the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Statistical reconstruction of daily temperature and sea-level pressure in Europe for the severe winter 1788/9
- 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
- 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.
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Duncan Pappert et al.
Status: final response (author comments only)
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RC1: 'Comment on cp-2022-10', Anonymous Referee #1, 16 Mar 2022
Summary:
the manuscript presents a spatially resolved reconstruction of daily temperature and SLP during the extremely cold European winter of 1789/1790. The reconstruction is based on early instrumental data from a set of stations , by applying a two-step procedure: first, a search for analogues in a gridded modern data set (after with a suitable pre-processing) and a refinement with an Ensemble Kalman Filter.
The reconstructed data are dynamically interpreted, essentially the cold temperatures being a result of an extreme meridional flow (negative North Atlantic Oscillation).
The novelty of this study is precisely the spatially resolved reconstructions ate a very short time scale (daily), which allows to have a glimpse into the temporal structure of a very cold winter. This structure is also compared with modern cold winters, revealing their similarities but also their differences.
Recommendation: I think this is a very interesting study. The manuscript is also well written, although some technical steps will require the attention of the reader. The method is not applied here for the first time and the interested reader can check out previous studies by the authors using this methodology.
In my opinion, the article can be published almost as is. I have a few minor comments that the authors may want to consider
1) The annual cycle is removed by filtering out the annual frequencies. However, the analogs themselves are chosen from candidates with a calender date within a temporal window centered on the target. I wonder whether the filtering of the annual cycle is really necessary, since all analogs candidates are located in the same 'season' as the target. I do not think the filtering is damaging, but in my view it is not necessary. Perhaps the authors may like to add a couple of sentences to inform the reader
2) Background state in the EnKF. The background state is chose as the best analog. I also wonder whether this is consistent with the calculation of the covariance matrix using all n-nearest neighbour analogs. It seems to me more logical to choose either the average of all n-analogs or possibly the member of the analog ensemble with median distance. Again, perhaps the auhors may want to comment on this
3) The Kalman filter set-up is generally used to combine two independent estimations, for instance one from a model run and one from a noisy observation. Both need to be independent for the method to be statistically sound. Here, however, both estimations are not independent: one is the best analog, which uses the observations, and the second is the observation itself. Thus, the separation is not clean, if I am not mistaken.
I would not be very picky here, since the authors test their results with independent observations and the method, pragmatically, indeed works: the EnSK is able to improve the analog-based estimation. However, the more theory-inclined reader may frown upon this dependency. The authors may again want to include a warning or a comment.
4) ' The RMSE also shows an improvement from 3.4 to 2.7 °C, as does the mean bias from 0.67 to -0.13'
The units for the bias are missing
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RC2: 'Good science, but needs a total re-write.', Philip Brohan, 10 May 2022
This document is not a research paper.
It is a description: of a diverse and valuable program of research relating to the European weather of 1788/9 - some basic data work, some reconstruction methodology work, some creation of new weather reconstructions, and some discussion of the specifics of the event. As a research program this is admirable, and I am sure that there is material here that is well worth publishing, but as a research paper it's confused - a paper really needs to tell it's readers a single new thing - to present an important result, or method, or dataset that is new to science. This paper presents lots of things, but I had great difficulty working out what was new, and what was important. The effect of this is that it's difficult to read, and it's hard to work out what value it is offering. So, despite the potential value of the work done, I don't think we can publish this because nobody will read it - reading it is much too much work.
This could be a paper on the winter of 1788/9, presenting new reconstructions of the weather, with their validation and where they were better than the previous state of the art. And maybe appendix 1 on collected station observations for the period and appendix 2 on the reconstruction method.
It could be a paper on a new weather-field reconstruction method combining analogues and EnKF, with a thorough validation using subsampled modern reconstructions, then a case-study on 1788/9. (Please be careful of terminology when describing the method, I'm still not quite sure if 'post-processing' and EnKF mean the same thing, or whether either of them is included in ARM.)
It could be a paper on a basic data collection for 1788/9, with an example reconstruction to show its value.
I would like the authors to rewrite their paper. Don't tell me what you did; decide what you discovered that I need to know, and write a paper that communicates that. (They might end up with more than one paper). I realise they won't be happy with this recommendation, but something drastic has to be done - I don't think the current version will work at all - and there is good science here, I'd like to see it successfully published.
Duncan Pappert et al.
Duncan Pappert et al.
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