We applied the topic modelling tool Topics2Themes to a collection of German tweets on the subject of climate change. Topics2Themes is currently being further developed and evaluated within Spr{\aa}kbanken Sam, which is a part of {\sc SWE-CLARIN}.  The tool automatically extracted 15 topics from the tweet collection. We used the graphical user interface of Topics2Themes to manually search for recurring themes among the eight tweets most closely associated with the topics extracted. Although the content of the tweets associated with a topic was often diverse, we were still able to identify recurring themes. More specifically, 14 themes that occurred at least three times were identified in the texts analysed.