SCOPE Climate: a 142-year daily high-resolution ensemble meteorological reconstruction dataset over France

Abstract

Abstract. SCOPE Climate (Spatially COherent Probabilistic Extended Climate dataset) is a 25-member ensemble of 142-year daily high-resolution reconstructions of precipitation, temperature and Penman-Monteith reference evapotranspiration over France, from 1 January 1871 to 29 December 2012. SCOPE Climate provides an ensemble of 25 spatially coherent gridded multivariate time series. It is derived from the statistical downscaling of the Twentieth Century Reanalysis (20CR) by the SCOPE method (Spatially COherent Probabilistic Extended method) which is based on the analogue approach. SCOPE Climate performs well in comparison to both dependent and independent data for precipitation and temperature. The ensemble aspect corresponds to the uncertainty related to the SCOPE method. SCOPE Climate is the first century-long gridded high-resolution homogeneous dataset available over France and thus paves the way for improving the knowledge on specific past meteorological events or for improving the knowledge on climate variability since the end of the 19th century. This dataset has also been designed as a forcing dataset for long-term hydrological applications and studies of the hydrological consequences of climate variability over France. SCOPE Climate is freely available for any non-commercial use, and can be downloaded as NetCDF files from http://doi.org/10.5281/zenodo.1299760 for precipitation, http://doi.org/10.5281/zenodo.1299712 for temperature, and http://doi.org/10.5281/zenodo.1251843 for reference evapotranspiration.

Moving beyond the catchment scale: Value and opportunities in large‐scale hydrology to understand our changing world

“…For such reasons, endeavours such as large‐scale hydrological data rescue (e.g., Le Gros et al, ), reconstructing long‐term and large‐scale high‐resolution climate datasets (Devers, Vidal, Lauvernet, Graff, & Vannier, ) and corresponding near‐natural hydrological datasets (e.g., Hanel et al, ; Moravec, Markonis, Rakovec, Kumar, & Hanel, ) are central in understanding the large temporal and spatial variations of hydrology. Compatibility between, or merging of, national‐scale datasets (e.g., Caillouet, Vidal, Sauquet, Graff, & Soubeyroux, ; Keller et al, ) would be a further advance, as would improved quality assessment of large repositories such as the Global Runoff Data Centre under the auspices of the World Meteorological Organisation.…”

A framework for high‐resolution meteorological surface reanalysis through offline data assimilation in an ensemble of downscaled reconstructions

“…This study thus proposes to assess methodological choices to develop a 140‐year high‐resolution meteorological surface reanalysis over France through offline assimilation of station observations of daily precipitation and temperature. The background considered is SCOPE Climate, an ensemble meteorological reconstruction of daily precipitation and temperature (Caillouet et al ., ). This choice is motivated by its temporal extension (since 1871), its spatial resolution (8 × 8 km) and its ensemble property (25 members).…”

“…This choice is motivated by its temporal extension (since 1871), its spatial resolution (8 × 8 km) and its ensemble property (25 members). The SCOPE Climate dataset comes from the downscaling of the 20CR mean (Caillouet et al ., ). The ensemble provided by the background naturally leads to the implementation of an Ensemble Kalman Filter (EnKF: Burgers et al ., ; Evensen, ), allowing us to estimate the associated uncertainty at the daily time step.…”

“…The currently available high‐resolution daily long‐term gridded times series of precipitation and temperature over France either are limited in their spatial extent (Kuentz et al ., ), do not take into account the in situ observations (Caillouet et al ., ), or do not provide any uncertainty estimation (Bonnet et al ., ). This study thus proposes to assess methodological choices to develop a 140‐year high‐resolution meteorological surface reanalysis over France through offline assimilation of station observations of daily precipitation and temperature.…”

Synthetic Weather Diaries: Concept and Application to Swiss Weather in 1816

“…In addition to data assimilation, providing global weather data at coarse resolution back to the early 19th century (Slivinski et al 2019), also other techniques such as analog resampling of regional weather fields (Caillouet et al, 2019;Devers et al, 2020;Pfister et al, 2020) have been used to reconstruct local daily weather 150-200 years back in time, with the potential to go even further back. These reconstructions provide a resource not just for climate science, but also for historians.…”