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The article explores the potential use of climate models to reproduce wine grape productivity at a local scale in Italy. To this end, both single and multiple regression approaches are used to link productivity data provided by two Italian wine consortia with bioclimatic indices. Temperature- and precipitation-based bioclimatic indices are computed using the observational dataset E-OBS, the high-resolution climate reanalysis product SPHERA, the regional climate model CNRM-ALADIN, and the kilometer-scale convection-permitting climate model CNRM-AROME. The multiple regression method outperforms the single regression systematically, enhancing the ability of bioclimatic indices to explain productivity variability. The results show that productivity is strongly tied to temperature-based bioclimatic indices in the area of the Consorzio per la tutela del Franciacorta in northern Italy, while for the Consorzio del Vino Nobile di Montepulciano area in central Italy both temperature- and precipitation-based indices are relevant. Climate models, providing similar results as E-OBS and SPHERA, appear to be a useful tool to explain productivity variance. In particular, the added value of convection-permitting resolution is evident when precipitation-based indices are considered. This assessment shows windows of opportunity for using climate models, especially at a convection-permitting scale, to investigate future climate change impact on wine production. [ABSTRACT FROM AUTHOR]

Snow cover modeling remains a major challenge in climate and numerical weather prediction (NWP) models even in recent versions of high-resolution coupled surface–atmosphere (i.e., at kilometer scale) regional models. Evaluation of recent climate simulations, carried out as part of the WCRP-CORDEX Flagship Pilot Study on Convection (FPSCONV) with the CNRM-AROME convection-permitting regional climate model at 2.5 km horizontal resolution, has highlighted significant snow cover biases, severely limiting its potential in mountain regions. These biases, which are also found in AROME numerical weather prediction (NWP) model results, have multiple causes, involving atmospheric processes and their influence on input data to the land surface models in addition to deficiencies of the land surface model itself. Here we present improved configurations of the SURFEX-ISBA land surface model used in CNRM-AROME. We thoroughly evaluated these configurations on their ability to represent seasonal snow cover across the European Alps. Our evaluation was based on coupled simulations spanning the winters of 2018–2019 and 2019–2020, which were compared against remote sensing data and in situ observations. More specifically, the study tests the influence of various changes in the land surface configuration, such as the use of multi-layer soil and snow schemes, the division of the energy balance calculation by surface type within a grid cell (multiple patches), and new physiographic databases and parameter adjustments. Our findings indicate that using only more detailed individual components in the surface model did not improve the representation of snow cover due to limitations in the approach used to account for partial snow cover within a grid cell. These limitations are addressed in further configurations that highlight the importance, even at kilometer resolution, of taking into account the main subgrid surface heterogeneities and improving representations of interactions between fractional snow cover and vegetation. Ultimately, we introduce a land surface configuration, which substantially improves the representation of seasonal snow cover in the European Alps in coupled CNRM-AROME simulations. This holds promising potential for the use of such model configurations in climate simulations and numerical weather prediction both for AROME and other high-resolution climate models. [ABSTRACT FROM AUTHOR]

Viticulture is tied to climate, it influences the suitability of an area, yield and quality of wine grapes. Therefore, traditional wine-growing regions could be threatened by a changing climate. Italy is at-risk being part of the Mediterranean climatic hotspot and judged in 2022 the second-largest exporter of wine worldwide. The article explores the potential of climate models to predict wine grape productivity at local scale. To this end, both single and multi-regression approaches are used to link productivity data provided by two Italian wine consortia with bioclimatic indices. Temperature and precipitation-based bioclimatic indices are computed by using the observational dataset E-OBS, the high-resolution climate reanalysis product SPHERA, and both the Regional and the Convection-permitting Climate Model (RCM and CPM). The potential of CPMs to represent the impact of climate variability on wine grape productivity at local scale in Italy is evaluated. The results indicate high correlations between some bioclimatic indices and productivity. Climate models appear to be a useful tool to explain productivity variance, however, the added value of CPM, became evident only when precipitation-based indices are considered. This assessment opens the path for using climate models, especially at convection-permitting scale, to investigate future climate change impact on wine production. [ABSTRACT FROM AUTHOR]

Taking advantage of a large ensemble of Convection Permitting‐Regional Climate Models on a pan‐Alpine domain and of an object‐oriented dedicated analysis, this study aims to investigate future changes in high‐impact fall Mediterranean Heavy Precipitation Events at high warming levels. We identify a robust multi‐model agreement for an increased frequency from central Italy to the northern Balkans combined with a substantial extension of the affected areas, for a dominant influence of the driving Global Climate Models for projecting changes in the frequency, and for an increase in intensity, area, volume and severity over the French Mediterranean. However, large quantitative uncertainties persist despite the use of convection‐permitting models, with no clear agreement in frequency changes over southeastern France and a large range of plausible changes in events' properties, including for the most intense events. Model diversity and international coordination are still needed to provide policy‐relevant climate information regarding precipitation extremes. Plain Language Summary: Despite growing computational resources and multiple model developments, projecting future changes in the high‐impact Mediterranean Heavy Precipitation Events remains both a numerical and scientific challenge. The present study takes advantage of the recent availability of a relatively large ensemble of high resolution Regional Climate Models (2–3 km), which represent a step change in the simulation of precipitation extremes, and of an object‐oriented approach, allowing us to track the convective precipitating systems on an hourly basis. Looking at future changes in fall Mediterranean Heavy Precipitation Events at high warming levels, we identify a robust multi‐model agreement for an increased frequency from central Italy to the northern Balkans combined with a substantial expansion of the affected areas, and an increase in intensity, area, volume and severity over the French Mediterranean. However, considerable uncertainties remain in terms of frequency over parts of the domain arising from uncertainty in changes in large scale weather patterns, and in terms of degree of intensification for the most intense events. It suggests the need for model diversity and for more coordinated high resolution climate projections with careful selection of different driving global models in order to provide policy‐relevant climate information regarding precipitation extremes. Key Points: High‐resolution ensemble and object‐oriented approach offer a unique opportunity to study changes in Mediterranean extreme precipitationRobust agreement is found for an increase in intensity, volume and severity for future French Mediterranean Heavy Precipitation EventsEven at convection‐permitting scale, considerable uncertainty remains regarding the degree of intensification of the most extreme events [ABSTRACT FROM AUTHOR]

Snow cover modeling remains a major challenge in climate and numerical weather prediction (NWP) models, even in recent versions of high-resolution coupled surface-atmosphere (i.e. at km-scale) regional models. Evaluation of recent climate simulations, carried out as part of WCRP-CORDEX Flagship Pilot Study on Convection with the CNRM-AROME convection permitting regional climate model at 2.5 km horizontal resolution, has highlighted significant snow cover biases, severely limiting its potential in mountain regions. These biases, which are also found for AROME NWP model results, have multiple causes, involving atmospheric processes and their influence on input data to the land surface models, in addition to deficiencies of the land surface model itself. Here we present improved configurations of the SURFEX-ISBA land surface model used in CNRM-AROME. We thoroughly evaluated these configurations on their ability to represent seasonal snow cover across the European Alps. Our evaluation was based on coupled simulations spanning the winters of 2018–2019 and 2019–2020, which were compared against remote sensing data and in situ observations. Specifically, the study tests the influence of various changes to the land surface configuration, such as using a multi-layer soil and snow scheme, multiple patches for land surface grid cells, new physiographic databases, and parameter adjustments. Our findings indicate that using more physically detailed individual components in the surface model using only one patch did not improve the representation of snow cover due to limitations in the approach used to account for partial snow cover within a grid cell. To address these limitations, we evaluated further configurations using three patches and improved representations of the interactions between fractional snow cover and vegetation. At the end, we introduce a land surface configuration that substantially improved the representation of seasonal snow cover in the European Alps. This holds promising potential for the use of such model configurations in climate simulations and numerical weather prediction, including AROME and other high-resolution climate models. [ABSTRACT FROM AUTHOR]