Balanced estimate and uncertainty assessment of European climate
change using the large EURO-CORDEX regional climate model
ensemble

Evin, Guillaume; Somot, Samuel; Hingray, B.

doi:10.5194/esd-2021-8

Cited by 5 publications

(8 citation statements)

References 22 publications

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“…A methodology to enlarge the number of simulations, which are strictly limited by the huge computational time of climate model runs, thus increasing the statistical representatives of the ensemble, has been previously presented by the authors in [54,55]. Statistical adjustment procedures based on data augmentation and Bayesian inference are presented in [56] and recently applied to large RCM ensemble for the assessment of uncertainty associated to the expected changes in mean temperature and total precipitation in Europe [57].…”

Section: Datasetsmentioning

confidence: 99%

Extreme Ground Snow Loads in Europe from 1951 to 2100

Croce

Formichi

Landi

2021

Climate

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Lightweight roofs are extremely sensitive to extreme snow loads, as confirmed by recently occurring failures all over Europe. Obviously, the problem is further emphasized in warmer climatic areas, where low design values are generally foreseen for snow loads. Like other climatic actions, representative values of snow loads provided in structural codes are usually derived by means of suitable elaborations of extreme statistics, assuming climate stationarity over time. As climate change impacts are becoming more and more evident over time, that hypothesis is becoming controversial, so that suitable adaptation strategies aiming to define climate resilient design loads need to be implemented. In the paper, past and future trends of ground snow load in Europe are assessed for the period 1950–2100, starting from high-resolution climate simulations, recently issued by the CORDEX program. Maps of representative values of snow loads adopted for structural design, associated with an annual probability of exceedance p = 2%, are elaborated for Europe. Referring to the historical period, the obtained maps are critically compared with the current European maps based on observations. Factors of change maps, referred to subsequent time windows are presented considering RCP4.5 and RCP8.5 emission trajectories, corresponding to medium and maximum greenhouse gas concentration scenarios. Factors of change are thus evaluated considering suitably selected weather stations in Switzerland and Germany, for which high quality point measurements, sufficiently extended over time are available. Focusing on the investigated weather stations, the study demonstrates that climate models can appropriately reproduce historical trends and that a decrease of characteristic values of the snow loads is expected over time. However, it must be remarked that, if on one hand the mean value of the annual maxima tends to reduce, on the other hand, its standard deviation tends to increase, locally leading to an increase of the extreme values, which should be duly considered in the evaluation of structural reliability over time.

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Section: Datasetsmentioning

confidence: 99%

Extreme Ground Snow Loads in Europe from 1951 to 2100

Croce

Formichi

Landi

2021

Climate

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“…RCMs are the most modern tool for projecting climate conditions in specific areas (Stefanidis et al, 2020). Estimates of future changes in the climate are expressed in terms of mean changes (Evin et al, 2021) or predictions of future trends in wind speeds, temperature, and radiation. These estimates can also be converted to impacts such as changes in energy production.…”

Section: Introductionmentioning

confidence: 99%

“…Their quantification in ensembles of climate projections supports the understanding of where targeting efforts reduce them (Evin et al, 2019). The methods to characterize the different sources of uncertainty associated with the projections of climate variables include advanced Bayesian analysis of variance (ANOVA) (Bichet et al, 2020) and QUALYPSO (Evin et al, 2021), which is another statistical approach to calculate the total uncertainty of projections. These methods are computationally expensive and time-consuming procedures.…”

Section: Introductionmentioning

confidence: 99%

Climate Change Impact Assessment for Future Wind and Solar Energy Installations in India

Zakari

Vuille²,

Lehning

2022

Front. Energy Res.

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Solar and wind assets are climate-dependent and changes in climate will result in variations in their generation and intermittency. Developers of solar and wind parks in India have observed changes in climate conditions and variability in solar irradiation and wind profiles at the seasonal and year-to-year timescales. Future climate change perturbations, including monsoon shifts, could lead to lower-than-predicted wind and solar energy production and affect the economics of solar and wind assets. Regional climate models (RCMs) are the basis of climate impact assessments and the most trusted source of information to extract knowledge about future trends in climate variables. However, RCM projections are tainted with variability and uncertainty about the future trends. For India as a case study, we use the RCMs generated by the Coordinated Regional Climate Downscaling Experiment West Asia project (CORDEX WAS) to calculate individual wind, radiation, and temperature trends at selected sites; estimate wind and solar PV energy time series; and embed them in portfolio methods to test the impact of combining wind and solar assets on the variability of the total production and the uncertainty about the predicted production. We include a comparison of CORDEX RCMs with the ERA5 reanalysis dataset and conclude that all available RCMs reasonably simulate the main annual and seasonality features of wind speed, surface solar radiation, and temperature in India. The analysis demonstrates that the uncertainty about the portfolio return can be reduced by optimizing the combination of wind and solar assets in a producer portfolio, thus mitigating the economic impact of climate change. We find that the reduction obtained with a mixed portfolio ranges from 33 to 50% compared to a wind only portfolio, and from 30 to 96% compared to a solar only portfolio.

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“…In order to deliver robust information about future local responses to climate change, it is necessary to explore the uncertainty associated with RCM simulations. Déqué et al (2007) and Evin et al (2021) assess that four sources of uncertainty are at play in a regional climate simulation: the choice of the driving GCM, the greenhouse gas scenario, the choice of the RCM itself and the internal variability. Their relative importance depends on the considered variables, spatial scale, and timeline.…”

Section: Introductionmentioning

confidence: 99%

“…Their relative importance depends on the considered variables, spatial scale, and timeline. According to these results, it is important (Déqué et al, 2012;Evin et al, 2021;Fernández et al, 2019) However, the main limitation of RCM is their high computational costs, and completion of such matrices is impossible.…”

Section: Introductionmentioning

confidence: 99%

Regional Climate Model Emulator based on Deep Learning: Concept and First Evaluation of a Novel Hybrid Downscaling Approach

Gadat

Corre

Doury

et al. 2021

Preprint

Self Cite

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Providing reliable information on climate change at local scale remains a challenge of first importance for impact studies and policymakers. Here, we propose a novel hybrid downscaling method combining the strengths of both empirical statistical downscaling methods and Regional Climate Models (RCMs). The aim of this tool is to enlarge the size of high-resolution RCM simulation ensembles at low cost.We build a statistical RCM-emulator by estimating the downscaling function included in the RCM. This framework allows us to learn the relationship between large-scale predictors and a local surface variable of interest over the RCM domain in present and future climate. Furthermore, the emulator relies on a neural network architecture, which grants computational efficiency. The RCM-emulator developed in this study is trained to produce daily maps of the near-surface temperature at the RCM resolution (12km). The emulator demonstrates an excellent ability to reproduce the complex spatial structure and daily variability simulated by the RCM and in particular the way the RCM refines locally the low-resolution climate patterns. Training in future climate appears to be a key feature of our emulator. Moreover, there is a huge computational benefit in running the emulator rather than the RCM, since training the emulator takes about 2 hours on GPU, and the prediction is nearly instantaneous. However, further work is needed to improve the way the RCM-emulator reproduces some of the temperature extremes, the intensity of climate change, and to extend the proposed methodology to different regions, GCMs, RCMs, and variables of interest.

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Balanced estimate and uncertainty assessment of European climate change using the large EURO-CORDEX regional climate model ensemble

Cited by 5 publications

References 22 publications

Extreme Ground Snow Loads in Europe from 1951 to 2100

Extreme Ground Snow Loads in Europe from 1951 to 2100

Climate Change Impact Assessment for Future Wind and Solar Energy Installations in India

Regional Climate Model Emulator based on Deep Learning: Concept and First Evaluation of a Novel Hybrid Downscaling Approach

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