Kristian Strommen

Abstract:

We introduce and study the impact of three stochastic schemes in the EC-Earth climate model: two atmospheric schemes and one stochastic land scheme. These form the basis for a probabilistic Earth system model in atmosphere-only mode. Stochastic parametrization have become standard in several operational weather-forecasting models, in particular due to their beneficial impact on model spread. In recent years, stochastic schemes in the atmospheric component of a model have been shown to improve aspects important for the models long-term climate, such as El Niño–Southern Oscillation (ENSO), North Atlantic weather regimes, and the Indian monsoon. Stochasticity in the land component has been shown to improve the variability of soil processes and improve the representation of heatwaves over Europe. However, the raw impact of such schemes on the model mean is less well studied. It is shown that the inclusion of all three schemes notably changes the model mean state. While many of the impacts are beneficial, some are too large in amplitude, leading to significant changes in the model's energy budget and atmospheric circulation. This implies that in order to maintain the benefits of stochastic physics without shifting the mean state too far from observations, a full re-tuning of the model will typically be required.

Abstract:

There is growing evidence that the atmospheric dynamics of the Euro-Atlantic sector during winter is driven in part by the presence of quasi-persistent regimes. However, general circulation models typically struggle to simulate these, with e.g. an overly weakly persistent blocking regime. Previous studies have showed that increased horizontal resolution can improve the regime structure of a model, but have so far only considered a single model with only one ensemble member at each resolution, leaving open the possibility that this may be either coincidental or model-dependent. We show that the improvement in regime structure due to increased resolution is robust across multiple models with multiple ensemble members. However, while the high resolution models have notably more tightly clustered data, other aspects of the regimes may not necessarily improve, and are also subject to a large amount of sampling variability that typically requires at least three ensemble members to surmount.

Abstract:

Studies conducted by the UK Met Office reported significant skill in predicting the winter North Atlantic Oscillation (NAO) index with their seasonal prediction system. At the same time, a very low signal‐to‐noise ratio was observed, as measured using the “ratio of predictable components” (RPC) metric. We analyse both the skill and signal‐to‐noise ratio using a new statistical toy model, which assumes NAO predictability is driven by regime dynamics. It is shown that if the system is approximately bimodal in nature, with the model consistently underestimating the level of regime persistence each season, then both the high skill and high RPC value of the Met Office hindcasts can easily be reproduced. Underestimation of regime persistence could be attributable to any number of sources of model error, including imperfect regime structure or errors in the propagation of teleconnections. In particular, a high RPC value for a seasonal mean prediction may be expected even if the model's internal level of noise is realistic.