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[1] There is widely believed to be a link between stratospheric flow variability and stationary, persistent "blocking" weather systems, but the precise nature of this link has proved elusive. Using data from the ERA-40 Reanalysis and an atmospheric general circulation model (GCM) with a well-resolved stratosphere (HadGAM), it is shown that there are in fact several different highly significant associations, with blocking in different regions being related to different patterns of stratospheric variability. This is true in both hemispheres and in both data sets. The associations in HadGAM are shown to be very similar to those in ERA-40, although the model has a tendency to underestimate both European blocking and the wave number 2 stratospheric variability to which this is related. Although the focus is on stratospheric variability in general, several of the blocking links are seen to occur in association with the major stratospheric sudden warmings. In general, the direction of influence appears to be upward, as blocking anomalies are shown to modify the planetary stationary waves, leading to an upward propagation of wave activity into the stratosphere. However, significant correlations are also apparent with the zonal mean flow in the stratosphere leading the occurrence of blocking at high latitudes. Finally, the underestimation of blocking is an enduring problem in GCMs, and an example has recently been given in which improving the resolution of the stratosphere improved the representation of blocking. Here, however, another example is given, in which increasing the stratospheric resolution unfortunately does not lead to an improvement in blocking.

The stratosphere can have a significant impact on winter surface weather on subseasonal to seasonal (S2S) timescales. This study evaluates the ability of current operational S2S prediction systems to capture two important links between the stratosphere and troposphere: (1) changes in probabilistic prediction skill in the extratropical stratosphere by precursors in the tropics and the extratropical troposphere and (2) changes in surface predictability in the extratropics after stratospheric weak and strong vortex events. Probabilistic skill exists for stratospheric events when including extratropical tropospheric precursors over the North Pacific and Eurasia, though only a limited set of models captures the Eurasian precursors. Tropical teleconnections such as the Madden‐Julian Oscillation, the Quasi‐Biennial Oscillation, and El Niño–Southern Oscillation increase the probabilistic skill of the polar vortex strength, though these are only captured by a limited set of models. At the surface, predictability is increased over the United States, Russia, and the Middle East for weak vortex events, but not for Europe, and the change in predictability is smaller for strong vortex events for all prediction systems. Prediction systems with poorly resolved stratospheric processes represent this skill to a lesser degree. Altogether, the analyses indicate that correctly simulating stratospheric variability and stratosphere‐troposphere dynamical coupling are critical elements for skillful S2S wintertime predictions.

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The stratosphere has been identified as an important source of predictability for a range of processes on subseasonal to seasonal (S2S) time scales. Knowledge about S2S predictability within the stratosphere is however still limited. This study evaluates to what extent predictability in the extratropical stratosphere exists in hindcasts of operational prediction systems in the S2S database. The stratosphere is found to exhibit extended predictability as compared to the troposphere. Prediction systems with higher stratospheric skill tend to also exhibit higher skill in the troposphere. The analysis also includes an assessment of the predictability for stratospheric events, including early and midwinter sudden stratospheric warming events, strong vortex events, and extreme heat flux events for the Northern Hemisphere and final warming events for both hemispheres. Strong vortex events and final warming events exhibit higher levels of predictability as compared to sudden stratospheric warming events. In general, skill is limited to the deterministic range of 1 to 2 weeks. High‐top prediction systems overall exhibit higher stratospheric prediction skill as compared to their low‐top counterparts, pointing to the important role of stratospheric representation in S2S prediction models.

[1] The European winter surface climate response to the Quasi-Biennial Oscillation (QBO) and its dependence on stratospheric resolution is cleanly assessed in idealized seasonal hindcasts with two versions of the Hadley Centre's atmospheric general circulation model. The standard 38-level version extends to an altitude of 39 km while the extended 60-level version has enhanced stratospheric resolution and reaches 84-km altitude. We show that both models generate a realistic stratospheric polar and surface European Arctic Oscillation (AO) response to the QBO for winter hindcasts initialized on 1 December and suggest that the better representation of the QBO in the L60 model will lead to improved forecasts at seasonal-to-multiannual timescales.

During austral summer 2015–2016, prolonged extreme ocean warming events, known as marine heatwaves (MHWs), occurred in the waters around tropical Australia. MHWs arose first in the southeast tropical Indian Ocean in November 2015, emerging progressively east until March 2016, when all waters from the North West Shelf to the Coral Sea were affected. The MHW maximum intensity tended to occur in March, coinciding with the timing of the maximum sea surface temperature (SST). Large areas were in a MHW state for 3–4 months continuously with maximum intensities over 2°C. In 2016, the Indonesian‐Australian Basin and areas including the Timor Sea and Kimberley shelf experienced the longest and most intense MHW from remotely sensed SST dating back to 1982. In situ temperature data from temperature loggers at coastal sites revealed a consistent picture, with MHWs appearing from west to east and peaking in March 2016. Temperature data from moorings, an Argo float, and Slocum gliders showed the extent of warming with depth. The events occurred during a strong El Niño and weakened monsoon activity, enhanced by the extended suppressed phase of the Madden‐Julian Oscillation. Reduced cloud cover in January and February 2016 led to positive air‐sea heat flux anomalies into the ocean, predominantly due to the shortwave radiation contribution with a smaller additional contribution from the latent heat flux anomalies. A data‐assimilating ocean model showed regional changes in the upper ocean circulation and a change in summer surface mixed layer depths and barrier layer thicknesses consistent with past El Niño events.

The Australian Bureau of Meteorology has recently enhanced its capability to make coupled model forecasts of intraseasonal climate variations. The Predictive Ocean Atmosphere Model for Australia (POAMA, version 2) seasonal prediction forecast system in operations prior to March 2013, designated P2-S, was not designed for intraseasonal forecasting and has deficiencies in this regard. Most notably, the forecasts were only initialized on the 1st and 15th of each month, and the growth of the ensemble spread in the first 30 days of the forecasts was too slow to be useful on intraseasonal time scales. These deficiencies have been addressed in a system upgrade by initializing more often and through enhancements to the ensemble generation. The new ensemble generation scheme is based on a coupled-breeding approach and produces an ensemble of perturbed atmosphere and ocean states for initializing the forecasts. This scheme impacts favorably on the forecast skill of Australian rainfall and temperature compared to P2-S and its predecessor (version 1.5). In POAMA-1.5 the ensemble was produced using time-lagged atmospheric initial conditions but with unperturbed ocean initial conditions. P2-S used an ensemble of perturbed ocean initial conditions but only a single atmospheric initial condition. The improvement in forecast performance using the coupled-breeding approach is primarily reflected in improved reliability in the first month of the forecasts, but there is also higher skill in predicting important drivers of intraseasonal climate variability, namely the Madden–Julian oscillation and southern annular mode. The results illustrate the importance of having an optimal ensemble generation strategy.