Articles | Volume 19, issue 20
https://doi.org/10.5194/acp-19-13129-2019
https://doi.org/10.5194/acp-19-13129-2019
Research article
 | 
24 Oct 2019
Research article |  | 24 Oct 2019

Recent trends in climate variability at the local scale using 40 years of observations: the case of the Paris region of France

Justine Ringard, Marjolaine Chiriaco, Sophie Bastin, and Florence Habets

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Cited articles

Bastin, S., Chiriaco, M., and Drobinski, P.: Control of radiation and evaporation on temperature variability in a WRF regional climate simulation: comparison with colocated long term ground based observations near Paris, Clim. Dynam., 51, 985–1003, https://doi.org/10.1007/s00382-016-2974-1, 2018. 
Bastin, S., Drobinski, P., Chiriaco, M., Bock, O., Roehrig, R., Gallardo, C., Conte, D., Dominguez-Alonso, M., Li, L., Lionello, P., and Parracho, A. C.: Impact of humidity biases on light precipitation occurrence: observations versus simulations, Atmos. Chem. Phys., 19, 1471–1490, https://doi.org/10.5194/acp-19-1471-2019, 2019. 
Boé, J.: Modulation of soil moisture–precipitation interactions over France by large scale circulation, Clim. Dynam., 40, 875–892, https://doi.org/10.1007/s00382-012-1380-6, 2013. 
Boé, J. and Habets, F.: Multi-decadal river flow variations in France, Hydrol. Earth Syst. Sci., 18, 691–708, https://doi.org/10.5194/hess-18-691-2014, 2014. 
Bonnet, R., Boé, J., Dayon, G., and Martin, E.: Twentieth-Century Hydrometeorological Reconstructions to Study the Multidecadal Variations of the Water Cycle Over France, Water Resour. Res., 53, 8366–8382, https://doi.org/10.1002/2017WR020596, 2017. 
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Short summary
This study characterizes the changes observed at Paris urban scale and attempts to identify the surface–atmosphere feedbacks likely to explain the trends observed as a function of the different configurations of large-scale dynamics. This article is interested in several atmospheric parameters and their possible retroactions. Finally, to study urban environments, the analysis at the local scale is essential because it is very poorly represented in the model.
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