For the given event date the closest N dynamical analogues are identified for each of two periods (past period and present period), over the given region. When available, ERA5 data is used. As ERA5 data is only available around a week behind real time, if it is not yet available data from the ECMWF forecast data is used instead. The analogues are identified from one of two variables: Mean Sea Level Pressure (MSL) or 500 hPa Geopotential Height (Z500). Only the season in which the event day occurs is assessed (DJF, MAM, JJA, or SON). Similar methods have been used in Thompson et al., 2024 and Thompson et al., preprint.
The methods used have evolved from those presented in Faranda et al., 2022.Analogues can be identified using two different methods: Euclidean distance or spatial correlation. For this the spatial mean over the given region of the field is first removed, then Euclidean distance or spatial correlation between the event day and every day within the time period and event season, over the given region, is calculated. For each of the periods, we take the N days with the smallest Euclidean distances or greatest spatial correlation compared to the event as the analogue set.
We impose a gap of 5 days between the analogues, resulting in N unique events. If within the time period, the event itself is excluded from analogues.
Figure 1: The first figure shows the fields of the given event and composites of the analogue sets. The upper row shows the anomaly field from which the analogues are defined, with (a) the event field; (b) composite of the past period analogues; (c) composite of the present period analogues; and (d) the difference between present and past analogues. Rainfall, temperature, and wind[1] of the given date and analogue sets are presented in the rows below.
Figure 2: To assess analogue similarity between periods we show violin plots of the Euclidean distance or correlation (depending on method chosen), for each set of analogues (past and present). This can be used to determine if the most similar events are becoming more (or less) like the given event between the two periods. For both methods, the distribution displaying a smaller mean includes events more similar to the event itself (note, correlation coefficient has been inversed).
Figure 3: Finally, timeseries of frequency of events of different similarity levels are produced. For this we determine the 5%, 10%, and 20% most similar days across the full time period - and count the days within each year, plotted as a timeseries. This figure allows trends in frequency of similar days through time to be assessed. Note - neighbouring days are not excluded, so one event can lead to multiple days.
Latitude/Longitude: It is recommended to use a region which is chosen based on the analogue field (SLP/Z500) of the event day (not a region based on the event impacts such as rainfall), ensuring the analogues are attempting to capture the key dynamical features of the event.
Period length: Longer time periods will give a better measure of long term changes in events, taking short time periods the results will be bias by interannual variability. Equal period length is recommended.N analogues to find: It is recommended to use N of ~number of years in the period, this will result in the analogues selected equating to the closest ∼1% of days. For less unusual event types higher N may be appropriate as there will be more events in the dataset with similar dynamical characteristics, the analogue quality plot can help determine an optimum value.
Choose Field (MSL/Z500): For extratropical heatwaves Z500 is recommended. For slow-moving, persistent, weather systems (such as cut-off low pressure systems over Europe) it is recommended that Z500 be used. For faster moving systems (such as extratropical cyclones) SLP is more appropriate.
As the research progresses, further guidelines for more event types will be developed.
Thompson, V., Coumou, D., Galfi, V.M., Happé, T., Kew, S., Pinto, I., Philip, S., de Vries, H. and van der Wiel, K., 2024. Changing dynamics of Western European summertime cut-off lows: A case study of the July 2021 flood event. Atmospheric Science Letters, 25(10), p.e1260.
Thompson, V., Philip, S.Y., Pinto, I. and Kew, S.F., 2024. The influence of the Atlantic Multidecadal Variability on Storm Babet-like events. preprint in EGUsphere, pp.1-15.
Faranda, D., Bourdin, S., Ginesta, M., Krouma, M., Messori, G., Noyelle, R., Pons, F., and Yiou, P., 2022. A climate-change attribution retrospective of some impactful weather extremes of 2021, Weather Clim. Dynam., 3, pp.1311-1340
Thompson, V., Pinto, I., Kew, S., and Philip, S.: Development of the Climate Explorer Circulation Analogues Tool , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8242, PDF of Poster
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[1] Wind is not included if the data is taken from the ECWMF forecast (for near real time assessments).