Preprints
https://doi.org/10.5194/cp-2022-10
https://doi.org/10.5194/cp-2022-10
 
02 Mar 2022
02 Mar 2022
Status: this preprint is currently under review for the journal CP.

Statistical reconstruction of daily temperature and sea-level pressure in Europe for the severe winter 1788/9

Duncan Pappert1,2, Mariano Barriendos3, Yuri Brugnara1,2, Noemi Imfeld1,2, Sylvie Jourdain4, Rajmund Przybylak5,6, Christian Rohr2,7, and Stefan Brönnimann1,2 Duncan Pappert et al.
  • 1Institute of Geography, University of Bern, Bern, Switzerland
  • 2Oeschger Centre for Climate Research, University of Bern, Bern, Switzerland
  • 3Department of History and Archaeology, University of Barcelona, Barcelona, Spain
  • 4Direction de la Climatologie et des Service Climatiques, Météo-France, Toulouse, France
  • 5Faculty of Earth Sciences and Spatial Management, Nicolaus Copernicus University, Torun, Poland
  • 6Centre for Climate Change Research, Nicolaus Copernicus University, Torun, Poland
  • 7Institute of History, University of Bern, Bern, Switzerland

Abstract. The winter 1788/9 was one of the coldest winters Europe had witnessed in the past 300 years. Fortunately for historical climatologists, this extreme event occurred at a time when many stations across Europe, both private and as part of coordinated networks, were making quantitative observations of the weather. This means that several dozens of early instrumental series are available to carry out an in-depth study of this severe cold spell. While there have been attempts to present daily spatial information for this winter, there is more to be done to understand the weather variability and day-to-day processes that characterised this weather extreme. In this study, we seek to reconstruct daily spatial high-resolution temperature and sea level pressure fields of the winter 1788/9 in Europe, from November through February. The reconstruction is performed with an analogue resampling method (ARM) that uses both historical instrumental data and a weather type classification. Analogue reconstructions are then post-processed through an ensemble Kalman fitting (EnKF) technique. Validation experiments show a good skill for both reconstructed variables, which manage to capture the dynamics of the extreme in relation to the large-scale circulation. These results are promising for more such studies to be undertaken, focusing on different extreme events and other regions in Europe and perhaps even further back in time. The dataset presented in this study may be of sufficient quality to allow historians to better assess the environmental and social impacts of the harsh weather.

Duncan Pappert et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on cp-2022-10', Anonymous Referee #1, 16 Mar 2022
  • RC2: 'Good science, but needs a total re-write.', Philip Brohan, 10 May 2022

Duncan Pappert et al.

Duncan Pappert et al.

Viewed

Total article views: 532 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
428 95 9 532 25 3 4
  • HTML: 428
  • PDF: 95
  • XML: 9
  • Total: 532
  • Supplement: 25
  • BibTeX: 3
  • EndNote: 4
Views and downloads (calculated since 02 Mar 2022)
Cumulative views and downloads (calculated since 02 Mar 2022)

Viewed (geographical distribution)

Total article views: 484 (including HTML, PDF, and XML) Thereof 484 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 20 May 2022
Download
Short summary
We present daily temperature and sea-level pressure fields for Europe for the severe winter 1788/9. They are based on historical meteorological measurements and an analog reconstruction approach. The resulting reconstruction skilfully reproduces temperature and pressure variations over Central and Western Europe. We find intense blocking systems over Northern Europe and several abrupt, strong cold air outbreaks, demonstrating that quantitative weather reconstruction of past extremes is possible.