedom, of the 500 hPa geopotential height at 45° N, wavenumbers 1 to 12. 6 degrees offreedom) are also plot ted. (From Wilson, 1975). 4: Zonal wavenumber speetrum for the interval July 1985 through June 1988 for the North Padfie site 4F N, 162°W.

6 Chapter 3: Climate Spectra and Stochastic Climate Models Variability in the Ocean Interior Few long time series are available in the ocean interior, so that the spectral characteristics are virtually unknown below the surface at periods longer than a few years. The longest series of hydrographie stations were made at station "S" off Bermuda (32 0 N, 64 0 W). The temperature spectra are dominated by mesoscale eddies because of the proximity of the Gulf Stream, and become red at very low frequencies (FrankignouI1981).

3) with Gaussian "white noise"N t , which is neither auto-correlated nor correlated with Xt-k for k 2:: 1. a is the lag-1 autocorrelation of X t . 1000 iid 3 time series of different lengths, varying form n 100 to n 1000 were generated and a Mann-Kendall test was performed. , 50 out of the 1000 tests should return the result "reject null hypothesis" . 2). 30. 3, we must expect that on average at 15% of all points a "statistically significant trend" is found even though there is no trend but only "red noise".

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