Our contribution sheds light on the argumentation patterns observed in discussions of climate contrarians on social media, where the possible outcomes of coordinated contrarian strategies can become visible in everyday discussions. Applying a transformer-based text classifier, we show that the most frequently en- countered claims against climate change are 1) attacks against the climate movement, 2) attacks against climate science and 3) questioning the effect of human activity on global warming. Furthermore, we show using a PCA that these claims constitute the main argumentative axes that divide threads in r/climateskeptics, next to discussions focusing on the harm caused by climate-change mitigating policies. Our method of thread embedding allows us to explore and close-read the relevant parts of our dataset to investigate fundamental questions of social influence and opinion exchange in controversial debates.