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| Titel |
Markov chain analysis of global water vapour and surface temperature interaction |
| VerfasserIn |
S. Mieruch, S. Noël, H. Bovensmann, J. P. Burrows |
| Konferenz |
EGU General Assembly 2009
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 11 (2009) |
| Datensatznummer |
250021638
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| Zusammenfassung |
| From thermodynamics the strong coupling between temperature and water vapour is well
known. This relationship has been shown e.g. for measurements of annual means of the near
surface temperature and the total water vapour column.
Environmental parameters such as temperature and water vapour are often autocorrelated and
also cross correlated. Our approach is to reduce the bivariate set of data (water vapour and
temperature) to a univariate sequence of symbols, which can be described as a discrete
stochastic process, a Markov chain. This Markov chain represents the water vapour -
temperature interaction or state of a region. Several descriptors, which are new in
environmental science, can be calculated, such as persistence, replacement of and entropy
and are characteristic for the climate system.
The method does not require normally distributed data and can also be applied to unevenly
sampled measurements. In the context of climate change the Markovian analysis is an
underrepresented, but powerful tool to account for the complexity of climate systems and for
the detection of shifts or changes. Here we show a first application of the Markov chain
analysis on the combined climate parameters, water vapour and temperature on a global
scale.
Total water vapour columns are provided by the Global Ozone Monitoring Experiment
(GOME) flying on ERS-2 which was launched in April 1995 and the SCanning Imaging
Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) onboard
ENVISAT and yield a time series from 1996 to now. Global near surface temperatures are
provided by the Goddard Institute of Space Studies (GISS) and cover a time span from 1880
to 2005. The temperature data are retrieved from ground stations and mainly based on the
Global Historical Climatology Network (GHCN). |
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