By Antonio Navarra (auth.), Hans von Storch, Antonio Navarra (eds.)
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Extra resources for Analysis of Climate Variability: Applications of Statistical Techniques
11) which provides explicit solutions on the t y time scale. For a stable solution, the matrix Vij must be negative definite. Since for t ~ t lll , v~ acts as a white noise generator, Eq. 11) represents a multivariate first-order Markov process. 12) k,l = with the matrix T (ilI - V)-l (I is the unit matrix). 14) where >. is the feedback. 4: Sea Surface Temperature Anomalies 39 which can even be tested when no simuItaneous information is available on the weather forcing. 11), providing more stringent signatures than the power spectra.
Increases. Although other factors (vegetation, varying soil characteristics, subsurface water flow) need to be considered, Delworth and Manabe's (1988) interpretation seems sturdy: soil moisture anomaly observations in the Soviet Union are indeed well-modeled by a first-order Markov process, with a damping time approximately equal to the ratio of field capacity to potential evaporation (Vinnikov and Yeserkepova, 1991). On monthly to yearly time scales, sea ice anomalies in the Arctic and Antarctic have been shown by Lemke et al.
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". This finding is mostly independent of the time series length. 2. 4) The "prewhitened" time series is considerably less plagued by serial correlation, and the same Monte Carlo test as above returns actual rejections rates elose to the nominal one, at least for moderate autocorrelations and not too short time series. 4) affects also any trend; however, other Monte Carlo experiments have revealed that the power of the test is reduced only weakly as long as a is not too large.
Analysis of Climate Variability: Applications of Statistical Techniques by Antonio Navarra (auth.), Hans von Storch, Antonio Navarra (eds.)