Jet latitude regimes and the predictability of the North Atlantic Oscillation

Abstract

In recent years, numerical weather prediction models have begun to show notable levels of skill at predicting the average winter North Atlantic Oscillation (NAO) when initialised one month ahead. At the same time, these model predictions exhibit unusually low signal-to-noise ratios, in what has been dubbed a 'signal-to-noise paradox'. We analyse both the skill and signal-to-noise ratio of the Integrated Forecast System (IFS), the European Center for Medium-range Weather Forecasts (ECMWF) model, in an ensemble hindcast experiment. Specifically, we examine the contribution to both from the regime dynamics of the North Atlantic eddy-driven jet. This is done by constructing a statistical model which captures the predictability inherent to to the trimodal jet latitude system, and fitting its parameters to reanalysis and IFS data. Predictability in this regime system is driven by interannual variations in the persistence of the jet latitude regimes, which determine the preferred state of the jet. We show that the IFS has skill at predicting such variations in persistence: because the position of the jet strongly influences the NAO, this automatically generates skill at predicting the NAO. We show that all of the skill the IFS has at predicting the winter NAO over the period 1980-2010 can be attributed to its skill at predicting regime persistence in this way. Similarly, the tendency of the IFS to underestimate regime persistence can account for the low signal-to-noise ratio, giving a possible explanation for the signal-to-noise paradox. Finally, we examine how external forcing drives variability in jet persistence, as well as highlight the role played by transient baroclinic eddy feedbacks to modulate regime persistence.

Jet Speed Variability Obscures Euro‐Atlantic Regime Structure

“…It can be seen that the K‐means clustering essentially reproduces the three jet regimes, a correspondence we will quantify in section 3.2. In fact, the curvature visible in Figures 1b and 1d corresponds exactly to the nonlinear relationship between the jet latitude and the NAO first observed in Woollings, Hannachi, and Hoskins (2010): See also Figure 2 of Strommen (2020), where this nonlinearity is more clearly highlighted.…”

“…In particular, this new clustering framework might be particularly suited for identifying the action of teleconnections on the North Atlantic, which can be difficult to study with either nonstationary data sets or data confined to a short, modern period. Because there is evidence that forecast models struggle to predict the jet speed (Parker et al., 2019; Strommen, 2020), there are also potential benefits of this approach to forecasting. Finally, the strong stationarity paves the way for a more robust assessment of potential future changes in regime behavior under global warming.…”

A Topological Perspective on Weather Regimes

“…By measuring the location of the maximum wind-speed of the jet, one can define the latitude of the jet on any given day: the histogram of this jet-latitude index is visibly and robustly trimodal, suggesting the existence of three distinct regimes [49]. The differences between these two perspectives, which would a priori be expected to be equivalent, can be reconciled by taking into account the added variability coming from the speed of the jet, after which both pressure and wind data suggest three very robust regimes [50,47]. Applications to predictability have been studied in both contexts, see, e.g., [51] and [47].…”

“…The general suitability of ERA20C for regime-based studies has been commented on in previous studies [46,47], and essentially relies on the fact that there is a long and consistent record of surface observations in the Euro-Atlantic sector, which will be our area of interest. The existence and properties of regimes in the wintertime Euro-Atlantic circulation has been extensively studied, either through the prism of pressure fields, typically geopotential height at 500hPa, or winds, in the form of zonal winds at 850hPa (hereafter ua850).…”

When Does the Lorenz 1963 Model Exhibit the Signal‐To‐Noise Paradox?

“…In contrast, Weisheimer et al (2019) focus on statistical uncertainties in the quantities used to assess the level of underconfidence. Furthermore, Strommen and Palmer (2018) and Strommen (2020) propose that the SNP might be related to deficiencies of the models in representing the regime behavior of the NAO. Despite these previous efforts, there is up to now no scientific consensus on the origin of the SNP.…”