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Building long-term and high spatio-temporal resolution precipitation and air temperature reanalyses by mixing local observations and global atmospheric reanalyses: the ANATEM model
SMHI, Samhälle och säkerhet.
2015 (engelsk)Inngår i: Hydrology and Earth System Sciences, ISSN 1027-5606, E-ISSN 1607-7938, Vol. 19, nr 6, s. 2717-2736Artikkel i tidsskrift (Fagfellevurdert) Published
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Abstract [en]

Efforts to improve the understanding of past climatic or hydrologic variability have received a great deal of attention in various fields of geosciences such as glaciology, dendrochronology, sedimentology and hydrology. Based on different proxies, each research community produces different kinds of climatic or hydrologic reanalyses at different spatio-temporal scales and resolutions. When considering climate or hydrology, many studies have been devoted to characterising variability, trends or breaks using observed time series representing different regions or climates of the world. However, in hydrology, these studies have usually been limited to short temporal scales (mainly a few decades and more rarely a century) because they require observed time series (which suffer from a limited spatio-temporal density). This paper introduces ANATEM, a method that combines local observations and large-scale climatic information (such as the 20CR Reanalysis) to build long-term probabilistic air temperature and precipitation time series with a high spatio-temporal resolution (1 day and a few km(2)). ANATEM was tested on the reconstruction of air temperature and precipitation time series of 22 watersheds situated in the Durance River basin, in the French Alps. Based on a multi-criteria and multi-scale diagnosis, the results show that ANATEM improves the performance of classical statistical models - especially concerning spatial homogeneity - while providing an original representation of uncertainties which are conditioned by atmospheric circulation patterns. The ANATEM model has been also evaluated for the regional scale against independent long-term time series and was able to capture regional low-frequency variability over more than a century (1883-2010).

sted, utgiver, år, opplag, sider
2015. Vol. 19, nr 6, s. 2717-2736
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Hydrologi
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URN: urn:nbn:se:smhi:diva-1973DOI: 10.5194/hess-19-2717-2015ISI: 000357125300014OAI: oai:DiVA.org:smhi-1973DiVA, id: diva2:923312
Tilgjengelig fra: 2016-04-26 Laget: 2016-03-03 Sist oppdatert: 2018-01-10bibliografisk kontrollert

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