Robust climate scenarios for sites with sparse observations: a two-step bias correction approach

Observed and projected climatic changes demand for robust assessments of climate impacts on various environmental and anthropogenic systems. Empirical-statistical downscaling (ESD) methods coupled to output from climate model projections are promising tools to assess impacts at regional to local scale. ESD methods correct for common model deficiencies in accuracy (e.g. model biases) and scale (e.g. grid vs point scale). However, most ESD methods require long observational time series at the target sites, and this often restricts robust impact assessments to a small number of sites. This paper presents a method to generate robust climate model based scenarios for target sites with short and (or) sparse observational data coverage. The approach is based on the well-established quantile mapping method and incorporates two major steps: (1) climate model bias correction to the most representative station with long-term measurements and (2) spatial transfer of bias-corrected model data to represent target site characteristics. Both steps are carried out using the quantile mapping technique. The resulting output can serve as end user–tailored input for climate impact models. The method allows for multivariate and multi-model ensemble scenarios and additionally enables to approximately reconstruct data for non-measured periods. The method's applicability is validated using (1) long-term weather stations across the topographically and climatologically complex territory of Switzerland and (2) sparse data sets from Swiss permafrost research sites located in challenging conditions at high altitudes. It is shown that the two-step approach performs well and offers attractive quality, even for extreme target locations. Uncertainties, however, remain and primarily depend on (1) data availability and (2) the considered variable. The two-step approach itself involves large uncertainties when applied to short reference data sets or spatially heterogeneous variables (e.g. precipitation, wind speed). For temperature, results are promising even when using very short calibration periods

Semi-automated calibration method for modelling of mountain permafrost evolution in Switzerland

Permafrost is a widespread phenomenon in mountainous regions of the world such as the European Alps. Many important topics such as the future evolution of permafrost related to climate change and the detection of permafrost related to potential natural hazards sites are of major concern to our society. Numerical permafrost models are the only tools which allow for the projection of the future evolution of permafrost. Due to the complexity of the processes involved and the heterogeneity of Alpine terrain, models must be carefully calibrated, and results should be compared with observations at the site (borehole) scale. However, for large-scale applications, a site- specific model calibration for a multitude of grid points would be very time-consuming. To tackle this issue, this study presents a semi-automated calibration method using the Generalized Likelihood Uncertainty Estimation (GLUE) as implemented in a 1-D soil model (CoupModel) and applies it to six permafrost sites in the Swiss Alps. We show that this semi-automated calibration method is able to accurately reproduce the main thermal condition characteristics with some limitations at sites with unique conditions such as 3-D air or water circulation, which have to be calibrated manually. The calibration obtained was used for global and regional climate model (GCM/RCM)-based long-term climate projections under the A1B climate scenario (EU-ENSEMBLES project) specifically downscaled at each borehole site. The projection shows general permafrost degradation with thawing at 10 m, even partially reaching 20 m depth by the end of the century, but with different timing among the sites and with partly considerable uncertainties due to the spread of the applied climatic forcing

Percentile indices for assessing changes in heavy precipitation events

Many climate studies assess trends and projections in heavy precipitation events using precipitation percentile (or quantile) indices. Here we investigate three different percentile indices that are commonly used. We demonstrate that these may produce very different results and thus require great care with interpretation. More specifically, consideration is given to two intensity-based indices and one frequency-based index, namely (a) all-day percentiles, (b) wet-day percentiles, and (c) frequency indices based on the exceedance of a percentile threshold. Wet-day percentiles are conditionally computed for the subset of wet events (with precipitation exceeding some threshold, e.g. 1 mm/d for daily precipitation). We present evidence that this commonly used methodology can lead to artifacts and misleading results if significant changes in the wet-day frequency are not accounted for. Percentile threshold indices measure the frequency of exceedance with respect to a percentile-based threshold. We show that these indices yield an assessment of changes in heavy precipitation events that is qualitatively consistent with all-day percentiles, but there are substantial differences in quantitative terms. We discuss the reasons for these effects, present a theoretical assessment, and provide a series of examples using global and regional climate models to quantify the effects in typical applications. Application to climate model output shows that these considerations are relevant to a wide range of typical climate-change applications. In particular, wet-day percentiles generally yield different results, and in most instances should not be used for the impact-oriented assessment of changes in heavy precipitation events

Effects of the solution and first aging treatment applied to as-built and post-HIP CM247 produced via Laser Powder Bed Fusion (LPBF)

In this work CM247LC, a low weldable Ni-Based alloy, was produced using selective laser melting (SLM). Despite the initial process parameter optimization, the low defect volume fraction was still uncompliant with manufacturing standards. This condition is principally caused by the high γ’ volume fraction which strongly affects the alloy weldability. Nonetheless, a crack free condition was eventually achieved applying a γ’-sub-solvus Hot Isostatic Pressing Cycle (HIP) which lowered the defects fraction down to 0.04%. The HIP cycle also demonstrated to play an important role in the stabilization of the microstructure, considerably limiting the carbides coarsening during the following heat treatment. Apart from the effectiveness of the healing process brought by HIP, the material microstructure still needs an optimization process which will be described along this paper. In fact, the Initial microstructure obtained after the printing process (the as-built condition) as well as the one obtained after HIP (post-HIP) won’t meet the desired requirements. Namely, the dendritic and γ’ free microstructure of the asbuilt material or the one with coarse and disordered particles obtained right after HIP, still need a tailored homogenization process. This paper will show how the combined effect of the solution and first aging treatment will profoundly alter the γ’ precipitation. More specifically, here, a new heat treatment recipe was developed to promote the precipitation of ordered cuboidal primary γ’ so as to improve creep and high temperature fatigue resistance. Moreover, the use of a γ’ super-solvus temperature allowed to achieve a γ’ volume fraction as high as 73% reducing its average size to 520 nm. At the same time, such heat treatment caused a profound alteration of the crystalline structures of the material promoting a general grain coarsening and the formation of equiaxial grain

Urban multi-model climate projections of intense heat in Switzerland

This paper introduces a straightforward approach to generate multi-model climate projections of intense urban heat, based on an ensemble of state-of-the-art global and regional climate model simulations from EURO-CORDEX. The employed technique entails the empirical-statistical downscaling method quantile mapping (QM), which is applied in two different settings, first for bias correction and downscaling of raw climate model data to rural stations with long-term measurements and second for spatial transfer of bias-corrected and downscaled climate model data to the respective urban target site. The resulting products are daily minimum and maximum temperatures at five urban sites in Switzerland until the end of the 21st century under three emission scenarios (RCP2.6, RCP4.5, RCP8.5). We test the second-step QM approach in an extensive evaluation framework, using long-term observational data of two exemplary weather stations in Zurich. Results indicate remarkably good skill of QM in present-day climate. Comparing the generated urban climate projections with existing climate scenarios of adjacent rural sites allows us to represent the urban heat island (UHI) effect in future temperature-based heat indices, namely tropical nights, summer days and hot days. Urban areas will be more strongly affected by rising temperatures than rural sites in terms of fixed threshold exceedances, especially during nighttime. Projections for the end of the century for Zurich, for instance, suggest more than double the number of tropical nights (Tmin above 20 ºC) at the urban site (45 nights per year, multi-model median) compared to the rural counterpart (20 nights) under RCP8.5.This research has been partly supported by the European Commission (HEAT-SHIELD 668786). EH is supported by the German Research Foundation under project number 40805747

CH2018 - National climate scenarios for Switzerland : how to construct consistent multi-model projections from ensembles of opportunity

The latest Swiss Climate Scenarios (CH2018), released in November 2018, consist of several datasets derived through various methods that provide robust and relevant information on climate change in Switzerland. The scenarios build upon the regional climate model projections for Europe produced through the internationally coordinated downscaling effort EURO-CORDEX. The simulations from EURO-CORDEX consist of simulations at two spatial horizontal resolutions, several global climate models, and three different emission scenarios. Even with this unique dataset of regional climate scenarios, a number of practical challenges regarding a consistent interpretation of the model ensemble arise. Here we present the methodological chain employed in CH2018 in order to generate a multi-model ensemble that is consistent across scenarios and is used as a basis for deriving the CH2018 products. The different steps involve a thorough evaluation of the full EURO-CORDEX model ensemble, the removal of doubtful and potentially erroneous simulations, a time-shift approach to account for an equal number of simulations for each emission scenario, and the multi-model combination of simulations with different spatial resolutions. Each component of this cascade of processing steps is associated with an uncertainty that eventually contributes to the overall scientific uncertainty of the derived scenario products. We present a comparison and an assessment of the uncertainties from these individual effects and relate them to probabilistic projections. It is shown that the CH2018 scenarios are generally supported by the results from other sources. Thus, the CH2018 scenarios currently provide the best available dataset of future climate change estimates in Switzerland

Climate Scenarios for Switzerland CH2018 – Approach and Implications

To make sound decisions in the face of climate change, government agencies, policymakers and private stakeholders require suitable climate information on local to regional scales. In Switzerland, the development of climate change scenarios is strongly linked to the climate adaptation strategy of the Confederation. The current climate scenarios for Switzerland CH2018 - released in form of six user-oriented products - were the result of an intensive collaboration between academia and administration under the umbrella of the National Centre for Climate Services (NCCS), accounting for user needs and stakeholder dialogues from the beginning. A rigorous scientific concept ensured consistency throughout the various analysis steps of the EURO-CORDEX projections and a common procedure on how to extract robust results and deal with associated uncertainties. The main results show that Switzerland’s climate will face dry summers, heavy precipitation, more hot days and snow-scarce winters. Approximately half of these changes could be alleviated by mid-century through strong global mitigation efforts. A comprehensive communication concept ensured that the results were rolled out and distilled in specific user-oriented communication measures to increase their uptake and to make them actionable. A narrative approach with four fictitious persons was used to communicate the key messages to the general public. Three years after the release, the climate scenarios have proven to be an indispensable information basis for users in climate adaptation and for downstream applications. Potential for extensions and updates has been identified since then and will shape the concept and planning of the next scenario generation in Switzerland

The effect of hot days on occupational heat stress in the manufacturing industry: implications for workers' well-being and productivity

Climate change is expected to exacerbate heat stress at the workplace in temperate regions, such as Slovenia. It is therefore of paramount importance to study present and future summer heat conditions and analyze the impact of heat on workers. A set of climate indices based on summer mean (Tmean) and maximum (Tmax) air temperatures, such as the number of hot days (HD: Tmax above 30 °C), and Wet Bulb Globe Temperature (WBGT) were used to account for heat conditions in Slovenia at six locations in the period 1981–2010. Observed trends (1961–2011) of Tmean and Tmax in July were positive, being larger in the eastern part of the country. Climate change projections showed an increase up to 4.5 °C for mean temperature and 35 days for HD by the end of the twenty-first century under the high emission scenario. The increase in WBGT was smaller, although sufficiently high to increase the frequency of days with a high risk of heat stress up to an average of a third of the summer days. A case study performed at a Slovenian automobile parts manufacturing plant revealed non-optimal working conditions during summer 2016 (WBGT mainly between 20 and 25 °C). A survey conducted on 400 workers revealed that 96% perceived the temperature conditions as unsuitable, and 56% experienced headaches and fatigue. Given these conditions and climate change projections, the escalating problem of heat is worrisome. The European Commission initiated a program of research within the Horizon 2020 program to develop a heat warning system for European workers and employers, which will incorporate case-specific solutions to mitigate heat stress.The work was supported by the European Union Horizon 2020 Research and Innovation Action (Project number 668786: HEATSHIELD)

The VALUE perfect predictor experiment: evaluation of temporal variability

Temporal variability is an important feature of climate, comprising systematic vari-ations such as the annual cycle, as well as residual temporal variations such asshort-term variations, spells and variability from interannual to long-term trends.The EU-COST Action VALUE developed a comprehensive framework to evaluatedownscaling methods. Here we present the evaluation of the perfect predictorexperiment for temporal variability. Overall, the behaviour of the differentapproaches turned out to be as expected from their structure and implementation.The chosen regional climate model adds value to reanalysis data for most consid-ered aspects, for all seasons and for both temperature and precipitation. Bias cor-rection methods do not directly modify temporal variability apart from the annualcycle. However, wet day corrections substantially improve transition probabilitiesand spell length distributions, whereas interannual variability is in some cases dete-riorated by quantile mapping. The performance of perfect prognosis (PP) statisticaldownscaling methods varies strongly from aspect to aspect and method to method,and depends strongly on the predictor choice. Unconditional weather generatorstend to perform well for the aspects they have been calibrated for, but underrepre-sent long spells and interannual variability. Long-term temperature trends of thedriving model are essentially unchanged by bias correction methods. If precipita-tion trends are not well simulated by the driving model, bias correction furtherdeteriorates these trends. The performance of PP methods to simulate trendsdepends strongly on the chosen predictors.VALUE has been funded as EU COST Action ES1102