Evaluation of the capability of global climate models in reproducing the temporal clustering in heavy precipitation over Europe

Abstract

Evaluating the capability of global climate models (GCMs) in reproducing the historical records represents a way of building confidence in their capability for future projections. Among the different ways of evaluating them, here we focus on their capability in reproducing the temporal clustering of heavy precipitation events across Europe in light of four climate modes [the Arctic Oscillation (AO), the North Atlantic Oscillation (NAO), the East Atlantic (EA) pattern, and the Scandinavia pattern (SCAND)], and GCMs from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and the large ensemble runs using the Community Earth System Model (CESM). We use a peak over threshold (POT) approach to identify heavy precipitation events, and Cox regression to relate the occurrence of these events to the climate modes. We find that the GCMs can capture the temporal clustering in heavy precipitation across Europe as a function of these four climate modes; moreover, our findings indicate the GCMs can better reproduce the relationship between heavy precipitation and AO/SCAND, than NAO/EA. Comparing the results based on CMIP5 models and CESM, we find that the inter-model uncertainties are larger than the intra-model ones in most of the cases, even though CESM tends to have a poorer performance for EA; this shortcoming for CESM is likely due to the pattern of the Z500 anomalies which is different from the reference data when EA is in the positive phase, affecting the transport of moisture across Europe.

Amplified potential for vegetation stress under climate change-induced intensifying compound extreme events in the Greater Mediterranean Region

“…These models, driven on a global basis, are capable of providing large-scale information on changes in atmospheric predictors, although their spatial resolution is generally low. While GCMs are able of providing reasonable results under specific circumstances, the quality of GCM output proves to be too low to be applied on a sub-global scale (Di Virgilio et al, 2022;Yang and Villarini, 2021;Wang et al, 2021;Hardiman et al, 2008). To improve the quality of these projections, regional climate models (RCM) driven by the GCM output can be consulted, by conducting simulations on a regional to local level and in a higher temporal and spatial resolution (Giorgi, 2019).…”

On the Temporal Clustering of European Extreme Precipitation Events and its Relationship to Persistent and Transient Large-Scale Atmospheric Drivers

“…Third the temporal clustering may generate unexpected financial implications for the insurance/reinsurance industry (Vitolo et al, 2009) Hence, a sequence of extreme rainfall events can amplify impacts when compared with a single hazard. It is therefore a temporally compounding reanalyzes (Yang and Villarini, 2019) and global climate models (Yang and Villarini, 2021) capture temporal clustering of heavy precipitation events across Europe. Building on the modes of variability, weather regimes (WRs) summarize regional climate variability on timescales of several weeks (e.g.…”