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Geomatic downscaling of temperatures in the Mont Blanc massif
Author(s) -
Joly Daniel,
Berger Alexandre,
Buoncristiani JeanFrançois,
Champagne Olivier,
Pergaud Julien,
Richard Yves,
Soare Paul,
Pohl Benjamin
Publication year - 2017
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.5300
Subject(s) - downscaling , climatology , massif , environmental science , linear regression , regression analysis , atmospheric temperature , scale (ratio) , mean squared error , mean radiant temperature , regression , meteorology , atmospheric sciences , statistics , mathematics , geology , geography , climate change , precipitation , cartography , oceanography
This article presents a novel downscaling method based on a geomatic spatial model. This model is constructed on the basis of regressions between topographical (explanatory) variables (digitized here at 200 m resolution) and climatic factors observed at meteorological stations. These regressions are used to calibrate the model in the form of two parameters: the frequency with which explanatory variables are significant at the 95% level, and the mean of the regression coefficient associated with each variable. One of the objectives of the article is to test the relevance of the method and the application made of it in this study bears on daily observed minimum (tn) and maximum (tx) temperatures in the Mont Blanc massif between 1979 and 2014. Estimations show weak statistical biases and the temperature variation from day to day is well represented. The root mean square errors of daily temperatures are of the order of 2 °C for tn and tx alike. The validation shows (1) the value of applying spatial models to two atmospheric configurations (weather regimes and local rainfall conditions) and (2) the limitations of the method. Eventually, this method shall be applied to the downscaling of large‐scale atmospheric parameters provided by earth system models for projecting the temperature of the lower layers of the atmosphere over future decades.