the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 19 Feb 2025
Assessing stratospheric contributions to subseasonal predictions of precipitation after the 2018 SSW from SNAPSI
Abstract. The sudden stratospheric warming (SSW) event in February 2018 was followed by dry spells in Scandinavia and record-breaking rainfall over the Iberian Peninsula through the following March. Here, we study the role of the 2018 SSW in subseasonal to seasonal (S2S) prediction of the 'Wet Iberia and Dry Scandinavia' precipitation signal, using a new database of S2S forecasts generated by the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project. This database includes three sets of forecast ensembles: a free ensemble in which the atmosphere evolves freely, a nudged ensemble in which the stratosphere is nudged to the observed zonal-mean evolution of the 2018 SSW, and a control ensemble in which the stratosphere is nudged to climatology. Each set of ensembles has two initialization dates: 25 January 2018 and 8 February 2018, both before the onset of the SSW on 12 February. We find that the 'Wet Iberia and Dry Scandinavia' pattern was captured by the late free ensemble (initialized at 8 February) which successfully predicted the stratospheric warming, but not by the early free ensemble (initialized at 25 January) which predicted a stratospheric cooling. Unlike the early free ensemble, the early nudged ensemble successfully captured the 'Wet Iberia and Dry Scandinavia' pattern, indicating that an accurate forecast of stratospheric variability can improve S2S predictability of precipitation. While the pattern of European precipitation anomalies is evidently connected to the stratosphere, we estimate that only roughly a quarter of the amplitude was expected given the stratospheric anomalies. Nonetheless, the likelihood of Iberian rainfall extremes comparable to or even stronger than the one observed doubles in the nudged ensemble, compared to the control ensemble. The increased likelihood in the nudged ensemble suggests that the weakened stratospheric polar vortex can increase the risk of Iberian rainfall extremes.
Competing interests: Some authors are members of the editorial board of journal Weather and Climate Dynamics.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.- Preprint
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RC1: 'Comment on egusphere-2025-484', Anonymous Referee #1, 14 Mar 2025
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The manuscript investigates the role of the 2018 SSW in S2S prediction of the "Wet Iberia and Dry Scandinavia" precipitation pattern by using observations and a new S2S forecasts project SNAPSI. Some of the conclusions are - the early nudged ensembles, unlike the early free ensembles, successfully capture the "Wet Iberia and Dry Scandinavia" precipitation pattern, suggesting that an accurate forecast of stratospheric variability is helpful to improve S2S predictability of precipitation. In addition, the SSW related precipitation anomaly accounts for about 1/4 of the observed precipitation anomaly, suggesting the importance of other tropospheric variability.
The manuscript covers an important topic in assessing and quantifying the contribution of SSW on the "Wet Iberia and Dry Scandinavia" pattern in 2018. It's also overall well-written. However, I have some major comments about assessing the role of tropospheric internal variability using ensemble spread.
Major comments:
1. About the role of tropospheric internal variability. While I understand that the majority of the analysis focuses on ensemble mean to extract the SSW-induced variability, I would suggest the authors examine more on the ensemble spread since the ensemble spread includes the tropospheric variability. Some of the questions that the authors could investigate include - Can some ensemble members capture the observed magnitude of the precipitation pattern? (This seems to be yes given Fig. 9 but the question is different from Section 7) What does the ensemble mean versus ensemble spread tell about the role of tropospheric variability? (The role of tropospheric variability is assessed in the paper by using the linear regression approach. Does the model simulations tell the same attribution?) What are the model-to-model differences?Â
2. About 2018 SSW compared to other SSWs. I would suggest the authors add some conclusions/discussion on how 2018 SSW is compared to other SSWs. As shown in Fig. 7, 2018 SSW is not particularly strong based on T100 but the SLP_Atlantic is the strongest. This suggests that the 2018 SSW is not very different from other SSWs and the SLP_Atlantic and precipitation pattern is mostly driven by other tropospheric variability. This information is here and there in the paper but would be very useful information to include in Conclusions/Discussion.
3. This might be minor but why all the free running models predict a stratospheric cooling?
Citation: https://doi.org/10.5194/egusphere-2025-484-RC1 -
RC2: 'Comment on egusphere-2025-484', Anonymous Referee #2, 19 Mar 2025
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The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-484/egusphere-2025-484-RC2-supplement.pdf
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