Institute of Mathematics


Modul:   STA671  Kolloquium über anwendungsorientierte Statistik

Characterizing forced climate signals and internal variability in observations and models

Talk by Dr. Sebastian Sippel

Date: 29.10.21  Time: 15.15 - 16.15  Room: ETH HG G 19.1

Joint work with: Nicolai Meinshausen, Erich Fischer, Eniko Székely, Flavio Lehner, Angeline Pendergrass, Reto Knutti Internal climate variability fundamentally limits short-​ and medium-​term climate predictability, and the separation of forced changes from internal variability is a key goal in climate change detection and attribution (D&A). In this talk, we discuss the identification of forced climate signals and internal variability in observations and models from spatial patterns of climate variables by using statistical learning techniques. We first introduce a detection approach using climate model simulations and a statistical learning algorithm to encapsulate the relationship between spatial patterns of daily temperature and humidity, and key climate change metrics such as annual global mean temperature. Observations are then projected onto this relationship to detect climatic changes, and it is shown that externally forced climate change can be assessed and detected in the observed global climate record at time steps such as months or days. Second, we discuss how these approaches can be extended to address key remaining uncertainties related to the role of decadal-​scale internal variability (DIV). DIV is difficult to quantify accurately from observations, and D&A requires that models simulate internal climate variability sufficiently accurately. We show that a recently developed statistical learning technique, anchor regression, allows to identify the externally forced global temperature response, while increasing the robustness towards different representations of DIV (via an explicit `anchor’ on decadal-​scale variability). The fraction of warming due to external factors, based on these optimized patterns, is more robust across different climate models even if DIV would be larger than current best estimates. These findings increase the confidence that warming over past decades is dominated by external forcing, irrespective of remaining uncertainties in the magnitude of climate variability