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Evaluating the potential climatic suitability for premium wine production is crucial for adaptation planning in Europe. While new wine regions may emerge out of the traditional boundaries, most of the present‐day renowned winemaking regions may be threatened by climate change. Here, we analyse the future evolution of the geography of wine production over Europe, through the definition of a novel climatic suitability indicator, which is calculated over the projected grapevine phenological phases to account for their possible contractions under global warming. Our approach consists in coupling six different de‐biased downscaled climate projections under two different scenarios of global warming with four phenological models for different grapevine varieties. The resulting suitability indicator is based on fuzzy logic and is calculated over three main components measuring (i) the timing of the fruit physiological maturity, (ii) the risk of water stress and (iii) the risk of pests and diseases. The results demonstrate that the level of global warming largely determines the distribution of future wine regions. For a global temperature increase limited to 2°C above the pre‐industrial level, the suitable areas over the traditional regions are reduced by about 4%/°C rise, while for higher levels of global warming, the rate of this loss increases up to 17%/°C. This is compensated by a gradual emergence of new wine regions out of the traditional boundaries. Moreover, we show that reallocating better‐suited grapevine varieties to warmer conditions may be a viable adaptation measure to cope with the projected suitability loss over the traditional regions. However, the effectiveness of this strategy appears to decrease as the level of global warming increases. Overall, these findings suggest the existence of a safe limit below 2°C of global warming for the European winemaking sector, while adaptation might become far more challenging beyond this threshold. [ABSTRACT FROM AUTHOR]

Decadal Climate Predictions (DCP) have gained considerable attention for their potential utility in promoting optimised plans of adaptation to climate change and variability. Their effective applicability to a targeted problem is nevertheless conditional on a detailed evaluation of their ability to simulate the near-term climate evolution under specific conditions. Here we explore the performance of the IPSL-CM5A-LR DCP system in predicting air temperature over Europe, by proposing a systematic assessessment of the prediction skill for different time windows (periods of the calendar time, forecast years and months/seasons). In this framework, we also compare raw and de-biased hindcasts, in which the temperature outputs have been corrected using a quantile matching method. The systematic analysis allows to discern certain conditions conferring larger predictability, which we find to be intermittent in time. The predictions appear more skilful around the 1960s and after the 1980s, in coincidence with large shifts of the Atlantic Multidecadal Variability, which are well reproduced in the hindcasts. Averages on longer forecast periods also generally imply better prediction skill, while the best predicted months appear to be mainly those between late spring and early autumn. Moreover, we find an overall added value due to initialisation, while de-biased predictions significantly outperform raw predictions only for a few specific time windows. Finally, we discuss the potential implications of the proposed systematic exploration of skill opportunities in DCPs for integrated applications in climate sensitive sectors. [ABSTRACT FROM AUTHOR]

The objective of this paper is to present a new data set of bias-corrected CMIP5 global climate models (GCMs) daily data over Africa. This dataset was obtained in using the Cumulative Distribution Function Transform (CDF-t) method, a method that has been applied on several regions and contexts but never on Africa. Here CDF-t is used over the period 1950–2099 combining historical runs and climate change scenarios on 6 variables, precipitation, mean near-surface air temperature, near-surface maximum air temperature, near-surface minimum air temperature, surface down-welling shortwave radiation, and wind speed, which are critical variables for agricultural purposes. Evaluation of the results is carried out over West Africa on a list of priority users-based metrics that was discussed and selected with stakeholders and on simulated yield using a crop model simulating maize growth. Bias-corrected GCMs data are compared with another available dataset of bias-corrected GCMs, and the impact of three different reference datasets on bias-corrections is also examined in details. CDF-t is very effective in removing the biases and in reducing the high inter-GCMs scattering. Differences with other bias-corrected GCMs data are mainly due to the differences between the reference datasets. This is particular true for surface down-welling shortwave radiation, which has impacts in terms of simulated maize yields. Projections of future yields over West Africa have quite different levels, depending on bias-correction method used, but they all show a similar relative decreasing trend over the 21st century. [ABSTRACT FROM AUTHOR]

In order to fulfill the society demand for climate information at the spatial scale allowing impact studies, long-term high-resolution climate simulations are produced, over an area covering metropolitan France. One of the major goals of this article is to investigate whether such simulations appropriately simulate the spatial and temporal variability of the current climate, using two simulation chains. These start from the global IPSL-CM4 climate model, using two regional models (LMDz and MM5) at moderate resolution (15-20 km), followed with a statistical downscaling method in order to reach a target resolution of 8 km. The statistical downscaling technique includes a non-parametric method that corrects the distribution by using high-resolution analyses over France. First the uncorrected simulations are evaluated against a set of high-resolution analyses, with a focus on temperature and precipitation. Uncorrected downscaled temperatures suffer from a cold bias that is present in the global model as well. Precipitations biases have a season- and model-dependent behavior. Dynamical models overestimate rainfall but with different patterns and amplitude, but both have underestimations in the South-Eastern area (Cevennes mountains) in winter. A variance decomposition shows that uncorrected simulations fairly well capture observed variances from inter-annual to high-frequency intra-seasonal time scales. After correction, distributions match with analyses by construction, but it is shown that spatial coherence, persistence properties of warm, cold and dry episodes also match to a certain extent. Another aim of the article is to describe the changes for future climate obtained using these simulations under Scenario A1B. Results are presented on the changes between current and mid-term future (2021-2050) averages and variability over France. Interestingly, even though the same global climate model is used at the boundaries, regional climate change responses from the two models significantly differ. [ABSTRACT FROM AUTHOR]