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(A) Predictability of the monthly-mean AO after a 10-day lead. Values are obtained by linear regression between the daily NAM time series and the monthly-mean AO beginning after 10 days and are displayed as percent variance of the monthly-mean AO. Daily values represent an average using Gaussian weighting with a FWHM of 60 days. (B) Cross sections through (A) at 1000 and 150 hPa. The 150-hPa NAM (blue curve) predicts the monthly-mean AO better than the AO itself (black curve) does.
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We use an empirical statistical model to demonstrate significant skill in making extended-range forecasts of the monthly-mean Arctic Oscillation (AO). Forecast skill derives from persistent circulation anomalies in the lowermost stratosphere and is greatest during boreal winter. A comparison to the Southern Hemisphere provides evidence that both th...
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Context 1
... performed least-squares regressions to calculate the percent variance of A ¯ (t L) that is accounted for by the predictor series N(t) (24), as a function of height and time of year ( Fig. 2A). Predictability of the AO was greatest during the extended winter season (October through April). The stratospheric NAM was a better predictor of the AO than the AO was of itself-and it did so for a longer season (Fig. 2B). The optimum single level for forecasting the AO is 150 hPa (13 km), which is the lowest data level that lies ...
Context 2
... variance of A ¯ (t L) that is accounted for by the predictor series N(t) (24), as a function of height and time of year ( Fig. 2A). Predictability of the AO was greatest during the extended winter season (October through April). The stratospheric NAM was a better predictor of the AO than the AO was of itself-and it did so for a longer season (Fig. 2B). The optimum single level for forecasting the AO is 150 hPa (13 km), which is the lowest data level that lies entirely above the tropopause in the ...
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... Predictability on these timescales is closely related to the memory of the oceans. Furthermore, the stratosphere and its connection to the troposphere also play a fundamental role in this regime (Baldwin and Dunkerton 2001;Baldwin et al. 2003). There is great potential in extending the assimilation of stratospheric data with appropriate accuracy, in particular for variables like wind velocity, which are difficult to observe. ...
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... by large negative time-persistent sea ice area anomalies during the interval prior to a resampling event. In contrast to the resampling time of r = 30 days, the resampling time of r = 5 days is not larger than the persistence time scale of the large-scale atmospheric circulation (Baldwin et al. 2003) and is only slightly larger than the typical persistence of synoptic-scale atmospheric fluctuations (Hoven 1957). We therefore expect that the experiments with a resampling time of r = 5 days efficiently sample extremely low sea ice states both due to anomalies in the oceanic thermal state and due to anomalies in the atmospheric circulation that are in the order of or larger than the upper range of the synoptic time scale. ...
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... (1) in Methods), represents the memory or the persistence of the climate anomalies from one year to the next. Climate persistence spans a range of timescales, from months to decades [24][25][26] . The multi-year to decadal memory of the climate system, resulting from complex interactions across various climate subsystems, has been a focal point in the study of global climate change 9,27 . ...
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... Previous studies showed that the anomalies in atmospheric circulation in the stratosphere and troposphere may be linked to the response of stratospheric ozone to SC (e.g., [25,30,33,61]). Figure 8 illustrates the vertical cross-section of composite differences in ozone, temperature, and wind fields in summer for the HSY and LSY in observations and models. In observation (Figure 8a) during the HSY, there was an increased concentration of ozone and higher temperatures in the SH (winter hemisphere), which aligns with the findings of Hood et al. [33]. ...
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... This result illustrates that the tropospheric responses to major events are greater than those of minor events, and the anomalous perturbations from the stratosphere in major events are more likely to be transmitted down to the troposphere. Such findings are consistent with the results of Baldwin and Dunkerton (2001) and Baldwin et al. (2003). In addition, our investigation has confirmed that after the occurrence of stratospheric warming, the troposphere will have a rapid (<20 days) response that is relatively large (Baldwin & Dunkerton, 2001). ...
Utilizing the Open Integrated Forecasting System, the responses of Ural blocking (UB) to different stratospheric warming scenarios are investigated. Numerical results show that stratospheric warming with moderate strength in minor patterns prolongs the UB duration and enhances its intensity, while strong stratospheric warming in minor patterns tends to shorten its duration and weaken its intensity, even leading to the collapse of the UB events. Further diagnosis reveals that the planetary wave activity flux propagates downward from the stratosphere to the troposphere after stratospheric warming. Moreover, the convergence of planetary wave activity flux is a key factor for UB enhancement and maintenance. In addition, the weakened meridional temperature gradients, decelerated zonal westerly winds, and a reduced meridional potential vorticity gradient (PVy) result in UB enhancement in response to stratospheric warming with moderate strength. As stratospheric warming strengthens, planetary wave activity flux diverges, westerly winds in the tropospheric mid‐latitudes accelerate and the PVy in the Ural sector enlarges, which further weakens UB. Regarding the stratospheric perturbations in major patterns, they have similar influences on UB events, that is, UB enhances with moderate stratospheric warming and weakens with strong warming. However, the strengthened warming would trigger UB re‐enhancement, which is closely associated with anomalous activities of tropospheric synoptic‐scale waves induced by stratospheric perturbations. These results reveal UB events respond differently to stratospheric warming with various intensities and patterns in the short term, which makes a contribution to understanding stratosphere‐troposphere coupling.
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The extended‐range predictability of three simulated extreme cold events in East Asia in the Community Atmosphere Model (version 4) control experiment, whose atmospheric circulation backgrounds are similar to two recent observational cases, is investigated. The results show that they have a predictability of four pentads. Then, we evaluate the extent of the forecast uncertainty in the 4th pentad caused by the initial atmospheric uncertainties in the Arctic, which are large due to sparse instrumental observations. The initial uncertainty leading to the largest forecast uncertainty is obtained by the conditional nonlinear optimal perturbation method, and referred to as the CNOP‐type initial uncertainty. The forecast of the cold surge in the 4th pentad fails after adding the CNOP‐type initial uncertainty at day 0. In comparison, the forecast uncertainties are much weaker when the initial conditions are perturbed by noises, and the weaker influence may be due to the noises' lack of spatial structure. In terms of how the CNOP‐type initial uncertainty develops, a baroclinic structure is seen in the uncertainties on the first 2 days, followed by a propagating Rossby wave feature from the 2nd pentad to the 4th pentad. Meanwhile, synoptic transient eddy feedback also plays an essential role. The results suggest that the CNOP‐type initial uncertainty has potential to identify sensitive areas for targeted observations; plus, it could serve as a member of ensemble initial perturbations, since it indicates the largest uncertainty growth.
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Extreme weather events have significant consequences, dominating the impact of climate on society. While high‐resolution weather models can forecast many types of extreme events on synoptic timescales, long‐term climatological risk assessment is an altogether different problem. A once‐in‐a‐century event takes, on average, 100 years of simulation time to appear just once, far beyond the typical integration length of a weather forecast model. Therefore, this task is left to cheaper, but less accurate, low‐resolution or statistical models. But there is untapped potential in weather model output: despite being short in duration, weather forecast ensembles are produced multiple times a week. Integrations are launched with independent perturbations, causing them to spread apart over time and broadly sample phase space. Collectively, these integrations add up to thousands of years of data. We establish methods to extract climatological information from these short weather simulations. Using ensemble hindcasts by the European Center for Medium‐range Weather Forecasting archived in the subseasonal‐to‐seasonal (S2S) database, we characterize sudden stratospheric warming (SSW) events with multi‐centennial return times. Consistent results are found between alternative methods, including basic counting strategies and Markov state modeling. By carefully combining trajectories together, we obtain estimates of SSW frequencies and their seasonal distributions that are consistent with reanalysis‐derived estimates for moderately rare events, but with much tighter uncertainty bounds, and which can be extended to events of unprecedented severity that have not yet been observed historically. These methods hold potential for assessing extreme events throughout the climate system, beyond this example of stratospheric extremes.
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Wave-mean flow interaction is usually regarded as accounting for the origin of the Arctic Oscillation/Northern Hemisphere Annular Mode (AO/NAM). It is inferred that the combination of the local wave-mean flow interactions at the AO/NAM’s three regional centers of action on three important time scales contributes to the main behavior of the AO/NAM index. To discuss the variations of the AO/NAM indices on the three prominent time scales, we take the 2007/08 and 2009/10 winters as two comparative examples to analyze the local wave-mean flow interactions at the AO/NAM’s three centers. The following three facets are identified: (1) Synoptic-scale wave breakings in the North Atlantic can explain the variances of the AO/NAM index on a time scale of 10–20 days. In the 2007/08 winter, there were both cyclonic and anticyclonic synoptic wave breakings, while in the 2009/10 winter, cyclonic synoptic wave breaking was dominant, and the flow characteristics were strikingly similar to the blocking. (2) In the 2007/08 and 2009/10 winters, the signals of the AO/NAM indices on the time scale of 30–60 days are mainly from the interactions between the upward propagating quasi-stationary waves and the polar vortex in the stratosphere. (3) This work also demonstrates that the AO/NAM is linked to the El Niño–Southern Oscillation (ENSO) by the Pacific–North American pattern (PNA) on the winter mean time scale. In the 2007/08 (2009/10) winter, La Niña (El Niño) forced the Pacific jet to shift poleward (equatorward), in favor of weakening (enhancing) the polar waveguide; thus, the polar vortex became stronger (weaker), corresponding to the positive (negative) winter mean AO/NAM index.
