The influence of topography and land cover on air temperature space-time variability is examined in an urban environment with contrasted topography through simple and multiple linear regression (SLR and MLR) models, ran for each hour of the period 2014–2021, to explain spatial patterns of air temperature measured by a dense network. The SLR models reveal a complementary influence of topography and land cover, with the largest influence during daytime and nighttime, respectively. The MLR significantly improves upon the SLR models despite persistent intensity errors at night and spatial errors in the early morning. Topography influences air temperatures all year round, with temperature decreasing with height during the day and frequent thermal inversions at night (up to 30% of the time). Impervious surfaces are more influential in summer and early fall, especially during the late afternoon for the fraction covered by buildings and during the early night for the distance from the city centre. They contribute to increase air temperature close to the city centre and where the fraction covered by buildings is large. By contrast, vegetation contributes to cool air temperature during the night, especially in spring and early summer for field crops, summer and early fall for forests, and late fall and winter for low vegetation. Our framework proves to be a low-cost and efficient way to assess how strongly and how recurrently the static surface conditions influence air temperature along the annual and diurnal cycles. It is easily transposable to other areas and study fields.

Canicules et fortes chaleurs induisent un stress thermique potentiellement accru en milieu urbain. Nous examinons ici la combinaison de ces différents éléments à Dijon, à partir d’un réseau dense de stations avec des mesures horaires sur la période 2014-2021. Pour cela, nous mettons en œuvre une analyse (i) de la circulation atmosphérique synoptique et locale et (ii) des déterminants géophysiques (occupation du sol et topographie). Les cinq canicules détectées persistent 4 à 5 jours et sont associées à des situations de blocage atmosphérique de large échelle favorisant le développement d’inversions thermiques. Sur les 24 nuits étudiées : 60% sont caractérisées par un Îlot de Chaleur Urbain (ICU) excédant +3°C, une inversion thermique souvent supérieure à 0,5°C/100 m et un vent faible (<2 m/s); 30% par un ICU plafonnant à +2°C, un gradient adiabatique et un vent non négligeable (>2 m/s); 10% par un faible ICU, une faible inversion thermique et des conditions de vent variables. Des statistiques comparables sont obtenues par jours de fortes chaleurs (105 jours). Canicules et fortes chaleurs sont associées à deux structures contrastées en fonction des conditions de vent. Un vent non négligeable (>2 m/s) contribue à ventiler l’excès de chaleur de la ville et à limiter le contrôle de la topographie. En résultent des températures très homogènes sur l’ensemble de l’aire d’étude. Au contraire, un vent faible (<2 m/s) maximise le contrôle de l’occupation du sol et de la topographie sur la température de l’air. En résulte un excès de chaleur en ville. La plaine, à l’est, est relativement plus fraîche que le plateau à l’ouest, de même qu’un axe traversant l’agglomération le long du talweg et du cours d’eau (vallée de l’Ouche). Cet axe frais naturel limite l’ICU ou, a minima, favorise de relatifs Îlots de Fraîcheur Urbains nocturnes. Cette étude montre la pertinence de l’analyse combinée d’un réseau de mesures de la température de l’air, de la circulation atmosphérique et des descripteurs géophysiques pour mettre à jour les déterminants de la température de l’air et la spatialiser.

Fifty per cent of European peatlands are in a damaged state. While intact peatlands are natural carbon sinks, degraded sites release important amounts of greenhouse gases into the atmosphere, contributing to global warming. Restoration of the hydrological functionality of peatlands has proved to be an efficient tool to avoid these emissions. In France, Tuffnell & Bignon's ministerial report (2019) emphasized the need for peatlands 'integration into the National Low Carbon Strategy, targeting carbon neutrality by 2050. However, current knowledge regarding French peatlands' distribution and carbon stocks is insufficient and does not allow decision makers and managers to prioritize areas for restoration. The most complete database to date is the 1949 Atlas, an inventory of exploitable peat deposits that was conducted during WWII for peat exploitation as fuel. Until its digitalization, the latter database was archived and never used in a scientific study. It provides detailed information about peatland surfaces, peat thicknesses and carbon contents at that time. We estimated peat carbon stocks from French peatlands to be 111 Mt C in 1949 for 63,290 ha identified as peaty sites, the equivalent of 3% of the organic carbon contained in the upper 30 centimetres of French soils. 34% of this stock was held in Lower Normandy (37.7 Mt C) and 12% in the Picardy's region (13.0 Mt C), in large lowland peatlands. However, not all peatlands were prospected in the 1949 inventory and the characteristics of the prospected peatlands may have changed with anthropic disturbances of the last decades, such as draining or climate change. These first results highlight the need for a recent inventory of French peatlands and carbon stocks based on local data aggregation. Data from the 1949 Atlas could help constituting this new inventory but should be validated before being used to describe the present.

Our database comprises daily minimum and maximum temperatures observed over 10 years at 859 pairs of meteorological stations throughout France. Each pairing associates a low and a high station. The influence of six predictors on the intensity, frequency, and duration of temperature inversions is measured by linear regressions. Five predictors are drawn from a 250 m-resolution DTM: elevation, depth of the valley where the low stations are located, magnitude of positive relief (ridge, hills), gradient of the slope of the hill or mountainside, and altitudinal amplitude between the high and the low station. The sixth descriptor used is the distance to the nearest sea. Topography exerts a major influence over the formation of thermal inversions. Three of the descriptors account for more than 80% of the variance of the inversion characters: distance to the sea, valley depth, and altitudinal amplitude. Elevation explains only 24% of that variance. The spatial distribution of the three characteristics of the inversions highlights several categorizations that fit into several nested scales. The 859 sites can be arranged into three classes relating to mountains, coastal areas, and plateaus. However, their distribution over the area under consideration is unclear and fails to indicate sharply delimited groupings.

This study analyses mobile measurements of urban temperatures in Dijon (eastern France) to quantify the influence of urban form on the micro-scale variability of air temperature. A route was ridden identically on 33 spring and summer evenings on a bike fitted out with measuring instruments (VeloClim). These evenings followed sunny calm days conducive to the formation of thermal contrasts and urban heat islands (UHIs). Two typologies, Corine Land Cover (CLC) and Local Climate Zones (LCZ), are used to assess the impact of urban form and land cover on air temperatures based on ANalysis Of VAriance (ANOVA). ANOVA is applied to the mean of runs to maximize the effect of surface states, and to each run individually to maximize the influence of weather conditions. The results show that both typologies prove relevant and complementary for studying the impact of vegetated and artificialized zones on urban temperature. Temperature variations on intra-urban scales are significantly modulated by urban form and land cover types. Vegetated areas are systematically cooler than impervious surfaces. Independently of meteorological conditions, urban form has a decisive influence on air temperature and each CLC or LCZ category has an original air temperature signature.

Soil Water Content (SWC) plays a key role in hydrological processes at the catchment scale. It is highly dependent on local variables (such as soil properties, vegetation type, and topography) and rainfall, which makes it vary in space and time. To characterize its complexity, scientists need SWC monitoring at short time-steps and at different spatial scales (plot, hillslope, sub-catchment). Today such monitoring is possible due to sensors based on geophysical methods that measure SWC at the plot scale, but assessing the space-time variation of SWC remains a challenge in mountainous catchments. This study focuses on a small headwater catchment (Laval, 0.86 km(2), Draix-Bleone observatory) composed primarily of black marls in which soil water content variations are assumed to occur primarily in the subsurface (0 cm-50 cm). To characterize the catchment inner hydrological response and to identify parameters controlling hydrological processes, 14 capacitance sensors were set up to monitor SWC at a 15 min time-step at depths of 10 cm to 20 cm; this network complements classic measurements (rainfall, outflows, erosion) in various soil-landscape-hydrological units. The model LISDQS (Interpolation of Quantitative and Spatial Data) based on local topographic variables (from a 1 m resolution Digital Elevation Model) (DEM), Normalized Difference Vegetation Index (NDVI), and soil depth made it possible to estimate SWC at the plot scale and at different time scales. Estimated SWC were compared to SWC time-series collected since May 2016. Finally, this method points out how local parameters that control SWC affect this variable according to rainfall and the catchment initial soil hydric status. At the hourly and daily time scales, SWC variations were highly dependent on curvature index and NDVI during wetting period, and primarily on soil depth and NDVI during drying periods. At 15 min time steps, 7 rainfall-runoff events have been investigated. SWC variations were highly dependent on rainfall intensity and initial SWC. At the same time step, matrix flow was characterized as the main subsurface flow type during fall flood events in every monitored environment while preferential flow appeared during summer flood events and mainly in grassland.

Public health institutions need high-resolution next-day forecasts so they can order appropriate measures when there is a risk of air pollution exceeding regulatory thresholds. The MOCAGE model, the chemistry transport model developed by Météo-France, forecasts hourly surface PM 10 concentrations at a resolution of 0.1° throughout France (7.6 km). To obtain more efficient forecasts, a downscaling method is applied using topographic data (250-m resolution) and inventory data (2.2 km). All these disparate inputs are spatially standardized in a geographical information system to construct continuous daily fields at 250-m resolution. This method is suitable for large territories with widely varying environments (mountains, lowlands, coastlinessnap, urban areas, etc.) and areas with a low density of monitoring stations. The parameters used to improve MOCAGE forecasts are derived from “global” and “local” regressions describing the links between the daily PM 10 concentration averages collected at 325 monitoring stations and seven explanatory variables (three topographic and four emission-inventory variables). One of the main results shows that the topographic and emission variables, respectively, explain 6% and 13% of PM 10 variance in France. Analysis by local regression accounts for 74% of the spatial variation of PM 10 concentration, while the global regression accounts for 49%. The results show above all that if the authorities responsible for human health protection had used the downscaling method instead of MOCAGE raw forecasts in 2016, they would have informed or alerted ten times as many people about the information and recommendation threshold (50 μg m −3 ) and alert threshold (80 μg m −3 ) being exceeded.

This analysis of the frequency, intensity, and duration of thermal inversions is based on daily minimum (tn) and maximum (tx) temperatures recorded over 3 years at 16 pairs of data loggers located under forest cover in the Jura Mountains of France. Each pair consists of a logger located at the bottom of a depression and another located higher up either nearby (local site) or more than 40 km away (regional site). The daily frequency of inversions is maximum at local sites for tn (50%) and minimum for tx at regional sites (4%). The maximum intensity of the inversions reaches 15.1 °C for tn and 16.2 °C for tx. The average intensity is about 2 °C: 1.5 °C for tx at local sites and 2.4 °C at regional sites. The duration of inversions is generally short: 60% of them last less than a day. Of the inversions that last for more than 1 day, 15% exceed 3 days and the maximum duration observed is 22 days. The relationship between the diurnal amplitude of temperature and the frequency, intensity, and duration of inversions indicates that mesoscale atmospheric conditions directly influence inversions.

Mountains are a sensitive indicator of climate change and these areas are an early glimpse of what could happen in lowland environments. Peaking at 4808 m asl, the Mont-Blanc summit, at the boundary between France and Italy, is the highest of the Alps, in Western Europe. Its Massif is world-famous for outdoor and extreme sport activities, especially since the 1924 Olympic games held in Chamonix. Here, we use a novel statistical downscaling approach to regionalize current and future climate change over the Mont-Blanc Massif at an unequalled spatial resolution of 200 m. The algorithm is applied to daily minimum and maximum temperature derived from global climate models used in the fifth assessment report of the International Panel on Climate Change (IPCC). This new high-resolution database allows for a precise quantification of frost occurrence and its evolution until 2100. In the winter season and by the end of the 21 st century, under a pessimistic scenario (RCP8.5), frost frequency in the morning could decrease by 30–35 percentage points in the valley of Chamonix, and in the afternoon, similar changes could occur for elevations comprised between 2000 and 3000 m. In summertime, changes are even larger, reaching a huge drop of 45–50 points in the afternoon between 3500 and 4500 m. These changes are much reduced under an optimistic scenario. They could have huge impacts on the environment (glacier shrinking, permafrost degradation, floods, changes in the distribution of species and ecosystems) and societies (summer tourism for climbing and hiking, and winter tourism for skiing).

The Arctic region has experienced significant warming during the past two decades with major implications on the cryosphere. The causes of Arctic amplification are still an open question within the scientific community, attracting recent interest. The goal of this study is to quantify the contribution of atmospheric circulation on temperature variability in the Atlantic–Arctic region at decadal to intra‐annual timescales from 1951 to 2014. Daily 20th Century reanalyses geopotential height anomalies at 500 hPa were clustered into different weather regimes to assess their contribution to observed temperature variability. The results show that in winter, 25% of the warming (cooling) in the North Atlantic Ocean (northeastern Canada) is due to temporal decreases of high geopotential anomalies in Greenland. This regime influences air mass migration patterns, bringing less cold (warm) air masses into these regions. Additionally, atmospheric warming or cooling has been attributed to a change in nearby oceanic basin surface conditions because of sea ice decline. In summer, about 15% of the warming observed in Norwegian/Greenland Seas is related to an increase in temporal anticyclonic patterns. This ratio reaches 37% in Norway due to an amplification from downwards solar radiation. This study allows for better understanding how natural climate variability modulates the regional signature of climate change and estimating the uncertainties in climate projections.

Temperature is a driving climate variable for grapevine development and grape ripening kinetics. The current study first reports interpolation of daily minimum and maximum temperature data by a weather station network from 2001 to 2005 in the Bordeaux (France) region by means of regression kriging using terrain, satellite and land-cover derived covariates. Second it analyses the interpolation procedure errors in agroclimatic indices by means of cross validation and then it compares the field observations of grapevine phenology to temperature-based predicted phenology applied to interpolated data. Finally it proposes a simple method to perform a zoning of Bordeaux vineyards based upon the spatialized prediction of the day on which grape sugar content reaches 200 g.L-1. The zoning performed shows large potential differences in grape maturity date (up to 20 days) induced by temperature spatial variability in a low relief area.

Sixteen temperature measurement sites under forest cover are distributed across the plateaus and mountains of the Jura (France). They are composed of pairs of stations located, one at the bottom of a topographic trough, the other at least 50 m higher in altitude. Three descriptors (station elevation, altitudinal difference (amplitude) between the two stations of each site, and topographical context) are used to explain how the frequency, intensity, and duration of inversions are spatially structured. Depending on whether one considers: 1) tn (minimum temperature) or tx (maximum temperature), 2) frequency or intensity, the sign of the correlation values changes. This reflects the fact that not all inversions can be explained in the same way. Elevation moderately explains the three characters of the inversions. Amplitude mainly explains their frequency (R = -0.83 for daily minima [tn]) and their intensity (R = 0.62 for daily maxima [tx]). The magnitude of the topographic depressions where the low stations are located mainly explains the tn inversions while the magnitude of the eminences where the high stations are located mainly explains the tx inversions. Finally, a multiple regression where the explanatory variables correspond to the topographic descriptors makes it possible to model the three inversion indicators.

As one of the most emblematic wine regions of cool climate terroir viticulture, Burgundy is endowed with a set of very specific natural features suitable to the production of high quality wines, where climate is arguably one of the main factors to profoundly influence vine physiology/phenology and grape composition. These environmental nuances have led to a wide variety of styles in Pinot noir and Chardonnay wines that have been largely acknowledged and appreciated by the international market and vitivinicultural industry. However, individual grape varieties optimum quality is known to be closely related to well-defined climate and geographical ranges. Climate change and global warming latest trends make them more susceptible to undergo modifications in terms of berry ripening processes and advancements in harvest dates due to short-term and long-term spatiotemporal fluctuations in climate variability. The impact of air temperature on grapevine development and harvest outcomes has been widely documented by the scientific community, its influence translating as quality and quantity fluctuations in space ( “terroir” effect) and time ( “vintage” effect). Through this study we aim to assess the extent of these threats by means of modelling and spatializing the regional climate variations based on 5 agricultural climatic indices: the number of days with temperatures equal or greater than 35°C (heat stress), the number of days indicating a frost risk (equal or greater than -1°C), the mid-flowering, the mid-véraison and the theoretical grape maturity (200g/l of sugar) occurrence dates. Mid-flowering, mid-véraison as well as the theoretical grape maturity were estimated through the summation of temperatures over 10°C (starting 1 st of March) based on the GFV (Grapevine Flowering Véraison) linear phenological model and were calculated for the 2 prime varieties cultivated in Burgundy (Chardonnay and Pinot noir). Daily minimum and maximum temperatures issued from a network of 64 weather stations scattered throughout the main 9 wine production subregions of Burgundy were spatially interpolated on a grid with a 75m resolution over a 41751ha area (74556 pixels). Spatial interpolations were performed at a daily time step integrating various topographical features through a regression-kriging model for the 2011-2015 period. Daily grid minimum and maximum temperatures were further used to calculate the 5 agroclimatic indices for each of the years of the study period. The entirety of the 74556 pixels were later classified at regular intervals in 6 groups which were assigned to each of the three phenological stages: “very early” , “early” , “ intermediate ”, “late” , “very late” and “variable” . The number of heat stress days as well as those presenting a frost risk were equally classified based on their occurrence as “very rare” , “rare” , “intermediate” , “frequent” and “very frequent” . The annual spatial structure of the individual classes was very similar due to temperature distribution being strongly governed by terrain features. We were able to identify observable differences between the north and the south subregions of Burgundy with a potential variation ranging from 7 to 15 days in terms of phenological and theoretical maturity occurrence dates. Côte de Nuits and Côte de Beaune vineyards indicate similar climate characteristics with early phenological timing (97% and 82% respectively of the area classified as “early” ) and little frost and heat risks. The number of days with a frost risk is a lot more elevated in the Côte Châtillonnaise and the Chablis subregions, while the number of heat stress days was larger in the subregions located in the south of Burgundy.

Vegetation in cities keeps climate warming down and improves the health of people and ecosystems while making for a pleasant urban setting. Contemporary urban planning promotes sustainable green cities. Green and blue infrastructures, which help maintain an eco-friendly environment, are the primary instruments of this movement. This paper attempts to show the relative weight of plant life (trees, grass, orchards, etc.) seen from buildings in different urban settings (local urban patterns of high or low density of buildings). Landscapes open to view are identified by combining a digital elevation model and an 11-class land-use layer (including buildings, facilities, grey infrastructures, green and blue surfaces) in a computational tool that calculates viewsheds. The results show that vegetation is very much present in urban landscapes. In high-density built areas of city centres, the landscape is varied although not open and is dominated by trees, low-rise residential buildings and grass. Grey infrastructures and bushes are also very common. In low-density built areas the rank order of objects in view is similar but the landscape is more panoramic.

As an important component of the quality of the living environment, landscape is increasingly addressed in terms of its visual dimension. In contrast to the point‐like character of in situ observations and photographic analyses, the modeling of landscape visibility from digital data has the advantage of scanning geographical space in a systematic way. However, the tools currently available for visibility modeling are limited to the mapping of viewsheds. They require complementary operations for a complete landscape assessment, including direct and easy computation of landscape metrics. Furthermore, none of those tools integrates recent technical advances to better characterize the visible landscape by tangential vision (i.e., from ground level as opposed to vision by viewshed from above). Starting from this, PixScape software proposes to integrate a large set of functions for modeling landscape visibility while remaining interfaced with GIS software. This software can be used to perform a complete landscape assessment by computing a wide range of original landscape metrics. It performs tangential analysis in addition to viewshed analysis, which can produce more realistic outcomes. Because landscape visibility analysis over large areas implies significant computation time, the software also integrates a multi‐resolution process intended to speed up calculations while also taking into account the cognitive abilities of human vision.

The association between elevation and temperature is analysed by simple linear correlations across several spatial scales. The minimum (tn) and maximum (tx) temperatures (response variables), expressed at two time scales (monthly and daily), are observed for 102 weather stations in east central France from 1980 to 2014 (12,784 days). Elevation (explanatory variable) is provided at 10 resolutions: 50, 100, 200, 500 m, 1, 2, 4, 8, 12, and 16 km. The coefficient of determination, R2, is used to determine which resolution gives the best results. The slope given by the regression is used to assess the drop in temperature per unit of elevation (temperature lapse rate [TLR]). In most situations, monthly and daily temperatures are optimally explained by the finest (50 m) resolution: the R2 is, respectively, 0.53 and 0.24 for tn and 0.78 and 0.39 for tx. The coarser resolutions produce results of much lower quality. However, in one circumstance (monthly mean of tn), the highest R2 value is obtained for the 4‐km resolution, which is a meaningful result as current regional climate models now achieve similar resolutions. Both monthly and daily TLRs of tn and tx are, on average, slightly lower than −0.5 °C/100 m at 50‐m resolution. The TLR decreases with resolution: it is only −0.23 °C/100 m for tn and −0.13 °C/100 m for tx at 16‐km resolution. Other insightful results involve the influence of the topographical context, which shows some additional effect with that of elevation and which was quantified through partial correlations.