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https://doi.org/10.5194/egusphere-2026-737
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/egusphere-2026-737
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
19 Feb 2026
| 19 Feb 2026
Status: this preprint is open for discussion and under review for Weather and Climate Dynamics (WCD).
Extratropical teleconnections in a multi-model ensemble nudged towards the observed QBO
Abstract. The Quasi-Biennial Oscillation (QBO), the dominant mode of interannual variability of monthly-mean zonal winds and temperatures in the equatorial stratosphere, has a significant influence on the circulation in other regions of the stratosphere and troposphere in boreal winter. To better understand these teleconnections, results from twelve models are analysed for three complementary experiments in which the QBO is either internally generated by the models (free-running), nudged towards the observed QBO, or nudged towards the observed climatology. For the multi-model ensemble mean we assess how well the free-running experiment captures the observed teleconnections, and whether the simulated teleconnections improve when the models have a more realistic QBO.
The observed relationship between the QBO phase and the strength of the boreal winter Polar Night Jet is weakly captured by the free-running experiment. This relationship is strengthened when the models are nudged towards the observed QBO, primarily because a larger proportion of the models exhibit this relationship. Both free-running and nudged-QBO experiments capture a robust but small and insignificant increase in Sudden Stratospheric Warning (SSW) frequency during the QBO-easterly phase. Neither experiment captures the observed QBO influence on the timings of SSWs during winter nor the QBO teleconnection to the North Atlantic Oscillation. Nudging the QBO does, however, influence the latitudinal profile of the North Pacific sub-tropical jet and strength of tropical precipitation.
We find that a more realistic representation of the QBO is beneficial for certain teleconnections, but that effective teleconnections are ultimately reliant on how well the models respond to the QBO.
How to cite. Andrews, M. B., Butchart, N., Anstey, J. A., Bednarz, E., Elsbury, D., García-Franco, J. L., Kumar, V., Palmeiro, F. M., Trencham, N. E., Yoshida, K., Chai, Z., Hong, D.-C., Huang, K., Jaison, A. M., Kawatani, Y., Knight, J. R., Lin, P., Lott, F., Lu, Y., Naoe, H., Osprey, S. M., Richter, J. H., Serva, F., Son, S.-W., Tang, Q., Watanabe, S., and Xie, J.: Extratropical teleconnections in a multi-model ensemble nudged towards the observed QBO, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2026-737, 2026.
Competing interests: At least one of the (co-)authors serves as co-editor for the special issue to which this paper belongs.
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- RC1: 'Comment on egusphere-2026-737', Chaim Garfinkel, 31 Mar 2026 reply
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Latest update: 01 Apr 2026
Martin B. Andrews
CORRESPONDING AUTHOR
Met Office Hadley Centre (MOHC), Exeter, UK
Neal Butchart
Met Office Hadley Centre (MOHC), Exeter, UK
James A. Anstey
Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, British Columbia, Canada
Ewa Bednarz
Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado Boulder, Boulder, CO, USA
NOAA Chemical Sciences Laboratory (CSL), Boulder, CO, USA
Dillon Elsbury
Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado Boulder, Boulder, CO, USA
NOAA Chemical Sciences Laboratory (CSL), Boulder, CO, USA
Jorge L. García-Franco
Lamont Doherty Earth Observatory, Columbia University
Escuela Nacional de Ciencias de la Tierra, Universidad Nacional Autónoma de México, México
Vinay Kumar
Radio and Atmospheric Physics Lab, Rajdhani College, University of Delhi, New Delhi, India
Froila M. Palmeiro
CMCC Foundation - Euro-Mediterranean Center on Climate Change, Italy
Natasha E. Trencham
Department of Applied Physics and Applied Mathematics, Columbia University
Kohei Yoshida
Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan
Zhaoyang Chai
Earth System Numerical Simulation Science Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Dong-Chan Hong
School of Earth and Environmental Sciences, Seoul National University, Seoul, Republic of Korea
Kai Huang
Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, VA, USA
Aleena M. Jaison
Atmospheric, Oceanic and Planetary Physics, University of Oxford, Oxford, United Kingdom
Yoshio Kawatani
Faculty of Environmental Earth Science, Hokkaido University
Jeff R. Knight
Met Office Hadley Centre (MOHC), Exeter, UK
Pu Lin
Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, USA
Geophysical Fluid Dynamics Laboratory(NOAA), Princeton, NJ, USA
François Lott
Laboratoire de Météorologie Dynamique (LMD)/IPSL, Paris, France
Yixiong Lu
Earth System Modeling and Prediction Centre, China Meteorological Administration, Beijing, China
Hiroaki Naoe
Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan
Scott M. Osprey
National Centre for Atmospheric Science, University of Oxford, Oxford, UK
Atmospheric, Oceanic and Planetary Physics, University of Oxford, Oxford, United Kingdom
Jadwiga H. Richter
Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
Federico Serva
Consiglio Nazionale delle Ricerche, Institute of Marine Sciences (CNR-ISMAR), Rome, Italy
Seok-Woo Son
School of Earth and Environmental Sciences, Seoul National University, Seoul, Republic of Korea
Lawrence Livermore National Laboratory, Livermore, CA, USA
Shingo Watanabe
Faculty of Environmental Earth Science, Hokkaido University
Advanced Institute for Marine Ecosystem Change (WPI-AIMEC), Tohoku University, Sendai, Japan
Jinbo Xie
Lawrence Livermore National Laboratory, Livermore, CA, USA
High Meadows Environmental Institute (HMEI), Princeton University, Princeton, NJ, USA
Short summary
The observed winds in the upper atmosphere over the equator have alternating easterly and westerly regions that descend towards the lower atmosphere before dissipating, with a period of approximately 28 months. This is known as the Quasi-Biennial Oscillation (QBO). The QBO is known to influence remote regions of the atmosphere. This paper details the results of multi-model experiments where the QBO is nudged towards the observed QBO allowing the assessment of these remote connections.
The observed winds in the upper atmosphere over the equator have alternating easterly and...
review of "Extratropical teleconnections in a multi-model ensemble nudged towards the observed QBO" by Andrews et al
This study presents first results from the QBOi phase 2 experiments in which the QBO is nudged to observations while the rest of the atmosphere is allowed to freely evolve, with the hopes of understanding how teleconnections improve with a "perfect" QBO. While there is a modest improvement in teleconnections both to the polar night jet and to the subtropical Pacific, the signals are still weaker than those observed. The remaining difference could be either to model deficiencies in the teleconnections processes themselves, or to the fact that part of the observed signal is a fortuitous alignment of internal variability.
This study is clearly on its way to being publishable in WCD, however moderate revisions are needed as detailed below. My main critique is that I'm not fully convinced that the problem is with the teleconnection process itself, while the authors appear to conclude otherwise; see details below. I'm going to sign my review, as the papers I'm referencing in my review probably give my identity away in any event.
Chaim Garfinkel
general comments:
1. The paper doesn't directly show that the nudging leads to a reasonable QBO in the lowermost stratosphere, and instead cites Anstey et al in prep (line 225). I realize how this can happen with community efforts, however, I think it is important to demonstrate that the lowermost stratospheric QBO (both amplitude and meridional width) are reasonable in the nudged runs, in order to confirm that the nudging is acting as intended and that we should expect teleconnections to be better. (For the purposes of this paper on teleconnections, only the lowermost stratosphere is what is relevant.). This concern is motivated by previous work which suggests that on the rare occasion that free-running models simulate a reasonable QBO in the lowermost stratosphere, the PNJ modulation is actually realistic in the extended winter mean (Rao et al 2020) even if the seasonality within winter is still biased.
One way to bypass this concern (and also the lack of any figure documenting the QBO itself in this paper) is to focus on regressions of the PNJ against QBO winds, instead of a compositing perspective. A regression approach will "correct" for any deficiency in the QBO, and instead focus directly on whether teleconnection processes are too weak. It would be interesting to compare regression coefficients of the QBO with the PNJ in Exp1 vs. in Exp1-obsQBO, and if they differ this would imply that the nudging is somehow interfering with teleconnection processes in models (e.g., the nudging could be messing with the waves, despite the fact it is zonal mean only, by perhaps inducing artificial wave reflection, though I would think the 5-day timescale is weak enough to mostly avoid this).
On a related noted note, some QBOi-phase2 models use full-field nudging and not zonal-mean nudging, and it might be helpful to see if those models have a systematically different QBO->PNJ effect than the ones using zonal-mean nudging only. Some of the downward impacts from the polar vortex to the near-surface in the SNAPSI runs are stronger in the full-field nudging runs (e.g., Australia T2m in Feng et al 2025) for reasons that aren't yet fully clear. To be specific, it would be helpful to add additional panels to figure 2 where you separately show the multimodel mean for full-nudged vs. zonal-mean nudged.
2 (but to some degree an extension of 1): I'm a bit confused as to how to interpret figure 2. Doesn't this figure show that the correlation of the QBO winds with U1060 is nearly perfect in the multi-model mean? There are still issues with January specifically, but if I mentally perform a wintertime average the correlations seem very reasonable. See comment #1 about using a regression approach rather than a compositing approach - based on Figure 2 I would guess that a regression approach would likely show that the models are doing a very good job in the winter mean, and that other than a specific issue in January, there is no evidence for a systematic inability to capture QBO->PNJ teleconnection. Rather, the bias is in whether the models are simulating the correct amount of variance, either in the PNJ or in the QBO. (A regression version of Figure 2 in addition to the correlation version currently used would also help clarify this point.) If the authors agree with this interpretation, then the overarching conclusion reached in this paper (i.e., models struggle with the PNJ teleconnection) may need to be toned down.
3. I'm also confused as to how to interpret the results of Table 2 in the context of figure 4 and 5. Figure 4 and 5 indicate that the QBO in Exp1 leads to a significant vortex response (albeit weaker than that of Exp1-ObsQBO). On the other hand, Table 2 indicates this experiment nonetheless has no skill. Is it surprising that skill is now essentially zero even as the HT effect is still there, just weaker? To be specific, I would be interested in seeing an analogous panel on Figure 2 but for the Exp1 runs: is the correlation weaker than for Exp1-ObsQBO? If it isn't weaker, then this would indicate that the primary problem is that the QBO itself is too weak in Exp1.
4. Section 5: it would be nice to see a map of slp or near surface U (or similar). It could be the signal doesn't project strongly onto the NAO pattern you predefine, but there still could be a surface signal. Also, I couldn't find the description of the NAO definition used in this paper. Are you allowing for each model to have a slightly different NAO loading pattern? Does the QBO downward impact project onto a slightly modified version of the NAO loading pattern?
5. The discussion near line 575-580 of the causes of the subtropical jet response seems very speculative to me - please explain in more detail why these tropical precip anomalies should matter. Garfinkel et al 2011 (already cited) instead argue that the subtropical Pacific response is related to downward arching of zonal wind anomalies which then interacts with baroclinic eddies, and actually the Introduction (line 124-130) and Discussion section (line 677-680) of this paper seems to focus on this downward arching mechanism and not the tropical precip mechanism. Note that Garfinkel et al 2011 (the dry model one) explicitly rejects the possibility that the subtropical jet modulation arises from a tropical convection route, but of course in other models or in observations this pathway cannot be ruled out just based on the dry model results. If you want to focus on this tropical convection mechanism, I suggest including it in the Introduction and Discussion.
To be specific, can you make figure 12 (or 13) for a range of pressure levels from 70hPa to 300hPa? If the Garfinkel et al mechanism is relevant, there should be a continuous connection from the QBO region itself to the subtropical Pacific in the upper troposphere.
6. I have a stylistic suggestion, motivated by the fact that the discussion of what may cause the remaining difference in teleconnection strength/position between the nudged runs and observations seems somewhat disjointed. Namely, some parts of the text focus on issues in teleconnection processes (PNJ response) while others focus on the possibility of internal variability or other processes aliasing the observed response (subtropical jet). My guess is that this reflects the intuition of whichever co-author wrote that particular section, though Figure 5 makes the case that internal variability isn't enough for the SPV response and there is a genuine bias in dynamical processes. Either way, it would seem that both possibilities are relevant to all of the teleconnections (though maybe the authors think otherwise! I would welcome being corrected.) If that is indeed the case, the paper could probably be better organized by laying down both possibilities already in the introduction section, then shortening/cutting as much as possible the discussion of these two possibilities from the Results section, and instead revisiting it in detail in the Discussion section in a more systematic way. [I think the paper would be clearer if this suggestion was followed, but it's entirely up to the authors.]
minor comments
line 49: "robust and insignificant" is an oxymoron. I wouldn't use this expression in the abstract, as there isn't space to explain the subtleties behind why this combination makes sense. (I think you intend for "robust" to refer to across model agreement, and "insignificant" whether anomalies meet 95% significance using standard statistical tests, right? You might want to state this explicitly in the methods section.)
line 209: also shown in the ozone-only LESFMIP experiments (Garfinkel et al 2025)
line 302: observe -> observed
line 327: "is an overestimate" could be worded more carefully. Do you intend "might reflect aliasing from unrelated variability"? See general comment 6 above.
line 350: on S2S timescales, there is no evidence for a S2N paradox in the stratospheric polar vortex (Garfinkel et al 2024)
line 414: "QBOE vs QBOE" one of these should be QBOW
Figure 10 seems to have lower resolution than the other figures.
Figure 13: it would be interesting to include the eastern Indian Ocean in these figures. Overall the pattern matches very well the recent simulations of
Schwartz et al 2026, including a tripole pattern in the tropics
Feng, Kexiang, Jian Rao, Chaim I. Garfinkel, Amy H. Butler, Weihua Jie, Tongwen Wu, Peter Hitchcock, Eun-Pa Lim, Andrew J. Dowdy, and William Seviour. "Can stratospheric nudging improve surface predictability? Insights from the 2019 Southern Hemisphere sudden stratospheric warming." npj Climate and Atmospheric Science 8, no. 1 (2025): 353.
Garfinkel, Chaim I., Jeff Knight, Masakazu Taguchi, Chen Schwartz, Judah Cohen, Wen Chen, Amy H. Butler, and Daniela IV Domeisen. "Development of the signal‐to‐noise paradox in subseasonal forecasting models: When? Where? Why?." Quarterly Journal of the Royal Meteorological Society 150, no. 764 (2024): 4417-4436.
Garfinkel, C. I., Avisar, D., Osprey, S., & Smith, D. (2025). The response of the QBO to external forcings: implications for disruption events. Journal of Geophysical Research: Atmospheres, 130(22), e2025JD044438.
Schwartz, C., Garfinkel, C. I., & Chen, W. (2026). Simulated tropical troposphere response to the QBO: Effect of vertical resolution, gravity waves parameterization, and boundary forcing. Geophysical Research Letters, 53, e2025GL120711. https://doi.org/10.1029/2025GL120711
Rao, J., C. I. Garfinkel, and I. P. White, 2020: Impact of the Quasi-Biennial Oscillation on the Northern Winter Stratospheric Polar Vortex in CMIP5/6 Models. J. Climate, 33, 4787–4813, https://doi.org/10.1175/JCLI-D-19-0663.1.