Combining different views on internal climate variability of temperature over Europe

Internal climate variability (ICV) represents the intrinsic fluctuations within the climate system. Understanding how ICV varies across regions, seasons, and climatic conditions is crucial for detecting externally forced climate change and assessing future climate risks. It can also provide a useful benchmark for assessing climate model performance and the emergence of anthropogenically forced climate change. This study aims to compare ICV estimates obtained using different methods and data types. We focus on seasonal mean near-surface air temperature over Europe, utilizing data from 15 CMIP6 models, a 100-member MPI-ESM large ensemble, and three observational datasets. The ICV estimates are based on pre-industrial control simulations (Pidata), the Hawkins and Sutton (2009) residual-based method (HS09), regression-based removal of external forcings, and a single-model large-ensemble framework. The magnitude of ICV estimates generally decreases from northeastern to southwestern Europe on both annual and seasonal timescales, except during JJA, when the highest estimates shift southward. The highest ICV estimates are observed in DJF and the lowest at the annual timescale. Among all the methods, Pidata generally yields the highest ICV estimates across all seasonal and annual timescales, except during JJA. Based on the HS09 method, the similarity between the model-based estimates and the observations is most pronounced during DJF and at the annual timescale. However, this similarity is weaker when the regression-based method is applied, with the model-based estimates generally exceeding the observation-based ones. The projected changes in ICV show a tendency for reduced (enhanced) values in DJF (JJA). Although a north-south contrast emerges in other seasons, with declining (increasing) ICV in northern (southern) Europe, the overall spatial pattern indicates a reduction in projected ICV estimates across large parts of Europe, except during JJA.