the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 Aug 2025
The role of the stratospheric state in upward wave flux prior to Sudden Stratospheric Warmings: a SNAPSI analysis
Abstract. Several studies highlight the relevance of considering polar winter stratospheric information such as the occurrence of Sudden Stratospheric Warmings (SSWs) for skillful Subseasonal to Seasonal (S2S) surface climate predictions. However, current S2S forecast systems can only predict these events about two weeks in advance. A potential way of increasing their predictability is to improve the models' representation of the triggering mechanisms of SSWs. Traditional theories indicate SSWs follow sustained wave dissipation in the stratosphere, but the relative role of tropospheric versus stratospheric conditions in the enhancement of stratospheric wave activity remains unclear.
This study aims to quantify the role of the stratospheric state in wave activity preceding SSWs by analyzing three recent SSWs: the boreal SSWs of 2018 and 2019 and the austral minor SSW of 2019, using specific sets of S2S experiments. These ensembles follow the SNAPSI (Stratospheric Nudging And Predictable Surface Impacts) guidelines and include free-evolving atmospheric runs and nudged simulations, where the zonally-symmetric stratospheric state is nudged to either observations of a certain SSW or a climatological state. Our results show that the models struggle to capture the strong enhancement of wave activity preceding the 2018 SSW, limiting predictability beyond 10 days. In contrast, both SSWs of 2019 are better simulated, consistent with a more accurate simulation of the wave activity. The zonal mean stratospheric state does not drastically influence the upward wave activity flux or tropospheric circulation anomalies prior to these SSWs, but it has some impact on the stratospheric wave activity, although this modulation depends on the event characteristics. The boreal 2019 SSW appears to be primarily driven by tropospheric processes. In contrast, stratospheric contributions may have also played an important role in triggering the boreal 2018 SSW and the austral 2019 SSW. Understanding these variations is key to improving SSW predictability in S2S models.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Weather and Climate Dynamics.
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Status: open (until 16 Oct 2025)
- RC1: 'Comment on egusphere-2025-3611', Anonymous Referee #1, 11 Sep 2025 reply
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Ayarzagüena et al. investigate how the stratospheric background state influences upward wave activity preceding Sudden Stratospheric Warmings (SSWs). Using ensembles of different models from the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project, the study compares free-running, stratosphere-nudged and control simulations to ERA5 for three events: the February 2018 boreal SSW, the January 2019 boreal SSW, and the September 2019 austral minor SSW. The study aims to isolate the effect of the stratospheric state on the triggers of SSWs. Overall the paper provides a detailed study of the processes and wave fluxes that go into contributing to the different SSWs and attempts to separate tropospheric and stratospheric influences. The paper itself is rather long but the analysis is thorough and the authors walk the reader through the plots and their interpretation. I would recommend publication after addressing the points below.
1) Figure 1 and Table 3. I have difficulty reconciling the fact that the ensemble mean line for CNRM in Fig 1(a) doesn’t show an SSW but 62% of the ensemble members do. Do you have a suitable plot to illustrate the spread please?
2) Figure 5 to 7: I think all the subplots should use the same colorbar within each figure. For ERA5, am I right in thinking that this is a difference from climatology whilst for the models it is a difference between the strongest and weakest ensemble members? As such I would expect that the model composite differences shown are larger than if you were able to do a comparison to the free running climatology of each model (like for ERA5). I would like to see a more careful discussion of what is being shown in these figures around line 320. Whilst there are similarities between the patterns in some models and ERA5, the strength is much weaker in all cases.
3) Figure 9: would it be helpful to add the multimodal mean?
4) Refractive index. The authors acknowledge around line 715 that the resonant growth is likely non-linear. It is my view that since the refractive index is derived from linear theory, it has serious limitations in how it can be applied and I would prefer if Fig 12 and 13 and associated discussion were omitted.
5) The authors mention gravity wave drag as a source of uncertainty in models and as being important for triggering wave resonance. Have you looked at this in the SNAPSI models?
6) Line 745: I do not think interactive chemistry plays a role in the onset of SSWs but does later in the year. All the time periods analysed are in polar night.
Minor comments:
A general comment here is that the typesetting of the maths could be improved. For example, subscripts are used for both the z and \phi components of F and for partial derivatives.
Equation (1): Bold F for vector here and across manuscript.
Equations (1.1) and (1.2): \overline{\theta}_z rather than \overline{\theta_z}
Line 190: Define z. Also de-italicize ‘and \theta’.
Equation (2) actually comes from Kushner and Polvani (2004) Eq (7). The way they present it is much easier to read.
Equation (4): Definition of q. Which PV? Quasi-geostrophic?
Table 3
- First column. Consider using the event names introduced in Table 2.
- Second row: 2018 should be 2019.
- Third row. Maybe round 47.5% to 48% for consistency.
Supplementary figures S2 to S4 are far too small.
References:
Kushner, P. J., and L. M. Polvani, 2004: Stratosphere–Troposphere Coupling in a Relatively Simple AGCM: The Role of Eddies. J. Climate, 17, 629–639, https://doi.org/10.1175/1520-0442(2004)017<0629:SCIARS>2.0.CO;2.