The Quasi-Biennial Oscillation (QBO) is one of the most prominent internal sources of natural variability within the Earth's climate system. It is characterized by alternating bands of easterly and westerly winds that gradually descend through the equatorial stratosphere before disappearing near ∼ 16 km altitude. The wind speeds associated with the QBO peak near 30 m s⁻¹ in the middle stratosphere . A full cycle of the QBO is completed approximately every 28 months, allowing for the future QBO phase to be skillfully predicted months or even years in advance . Because the QBO can influence climate outside of the tropical stratosphere, its intrinsic predictability offers the potential for surface predictability on seasonal to annual timescales.

The QBO influences surface climate through at least three distinct mechanisms . First, the QBO variation in equatorial winds modulates the winter waveguide for extratropical planetary waves, which can affect the strength of the stratospheric polar vortex and thereby trigger a response in the North Atlantic Oscillation NAO; i.e., the Holton Tan or H–T effect;. Second, the tropical QBO winds arch downwards into the subtropical lowermost stratosphere and upper troposphere where they can affect tropospheric eddies . This effect is particularly pronounced in the East Asia and Pacific sectors . Finally, the QBO can directly affect winds and static stability in the tropical tropopause layer below the cold point and subsequently convection in the tropics . This effect is most robust in observations when focusing on intraseasonal variations in convective activity associated with the Madden–Julian Oscillation MJO;.

Previous work has demonstrated that these teleconnections are systematically weaker than observed in climate models and forecasting models . Part of the problem is that the QBO signal in the lowermost stratosphere is too weak in nearly all models ; however, even simulations with a nudged QBO still tend to simulate a weaker-than-observed response . Most previous studies have used relatively small ensembles to compare against the limited observational record, making it difficult to cleanly separate sampling variability from true model–observation discrepancies . It is also difficult to cleanly isolate surface responses to the QBO from the El Niño–Southern Oscillation (ENSO) .

Here, we evaluate the teleconnections of the QBO in models contributing to the new Large Ensemble Single Forcing Model Intercomparison Project (LESFMIP) , in which CMIP6-era models are used to isolate the impacts of different external drivers on the Earth system. The core LESFMIP experiments target the separate influences of historical changes in anthropogenic greenhouse gases (the hist-GHG experiment), aerosols (hist-aer), total-column ozone (hist-totalO3), volcanic eruptions (hist-volc), and solar variability (hist-sol). These experiments are a cornerstone in the analysis plan of the World Climate Research Program Lighthouse Activity on Explaining and Predicting Earth System Change , which aims to develop operational capabilities to project and attribute changes in the atmospheric circulation on annual-to-decadal timescales. The QBO represents a key source of predictability on these timescales. Participating models have contributed at least 10 ensemble members for each experiment covering the years 1850–2020 (in some cases 1850–2014). The large number of ensemble members allows for a clearer quantification of trends and variability in teleconnection strength than has been previously achievable. The use of single-forcing experiments also allows us to revisit the possibility that increases in greenhouse gases may alter the teleconnection strength over time , while allowing for the first time the isolation of the role of other forcings.

Four of the models participating in the LESFMIP are capable of spontaneously simulating a QBO : HadGEM3-GC31-LL , IPSL-CM6A-LR , MIROC6 , and CNRM-CM6-1 . assessed the surface and polar stratospheric response to the QBO in single ensemble members from the historical runs conducted for CMIP6 using these models. They found that all four models underestimated the observed H-T response (which exceeds 10 m s⁻¹). Among the models, HadGEM3-GC31-LL produced no H-T response at all, MIROC6 showed a significant response but only half as strong as that observed (∼ 5 m s⁻¹), and the other two models showed a weak response that did not rise to the level of statistical significance (see Fig. 1 of ; similar results were shown by ). Only IPSL-CM6A-LR simulated the observed downward arching of QBO wind anomalies in the Pacific sector (see Fig. 4 of ). All four models showed a decline in precipitation over the Maritime Continent and tropical East Indian Ocean (see Fig. 8 of ); however, the regional structure differed from one model to the next and none of the models reproduced the observed response. The models also disagreed as to whether there is a preferred relationship between ENSO and the QBO, with CNRM-CM6-1 showing no relationship while the others showed a La Niña response during the easterly phase of the QBO (eQBO) (see Fig. 11 of ). None of these models showed a relationship between the QBO and the MJO . Here, we revisit these relationships using a factor of 20 to 100 more model output, while also discovering new surface impacts of the QBO that appear to not have been noted before and isolating how external forcings may modulate their strength.

After introducing the LESFMIP dataset in Sect. , we quantify the teleconnections of the QBO in three core regions – the annular mode response via the polar vortex, the subtropical wind response in the Pacific, and tropical precipitation – in Sect. . We then assess sensitivity of these teleconnections to external forcings in Sect. , before discussing the implications of our results in Sect. .

This paper focuses on models contributing to the LESFMIP project. Four of the LESFMIP models spontaneously simulate a QBO: HadGEM3-GC31-LL , IPSL-CM6A-LR , MIROC6 , and CNRM-CM6-1 . We focus on five of the single-forcing experiments included in Phase 1 of the LESFMIP protocol: hist-GHG, hist-aer, hist-volc, hist-sol, and hist-totalO3. For each model–experiment pair, at least 10 ensemble members have been simulated over the period 1850 to 2020; however, only two of the models have provided output for the hist-volc, hist-sol, and hist-totalO3 experiments. The specific ensemble sizes for each run are shown in Table . Full details of the LESFMIP protocol have been provided by . All data are freely available via the Earth System Grid Federation (ESGF).

While the large ensemble sizes available as part of LESFMIP allow isolation of the simulated impacts of each forcing on the QBO and its teleconnections, there is a downside: the data volume is huge (approximately 65 000 years of model output for these four models alone), and some diagnostics that could help to clarify why the QBO and its teleconnections respond in the ways they do are simply unavailable. For example, only HadGEM3 has made available w*‾ .

Monthly zonal-mean zonal winds from 5° S to 5° N are used to define the QBO. All four models considered here simulate a spontaneous QBO. We demonstrate this by showing two 17-year snapshots of the QBO winds for the four models in Figs. S1–S4 in the Supplement. Additional diagnostics of the QBO have been shown by . Composites of westerly and easterly QBO events include all months when tropical zonal wind anomalies at 50 hPa exceed ±2 m s⁻¹. Results are generally similar (though weaker) for 30 hPa winds, as shown in the Supplement. The QBO is poorly defined at 70 hPa for some of these models, and the aliasing with ENSO discussed in Sect.  is most pronounced at that altitude. Hence, we do not use the 70 hPa level to specify the QBO phase. The zonal wind at 50 hPa within the 5° S–5° N tropical band is referred to as QBO50.

These four models have made daily outgoing longwave radiation (OLR) available for at least parts of their runs. We therefore apply the analysis framework proposed by to diagnose equatorial wave modes. The results presented below were obtained by applying the framework to daily OLR within the 15° S–15° N tropical band using overlapping time windows of 96 d with starting dates spaced at 25 d intervals (i.e., overlaps of 71 d between consecutive windows). We overlay on the spectra the theoretical dispersion relations obtained by for equivalent depths of 10, 30, and 90 m. Differences between the plane solutions of and the exact spherical solutions are small for the parameter regime characteristic of Earth's tropics .

In this section, we evaluate QBO teleconnections in the hist-GHG runs, as this experiment has the largest ensemble sizes. We discuss differences between hist-GHG and the other experiments in Sect. .

The LESFMIP data allows for an unprecedented examination of the ability of external forcings to affect the strength of QBO teleconnections. Previous work has found that the polar vortex response to the QBO strengthens in a future climate with increased GHGs even as the QBO itself weakens , and we now revisit this effect by examining the response in the hist-GHG experiment for the period since 1970 (Fig. ). Although regression coefficients between the QBO winds and winds at 10 hPa and 60° N increase by 7 %, this increase is not statistically significant in individual models, however it is robust across models. When evaluated as a composite difference, on the other hand, the teleconnection strength weakens in 2 of 4 models (Fig. S5), likely due to weakening of the QBO in response to increased GHGs . The downward arching winds to the subtropical troposphere also strengthen after 1970, with the regression coefficient between the QBO and U700 over the North Pacific increasing by 15 % in the multi-model mean (Fig. S11). HadGEM and MIROC6 (the two models that better capture this relationship) show even larger increases. However, these increases still do not reach the level of statistical significance despite the enormous amount of data in individual models. The cold anomaly over northwestern North America is also 36 % stronger after 1970 in HadGEM and MIROC6 (Figs. , S12). This difference barely misses the threshold for statistical significance at the 95 % level and is significant at the 90 % level. In contrast, the Atlantic sector U700 response shows no strengthening after 1970. Overall, the intensification of QBO teleconnections in response to increased GHGs is more pronounced in the Pacific sector via the downward arching subtropical horseshoe than in the North Atlantic via the polar stratosphere.

To understand why the subtropical horseshoe strengthens, we turn to thermal wind balance on an equatorial β-plane upon applying L'Hopital's Rule (Sect. 8.2 of ) and the thermodynamic equation (Sect. 7.2 of ): 1∂u∂z=-RHβ∂2T∂y22∂T∂t+v⋅∇T+HN2Rw‾*=-T-Teqτ

Assuming a quasi-steady state and weak horizontal temperature gradients (both ∂T∂t and v⋅∇T are near zero), vertical shear of the zonal wind must be balanced by a temperature anomaly, which in turn must be balanced by vertical motion. Static stability decreases near the tropopause and in the lower stratosphere under increased GHGs regardless of the QBO. This reduced static stability under climate change necessitates a stronger vertical velocity, and hence a stronger mass circulation, to achieve the same temperature anomaly to balance the vertical shear of the zonal wind. This point is demonstrated explicitly in Fig. S13: for each 10 m s⁻¹ strengthening of easterly QBO winds at 50 hPa, the upwelling anomaly in w*‾ at 60 hPa doubles at the end of the hist-GHG experiment as compared to the beginning, and this effect is still evident for w*‾ at 90hPa (∼ 50 % increase at the end of the hist-GHG experiment). This increase in vertical velocity per m s⁻¹ change in QBO wind necessitates a corresponding increase in the meridional velocity of the QBO's MMC due to mass continuity, including in the MMC in the vicinity of the tropopause. The Coriolis torque from this increase in near-tropopause meridional velocity induces a larger zonal wind anomaly in the subtropical upper troposphere in the presence of elevated GHGs, in turn altering eddy activity and the tropospheric jets .

There is little agreement across models as to whether elevated aerosol concentrations since 1970 affected surface teleconnections. Teleconnections are weaker in HadGEM3 under hist-aer but stronger in MIROC6 and CNRM6.1 (Figs. S11 and S12). In hist-volc, simulated QBO teleconnections are stronger in the 1880s (Krakatoa) and 1990s (Pinatubo) in MIROC6 but weaker during those same decades in HadGEM3 (not shown). Sensitivities of QBO teleconnections to the solar cycle will be evaluated in an upcoming paper. Finally, prescribed time-evolving ozone leads to stronger surface teleconnections in the Pacific sector wind response but weaker surface teleconnections in the Atlantic sector using HadGEM3, but weaker teleconnections in both regions using MIROC6 (Figs. e and e). Prescribed ozone changes invigorate the QBO winds in both models , and so the stronger teleconnections in HadGEM are more in line with expectations. Restricting the analysis to after 1970 (the era with ozone depletion) has little impact (Figs. S11e and S12e).

The Quasi-Biennial Oscillation (QBO) can be skillfully predicted months or even years in advance . Because the QBO can influence climate outside of the tropical stratosphere, the predictability of the QBO offers a potential for improved predictability in several key regions on seasonal to multi-annual timescales. Previous work has found that QBO teleconnections tend to be too weak in models . The recent availability of large ensembles from models with spontaneously-generated QBOs allows us to revisit and refine this conclusion.

The results demonstrate that the biggest contributor to this under-estimate is that the QBO is too weak in the lowermost stratosphere in nearly all models . It is therefore important to consider the possibility that too-weak teleconnections are a byproduct of biases in the QBO itself rather than biases in the mechanisms that connect the QBO to remote regions. This possibility can be evaluated by regressing climate variables in remote regions onto QBO winds. This approach sharpens focus on the teleconnection mechanisms while side-stepping issues in the QBO amplitude, while compositing approaches could conflate the two.

All four models show a Holton–Tan effect linking the QBO to the polar stratosphere (Figs.  and ), with regression results indicating that the amplitude of this effect is similar to that observed in three of the four models. By contrast, a compositing approach yields a pronounced underestimate (Fig. S5) due to the weaker-than-observed QBO amplitude in all models (Figs. S1–S4), a common model bias that is nonetheless difficult to fix though not insurmountable . The Holton–Tan response is consistent with a meridional dipole in zonal wind in the North Atlantic sector (Fig. ), anomalies in surface temperature over most of Eurasia (Fig. ), and a meridional dipole in precipitation anomalies over Europe (Fig. ). These effects are to be expected, given the well-known role of the polar stratosphere in regulating the NAO .

The large ensemble sizes also allow for clarifying the QBO level which most strongly affects the vortex. Both the Arctic stratospheric response and the downward arching horseshoe response to the QBO are weaker when winds at 30 hPa (instead of 50 hPa) are used to define QBO phases Figs. S8–S9;. We therefore recommend using 50 hPa winds to define the QBO when possible. The large ensemble sizes also allow us to confidently identify a delay in the Holton–Tan effect by a month or two in models (peaking in February) relative to observations (Fig. a). Likewise, the simulated Holton–Tan effects are weaker than observed in November and December. This delay in the Holton–Tan effect leads to a corresponding delay in the Atlantic sector and Eurasian surface responses (Fig. c). These discrepancies stand out more clearly in our study than in any previous work due to the huge amount of data examined. Despite these discrepancies, this study confirms a genuine influence of the QBO on the North Atlantic sector as represented by these models .

All four models simulate a subtropical temperature response in the winter hemisphere quantitatively similar to that observed, indicating that these models can simulate the MMC of the QBO. However, the models underestimate the temperature response in the equatorial lowermost stratosphere (Fig. ). This underestimate in the equatorial lowermost stratosphere is evident regardless of whether a regression approach or a compositing approach is used, and is therefore not solely a consequence of a too-weak QBO.

The models also successfully capture other aspects of the remote response to the QBO, including the downward arching of easterly wind anomalies towards the troposphere in the subtropics during eQBO (in 3 models, with IPSL6 the exception; Fig. ) and the cold tropical TTL anomalies associated with eQBO's MMC (all four models; Fig. ). This effect is particularly pronounced in the Pacific sector (Fig. ), however, the models simulate the largest impact in mid-winter, rather than in March when the observed effect peaks (Fig.b). These Pacific sector wind anomalies affect surface temperature and precipitation over western North America (Figs.  and ). This impact of the QBO on North American temperature and precipitation does not appear to have been noted before and, while present in ERA5 data (Fig. S10), it is not statistically significant. We expect that as the observational record lengthens, the signal may begin to emerge. One of the models also simulates a robust connection between the QBO and ENSO (Fig. ), however these remote teleconnections are robust even after regressing out the (linear) ENSO influence.

Precipitation anomalies are also evident in the tropics, but with substantial intermodel and inter-experiment spread. In DJF, eQBO leads to precipitation expanding away from the region with strongest precipitation climatologically, in contrast to contraction of these regions during wQBO. In JJA, precipitation increases in the core Southeast monsoon region in eQBO relative to wQBO. However, inter-model and inter-experiment differences in both JJA and DJF indicate that the precipitation response to the QBO is poorly constrained even when using large ensembles, and may instead reflect genuine model differences . Furthermore, these precipitation signals are typically not statistically significant unless more than 10 ensemble members are available, and so require more than 15 000 years of output to stand out from the noise. The relative weakness and scattered nature of these precipitation signals likely is a consequence of the QBO amplitude being too weak in the lowermost stratosphere, and particularly the too-small response of temperature near the tropical tropopause Fig. ;. Finally, these tropical precipitation responses are overwhelmed by ENSO effects if we do not regress these influences out, and we cannot rule out the possibility that other modes of tropical SST variability also alias into the QBO precipitation response.

One model (MIROC6) simulates the observed relationship between the QBO and the MJO (Fig. ). Despite the effect being weaker than observed, this model is remarkably more successful than previous CMIP-class models in reproducing this teleconnection. Future work is needed to constrain the regions in which this signal is most pronounced, and to more fully explore why this model is relatively more successful than the others.

Finally, the Holton–Tan mechanism strengthens (though not significantly in individual models) in response to increased GHGs when evaluated as a regression in agreement with, but not when evaluated as a composite difference. This sensitivity of the conclusions to the precise methodology can be attributed to the weakening of the QBO in response to increases in GHGs . The downward arching wind response in the subtropical Pacific sector also strengthens after 1970 in the hist-GHG experiment, likely because the QBO's lower stratospheric MMC strengthens in response to a reduction of lower stratospheric static stability forced by increased GHGs (Fig. S13). Other external forcings have no clear impacts on teleconnections that are robust across models.

Overall, the use of large ensembles has allowed us to develop a clearer picture of how well models simulate QBO teleconnections while revealing surface impacts over North America that appear to not have been noticed before. Some previous claims that models systematically underestimate QBO teleconnections are likely the result of insufficient sample size or the use of a compositing approach to evaluate teleconnections, which mixes together the well-known bias in QBO amplitude in the lower stratosphere with biases in the processes that connect QBO winds with remote regions. Regardless, there remain important biases in the timing of the teleconnections, and individual models show pronounced biases in the surface responses even after accounting for the relative weakness of the QBO in the lower stratosphere.

Despite these persistent biases, our results indicate that the QBO in better-performing models can already be used to improve surface climate predictability on seasonal to multi-annual timescales. However, it is critically important to improve the representation of the QBO in the lowermost stratosphere in all models. Without a realistic QBO in this region, teleconnections will need to be bias corrected to account for systematic underestimation of the lower stratospheric signal, potentially contributing to surface expressions of the signal-to-noise paradox .