Christoph Frei

The role of increasing temperature variability in European summer heatwaves.

Instrumental observations and reconstructions of global and hemispheric temperature evolution reveal a pronounced warming during the past ∼150 years. One expression of this warming is the observed increase in the occurrence of heatwaves. Conceptually this increase is understood as a shift of the statistical distribution towards warmer temperatures, while changes in the width of the distribution are often considered small. Here we show that this framework fails to explain the record-breaking central European summer temperatures in 2003, although it is consistent with observations from previous years. We find that an event like that of summer 2003 is statistically extremely unlikely, even when the observed warming is taken into account. We propose that a regime with an increased variability of temperatures (in addition to increases in mean temperature) may be able to account for summer 2003. To test this proposal, we simulate possible future European climate with a regional climate model in a scenario with increased atmospheric greenhouse-gas concentrations, and find that temperature variability increases by up to 100%, with maximum changes in central and eastern Europe.

Statistical and dynamical downscaling of precipitation: An evaluation and comparison of scenarios for the European Alps

[1] This paper compares six statistical downscaling models (SDMs) and three regional climate models (RCMs) in their ability to downscale daily precipitation statistics in a region of complex topography. The six SDMs include regression methods, weather typing methods, a conditional weather generator, and a bias correction and spatial disaggregation approach. The comparison is carried out over the European Alps for current and future (2071–2100) climate. The evaluation of simulated precipitation for the current climate shows that the SDMs and RCMs tend to have similar biases but that they differ with respect to interannual variations. The SDMs strongly underestimate the magnitude of the year-to-year variations. Clear differences emerge also with respect to the year-to-year anomaly correlation skill: In winter, over complex terrain, the better RCMs achieve significantly higher skills than the SDMs. Over flat terrain and in summer, the differences are smaller. Scenario results using A2 emissions show that in winter mean precipitation tends to increase north of about 45N and insignificant or opposite changes are found to the south. There is good agreement between the downscaling models for most precipitation statistics. In summer, there is still good qualitative agreement between the RCMs but large differences between the SDMs and between the SDMs and the RCMs. According to the RCMs, there is a strong trend toward drier conditions including longer periods of drought. The SDMs, on the other hand, show mostly nonsignificant or even opposite changes. Overall, the present analysis suggests that downscaling does significantly contribute to the uncertainty in regional climate scenarios, especially for the summer precipitation climate.

High Resolution Sensitivity Studies with the Regional Climate Model CCLM in the Alpine Region

The presented study investigates the performance of the regional climate model CCLM operated at high resolution (10km grid) over the European Alpine region to find a suitable setup for long term regional climate simulations over mountainous terrain and to quantify various error sources. CCLM is run in 8 different setups, spanning a substantial range of possible settings of the model and is driven by the ECMWFs ERA-40 re-analysis. The results are systematically evaluated focussing on 2 m air temperature and precipitation amount. The annual mean error in the entire Alpine region lies between -0.14 and -0.42 mm/d for precipitation and -0.98 and -1.44K for temperature, depending on the model setup. By objectively defining climate subregions covering the Alpine region we also take advantage of the high model resolution to analyze subregional performance of the CCLM. It is shown that modifications of the domain size and vertical resolution have the greatest impact on the results, while altering the parameterization and numerics does not yield a significantly different outcome.

Data Transformation and Uncertainty in Geostatistical Combination of Radar and Rain Gauges

AbstractGeostatistics provides a popular framework for deriving high-resolution quantitative precipitation estimates (QPE) by combining radar and rain gauge data. However, the skewed and heteroscedastic nature of precipitation is in contradiction to assumptions of classical geostatistics. This study examines the potential of trans-Gaussian kriging to overcome this contradiction. Combination experiments are undertaken with kriging with external drift (KED) using several settings of the Box–Cox transformation. Hourly precipitation data in Switzerland for the year 2008 serve as test bed to compare KED with and without transformation. The impact of transformation is examined with regard to compliance with model assumptions, accuracy of the point estimate, and reliability of the probabilistic estimate. Data transformation improves the compliance with model assumptions, but some level of contradiction remains in situations with many dry gauges. Very similar point estimates are found for KED with untransformed a...

Quantile-based short-range QPF evaluation over Switzerland

Quantitative precipitation forecasts (QPFs) are often verified using categorical statistics. The traditionally used 2×2 contingency table is modified here by applying sample quantiles instead of fixed amplitude thresholds. This calibration is based on the underlying precipitation distribution and has beneficial implications for categorical statistics. The quantile difference and the debiased Peirce skill score split the total error into the complementary components of bias and debiased pixel overlap. It is shown that they provide a complete verification set with the ability to assess the full range of rainfall intensities. The technique enables the potential skill in a calibrated forecast to be estimated without spurious influences from the marginal totals and the problem of hedging is therefore avoided. To exemplify the feasibility of quantile-based contingencies, the method is applied to 6.5 years of operational rainfall forecasts from the Swiss Federal Office of Meteorology and Climatology (MeteoSwiss). Daily accumulations of the COSMO model at 7 km grid size are compared to a high-quality gridded observational record of spatially interpolated rain gauge data. The quantile-based scores are applied to single grid points and to predefined regions. A high-resolution error climatology is then built up and reviewed in terms of typical error characteristics in the model. The seasonal QPF performance exhibits the most severe overestimation over the Northern Alps during winter, indicative of the impact of the model ice phase. The QPF performance related to model updates, such as the introduction of the prognostic precipitation scheme, is also evaluated. It is demonstrated that the potential skill continuously increases for subsequent versions of the COSMO model. Over the entire time period, a strong gradient of the debiased Peirce skill score is evident over the Alps, meaning that the potential skill is much higher on the Alpine south side than on the north side.