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In climate science, observational gridded climate datasets that are based on in situ measurements serve as evidence for scientific claims and they are used to both calibrate and evaluate models. However, datasets only represent selected aspects of the real world, so when they are used for a specific purpose they can be a source of uncertainty. Here, we present a framework for understanding this uncertainty of observational datasets which distinguishes three general sources of uncertainty: (1) uncertainty that arises during the generation of the dataset; (2) uncertainty due to biased samples; and (3) uncertainty that arises due to the choice of abstract properties, such as resolution and metric. Based on this framework, we identify four different types of dataset ensembles—parametric, structural, resampling, and property ensembles—as tools to understand and assess uncertainties arising from the use of datasets for a specific purpose. We advocate for a more systematic generation of dataset ensembles by using these sorts of tools. Finally, we discuss the use of dataset ensembles in climate model evaluation. We argue that a more systematic understanding and assessment of dataset uncertainty is needed to allow for a more reliable uncertainty assessment in the context of model evaluation. The more systematic use of such a framework would be beneficial for both scientific reasoning and scientific policy advice based on climate datasets. ISSN:1757-7780 ISSN:1757-7799

Climate information provided by global or regional climate models (RCMs) are often too coarse and prone to substantial biases for local assessments or use in impact models. Hence, statistical downscaling becomes necessary. For the Swiss National Climate Change Initiative (CH2011), a delta-change approach was used to provide daily climate scenarios at the local scale. Here, we analyse a Richardson-type weather generator (WG) as an alternative method to downscale daily precipitation, minimum and maximum temperature. The WG is calibrated for 26 Swiss stations and the reference period is 1980–2009. It is perturbed with change factors derived from RCMs (ENSEMBLES) to represent the climate of 2070–2099 assuming the SRES A1B emission scenario. The WG can be run in multi-site mode, making it especially attractive for impact-modellers that rely on a realistic spatial structure in downscaled time-series. The results from the WG are benchmarked against the original delta-change approach that applies mean additive or multiplicative adjustments to the observations. According to both downscaling methods, the results reveal mean temperature increases and a precipitation decrease in summer, consistent with earlier studies. For the summer drying, the WG indicates primarily a decrease in wet-day frequency and correspondingly an increase in mean dry spell length of between 18 and 40% at low-elevation stations. By definition, these potential changes cannot be represented by a delta-change approach. In winter, both methods project a shortening of the frost period (−30 to −60 days) and a decrease of snow days (−20 to −100%). The WG demonstrates though, that almost present-day conditions in snow-days could still occur in the future. As expected, both methods have difficulties in representing extremes. If users focus on changes in temporal sequences and need a large number of future realizations, it is recommended to use data from a WG instead of a delta-change approach.

Pattern scaling can be used to linearly relate changes in extreme indices to changes in the annual or seasonal mean temperature. This study demonstrates the skills and limitations of two often used pattern scaling approaches in filling-in gaps in the time series of six temperature-related extreme indices. The extreme indices over Europe are derived from daily temperature output of 12 regional climate models of the multi-model project ENSEMBLES. The response pattern is estimated using one of the two future time periods (2021–2050 or 2070–2099) and the reference period (1961–1990). The simulated values from the remaining future time period are used for evaluating the skills. Both pattern scaling approaches perform reasonably well particularly for percentile-based and over most of the regions also for fixed temperature indices. Uncertainties due to internal variability can be large if the time period used for estimating the response pattern is close to the reference period. Limitations of pattern scaling due to violations of the linearity assumption are related to the shape of the temperature distribution. As a result, differences in the skills among the extreme indices can be related to the magnitude and shift direction of the whole temperature distribution. Therefore, skills for estimated extreme indices derived from the upper tail of the underlying temperature distribution are generally high. Over some areas, linear regression models used in this study are not appropriate statistical models because of the bounded and discrete nature of the data. Alternative pattern scaling methods such as, for instance, the logistic regression model leads to improvements over particular areas but not over the whole integration area. Copyright © 2013 Royal Meteorological Society

Climate model projections are used to inform policy decisions and constitute a major focus of climate research. Confidence in climate projections relies on the adequacy of climate models for those projections. The question of how to argue for the adequacy of models for climate projections has not gotten sufficient attention in the climate modeling community. The most common way to evaluate a climate model is to assess in a quantitative way degrees of ‘model fit’; that is, how well model results fit observation-based data (empirical accuracy) and agree with other models or model versions (robustness). However, such assessments are largely silent about what those degrees of fit imply for a model's adequacy for projecting future climate. We provide a conceptual framework for discussing the evaluation of the adequacy of models for climate projections. Drawing on literature from philosophy of science and climate science, we discuss the potential and limits of inferences from model fit. We suggest that support of a model by background knowledge is an additional consideration that can be appealed to in arguments for a model's adequacy for long-term projections, and that this should explicitly be spelled out. Empirical accuracy, robustness and support by background knowledge neither individually nor collectively constitute sufficient conditions in a strict sense for a model's adequacy for long-term projections. However, they provide reasons that can be strengthened by additional information and thus contribute to a complex non-deductive argument for the adequacy of a climate model or a family of models for long-term climate projections., WIREs Climate Change, 8 (3), ISSN:1757-7780, ISSN:1757-7799

Regional projections of future climate with associated uncertainty estimates are increasingly being demanded. Generally, such scenarios rely on a finite number of model projections and are accompanied by considerable uncertainties which cannot be fully quantified. Consequently, probabilistic climate projections are conditioned on several subjective assumptions which can be treated in a Bayesian framework. In this study, a recently developed Bayesian multi-model combination algorithm is applied to regional climate model simulations from the ENSEMBLES project to generate probabilistic projections for Switzerland. The seasonal temperature and precipitation scenarios are calculated relative to 1980–2009 for three 30-year scenario periods (centred at 2035, 2060, and 2085), three regions, and the A1B emission scenario. Projections for two further emission scenarios are obtained by pattern scaling. Key to the Bayesian algorithm is the determination of prior distributions about climatic parameters. It is shown that the prior choice of model projection uncertainty ultimately determines the uncertainty in the climate change signal. Here, we assume that model uncertainty is fully sampled by the climate models available. We have extended the algorithm such that internal decadal variability is also included in all scenario calculations. The A1B scenarios show a significant rise in temperature increasing from 0.9–1.4 °C by 2035 (depending upon region and season), to 2.0–2.9 °C by 2060, and to 2.7–4.1 °C by 2085. Mean precipitation changes are subject to large uncertainties with median changes close to zero. Significant signals are seen towards the end of the century with a summer drying of 18–24% depending on region, and a likely increase of winter precipitation in Switzerland south of the Alps. The A2 scenario implies a warming of 3.2–4.8 °C, and a summer drying of 21–28% by 2085, while in case of the mitigation scenario RCP3PD, climate change could be stabilized to 1.2–1.8 °C of warming and 8–10% of drying. Copyright © 2011 Royal Meteorological Society

Climate change is clearly discernible in observed climate records in Switzerland. It impacts on natural systems, ecosystems, and economic sectors such as agriculture, tourism, and energy, and it affects Swiss livelihood in various ways. The observed and projected changes call for a response from the political system, which in Switzerland is characterized by federalism and direct democratic instruments. Swiss climate science embraces natural and social sciences and builds on institutionalized links between researchers, public, and private stakeholders. In this article, we review the physical, institutional, and political aspects of climate change in Switzerland. We show how the current state of Swiss climate science and policy developed over the past 20 years in the context of international developments and national responses. Specific to Switzerland is its topographic setting with mountain regions and lowlands on both sides of the Alpine ridge, which makes climate change clearly apparent and for some aspects (tourist sector, hydropower, and extreme events) highly relevant and better perceivable (e.g., retreating glaciers). Not surprisingly the Alpine region is of central interest in Swiss climate change studies.