Accounting for the NAO when applying observational constraints to future European climate projections

The North Atlantic Oscillation (NAO) is the leading mode of climate variability over the North Atlantic region, affecting temperature and rainfall over timescales from days through to seasons and decades. Previous studies have shown that variations in the NAO yield significant multi-decadal trends in European rainfall, especially in winter, and the magnitude of past multi-decadal NAO variability is generally not reproduced by CMIP class models This has important implications for deriving observational constraints, and the application of these scaling factors to projections of future European rainfall.

We have constructed two sets of multi-model-mean spatiotemporal fingerprints of European rainfall: one set that retains NAO variability, and another set that excludes the variability associated with the NAO (removing it using a simple regression technique). Following the so-called Allen-Scott-Kettleborough ‘ASK’ method, we conduct total-least-squares regressions using the two different sets of fingerprints against the observations in order to analyse the impact of removing the NAO in potentially enhancing the signal-to-noise. The derived scaling factors shed light on the ability of CMIP6 models to reproduce the magnitude of the forced response in precipitation, and confidence intervals for each of the scaling factors describes the range of magnitudes of the model response that are consistent with the observed signal.

Here we focus on one clear example, northern European rainfall, although we have also analysed additional European regions and surface air temperature across all of the seasons. There is an increasing trend in observed rainfall anomalies associated with the NAO over northern Europe, most pronounced in winter, which is not replicated in models. Once the variability associated with the NAO is removed the magnitude of the observed trends is reduced, and the scaling factors (derived in the ASK framework) are similarly reduced.

Along with a shift in the magnitude that comes from the modified observations, the constraint also tightens (the spread in consistent scaling factors narrows) due to the increased signal-to-noise in the modelled response once the NAO is removed from the simulations. This has important implications for the use of scaling factors to constrain future projections of European climate. The observed decadal to multi-decadal trends resulting from known modes of internal variability should be accounted for in the derivation of scaling factors to better capture the forced signal and bolster confidence in the constrained projections.