the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 25 Sep 2024
The Role of Weather Regimes for Subseasonal Forecast Skill of Cold-Wave Days in Central Europe
Abstract. Weather regimes (WRs) represent the large-scale tropospheric flow and therefore may contain useful information about the subseasonal predictability of cold waves, one of the most severe weather extremes in Central Europe. Firstly, we investigate in how far the succession of WRs during a forecast can be used to explain skill differences of forecasts initialized during different WRs. As an example, we use the skill differences of mean-bias-corrected 14-day reforecasts of the European Centre for Medium-Range Weather Forecasts for the occurrence of wintertime cold-wave days in Central Europe. Reforecasts initialized during the WR Greenland Blocking (GL; characterized by a high pressure system over Greenland) show the best Brier skill while those initialized during the WR Scandinavian Trough (ScTr; characterized by a low pressure system over Scandinavia) show the worst skill compared to a climatological ensemble for the winters 2000/2001–2019/2020. We find, that for forecasts initialized during GL, more often WR succession which follow typical climatological pattern are found during the 14 days of forecasts than for forecasts initialized during ScTr. We suggest that this is one of the main reasons for an increased forecast skill of predictions initialized during GL in contrast to predictions initialized during ScTr. Secondly, we analyze the WR succession for the best (worst) predicted days within the observed cold waves in the winters 2000/2001–2019/2020 independent from the WR present at initialization. We find, that forecast skill is significantly higher, when the European Blocking WR (characterized by a high pressure system over the British Isles and southern Scandinavia) is present a few days before the predicted cold-wave day. These results can be used to assess the reliability of cold-wave day predictions at the subseasonal lead time of 14 days.
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Status: open (until 27 Nov 2024)
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RC1: 'Comment on egusphere-2024-2955', Anonymous Referee #1, 10 Oct 2024
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This work described in this manuscript extends earlier studies by the same authors, focusing on a thorough analysis of the connection of weather regimes (and their succession) with the predictability of cold-wave days in Central Europe. The analysis shows that more common ('climatological') WR successions tend to be more predictable than uncommon WR successions, while other factors like the number of regime transitions between forecast initialization and valid time did not show a clear association with forecast skill. The paper is interesting, but some clarifications and more evidence for the main conclusion is required as detailed below.
General comment:
The main conclusion of the manuscript is that among the different WR-related explanations of increased/decreased predictability the frequency of WR successions following climatological patterns plays an important role. This conclusion is primarily based on the observation that 61.6% vs. 53.9% of a subselected set of cases follows such climatological patterns. That difference is noticeable but not huge, and given the additional complication due to the subselection criterion (only the most frequent WR successions per WR at the target date are considered), which presumably has the effect of amplifying the observed difference, I feel that more evidence for this conclusion should be provided. Would it be possible, for example, to calculate the Brier score for forecasts with the GL/ScTr WR at initialization time separately for the cases where the WR successions do and do not follow a climatological pattern and test whether the score differences are statistically significant?
Specific comments:
- Section 2.2: Are the ECMWF reforecasts also temporally smoothed (like the observation data), or is that unnecessary due to the subsequent post-processing?
- 131: Aren't these just forecast errors of an ensemble mean forecast? I find it strange to call them biases, which to me is a systematic error, while without further aggregation the quantities calculated here contain (a substantial amount of) random forecast errors as well.
- Section 3.2, 2nd paragraph: More detail is required for this ERA5-based predictor. Is ERA5 data at the different hours from the day before initialization time used here? Can you briefly describe the preprocessing operations mentioned in 149?
- 189: I was very confused about this concept of 'hypothetical' forecasts when I read it here, and understood only later that it's not really a forecast, but that the weather regimes on these dates can still be analyzed. Maybe this can already be clarified here.
- 235-236: I don't understand what is meant by 'single actual WR successions', and found this sentence very confusing. This paragraph is generally hard to follow, but it becomes clear what is studied here in connection with Figure 4. The aforementioned sentence, however, could easily be removed without loss of information.
- 267-268: I don't understand what is meant by 'without taking persistence of the individual WRs per se into account'. What if the WR at initialization time persists for the 14 days lead time? Please rephrase and/or explain.
- 275: Perhaps clearer to say '..., the number of possible WR successions varies ...'
Typos and language:
72: -> their skill
130: Therefore -> To this end
139: Either "the ECMWF S2S reforecast ensemble" or "ECMWF's S2S reforecast ensemble"
159: Please check this reference, I have never seen a citation with a range of publication years beforeCitation: https://doi.org/10.5194/egusphere-2024-2955-RC1
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