Internal variability plays a dominant role in global climate projections of temperature and precipitation extremes

Abstract

Climate projection uncertainty can be partitioned into model uncertainty, scenario uncertainty and internal variability. Here, we investigate the different sources of uncertainty in the projected frequencies of daily maximum temperature and precipitation extremes, which are defined as events that exceed the 99.97th percentile. This is done globally using large initial-condition ensembles. For maximum temperature extremes, internal variability that generates deviations about the ensemble average, dominates in the next 2 decades. Around the middle of the twenty-first century model and scenario uncertainty become the dominant contribution in the tropics but internal variability remains dominant in the extra-tropics. Towards the end of the century, model and scenario uncertainty increase to near equal contributions of $\sim$ 40% each globally with large regional fluctuations. For precipitation extremes, internal variability dominates throughout the twenty-first century, except for some tropical regions, for example, West Africa. In regions where internal variability constitutes the major source of uncertainty, the potential impact of reducing model uncertainty on the signal-to-noise ratio of the climate projection is estimated to be small. We discuss the caveats of the methodology used and impact of our findings for the design of future climate models. The importance of internal variability found here emphasizes that large ensembles are a vital tool for understanding climate projections.