A new climate classification based on Markov chain analysis
Abstract
Existing climate classifications comprise the genetic classification, which is based on climate genesis factors such as winds and oceanic and continental climate, and the empiric classification based on temperature, precipitation, vegetation and more. We present a novel method for climate classification that is based on dynamic climate descriptors, which are persistence, recurrence time and entropy and coin a dynamic classification of climate. These descriptors are derived from a coarse-grained categorical representation of multivariate time series and a subsequent Markov chain analysis. They are useful for a comparative analysis of different climate regions and, in the context of global climate change, for a regime shift analysis. We apply the method to the bivariate set of water vapor and temperature of two regional climates, the Iberian Peninsula and the islands of Hawaii in the central Pacific Ocean. Through the Markov chain analysis and via the derived descriptors we are able to quantify significant differences between the two climate regions. We discuss how these descriptors reflect properties such as climate stability, rate of changes and short term predictability.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2009
- Bibcode:
- 2009AGUFM.A44C..02M
- Keywords:
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- 3235 MATHEMATICAL GEOPHYSICS / Persistence;
- memory;
- correlations;
- clustering;
- 3265 MATHEMATICAL GEOPHYSICS / Stochastic processes;
- 3270 MATHEMATICAL GEOPHYSICS / Time series analysis;
- 3399 ATMOSPHERIC PROCESSES / General or miscellaneous