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Title: Probabilistic precipitation and temperature downscaling of the Twentieth Century Reanalysis over France

Abstract

This work proposes a daily high-resolution probabilistic reconstruction of precipitation and temperature fields in France over the 1871–2012 period built on the NOAA Twentieth Century global extended atmospheric reanalysis (20CR). The objective is to fill in the spatial and temporal data gaps in surface observations in order to improve our knowledge on the local-scale climate variability from the late nineteenth century onwards. The SANDHY (Stepwise ANalogue Downscaling method for HYdrology) statistical downscaling method, initially developed for quantitative precipitation forecast, is used here to bridge the scale gap between large-scale 20CR predictors and local-scale predictands from the Safran high-resolution near-surface reanalysis, available from 1958 onwards only. SANDHY provides a daily ensemble of 125 analogue dates over the 1871–2012 period for 608 climatically homogeneous zones paving France. Large precipitation biases in intermediary seasons are shown to occur in regions with high seasonal asymmetry like the Mediterranean. Moreover, winter and summer temperatures are respectively over- and under-estimated over the whole of France. Two analogue subselection methods are therefore developed with the aim of keeping the structure of the SANDHY method unchanged while reducing those seasonal biases. The calendar selection keeps the analogues closest to the target calendar day. The stepwise selection applies two newmore » analogy steps based on similarity of the sea surface temperature (SST) and the large-scale 2 m temperature ( T). Comparisons to the Safran reanalysis over 1959–2007 and to homogenized series over the whole twentieth century show that biases in the interannual cycle of precipitation and temperature are reduced with both methods. The stepwise subselection moreover leads to a large improvement of interannual correlation and reduction of errors in seasonal temperature time series. When the calendar subselection is an easily applicable method suitable in a quantitative precipitation forecast context, the stepwise subselection method allows for potential season shifts and SST trends and is therefore better suited for climate reconstructions and climate change studies. Furthermore, the probabilistic downscaling of 20CR over the period 1871–2012 with the SANDHY probabilistic downscaling method combined with the stepwise subselection thus constitutes a perfect framework for assessing the recent observed meteorological events but also future events projected by climate change impact studies and putting them in a historical perspective.« less

Authors:
 [1];  [1];  [1];  [2]
  1. Irstea, Villeurbanne Cedex (France)
  2. Compagnie Nationale du Rhone (CNR), Lyon (France)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Irstea, Villeurbanne Cedex (France)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER); USDOE Office of Science (SC), Innovative and Novel Computational Impact on Theory and Experiment (INCITE); NOAA
OSTI Identifier:
1375434
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Climate of the Past
Additional Journal Information:
Journal Volume: 12; Journal Issue: 3; Journal ID: ISSN 1814-9332
Publisher:
European Geophysical Union - Copernicus
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES

Citation Formats

Caillouet, Laurie, Vidal, Jean -Philippe, Sauquet, Eric, and Graff, Benjamin. Probabilistic precipitation and temperature downscaling of the Twentieth Century Reanalysis over France. United States: N. p., 2016. Web. doi:10.5194/cp-12-635-2016.
Caillouet, Laurie, Vidal, Jean -Philippe, Sauquet, Eric, & Graff, Benjamin. Probabilistic precipitation and temperature downscaling of the Twentieth Century Reanalysis over France. United States. doi:10.5194/cp-12-635-2016.
Caillouet, Laurie, Vidal, Jean -Philippe, Sauquet, Eric, and Graff, Benjamin. Wed . "Probabilistic precipitation and temperature downscaling of the Twentieth Century Reanalysis over France". United States. doi:10.5194/cp-12-635-2016. https://www.osti.gov/servlets/purl/1375434.
@article{osti_1375434,
title = {Probabilistic precipitation and temperature downscaling of the Twentieth Century Reanalysis over France},
author = {Caillouet, Laurie and Vidal, Jean -Philippe and Sauquet, Eric and Graff, Benjamin},
abstractNote = {This work proposes a daily high-resolution probabilistic reconstruction of precipitation and temperature fields in France over the 1871–2012 period built on the NOAA Twentieth Century global extended atmospheric reanalysis (20CR). The objective is to fill in the spatial and temporal data gaps in surface observations in order to improve our knowledge on the local-scale climate variability from the late nineteenth century onwards. The SANDHY (Stepwise ANalogue Downscaling method for HYdrology) statistical downscaling method, initially developed for quantitative precipitation forecast, is used here to bridge the scale gap between large-scale 20CR predictors and local-scale predictands from the Safran high-resolution near-surface reanalysis, available from 1958 onwards only. SANDHY provides a daily ensemble of 125 analogue dates over the 1871–2012 period for 608 climatically homogeneous zones paving France. Large precipitation biases in intermediary seasons are shown to occur in regions with high seasonal asymmetry like the Mediterranean. Moreover, winter and summer temperatures are respectively over- and under-estimated over the whole of France. Two analogue subselection methods are therefore developed with the aim of keeping the structure of the SANDHY method unchanged while reducing those seasonal biases. The calendar selection keeps the analogues closest to the target calendar day. The stepwise selection applies two new analogy steps based on similarity of the sea surface temperature (SST) and the large-scale 2 m temperature (T). Comparisons to the Safran reanalysis over 1959–2007 and to homogenized series over the whole twentieth century show that biases in the interannual cycle of precipitation and temperature are reduced with both methods. The stepwise subselection moreover leads to a large improvement of interannual correlation and reduction of errors in seasonal temperature time series. When the calendar subselection is an easily applicable method suitable in a quantitative precipitation forecast context, the stepwise subselection method allows for potential season shifts and SST trends and is therefore better suited for climate reconstructions and climate change studies. Furthermore, the probabilistic downscaling of 20CR over the period 1871–2012 with the SANDHY probabilistic downscaling method combined with the stepwise subselection thus constitutes a perfect framework for assessing the recent observed meteorological events but also future events projected by climate change impact studies and putting them in a historical perspective.},
doi = {10.5194/cp-12-635-2016},
journal = {Climate of the Past},
issn = {1814-9332},
number = 3,
volume = 12,
place = {United States},
year = {2016},
month = {3}
}

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Cited by: 12 works
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