Constantin Ardilouze

An accurate representation of the land surface is essential for simulating the exchange of energy, water and carbon between the land and the atmosphere. This study evaluates the impact of land cover (LC) representation on snow simulations in the Interactions Between Soil, Biosphere and Atmosphere (ISBA) land surface model in Europe between 2010 and 2022. The study employs the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 atmospheric forcing dataset. Offline simulation experiments were conducted using two different versions of the model to prescribe LC. The most recent version uses the latest LC data from the European Space Agency's (ESA) Climate Change Initiative (CCI). The model's ability to reproduce snow dynamics was evaluated through a comparison of the simulations with ESA CCI satellite snow water equivalent (SWE) and land surface temperature (LST) retrievals and ERA5 snow analyses. The ERA5 analysis shows the highest level of agreement with satellite-derived SWE at the domain scale. On average, both the ERA5 and ISBA simulations tend to overestimate SWE compared to the CCI SWE. However, it is also possible that the CCI SWE product underestimates the actual SWE. This bias is particularly large during the warm winter of 2020, while the scaled SWE anomalies are comparable to those observed by ESA CCI and ERA5. Using ESA CCI LC data reduces the ISBA SWE bias by around 23 %, with this reduction being observed over most of the domain. Further analysis of LC transitions shows that the reduction in SWE bias is driven primarily by a few vegetation changes, particularly the transition from grasslands to forests. Changes in SWE are also found in areas where the dominant LC remains unchanged. This indicates that modifications in sub-grid vegetation fractions can affect snow-vegetation interactions and SWE bias. The updated LC has a very limited impact on the model's performance for land surface temperature, indicating that the impact of LC updates is more noticeable for SWE than for LST. These findings emphasise the importance of accurate land cover data for improving snow representation in land surface models and highlight the need for updated vegetation information in future snow-related applications.

An accurate representation of the land surface is essential for simulating the exchange of energy, water and carbon between the land and the atmosphere. This study evaluates the impact of land cover representation on snow simulations in the Interactions Between Soil, Biosphere and Atmosphere (ISBA) land surface model in Europe between 2010 and 2022. The study employs the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 atmospheric forcing dataset. Offline simulation experiments were conducted using two different versions of the model to prescribe land cover. The most recent version uses the latest land cover data from the European Space Agency's (ESA) Climate Change Initiative (CCI). The model's ability to reproduce snow dynamics was evaluated through a comparison of the simulations with ESA CCI satellite snow water equivalent (SWE) retrievals and ERA5 snow analyses. The ERA5 analysis shows the highest level of agreement with satellite observations of SWE at the domain scale. On average, both the ERA5 and ISBA simulations tend to overestimate SWE compared to the CCI SWE. However, it is also possible that the CCI SWE product underestimates the actual SWE. This bias is particularly large during the warm winter of 2020, while the scaled SWE anomalies are comparable to those observed by ESA CCI and ERA5. Using ESA CCI land cover data reduces the ISBA SWE bias by around 33 %, with this reduction being observed over most of the domain. These findings emphasise the importance of accurate land cover data for improving snow representation in land surface models and highlight the need for updated vegetation information in future snow-related applications.

An accurate representation of the land surface is essential for simulating the exchange of energy, water and carbon between the land and the atmosphere. This study evaluates the impact of land cover representation on snow simulations in the Interactions Between Soil, Biosphere and Atmosphere (ISBA) land surface model in Europe between 2010 and 2022. The study employs the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 atmospheric forcing dataset. Offline simulation experiments were conducted using two different versions of the model to prescribe land cover. The most recent version uses the latest land cover data from the European Space Agency's (ESA) Climate Change Initiative (CCI). The model's ability to reproduce snow dynamics was evaluated through a comparison of the simulations with ESA CCI satellite snow water equivalent (SWE) retrievals and ERA5 snow analyses. The ERA5 analysis shows the highest level of agreement with satellite observations of SWE at the domain scale. On average, both the ERA5 and ISBA simulations tend to overestimate SWE compared to the CCI SWE. However, it is also possible that the CCI SWE product underestimates the actual SWE. This bias is particularly large during the warm winter of 2020, while the scaled SWE anomalies are comparable to those observed by ESA CCI and ERA5. Using ESA CCI land cover data reduces the ISBA SWE bias by around 33 %, with this reduction being observed over most of the domain. These findings emphasise the importance of accurate land cover data for improving snow representation in land surface models and highlight the need for updated vegetation information in future snow-related applications.

Les épisodes de sécheresse et de canicule qui frappent épisodiquement les régions tempérées ont des conséquences préjudiciables sur les plans sanitaire, économique, social et écologique. Afin de pouvoir enclencher des stratégies de préparation et de prévention avec quelques semaines ou mois d'anticipation, les attentes sociétales en matière de prévision sont élevées, et ce d'autant plus que les projections climatiques font craindre la multiplication de ces épisodes au cours du 21ème siècle. Néanmoins, la saison d'été est la plus difficile à prévoir aux moyennes latitudes. Les sources connues de prévisibilité sont plus ténues qu'en hiver et les systèmes de prévision climatique actuels peinent à représenter correctement les mécanismes de téléconnexion associés. Un nombre croissant d'études a mis en évidence un lien statistique dans certaines régions entre l'humidité du sol au printemps et les températures et précipitations de l'été qui suit. Ce lien a été partiellement confirmé dans des modèles numériques de climat mais de nombreuses interrogations subsistent. L'objectif de cette thèse est donc de mieux comprendre le rôle joué par l'humidité du sol sur les caractéristiques et la prévisibilité du climat de l'été dans les régions tempérées. Grâce notamment au modèle couplé de circulation générale CNRM-CM, nous avons mis en œuvre des ensembles de simulations numériques qui nous ont permis d'évaluer le degré de persistance des anomalies d'humidité du sol printanière. En effet, une longue persistance est une condition nécessaire pour que ces anomalies influencent le climat à l'échelle de la saison, via le processus d'évapotranspiration de la surface. En imposant dans notre modèle des conditions initiales et aux limitées idéalisées d'humidité du sol, nous avons mis en évidence des régions du globe pour lesquelles l'état moyen et la variabilité des températures et des précipitations en été sont particulièrement sensibles à ces conditions. C'est notamment le cas sur une grande partie de l'Europe et de l'Amérique du nord, y compris à des latitudes élevées. Pour toutes ces régions, l'humidité du sol est une source prometteuse de prévisibilité potentielle du climat à l'horizon saisonnier, bien que de fortes incertitudes demeurent localement sur le degré de persistance de ses anomalies. Une expérience de prévisibilité effective coordonnée avec plusieurs systèmes de prévision montre qu'une initialisation réaliste de l'humidité du sol améliore la prévision de températures estivales principalement dans le sud-est de l'Europe. Dans d'autres régions, comme l'Europe du Nord, le désaccord des modèles provient de l'incertitude sur la persistance des anomalies d'humidité du sol. En revanche, sur les Grandes Plaines américaines, aucun modèle n'améliore ses prévisions qui restent donc très médiocres. La littérature ainsi que nos évaluations de sensibilité du climat à l'humidité du sol ont pourtant identifié cette région comme un "hotspot" du couplage entre l'humidité du sol et l'atmosphère. Nous supposons que l'échec de ces prévisions est une conséquence des forts biais chauds et secs présents dans tous les modèles sur cette région en été, qui conduisent à un dessèchement excessif des sols. Pour le vérifier, nous avons développé une méthode qui corrige ces biais au cours de l'intégration des prévisions avec CNRM-CM6. Les prévisions qui en résultent sont nettement améliorées sur les Grandes Plaines. La compréhension de l'origine des biais continentaux en été et leur réduction dans les prochaines générations de modèles de climat sont des étapes essentielles pour tirer le meilleur parti de l'humidité du sol comme source de prévisibilité saisonnière dans les régions tempérées.

An accurate representation of the land surface is essential for simulating the exchange of energy, water and carbon between the land and the atmosphere. This study evaluates the impact of land cover representation on snow simulations in the Interactions Between Soil, Biosphere and Atmosphere (ISBA) land surface model in Europe between 2010 and 2022. The study employs the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 atmospheric forcing dataset. Offline simulation experiments were conducted using two different versions of the model to prescribe land cover. The most recent version uses the latest land cover data from the European Space Agency's (ESA) Climate Change Initiative (CCI). The model's ability to reproduce snow dynamics was evaluated through a comparison of the simulations with ESA CCI satellite snow water equivalent (SWE) retrievals and ERA5 snow analyses. The ERA5 analysis shows the highest level of agreement with satellite observations of SWE at the domain scale. On average, both the ERA5 and ISBA simulations tend to overestimate SWE compared to the CCI SWE. However, it is also possible that the CCI SWE product underestimates the actual SWE. This bias is particularly large during the warm winter of 2020, while the scaled SWE anomalies are comparable to those observed by ESA CCI and ERA5. Using ESA CCI land cover data reduces the ISBA SWE bias by around 33 %, with this reduction being observed over most of the domain. These findings emphasise the importance of accurate land cover data for improving snow representation in land surface models and highlight the need for updated vegetation information in future snow-related applications.

Leaf area index (LAI) regulates land–atmosphere exchanges and influences evapotranspiration (ET), seasonal water storage, and streamflow, but, in hydrological modelling, the value of constraining LAI with observations at the global scale remains poorly quantified at the basin scale. We assess the impact of assimilating satellite-derived LAI (GEOV2-AVHRR) in the global land data assimilation system LDAS-Monde coupled to ISBA-CTRIP over 1982–2017, evaluated against observation-based products and discharge records from 2139 gauged basins. Basins are grouped into hydro-biospheric regimes using observation-based descriptors to interpret regime-dependent responses. LAI assimilation improves vegetation climatology and phenology and yields spatially coherent gains in ET skill, alongside robust reductions in errors of snow water equivalent variations (ΔSWE) across regimes. Streamflow improvements are more modest and heterogeneous—consistent with unchanged precipitation forcing and compensating errors among flux and storage terms—but discharge biases are reduced in several regimes. Overall, LAI assimilation provides a practical pathway to improve key water-cycle components (ET and ΔSWE) and to deliver incremental, process-consistent benefits for basin-scale streamflow realism.

Soil moisture plays a critical role in water-limited regions through its strong coupling and feedbacks with vegetation. However, state-of-the-art Land Surface Models (LSMs) used in reanalysis and near-term prediction systems still lack a realistic coupling of vegetation, limiting their ability to properly account for the fundamental role of vegetation in modulating the feedback with soil–moisture.In this study, we incorporate Leaf Area Index (LAI) variability from observations - derived from the latest-generation satellite products provided by the Copernicus Land Monitoring Service - into three different LSMs. The models perform a coordinated set of offline, land-only simulations forced by hourly atmospheric fields from the ERA5 reanalysis. An experiment using interannually varying LAI (SENS) is compared with a control simulation based on climatological LAI (CTRL) in order to quantify vegetation feedbacks and their impact on simulated near-surface soil moisture.Our results show that interannually varying LAI substantially affects near-surface soil moisture anomalies across all three models and over the same water-limited regions. However, the response differs markedly among models. Compared with ESA-CCI observations, near-surface soil moisture anomalies significantly improve in one model (HTESSEL–LPJ-GUESS), whereas the other two models (ECLand and ISBA–CTRIP) exhibit a significant degradation in anomaly correlation. The improved performance in HTESSEL–LPJ-GUESS is attributed to the activation of a positive soil moisture–vegetation feedback enabled by its effective vegetation cover (EVC) parameterization. In HTESSEL–LPJ-GUESS, EVC varies dynamically with LAI following an exponential relationship constrained by satellite observations. Enhanced (reduced) soil moisture limitation during dry (wet) periods leads to negative (positive) LAI and EVC anomalies, which in turn generate a dominant positive feedback on near-surface soil moisture by increasing (decreasing) bare-soil exposure to direct evaporation from the surface. In contrast, ECLand and ISBA–CTRIP prescribe EVC as a fixed parameter that does not respond to LAI variability, preventing the activation of this positive feedback. In these models, the only active feedback on near-surface soil moisture anomalies is negative and arises from reduced (enhanced) transpiration associated with negative (positive) LAI anomalies.Our findings demonstrate that simply prescribing observed vegetation properties in LSMs does not guarantee a realistic coupling between vegetation and soil moisture. Instead, it is shown that the explicit representation of the underlying vegetation processes is essential to activate the proper feedback and capture the correct soil moisture response.

This review paper examines the development and application of climate predictions, primarily at seasonal timescales, as actionable information for decision‐making, with a specific focus on the Mediterranean region. It illustrates and analyses the steps required to transform climate model probabilistic forecasts into user‐defined information, emphasising the iterative nature of climate service development. By leveraging tools such as bias adjustment, downscaling, probabilistic calibration and impact models, these services provide tailored solutions to key sectors including energy, water supply, transport and agriculture. The paper discusses the contributions of global initiatives, such as the Global Framework for Climate Services (GFCS), and EU‐funded projects like EUPORIAS, MEDSCOPE and MED‐GOLD, which have advanced sector‐specific applications of climate predictions in the region. These initiatives illustrate how tailored climate services can address critical challenges ranging from renewable energy planning to drought risk mitigation and agricultural yield forecasting. The review highlights persistent challenges, including limited forecasting skill in the Mediterranean area, data accessibility issues and the need for robust impact‐based verification. To enhance the uptake and effectiveness of climate services, the review recommends fostering interdisciplinary collaboration, promoting iterative co‐production with users, improving the communication of uncertainty and forecast skill, and ensuring the sustainability of services beyond project lifetimes. This work underscores the potential of climate services to enhance resilience in the Mediterranean region, bridging the gap between scientific advances and practical applications.

An accurate representation of the land surface is essential for simulating the exchange of energy, water and carbon between the land and the atmosphere. This study evaluates the impact of land cover representation on snow simulations in the Interactions Between Soil, Biosphere and Atmosphere (ISBA) land surface model in Europe between 2010 and 2022. The study employs the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 atmospheric forcing dataset. Offline simulation experiments were conducted using two different versions of the model to prescribe land cover. The most recent version uses the latest land cover data from the European Space Agency's (ESA) Climate Change Initiative (CCI). The model's ability to reproduce snow dynamics was evaluated through a comparison of the simulations with ESA CCI satellite snow water equivalent (SWE) retrievals and ERA5 snow analyses. The ERA5 analysis shows the highest level of agreement with satellite observations of SWE at the domain scale. On average, both the ERA5 and ISBA simulations tend to overestimate SWE compared to the CCI SWE. However, it is also possible that the CCI SWE product underestimates the actual SWE. This bias is particularly large during the warm winter of 2020, while the scaled SWE anomalies are comparable to those observed by ESA CCI and ERA5. Using ESA CCI land cover data reduces the ISBA SWE bias by around 33 %, with this reduction being observed over most of the domain. These findings emphasise the importance of accurate land cover data for improving snow representation in land surface models and highlight the need for updated vegetation information in future snow-related applications.

European droughts have far-reaching socio-economic and ecological impacts, yet their prediction remains challenging due to the complex interplay between large-scale atmospheric circulation patterns and local thermodynamic processes. This study investigates how persistent North Atlantic weather regimes influence the occurrence and spatial distribution of seasonal meteorological droughts across Europe throughout the year. Using reanalysis datasets and a tailored regionalization based on drought synchronicity, we identify six coherent European sub-regions and relate drought events, defined by the standardized precipitation index (SPI3), to year-round weather regimes derived from 500 hPa geopotential height anomalies. Our analysis shows that while each weather regime exhibits distinctive and relatively stable precipitation patterns throughout the year, only a fraction of droughts – primarily in western Europe and during winter – can be directly attributed to anomalies in regime frequency. The findings underline the partial but regionally significant role of North Atlantic circulation patterns in shaping European drought risk, highlighting opportunities and limitations for improving drought forecasts on sub-seasonal to seasonal timescales. This study emphasizes the added value of using year-round weather regimes as a unifying framework for understanding drought drivers, overcoming the constraints of purely seasonal classifications. Improved understanding of these links could help refine climate models and support more robust early-warning systems for drought management across diverse European climates.