The debate on responding to climate change is complex, influenced by both global and local factors. Scientists cannot conduct real climate experiments, so they rely on climate models to simulate man-induced changes by experimenting with digital climates and extrapolating data. The effectiveness of a model hinges on its perceived accuracy in representing natural processes. However, climate models, which involve numerous hypotheses, are only partially testable. An advanced evaluation infrastructure has been established, incorporating model intercomparison, ensemble forecasts, and uncertainty metrics. The complexity of these models introduces uncertainties that often clash with socio-political expectations. Some predictions reflect desires rather than scientific application. Current efforts to define uncertainty in terms of likelihood and confidence acknowledge its integral role in understanding potential future developments. This collection offers insights into scientific strategies for managing uncertainty through simulation models, alongside the social, political, and economic factors influencing climate change projections. The contributions pose critical questions about whether these strategies align with societal requirements or fall short, emphasizing the ongoing challenge of addressing climate change effectively.
