The ‘signal-to-noise paradox’ for seasonal forecasts of the winter NAO is often described as an ‘underconfident’ forecast and measured using the ratio-of-predictable components metric (RPC). However, comparison of RPC with other measures of forecast confidence, such as spread-error ratios, can give conflicting impressions, challenging this informal description. We show, using a linear statistical model, that the ‘paradox’ is equivalent to a situation where the reliability diagram of any percentile forecast has a slope exceeding 1. The relationship with spread-error ratios is shown to be far less direct. We furthermore compute reliability diagrams of winter NAO forecasts using seasonal hindcasts from the European Centre for Medium-range Weather Forecasts and the UK Meteorological Office. While these broadly exhibit slopes exceeding 1, there is evidence of asymmetry between upper and lower terciles, indicating a potential violation of linearity/Gaussianity. The limitations and benefits of reliability diagrams as a diagnostic tool are discussed.

The role of internal variability in generating an apparent link between autumn Barents-Kara sea ice (BKS) and the winter North Atlantic Oscillation (NAO) has been intensely debated. In particular, the robustness and causality of the link has been questioned by showing that BKS-NAO correlations exhibit nonstationarity in both reanalysis and climate model simulations. We show that the lack of ice observations makes analysis of nonstationarity using reanalysis questionable in the period 1950-1970 and effectively impossible prior to 1950. Model simulations are used to corroborate an argument that nonstationarity is nevertheless expected due to changes in the ice edge variability due to global warming. Consequently, changes in BKS-NAO correlations over time may simply reflect that the ice edge has moved, rather than that there is no causal link. We discuss potential implications for analysis based on coupled climate models, which exhibit large ice edge biases.

A document by Kristian Strommen. Click on the document to view its contents.

To better understand the impacts of climate change on Europe, it is important to understand changes in the wintertime large-scale circulation. The framework of weather regimes provides a powerful tool for studying the highly nonlinear Euro-Atlantic circulation, but exactly how these regimes will be altered by anthropogenic climate change is still imperfectly understood. Using the recently developed approach of geopotential-jet regimes, applied to an ensemble of state-of-the-art CMIP6 models, we show that the centres of action of anticyclonic regimes are not projected to change substantially by the end of century, even under an extreme warming scenario. Instead, the regimes are expected to become less persistent, making long-lived blocking events less likely. We show that these two key elements of the regime response can be captured in a simple Lorenz-like model subjected to parameter variations, emphasising the conceptual link between observed atmospheric regimes and the regimes identified in basic mathematical systems.