The variability atlas: how internal climate variability affects the estimation of climate extreme indices

Abstract Robustly quantifying climate extremes is crucial for understanding their occurrence, reliable impact assessments, and efficient adaptation planning. To allow objective and comparable assessments of various climate extremes across regions, time periods, and datasets, standardized indices like those from the Expert Team on Climate Change Detection and Indices (ETCCDI) have been developed. These indices are routinely used for climate impact assessment. However, the occurrence of extremes depends not only on the climate state but also on the manifestation of internal climate variability, which is often not explicitly considered in such impact assessments. Here, we complement existing work on climate extremes by isolating the effect of internal climate variability on the estimation of extremes and raise awareness of potential implications for the community. We use a 50-member ensemble from the MPI-ESM global climate model to comprehensively map the effect of internal variability on various climate extremes as captured by the temperature- and precipitation-based ETCCDI indices in the period 1995–2014. We investigate the extent to which the assessment of extremes is affected by internal variability and discuss implications for users. These implications depend on many case-specific priorities, including the indices used, the region considered, and the focus on expected state versus worst-case scenario or single observed manifestation. To provide practical guidance, we introduce the Variability Atlas , an interactive online interface that allows for the investigation of the effect of internal variability for flexible user-defined cases and without coding experience.