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AbstractEuropean climate is associated with variability and changes in the mid-latitude atmospheric circulation. In this study, we aim to investigate potential future change in circulation over Europe by using the EURO-CORDEX regional climate projections at 0.11° grid mesh. In particular, we analyze future change in 500-hPa geopotential height (Gph), 500-hPa wind speed and mean sea level pressure (MSLP) addressing different warming levels of 1 °C, 2 °C and 3 °C, respectively. Simple scaling with the global mean temperature change is applied to the regional climate projections for monthly mean 500-hPa Gph and 500-hPa wind speed. Results from the ensemble mean of individual models show a robust increase in 500-hPa Gph and MSLP in winter over Mediterranean and Central Europe, indicating an intensification of anticyclonic circulation. This circulation change emerges robustly in most simulations within the coming decade. There are also enhanced westerlies which transport warm and moist air to the Mediterranean and Central Europe in winter and spring. It is also clear that, models showing different responses to circulation depend very much on the global climate model ensemble member in which they are nested. For all seasons, particularly autumn, the ensemble mean is much more correlated with the end of the century than most of the individual models. In general, the emergence of a scaled pattern appears rather quickly.

AbstractEuropean climate is associated with variability and changes in the mid-latitude atmospheric circulation. In this study, we aim to investigate potential future change in circulation over Europe by using the EURO-CORDEX regional climate projections at 0.11° grid mesh. In particular, we analyze future change in 500-hPa geopotential height (Gph), 500-hPa wind speed and mean sea level pressure (MSLP) addressing different warming levels of 1 °C, 2 °C and 3 °C, respectively. Simple scaling with the global mean temperature change is applied to the regional climate projections for monthly mean 500-hPa Gph and 500-hPa wind speed. Results from the ensemble mean of individual models show a robust increase in 500-hPa Gph and MSLP in winter over Mediterranean and Central Europe, indicating an intensification of anticyclonic circulation. This circulation change emerges robustly in most simulations within the coming decade. There are also enhanced westerlies which transport warm and moist air to the Mediterranean and Central Europe in winter and spring. It is also clear that, models showing different responses to circulation depend very much on the global climate model ensemble member in which they are nested. For all seasons, particularly autumn, the ensemble mean is much more correlated with the end of the century than most of the individual models. In general, the emergence of a scaled pattern appears rather quickly.

Les conséquences du changement climatique sont particulièrement aiguës au Kenya, où 80 % des terres sont arides ou semi-arides. Quelle perspective critique et constructive le concept d’habitabilité peut-il offrir dans ce contexte ? Fondé sur un travail de terrain réalisé au Kenya en 2022 et 2023, cet article entend affiner une définition in situ de l’habitabilité, en utilisant exclusivement des données qualitatives issues de deux phases de recherche, complétées par des exercices de vidéo-cartographie. Loin de proposer une lecture positiviste de l’habitabilité, portée par une conception exclusivement mécanique et linéaire des dynamiques de mobilité, cette étude propose de mieux relier l’habitabilité aux pratiques quotidiennes, aux habitudes, aux solidarités, voire aux résistances qui constituent la vie ordinaire des habitants des communautés et des personnes déplacées. L’habitabilité se situerait alors au cœur de l’intrication entre socialité et spatialité, entre social et biologique, qui définit – selon Stanley Cavell – la forme de vie humaine. The consequences of climate change are particularly acute in Kenya, where 80% of the land is arid or semi-arid. What critical and constructive perspective can the concept of habitability offer in this context? Based on fieldwork carried out in Kenya in 2022 and 2023, this article aims to refine an in-situ definition of habitability, using exclusively qualitative data from two research phases, supplemented by video-mapping exercises. Far from proposing a positivist reading of habitability, driven by an exclusively mechanical and linear conception of mobility dynamics, this study proposes to better link habitability to the daily practices, habits, solidarities and even resistances that make up the ordinary lives of community dwellers and displaced persons. Habitability would then lie at the heart of the intricacy between sociality and spatiality, between the social and the biological, which defines – according to Stanley Cavell – “the human form of life.”

Les conséquences du changement climatique sont particulièrement aiguës au Kenya, où 80 % des terres sont arides ou semi-arides. Quelle perspective critique et constructive le concept d’habitabilité peut-il offrir dans ce contexte ? Fondé sur un travail de terrain réalisé au Kenya en 2022 et 2023, cet article entend affiner une définition in situ de l’habitabilité, en utilisant exclusivement des données qualitatives issues de deux phases de recherche, complétées par des exercices de vidéo-cartographie. Loin de proposer une lecture positiviste de l’habitabilité, portée par une conception exclusivement mécanique et linéaire des dynamiques de mobilité, cette étude propose de mieux relier l’habitabilité aux pratiques quotidiennes, aux habitudes, aux solidarités, voire aux résistances qui constituent la vie ordinaire des habitants des communautés et des personnes déplacées. L’habitabilité se situerait alors au cœur de l’intrication entre socialité et spatialité, entre social et biologique, qui définit – selon Stanley Cavell – la forme de vie humaine. The consequences of climate change are particularly acute in Kenya, where 80% of the land is arid or semi-arid. What critical and constructive perspective can the concept of habitability offer in this context? Based on fieldwork carried out in Kenya in 2022 and 2023, this article aims to refine an in-situ definition of habitability, using exclusively qualitative data from two research phases, supplemented by video-mapping exercises. Far from proposing a positivist reading of habitability, driven by an exclusively mechanical and linear conception of mobility dynamics, this study proposes to better link habitability to the daily practices, habits, solidarities and even resistances that make up the ordinary lives of community dwellers and displaced persons. Habitability would then lie at the heart of the intricacy between sociality and spatiality, between the social and the biological, which defines – according to Stanley Cavell – “the human form of life.”

Energy management within microgrids under the presence of large number of renewables such as photovoltaics is complicated due to uncertainties involved. Randomness in energy production and consumption make both the prediction and optimality of exchanges challenging. In this paper, we evaluate the impact of uncertainties on optimality of different robust energy exchange strategies. To address the problem, we present AIROBE, a data-driven system that uses machine-learning-based predictions of energy supply and demand as input to calculate robust energy exchange schedules using a multiband robust optimization approach to protect from deviations. AIROBE allows the decision maker to tradeoff robustness with stability of the system and energy costs. Our evaluation shows, how AIROBE can deal effectively with asymmetric deviations and how better prediction methods can reduce both the operational cost while at the same time may lead to increased operational stability of the system.

Energy management within microgrids under the presence of large number of renewables such as photovoltaics is complicated due to uncertainties involved. Randomness in energy production and consumption make both the prediction and optimality of exchanges challenging. In this paper, we evaluate the impact of uncertainties on optimality of different robust energy exchange strategies. To address the problem, we present AIROBE, a data-driven system that uses machine-learning-based predictions of energy supply and demand as input to calculate robust energy exchange schedules using a multiband robust optimization approach to protect from deviations. AIROBE allows the decision maker to tradeoff robustness with stability of the system and energy costs. Our evaluation shows, how AIROBE can deal effectively with asymmetric deviations and how better prediction methods can reduce both the operational cost while at the same time may lead to increased operational stability of the system.