Abstract
Abstract. Surface observations are usually too few and far between to properly assess multidecadal variations at the local scale and characterize historical local extreme events at the same time. A data assimilation scheme has been recently presented to assimilate daily observations of temperature and precipitation into downscaled reconstructions from a global extended reanalysis through an Ensemble Kalman fitting approach and to derive high-resolution fields. Recent studies also showed that assimilating observations at high temporal resolution does not guarantee correct multidecadal variations. The current paper thus proposes (1) to apply the data assimilation scheme over France and over the 1871–2012 period based on the SCOPE Climate reconstructions background dataset and all available daily historical surface observations of temperature and precipitation, (2) to develop an assimilation scheme at the yearly timescale and to apply it over the same period and lastly, (3) to derive the FYRE Climate reanalysis, a 25-member ensemble hybrid dataset resulting from the daily and yearly assimilation schemes, spanning the whole 1871–2012 period at a daily and 8 km resolution over France. Assimilating daily observations only allows reconstructing accurately daily characteristics, but fails in reproducing robust multidecadal variations when compared to independent datasets. Combining the daily and yearly assimilation schemes, FYRE Climate clearly performs better than the SCOPE Climate background in terms of bias, error, and correlation, but also better than the Safran reference surface reanalysis over France available from 1958 onward only. FYRE Climate also succeeds in reconstructing both local extreme events and multidecadal variability. It is freely available at https://doi.org/10.5281/zenodo.4005573 (precipitation, Devers et al., 2020b) and https://doi.org/10.5281/zenodo.4006472 (temperature, Devers et al., 2020c).
Funder
Institut National de la Recherche Agronomique
Subject
Paleontology,Stratigraphy,Global and Planetary Change
Reference96 articles.
1. Anderson, J. L.: Localization and Sampling Error Correction in Ensemble
Kalman Filter Data Assimilation, Mon. Weather Rev., 140, 2359–2371,
https://doi.org/10.1175/MWR-D-11-00013.1, 2012. a
2. Angot, A.: Annales du Bureau Central Météorologique de France – Année
1879. Tome II. Bulletin des observations françaises et revue
climatologique. Revue climatologique mensuelle pour la France et les
contrées voisines, Gauthiers-Villars, Paris, France, 1881. a
3. Annan, J. D. and Hargreaves, J. C.: Identification of climatic state with limited proxy data, Clim. Past, 8, 1141–1151, https://doi.org/10.5194/cp-8-1141-2012, 2012. a
4. Asch, M., Bocquet, M., and Nodet, M.: Data assimilation: methods, algorithms, and applications, in: Fundamentals of Algorithms, Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA, USA, available at: https://hal.inria.fr/hal-01402885 (last access: 1 September 2021), 2016. a
5. Ben Daoud, A., Sauquet, E., Bontron, G., Obled, C., and Lang, M.: Daily
quantitative precipitation forecasts based on the analogue method:
Improvements and application to a French large river basin, Atmos.
Res., 169, 147–159, https://doi.org/10.1016/j.atmosres.2015.09.015, 2016. a
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