Description: Climate model simulations are routinely compared to observational data sets for evaluation purposes. The resulting differences can be large and induce artifacts if propagated through impact models. They are usually termed ‘model biases’, suggesting that they exclusively stem from systematic models errors. Here we explore for Switzerland the contribution of two other components of this mismatch, which are usually overlooked: interpolation errors and natural variability. Precipitation and temperature simulations from the RCM COSMO-CLM were compared to two observational data sets, for which estimates of interpolation errors were derived. Natural variability on the multi-decadal time scale was estimated using three approaches relying on homogenized time series, multiple runs of the same climate model and bootstrapping of 30-year meteorological records. We find that although these methods yield different estimates, the contribution of the natural variability to RCM-observation differences in 30-year means is usually small. In contrast, uncertainties in observational data sets induced by interpolation errors can explain a substantial proportion of the mismatch of 30-year means. In those cases, we argue that the model biases can hardly be distinguished from interpolation errors, making the characterization and reduction of model biases particularly delicate. In other regions, RCM biases clearly exceed the estimated contribution of natural variability and interpolation errors, enabling bias characterization and robust model evaluation. Overall, we argue that bias correction of climate simulations needs to account for observational uncertainties and natural variability. We particularly stress the need for reliable error estimates to accompany observational datasets.

Description: Bias-adjustment methods usually do not account for the origins of biases in climate models and instead perform empirical adjustments. Biases in the synoptic circulation are for instance often overlooked when post-processing regional climate model (RCM) simulations driven by general circulation models (GCMs). Yet considering atmospheric circulation helps to establish links between the synoptic and the regional scale, and thereby provides insights into the physical processes leading to RCM biases. Here we investigate how synoptic circulation biases impact regional climate simulations and influence our ability to mitigate biases in precipitation and temperature using quantile mapping. We considered 20 GCM-RCM combinations from the ENSEMBLES project and characterized the dominant atmospheric flow over the Alpine domain using circulation types. We report in particular a systematic overestimation of the frequency of westerly flow in winter. We show that it contributes to the generalized overestimation of winter precipitation over Switzerland and this wet regional bias can be reduced by improving the simulation of synoptic circulation. We also demonstrate that statistical bias-adjustment relying on quantile mapping is sensitive to circulation biases, which leads to residual errors in the post-processed time series. Overall, decomposing GCM-RCM time series using circulation types reveals connections missed by analyses relying on monthly or seasonal values. Our results underscore the necessity to better diagnose process misrepresentation in climate models to progress with bias-adjustment and impact modeling.

Description: In the context of climate change, both climate researchers and decision-makers deal with uncertainties, but these uncertainties differ in fundamental ways. They stem from different sources, cover different temporal and spatial scales, might or might not be reducible or quantifiable, and are generally difficult to characterize and communicate. Hence, for adaptation strategies and planning to progress, mutual understanding between current and future climate researchers and decision-makers needs to evolve. Iterative two-way dialogue can help to improve the decision-making process and bridge current top-down and bottom-up approaches. One way to cultivate such interactions is by providing venues for these actors to interact and exchange about the uncertainties they face. We use a workshop-seminar series including academic researchers, students, and decision-makers as an opportunity to put this idea into practice and evaluate it. Seminars, case studies and a round table allowed participants to reflect upon and experiment with uncertainties. An opinion survey conducted before and after the workshop-seminar series allowed us to qualitatively evaluate its influence on the participants. We find that the event stimulated new perspectives on communication processes and research priorities, and suggest that similar events may ultimately contribute to the mid-term goal of improving support for decision-making in a changing climate. Therefore, we recommend integrating interdisciplinary bridging events into university curriculum with the goal of exposing researchers, decision-makers and students to these concepts.

Description: Projections of discharge are key for future water resources management. These projections are subject to uncertainties, which are difficult to handle in the decision process on adaptation strategies. Uncertainties arise from different sources such as the emission scenarios, the climate models and their post-processing, the hydrological models and natural variability. Here we present a detailed and quantitative uncertainty assessment, based on recent climate scenarios for Switzerland (CH2011 data set) and covering catchments representative for mid-latitude alpine areas. This study relies on a particularly wide range of discharge projections resulting from the factorial combination of 3 emission scenarios, 10 to 20 regional climate models, 2 post-processing methods and 3 hydrological models of different complexity. This enabled us to decompose the uncertainty in the ensemble of projections using analyses of variance (ANOVA). We applied the same modeling setup to 6 catchments to assess the influence of catchment characteristics on the projected streamflow and focused on changes in the annual discharge cycle. The uncertainties captured by our setup originate mainly from the climate models and natural climate variability, but the choice of emission scenario plays a large role by the end of the century. The respective contribution of the different sources of uncertainty varied strongly among the catchments. The discharge changes were compared to the estimated natural decadal variability, which revealed that a climate change signal emerges even under the lowest emission scenario (RCP2.6) by the end of the century. Limiting emissions to RCP2.6 levels would nevertheless reduce the largest regime changes at the end of the 21 st century by approximately a factor of two, in comparison to impacts projected for the high emission scenario SRES A2. We finally show that robust regime changes emerge despite the projection uncertainty. These changes are significant and are consistent across a wide range of scenarios and catchments. We propose their identification as a way to aid decision-making under uncertainty.