Field Intercomparison



Frequently analyses of climate data, whether model-generated or observed, require the simultaneous assessment of the significance of multiple statistics. A commonly ocurring situation is the test of a hypothesis for the difference in means of two fields of data, e.g. the average wintertime temperature anomaly pattern over a net of European stations in each of two sets of GCM simulations. Regardless of whether the problem is approached with the multivariate methods described in Chapter 8 or as a collection of individual or local significance tests, the collective or field significance of the results depends crucially on the number of data points or tests and their interdependence.


Serial Correlation Empirical Distribution Function Multivariate Test Field Significance Monte Carlo Test 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 1995

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