The role of internal variability in global climate projections of extreme events

TL;DR

This study analyzes how internal variability influences the uncertainty in climate projections of extreme temperature and precipitation events, highlighting its dominance especially in the near term and the importance of large ensembles for accurate predictions.

Contribution

It provides a global assessment of the sources of uncertainty in climate extremes, emphasizing the role of internal variability and the need for large ensembles in climate modeling.

Findings

Internal variability dominates temperature extremes in the next two decades.

Internal variability remains the main uncertainty for precipitation extremes throughout the 21st century.

Model and scenario uncertainties become comparable globally towards the end of the century.

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 initial-condition large ensembles. For maximum temperature extremes, internal variability dominates in the next two decades. Around the middle of the 21st 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 ~40% each globally with large regional fluctuations. For precipitation extremes, internal variability dominates throughout the 21st 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.