Dr. Sebastian Sippel talk
Date: 29.03.19 Time: 15.15 - 16.00 Room: ETH HG G 19.1
Internal atmospheric variability fundamentally constrains short- and medium-term climate predictability and obscures detection of anthropogenic climate change on regional scales. Dynamical adjustment is a traditional climate science technique to characterize circulation-induced variability in temperature or precipitation; the residual contains an estimate of the external “forced”, i.e. circulation-independent response. Here, we present a novel dynamical adjustment technique that makes use of statistical learning principles within the context of (1) a set of climate model simulations and (2) a real-world application of the method to variability in winter temperatures and snow coverage in Switzerland. The statistical learning methods establish a consistent relationship between internal circulation variability and atmospheric target variables on a daily time scale with around 80% variance explained in European monthly winter temperature and precipitation; and to a similar degree for global annual mean temperature and zonal mean precipitation. A real-world application to the Swiss winter temperature series, a long-term homogenized observational record reveals that a large fraction of winter temperature variability, and to a smaller degree variability in snow coverage, can be explained by internal atmospheric variability. The adjusted residual time series reveals a smooth, increasing trend since around the late 1970s, mostly driven by thermodynamic changes. Overall, statistical learning techniques for dynamical adjustment help to uncover the external forced response at regional and global scales, thus strengthening process understanding and facilitating detection of climate change.