Consistency of Seasonal Mean and Extreme Precipitation Projections Over Europe Across a Range of Climate Model Ensembles

Uncertainties of regional precipitation projections are substantial, and users of such projections face the so-called practitioners dilemma: a plethora of projections with different models from different ensembles of different types and generations are available. But the consistency of these projections has not been systematically assessed, such that no clear guidance about the use of these ensembles exists. Therefore, we systematically compare, separately for each season, projections of mean precipitation and extremes of daily precipitation over Europe across a wide range of climate model ensembles. We specifically consider the global climate model ensembles CMIP3, CMIP5, Coupled Model Intercomparison Project Phase 6 (CMIP6), and HighresMIP as well as the regional climate model ensembles ENSEMBLES and EURO-CORDEX. All considered ensembles agree in their large-scale patterns of changes for both mean and extreme daily precipitation, but at the regional scale, substantial discrepancies and inconsistencies are evident. Within and across ensemble spread is strongest in summer, in particular for the drying trend over the Mediterranean and Eastern Europe. CMIP5 and CMIP6 are broadly consistent. The regional climate model (RCM) ensembles modify the signals of the driving global climate models indicating potential added value. The high resolution of the RCM and HighresMIP ensembles seems to be key over the Alps for summer precipitation. Our study provides important information for users of climate projections as it helps to establish continuity across generations and types of climate models, and aids the design of new climate impact studies.

Higher probability of compound flooding from precipitation and storm surge in Europe under anthropogenic climate change

In low-lying coastal areas, the co-occurrence of high sea level and precipitation resulting in large runoff may cause compound flooding (CF). When the two hazards interact, the resulting impact can be worse than when they occur individually. Both storm surges and heavy precipitation, as well as their interplay, are likely to change in response to global warming. Despite the CF relevance, a comprehensive hazard assessment beyond individual locations is missing, and no studies have examined CF in the future. Analyzing co-occurring high sea level and heavy precipitation in Europe, we show that the Mediterranean coasts are experiencing the highest CF probability in the present. However, future climate projections show emerging high CF probability along parts of the northern European coast. In several European regions, CF should be considered as a potential hazard aggravating the risk caused by mean sea level rise in the future.

Higher probability of compound flooding from precipitation and storm surge in Europe under anthropogenic climate change

In low-lying coastal areas, the co-occurrence of high sea level and precipitation resulting in large runoff may cause compound flooding (CF). When the two hazards interact, the resulting impact can be worse than when they occur individually. Both storm surges and heavy precipitation, as well as their interplay, are likely to change in response to global warming. Despite the CF relevance, a comprehensive hazard assessment beyond individual locations is missing, and no studies have examined CF in the future. Analyzing co-occurring high sea level and heavy precipitation in Europe, we show that the Mediterranean coasts are experiencing the highest CF probability in the present. However, future climate projections show emerging high CF probability along parts of the northern European coast. In several European regions, CF should be considered as a potential hazard aggravating the risk caused by mean sea level rise in the future.

Nonstationary Gaussian processes in wavelet domain: synthesis, estimation, and significance testing

We propose an equivalence class of nonstationary Gaussian stochastic processes defined in the wavelet domain. These processes are characterized by means of wavelet multipliers and exhibit well-defined time-dependent spectral properties. They allow one to generate realizations of any wavelet spectrum. Based on this framework, we study the estimation of continuous wavelet spectra, i.e., we calculate variance and bias of arbitrary estimated continuous wavelet spectra. Finally, we develop an areawise significance test for continuous wavelet spectra to overcome the difficulties of multiple testing; it uses basic properties of continuous wavelet transform to decide whether a pointwise significant result is a real feature of the process or indistinguishable from typical stochastic fluctuations. This test is compared to the conventional one in terms of sensitivity and specificity. A software package for continuous wavelet spectral analysis and synthesis is presented.