Mean and extreme climate in Europe under 1.5, 2, and 3°C global warming

Based on high-resolution regional climate models, the change over Europe in mean climate and extremes, including impact-relevant indicators, are investigated under different levels of global warming (1.5°C, 2°C, and 3°C). A suit of indices describing both hot and cold events are employed and, for precipitation, wet and dry conditions; in particular, we examine the evolution of threshold-based indices, such as the number of frost days or tropical nights, which may be relevant for impact assessment on specific sectors.JRC.E.1-Disaster Risk Managemen

PESETA III – Task1: Climate change projections, bias-adjustment, and selection of model runs

Global warming will greatly affect the climate at regional and local scale through, e.g., the increase of intensity and frequency of extreme weather events (floods, droughts, heat waves, etc.). In order to assess the impact of climate change at such scale (on, e.g., the hydrological cycle or crop production) it is necessary to attain meteorological information with a spatial detail much finer than that provided by global climate models (GCMs). High-resolution climate projections are usually obtained by employing regional climate models (RCMs), which are able to better resolve small-scale features such as topography and heterogeneous land use. When compared to present-day observations, however, the results of climate models can present large biases; in order to be used as an input for process-based impact models (like in PESETA III) outputs from RCMs are usually further post-processed by means of statistical techniques known as bias-correction (or bias-adjustment). Here, we describe the projections of climate change used in PESETA III and the bias-adjustment method applied to them, focusing on the analysis of a series of climate change indices for both the mean climate and extreme events (such as the number of frost days, of the number of consecutive dry days) relevant for impact assessment studies. Results show that, under the RCP8.5 emission scenario, at the end of the Century, maximum temperature is expected to increase, in winter, between about 2.5∘C over the British Isles and 4.8∘C over Scandinavia. In summer, the projected change ranges between 2.5∘C over Britain and 4.7∘C over the Iberian Peninsula. Winter precipitation is projected to increase over most of central and northern Europe in both frequency, and intensity, with a consequent increase of the number of consecutive wet days, and reduction of consecutive dry days. The change in precipitation frequencies distribution is not uniform, though, and a reduction in low precipitation intensity is accompanied by an increase of extreme events, even for the Mediterranean regions where total precipitation is projected to decrease. In summer, a general reduction in precipitation is projected for all regions except Scandinavia and Eastern Europe; as for winter, there is a tendency toward less frequent but more severe precipitation episodes. A set of 12 RCMs’ bias-adjusted climate change projections is provided to the PESETA III impact modellers; the use of such a large ensemble of runs is essential to quantify the uncertainty in climate projections (the so-called inter-model variability). In fact, each model’s run (driven by the same emission scenario) represents an equally plausible projection of the future evolution of the climate. However, due to differences in the models’ formulation and physical parameterization, the climate change signal projected by different models may present significant differences. Due to resource limitations, some impact model groups may not be able to use all the 12 provided runs; in this case, a sub-set of 5 runs is selected to be used by all impact models (compulsory core runs). The sub-set of core runs needs to be able to reproduce, as accurately as possible, the inter-model variability of the entire ensemble. The selection of the sub-set has been performed by means of Principal Component Analysis (PCA) on the bias adjusted climate change indices. Finally, the PESETA III protocol also requires investigating the impacts of a 2ᵒC global warming, compared to the preindustrial period. Here for each RCM run, the timing of reaching 2ᵒC warming is provided following the same procedure used in the FP7 project IMPACT2C, namely: - It is assumed that the climate in a +2ᵒC world is comparable irrespective of when and how fast this warming is reached - An RCM is defined to project a 2ᵒC global warming when the corresponding driving GCM reaches the 2ᵒC threshold, under RCP8.5 emission scenario - For each GCM-RCM run, the +2ᵒC period is defined as the 30 year period centred around the year when the 2ᵒC global warming is first reachedJRC.E.1-Disaster Risk Managemen

Projections of indices of daily temperature and precipitation based on bias-adjusted CORDEX-Africa regional climate model simulations

AbstractWe present a dataset of daily, bias-adjusted temperature and precipitation projections for continental Africa based on a large ensemble of regional climate model simulations, which can be useful for climate change impact studies in several sectors. We provide guidance on the benefits and caveats of using the dataset by investigating the effect of bias-adjustment on impact-relevant indices (both their future absolute value and change). Extreme threshold-based temperature indices show large differences between original and bias-adjusted values at the end of the century due to the general underestimation of temperature in the present climate. These results indicate that when biases are accounted for, projected risks of extreme temperature-related hazards are higher than previously found, with possible consequences for the planning of adaptation measures. Bias-adjusted results for precipitation indices are usually consistent with the original results, with the median change preserved for most regions and indices. The interquartile and full range of the original model ensemble is usually well preserved by bias-adjustment, with the exception of maximum daily precipitation, whose range is usually greatly reduced by the bias-adjustment. This is due to the poor simulation and extremely large model range for this index over the reference period; when the bias is reduced, most models converge in projecting a similar change. Finally, we provide a methodology to select a small subset of simulations that preserves the overall uncertainty in the future projections of the large model ensemble. This result can be useful in practical applications when process-based impact models are too expensive to be run with the full ensemble of model simulations

Dynamical downscaling of CMIP5 1 Global Circulation Models over CORDEX-Africa with COSMO-CLM: evaluation over the present climate and analysis of the added value.

In this work we present the results of the application 8 of the Consor- tium for Small-scale Modeling (COSMO) Regional Climate Model (COSMO-CLM, hereafter, CCLM) over Africa in the context of the Coordinated Regional Climate Downscaling Experiment (CORDEX). An ensemble of climate change projections has been created by downscaling the simulations of four Global ClimateModels (GCM), namely:MPI-ESM-LR, HadGEM2- ES, CNRM-CM5, and EC-Earth. Here we compare the results of CCLM to those of the driving GCMs over the present climate, in order to investigate whether RCMs are effectively able to add value, at regional scale, to the performances of GCMs. It is found that, in general, the geographical distribution of mean sea level pressure, surface temperature and seasonal precipitation is strongly affected by the boundary conditions (i.e. driving GCMs), and seasonal statistics are not always improved by the downscaling. However, CCLM is generally able to better represent the annual cycle of precipitation, in particular over Southern Africa and the West Africa Monsoon (WAM) area. By performing a Singular Spectrum Analysis (SSA) it is found that CCLM is able to reproduce satisfactorily the annual and sub-annual principal components of the precipitation time series over the Guinea Gulf, whereas the GCMs are in general not able to simulate the bimodal distribution due to the passage of the WAM and show a unimodal precipitation annual cycle. Furthermore, it is shown that CCLM is able to better reproduce the Probability Distribution Function (PDF) of precipitation and some impact-relevant indices such as the number of consecutive wet and dry days, and the frequency of heavy rain events.JRC.H.7-Climate Risk Managemen

Frequency analysis of critical meteorological conditions in a changing climate - Assessing future implications for railway transportation in Austria

Meteorological extreme events have great potential for damaging railway infrastructure and posing risk to the safety of train passengers. In the future, climate change will presumably have serious implications on meteorological hazards in the Alpine region. Hence, attaining insights on future frequencies of meteorological extremes with relevance for the railway operation in Austria is required in the context of a comprehensive and sustainable natural hazard management of the railway operator. In this study, possible impacts of climate change on the frequencies of so-called critical meteorological conditions (CMCs) between the periods 1961-1990 and 2011-2040 are analysed. Thresholds for such CMCs have been defined by the railway operator and used in its weather monitoring and early warning system. First, the seasonal climate change signals for air temperature and precipitation in Austria are described on the basis of an ensemble of high-resolution Regional Climate Model (RCM) simulations for Europe. Subsequently, the RCM-ensemble was used to investigate changes in the frequency of CMCs. Finally, the sensitivity of results is analysed with varying threshold values for the CMCs. Results give robust indications for an all-season air temperature rise, but show no clear tendency in average precipitation. The frequency analyses reveal an increase in intense rainfall events and heat waves, whereas heavy snowfall and cold days are likely to decrease. Furthermore, results indicate that frequencies of CMCs are rather sensitive to changes of thresholds. It thus emphasizes the importance to carefully define, validate and – if needed – to adapt the thresholds that are used in the weather monitoring and warning system of the railway operator. For this, continuous and standardized documentation of damaging events and near-misses is a pre-requisite.JRC.H.7-Climate Risk Managemen

Towards new European snow load map: Support to policies and standards for sustainable construction

The Mandate M/515 of the European Commission to CEN requested the assessment of the climate change implications for the Eurocodes, the European standards for structural design. The European Commission Mandate M/526 requested the European Standards Organisations (ESOs) to contribute to building and maintaining a more climate resilient infrastructure throughout the EU in the three priority sectors: transport infrastructure, energy infrastructure, and buildings/construction. To proceed with the envisaged adaptation of the European standards to the implications of climate change, the expected changes in the climatic loading shall be assessed in terms of the Eurocodes concept for the characteristic values of the variable climatic actions. The present report justifies the need of a European research project to develop an advanced procedure for deriving snow load on structures, taking into account climate change projections, and to set up a new European snow load map based on this procedure

The snow load in Europe and the climate change

It is often assumed that, as a consequence of global warming, a reduction of snow load on the ground should be expected. In reality, snow load is often depending on local orographic situations that can determine an increase of its height, even when the average snow height over the surrounding areas is reduced. Large snow loads on roofs during the winter season of 2005–2006 led to over 200 roof collapses in Central Europe. To proceed with the adaptation of the European standards for important buildings and infrastructures to the implications of climate change, the expected changes in the climatic loading shall be assessed in terms of the Eurocodes concept for characteristic values of variable climatic actions. The paper presents a procedure for derivation of snow load on ground from data on daily temperatures and precipitation. In addition, it allows to derive the characteristic snow loads from climate change projections and thus to evaluate the future trends in variation of snow loading. Analysis of these trends for the Italian territory is performed by comparing the results for several subsequent time periods of thirty years, with those obtained for the reference period 1951–1980. Results presented show a significant increase in the snow loading for the period 1981–2010 in many regions in north and east Italy in comparison with the reference period. It is suggested that a European project on snow load map shall be started, in order to help National Competent Authorities to redraft the national snow load maps for design with the Eurocodes

Global population‐weighted degree‐day projections for a combination of climate and socio‐economic scenarios

AbstractThe projected global temperature increase in the 21st century is expected to have consequences on energy consumption due to increase (decrease) in energy demand to cool (heat) the built environments. Such increase (decrease) also depends on the number of end users for such energy, thus it is crucial to include population into the analyses. This study presents population‐weighted (w) cooling (CDD), heating (HDD), and energy (EDD) degree‐day projections at global, regional, and local scales for the 21st century. We used a large ensemble of high‐resolution (0.44°) climate simulations from the COordinated Regional‐climate Downscaling EXperiment (CORDEX) to compute degree‐days for baseline (1981–2010) and global warming levels (GWLs from 1.5°C to 4°C), based on two representative concentration pathways. We used population projections from the NASA‐SEDAC datasets, driven by five socio‐economic scenarios (SSPs). The progressive increase in CDD outbalances the decrease in HDD in Central and South America, Africa, and Oceania and the opposite situation is likely to occur in North America, Europe, and Asia; at global scale, they are balanced. However, if results are weighted according to population, the increase in wCDD outbalances the decrease in wHDD almost everywhere for most GWLs and SSPs. Few regions show a decreasing tendency in wEDD at high GWLs for all SSPs: central Europe, northwestern, northeastern, and eastern Asia. Globally, wEDD are likely to double at 2°C compared to 1981–2010 independently of the SSP. Under the worst‐case scenario (SSP3), at 4°C wCDD are approximately 380% higher and wHDD approximately 30% lower than in the recent past, leading to an increase in wEDD close to 300%

Testing bias adjustment methods for regional climate change applications under observational uncertainty and resolution mismatch

Systematic biases in climate models hamper their direct use in impact studies and, as a consequence, many statistical bias adjustment methods have been developed to calibrate model outputs against observations. The application of these methods in a climate change context is problematic since there is no clear understanding on how these methods may affect key magnitudes, for example, the climate change signal or trend, under different sources of uncertainty. Two relevant sources of uncertainty, often overlooked, are the sensitivity to the observational reference used to calibrate the method and the effect of the resolution mismatch between model and observations (downscaling effect). In the present work, we assess the impact of these factors on the climate change signal of temperature and precipitation considering marginal, temporal and extreme aspects. We use eight standard and state-of-the-art bias adjustment methods (spanning a variety of methods regarding their nature—empirical or parametric—, fitted parameters and trend-preservation) for a case study in the Iberian Peninsula. The quantile trend-preserving methods (namely quantile delta mapping (QDM), scaled distribution mapping (SDM) and the method from the third phase of ISIMIP-ISIMIP3) preserve better the raw signals for the different indices and variables considered (not all preserved by construction). However, they rely largely on the reference dataset used for calibration, thus presenting a larger sensitivity to the observations, especially for precipitation intensity, spells and extreme indices. Thus, high-quality observational datasets are essential for comprehensive analyses in larger (continental) domains. Similar conclusions hold for experiments carried out at high (approximately 20 km) and low (approximately 120 km) spatial resolutions. © 2020 The Authors. Atmospheric Science Letters published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society