Award 2018: Dr. Marlene Kretschmer

The 2018 Wladimir Peter Köppen Prize was awarded to Dr. Marlene Kretschmer for her remarkable dissertation, which she completed at the Potsdam Institute for Climate Impact Research and the University of Potsdam. The jury of the Hamburg-based Cluster of Excellence CliSAP praised the work, claiming its “importance for climate research, in terms of its innovativeness and relevance, is outstanding.”

Kretschmer, a climate physicist, explored the extent to which global warming has increased the frequency of extremely harsh winters in the temperate latitudes over the past few decades. The Arctic continues to thaw, but extreme-cold events have been recorded in the USA, northern Asia and parts of Europe. In fact, on average winters here in Europe have grown colder over the past 25 years. A paradox?

In her work, Kretschmer investigates new mechanisms, how the stratosphere and troposphere interact. For example, the retreat of Arctic sea ice can weaken the polar vortex in the stratosphere, which circles the Arctic in winter. In turn, the vortex influences the circulation in the troposphere, and with it, the weather and climate in the temperate latitudes. If the polar vortex falters, it promotes colder winters in northern Europe. In addition, Kretschmer identifies more complex mechanisms, e.g. how the shape and intensity of the polar vortex affect winters in Europe and the USA.

Moreover, using an algorithm she developed, Kretschmer can for the first time use these causal connections for forecasting. With the aid of innovative statistical methods from the field of machine learning, certain regional indicators can be identified and used as the basis for predicting the strength of the polar vortex – which also implies that longer-term forecasts can be made for winters in the temperate latitudes. Kretschmer’s work supplies new clarity in a hotly debated field of research. At the same time, it provides a valuable new statistical tool, based on so-called “Causal Discovery Algorithms,” that can identify causal connections in climate data, with many more applications to other central questions in climate research.