The numerical weather prediction (NWP) models have exhibited a lot of volatility over the past week with regards to predicting the evolution of pressure patterns past day 5, which each twice daily run of the ECMWF and UKMO and four daily runs of GFS showing wild swings in evolutions between each run, some bringing a cold easterly by the end of next week, then the next run bringing a taste of spring by the end of next week.

Ever since a sudden stratospheric warming (SSW) occurred at the start of the week, where winds in the stratosphere reversed from westerly to easterly, there has been low predictability afforded by the operational runs of the GFS, UKMO and ECMWF and even the ensembles have been showing large spread early on.

So what’s sending the world’s best weather models into a spin?

Well, it appears it could be to do with issues over how the models are handling the coupling of the stratosphere and troposphere below (where our weather occurs), as the downwelling of the SSW eventually impacts the troposphere, as the reversal to easterly winds propagates downwards.

Cross section of atmosphere shows wind reversal to easterly (blues) earlier this week propagating downwards

The SSW early this week has caused the stratospheric polar vortex to split into two, with ‘daughter vortices’, one larger one heading over Canada and the other smaller vortex wandering into NE Europe. But the models may have some resolution issues around the tropopause where the stratosphere – troposphere coupling takes place. The NWP model performance could be compromised by their limited resolution of handling of the movement and strength of the daughter vortices and how this imprints on the troposphere and thus upper flow configuration that drives our weather patterns. The NE Europe vortex looks like moving west but remaining detached from the stronger Canadian vortex –which itself may endure weakening as further wave breaking working up from the N Atlantic domain of the troposphere impacts the vortex. It’s how the models handle and correctly model these interactions that maybe causing them to falter, which could ultimately be to do with how individual models are calibrated.