Atmospheric processes

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:

© 2019, The Author(s). The article The impact of stochastic physics on the El Niño Southern Oscillation in the EC-Earth coupled model, written by Chunxue Yang, Hannah M. Christensen, Susanna Corti, Jost von Hardenberg and Paolo Davini, was originally published electronically on the publisher’s internet portal (currently SpringerLink) on 07 February 2019 without open access.

Abstract:

While weather forecasting models can be tested by performing and evaluating many hindcasts, the limited observational record restricts the degree to which climate projections can be evaluated. Therefore a question of interest is: to what degree can we evaluate the potential skill of a climate model's response to forcing by assessing the reliability of short‐range weather and seasonal forecasts produced by the same model? We address this question using a dynamical systems framework. We use linear response theory to provide the mean climate response of a general dynamical system to a small external forcing. We relate this response to the reliability of initial value forecasts. We find that, in order to capture the mean climate response, the forecast model must correctly represent the slowest evolving modes of variability in the system. The reliability of forecasts on seasonal and longer time‐scales, which is sensitive to the representation of these slow modes, could therefore indicate if the forecast model has the correct climate sensitivity and so will respond correctly to an applied external forcing. In this way, the skill of initialized forecasts could act as an ‘emergent constraint’ on climate sensitivity. However, we also highlight that unreliable seasonal forecasts do not necessarily indicate an incorrect climate projection. This is because correctly representing rapidly evolving modes is also necessary for reliable seasonal forecasts.