Non-linear loss of suitable wine regions over Europe in response to increasing global warming.

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.

Extending the global high-resolution downscaled projections dataset to include CMIP6 projections at increased resolution coherent with the ERA5-Land reanalysis.

This paper describes the extension of the previously CMIP5 based high-resolution climate projections with additional ones based on the more recent climate projections from the CMIP6 experiment. The downscaling method and data processing are the same but the reference dataset is now the ERA5-Land reanalysis (compared to ERA5 previously) allowing to increase the resolution of the new downscaled projections from 0.25° x 0.25° to 0.1°x 0.1°. The extension comprises 5 climate models and includes 2 surface variables at daily resolution: air temperature and precipitation. Three greenhouse gas emissions scenarios are available: Shared Socioeconomic Pathways with mitigation policy (SSP1-2.6), an intermediate one (SSP2-4.5), and one without mitigation (SSP5-8.5).

A high-resolution downscaled CMIP5 projections dataset of essential surface climate variables over the globe coherent with the ERA5 reanalysis for climate change impact assessments.

A high-resolution climate projections dataset is obtained by statistically downscaling climate projections from the CMIP5 experiment using the ERA5 reanalysis from the Copernicus Climate Change Service. This global dataset has a spatial resolution of 0.25°x 0.25°, comprises 21 climate models and includes 5 surface daily variables at monthly resolution: air temperature (mean, minimum, and maximum), precipitation, and mean near-surface wind speed. Two greenhouse gas emissions scenarios are available: one with mitigation policy (RCP4.5) and one without mitigation (RCP8.5). The downscaling method is a Quantile Mapping method (QM) called the Cumulative Distribution Function transform (CDF-t) method that was first used for wind values and is now referenced in dozens of peer-reviewed publications. The data processing includes quality control of metadata according to the climate modeling community standards and value checking for outlier detection.

Seasonal variations of systemic lupus erythematosus flares in southern France.

OBJECTIVE: Exposure to sunlight is one of the environmental factors involved in the pathogenesis of systemic lupus erythematosus. We investigated whether there is seasonal variation in the incidence of cutaneous and noncutaneous severe lupus flares in southern France. METHODS: We retrospectively reviewed clinical and biological data from all SLE patients hospitalized for a flare of the disease during a two year period in our centre and collected corresponding meteorological data from the official website of MeteoFrance. RESULTS: Forty one patients, mean age 36.7 ± 13.8 years, were included. Twenty-six patients (63.4%) had kidney biopsy performed, showing in all cases proliferative nephritis, associated with membranous nephritis in 9 (22%). We found a clear seasonal pattern for overall lupus flares with 39% of flares occurred in Spring. Among patients without any cutaneous involvement, this seasonal pattern was still observed (p=0.024). Patients under antimalarials presented flares significantly later in the sunny season than those without (respectively median in July versus May, p=0.044). There were strong positive correlations between occurrence of lupus flares and maximum temperature increase (ρ=0.87, p<0.001), minimum temperature increase (ρ=0.87, p<0.001), and duration of sunshine increase (ρ=0.78, p=0.003). These correlations were also observed in patients with renal flares. CONCLUSION: We confirmed a seasonal pattern for lupus flares among patients living in Southern France, with most flares in spring, in correlation with an increase in temperature and duration of sunshine. A similar seasonal pattern was observed in patients with no cutaneous involvement and with visceral involvement.

Mathematical modelling of the impact of climatic conditions in France on Rhipicephalus sanguineus tick activity and density since 1960.

Rhipicephalus sanguineus, the brown dog tick, has a worldwide distribution in areas with a relatively warm climate, including mild winters. This tick plays an important role as vector for various animal and human pathogens, including bacteria and protozoa. Based on precise daily meteorological data from the past 40 years, combined with mathematical modelling designed to predict tick activity, two modelling approaches were developed. The first examined the evolution of the number of weeks with favourable biological conditions for ticks in four French cities located at various latitudes of the country: Nîmes in the south, Paris in the north, Lyon in the east and Nantes in the west. The second analysed the extension of the geographical surface area in km(2) where the biological conditions favour tick activity for at least 12 weeks per year. Both analyses revealed clear evidence of increased temperatures coupled with an augmented tick activity index in three of the four cities. However, the change was not significant in Nîmes, where the climate is Mediterranean and the tick is already endemic. For Paris, Lyon and Nantes, the activity index values have increased significantly, i.e. by 4.4%, 4.0% and 3.4%, respectively. The distribution of the activity index values is evolving strongly with significantly fewer values below 50% since the 1960s and a clear decrease of values between 20% and 50% during the latest decade. Between 1960 and 2000, the theoretical extension of the surface area where the climatic index is suitable for R. sanguineus has increased by 66%. Even though several other important factors, such as changes in biotopes or human activity, are not included in this study, the resulting patterns and trends are noticeable. Our models constitute the first demonstration of the impact of climate change on the activity and distribution of ticks and confirm the observed northward migration trend for this Mediterranean domestic tick.

FleaTickRisk: a meteorological model developed to monitor and predict the activity and density of three tick species and the cat flea in Europe.

Mathematical modelling is quite a recent tool in epidemiology. Geographical information system (GIS) combined with remote sensing (data collection and analysis) provide valuable models, but the integration of climatologic models in parasitology and epidemiology is less common. The aim of our model, called "FleaTickRisk", was to use meteorological data and forecasts to monitor the activity and density of some arthropods. Our parasitological model uses the Weather Research and Forecasting (WRF) meteorological model integrating biological parameters. The WRF model provides a temperature and humidity picture four times a day (at 6:00, 12:00, 18:00 and 24:00 hours). Its geographical resolution is 27 x 27 km over Europe (area between longitudes 10.5 degrees W and 30 degrees E and latitudes 37.75 degrees N and 62 degrees N). The model also provides weekly forecasts. Past data were compared and revalidated using current meteorological data generated by ground stations and weather satellites. The WRF model also includes geographical information stemming from United States Geophysical Survey biotope maps with a 30'' spatial resolution (approximately 900 x 900 m). WRF takes into account specific climatic conditions due to valleys, altitudes, lakes and wind specificities. The biological parameters of Ixodes ricinus, Dermacentor reticulatus, Rhipicephalus sanguineus and Ctenocephalides felis felis were transformed into a matrix of activity. This activity matrix is expressed as a percentage, ranging from 0 to 100, for each interval of temperature x humidity. The activity of these arthropods is defined by their ability to infest hosts, take blood meals and reproduce. For each arthropod, the matrix was calculated using existing data collected under optimal temperature and humidity conditions, as well as the timing of the life cycle. The mathematical model integrating both the WRF model (meteorological data + geographical data) and the biological matrix provides two indexes: an activity index (ranging from 0 to 100), calculated for the previous week and predictive for the coming week, and a cumulative index (ranging from 0 to 1000) which takes into account the past 12 weeks. The indexes are calculated twice a day for each geographical point all over Europe and are corrected based on three types of defined biotopes: urban and sub-urban areas, rural areas, and wilderness and forests. To clarify the presentation, indexes are calculated within intervals and are presented as colour maps grouping index isoclines. We hypothesised that the populations of tick and flea hosts are not lacking and therefore do not affect the numbers of arthropods. However, microclimates and biotopes have a major impact, especially on tick populations, and the results provided by the model must therefore be adjusted to local conditions by specialists, such as local veterinarians. Where fleas are concerned, the model takes into account their outdoor activity and ignores their indoor life cycle. The accuracy of the data was verified throughout 2007 and 2008, using sentinel veterinary clinics and tick samples, as well as comparisons with published surveys. The maps constructed with the model are available to veterinary practitioners on www.FleaTickRisk.com.