Forced changes in internal variability: an additional uncertainty to deal with

Abstract

<p>Anthropogenic activities contribute to the rising level of greenhouse gas concentrations in the atmosphere at a rate of approximately 1% per year providing a time-dependent external radiative forcing on the climate. In addition to tangible consequences of anthropogenic forcing affecting the climate system, simultaneous, less apparent changes occurring on low-frequency timescales demand effort to deal with. These include changes in natural internal processes of the climate system due to the non-stationary anthropogenic forcing. This represents additional uncertainty affecting future model projections on top of internal variability, scenario and model uncertainty. Here, with the application of state-of-the-art Single Model Initial-condition Large Ensemble (SMILE) simulations – that account for the chaotic behavior of the climate system with perturbed initial condition runs of the same model – we offer a way forward for new perspectives on externally-forced changes in internal variability. In doing so, we utilize an approach for analyzing SMILEs called the <em>snapshot view</em>, which offers a mathematically exact and elegant formulation and the potential to complement previous, time-series-based diagnostics with ensemble-based statistics. We reveal how the <em>snapshot view</em> allows for surprisingly simple practices to detect anthropogenically forced changes in modes of large-scale internal atmospheric circulation variability (so-called “teleconnection patterns”) as well as coupled modes of atmospheric variability with Arctic sea ice. A crucial message of the <em>snapshot view</em> is that all of the traditional, time series-based methods can be reformulated for ensembles and thus, on the one hand, ambiguous results arising from subjective choices of traditional methods (e.g. length and center of time windows) can be avoided, and on the other hand, new perspectives open for detecting forced changes in internal variability.</p>