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Climate of the Past An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/cpd-6-2625-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/cpd-6-2625-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.

  01 Dec 2010

01 Dec 2010

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This preprint was under review for the journal CP but the revision was not accepted.

Multiscale regression model to infer historical temperatures in a central Mediterranean sub-regional area

N. Diodato1, G. Bellocchi1,2, C. Bertolin3, and D. Camuffo3 N. Diodato et al.
  • 1MetEROBS – Met European Research Observatory, GEWEX-CEOP Network, World Climate Research Programme, via Monte Pino snc, 82100 Benevento, Italy
  • 2Grassland Ecosystem Research Unit, French National Institute of Agricultural Research, avenue du Brézet 234, 63000 Clermont-Ferrand, France
  • 3Atmosphere and Ocean Science Institute, National Research Council of Italy, corso Stati Uniti 4, 35127 Padua, Italy

Abstract. To reconstruct sub-regional European climate over the past centuries, several efforts have been made using historical datasets. However, only scattered information at low spatial and temporal resolution have been produced to date for the Mediterranean area. This paper has exploited, for Southern and Central Italy (Mediterranean Sub-Regional Area), an unprecedented historical dataset as an attempt to model seasonal (winter and summer) air temperatures in pre-instrumental time (back to 1500). Combining information derived from proxy documentary data and large-scale simulation, a statistical methodology in the form of multiscale-temperature regression (MTR)-model was developed to adapt larger-scale estimations to the sub-regional temperature pattern. The modelled response lacks essentially of autocorrelations among the residuals (marginal or any significance in the Durbin-Watson statistic), and agrees well with the independent data from the validation sample (Nash-Sutcliffe efficiency coefficient >0.60). The advantage of the approach is not merely increased accuracy in estimation. Rather, it relies on the ability to extract (and exploit) the right information to replicate coherent temperature series in historical times.

N. Diodato et al.

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N. Diodato et al.

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