Atmospheric blocking events are regularly observed mid-latitude weather patterns, which obstruct the usual path of the jet streams. However, there is no well-defined historical dataset of blocking events and the effect of climate change on atmospheric blocking is uncertain. In this thesis, I explore how climate change influences European summer blocking (ESB). I develop a new algorithm to identify regional blocking events (the SOM-BI index), combining supervised and unsupervised learning. This is compared to other methods and a new ground truth dataset. I find the SOM-BI has an improved detection skill over other methods, particularly for climate models.
I apply the SOM-BI to study ESB in the abrupt-4xCO2 experiments from phases 5 and 6 of the Coupled Model Intercomparison Project. These runs maximise the forcing and have not previously been used to study atmospheric blocking. I identify a strong negative correlation between the historical occurrence of ESB and the change in occurrence of ESB. This enables a prediction of the ESB climate response from the historical model bias.
Further, I identify the two main physical mechanisms which affect the ESB climate response: the poleward shift of the North Atlantic jet; and the propagation of Rossby waves across the North Pacific from diabatic heating in the tropical Pacific. I develop an informed physical understanding of these mechanisms, which have not been discussed in the literature as positive influences on the ESB climate response. I then define two metrics as proxies for these physical mechanisms and estimate a positive climate feedback on ESB: 0.22±0.35 days / °C.
My thesis demonstrates the potential for machine learning in studying atmospheric blocking, highlights the importance of tropical forcing in influencing the climate feedback on ESB, and identifies new mechanisms that can be further explored to develop our understanding of how climate change will influence atmospheric blocking.Open Acces
