Observed by satellites for more than a decade, surface soil moisture (SSM) is an essential component of the Earth system. Today, with the Sentinel missions, SSM can be derived at a sub-kilometer spatial resolution. In this work, aggregated 1 km × 1 km SSM observations combining Sentinel-1 (S1) and Sentinel-2 (S2) data are assimilated for the first time into the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface model using the global Land Data Assimilation System (LDAS-Monde) tool of Meteo-France. The ISBA simulations are driven by atmospheric variables from the Application of Research to Operations at Mesoscale (AROME) numerical weather prediction model for the period 2017–2019 for two regions in Southern France, Toulouse and Montpellier, and for the Salamanca region in Spain. The S1 SSM shows a good agreement with in situ SSM observations. The S1 SSM is assimilated either alone or together with leaf area index (LAI) observations from the PROBA-V satellite. The assimilation of S1 SSM alone has a small impact on the simulated root zone soil moisture. On the other hand, a marked impact of the assimilation is observed over agricultural areas when LAI is assimilated, and the impact is larger when S1 SSM and LAI are assimilated together.

Local short-term temperature variations at the surface are mainly dominated by small-scale processes coupled through the surface energy balance terms, which are well known but whose specific contribution and importance on the hourly scale still need to be further analyzed. A method to determine each of these terms based almost exclusively on observations is presented in this paper, with the main objective being to estimate their importance in hourly near-surface temperature variations at the SIRTA observatory, near Paris. Almost all terms are estimated from the multi-year dataset SIRTA-ReOBS, following a few parametrizations. The four main terms acting on temperature variations are radiative forcing (separated into clear-sky and cloudy-sky radiation), atmospheric heat exchange, ground heat exchange, and advection. Compared to direct measurements of hourly temperature variations, it is shown that the sum of the four terms gives a good estimate of the hourly temperature variations, allowing a better assessment of the contribution of each term to the variation, with an accurate diurnal and annual cycle representation, especially for the radiative terms. A random forest analysis shows that whatever the season, clouds are the main modulator of the clear-sky radiation for 1 h temperature variations during the day and mainly drive these 1 h temperature variations during the night. Then, the specific role of clouds is analyzed exclusively in cloudy conditions considering the behavior of some classical meteorological variables along with lidar profiles. Cloud radiative effect in shortwave and longwave and lidar profiles show a consistent seasonality during the daytime, with a dominance of mid- and high-level clouds detected at the SIRTA observatory, which also affects near-surface temperatures and upward sensible heat flux. During the nighttime, despite cloudy conditions and having a strong cloud longwave radiative effect, temperatures are the lowest and are therefore mostly controlled by larger-scale processes at this time.

The objective of this work is to assess the potential of the assimilation of satellite solar‐induced fluorescence (SIF) retrievals at eight‐day and 0.1° resolutions, derived from the TROPOMI instrument on board Copernicus Sentinel‐5P, in the integrated forecast system (IFS) developed at the European Centre for Medium‐Range Weather Forecasts (ECMWF), to update the IFS leaf area index (LAI) climatology and improve the IFS forecasts of gross primary productivity (GPP). This work represents a first attempt to assimilate SIF in a numerical weather prediction (NWP) framework at global scale by leveraging a machine‐learning (ML)‐based observation operator. Two configurations of the ML‐based observation operator, based on the XGBoost gradient‐boosted trees technique, were assessed: one trained using the IFS model fields which include the IFS LAI climatology, and one trained using only the actual satellite LAI observations along with spatiotemporal localization variables. The model directly trained on the satellite LAI shows the highest prediction scores. Besides, it produces more meaningful spatial patterns of LAI increments in response to climate anomalies compared to the model trained using the IFS predictors which do not include vegetation interannual variability. A key outcome is that the assimilation of SIF in the IFS improves LAI over cropland, which is promising for the monitoring of anthropogenic emissions over agriculture regions. However, at these spatial and temporal resolutions, the SIF ML model mainly resolves the seasonal variability of LAI which explains the degradations of LAI over tropical rainforests where the SIF signal is mainly driven by the variability in light use efficiency and the satellite LAI retrievals are more uncertain. Finally, the use of the updated LAI in the IFS coupled model leads to limited improvement of GPP forecasts, which is likely due to the prevailing effects of other sources of biases in the coupled IFS model.

Biogenic volatile organic compound (BVOC) emissions from vegetation represent a major source of volatile compounds globally and play an important role as precursors for tropospheric ozone. Understanding their emissions is therefore crucial for quantifying the impact of ozone on air quality. We present two datasets of biogenic volatile organic compound emissions that cover the European modelling domain of the Copernicus Atmospheric Monitoring Service at a resolution of 0.1° × 0.1° to support the study of European scale air quality. The compounds included in the dataset follow the VOCs included in the regional atmospheric chemistry model mechanism (RACM). The datasets were produced within the framework of the EU's SEEDS project. We produced each dataset by coupling modelling output variables from the SURFEX land surface model with the MEGAN3.0 BVOC emission model. In one instance, the SURFEX model was run in free-running mode, which we term the open-loop (OL) and in the other case we assimilated satellite observations of leaf area index (LAI), which we term the analysis. The OL and analysis land surface model outputs form the basis for each emission dataset that are called SURFEX-MEGAN3.0 OL (https://doi.org/10.7910/DVN/LAUVTU, Hamer et al., 2025a) and SURFEX-MEGAN3.0 analysis (https://doi.org/10.7910/DVN/69G1FX, Hamer et al., 2025b), respectively. The OL dataset is available over a five-year period from 2018–2022 and the analysis dataset is available over the three-year period 2018–2020. SURFEX was run for both the OL and analysis simulations in a configuration that allowed simulated vegetation to respond to variations in meteorology over time to more realistically track vegetation phenology. Evaluation of the land surface model output LAI and root-zone soil moisture (RZSM) showed that the OL and analysis simulations had good skill at tracking temporal changes in both variables, with the analysis performing better in each instance. We perform a variety of evaluations on the isoprene emissions specifically given the importance of this compound for atmospheric chemistry. We evaluated the temporal variability of isoprene emissions in both datasets and found that the majority of the interannual and monthly variability was linked to variability in LAI that in specific cases, like the summer of 2019, could be linked to drought impacts on vegetation growth simulated by SURFEX. We evaluated the daily temporal variability of the OL and analysis isoprene emission datasets against in-situ online observations of isoprene concentrations at 8 sites in western Europe and found moderate to strong correlation between the emissions and observations in almost all location-year pairings. We also evaluated the OL and analysis emission datasets against other published bottom-up isoprene emission datasets over the same European domain used in this study. We found that the SURFEX-MEGAN3.0 OL and analysis isoprene emission datasets lie between the minimum (CAMS-GLOB-BIOv3.1) and maximum (MEGAN-MACC) published emission datasets based on bottom-up approaches. Furthermore, we were able to attribute differences in seasonality between SURFEX-MEGAN3.0 and other emission inventories to differences in the temporal variability of the underlying LAI dataset used to compile them. Overall, our findings show the importance of variability in LAI in controlling isoprene emissions on monthly to annual timescales. Combining this with the demonstrated skill of the emissions in evaluation with independent data, this points towards the value of an Earth-system approach to BVOC emission modelling.

In a warming climate where the frequency and intensity of extreme events (such as droughts and floods) are increasing, a better representation and estimation of land surface variables remains a crucial step to study their response to climate change. Soil moisture is a key variable of the water cycle. Monitoring soil moisture, either by in situ measurements or by satellite observations allows better prediction and anticipation of droughts and floods, especially in agricultural regions. In order to fully exploit the growing number of satellite observations data, assimilation techniques can be used to integrate these data into land surface models.In this work, surface soil moisture (SSM) observations from Sentinel-1 (S1) satellite are assimilated into the ISBA model at the kilometer scale. The main objective is to evaluate the added value of the SSM assimilation and its impact on the ISBA model simulations, driven by atmospheric variables from the AROME weather forecast model. The Land Data Assimilation System tool (LDAS-Monde) of Météo-France is used. The SSM S1 product covers the period 2017-2019, over two regions in south of France and one in Spain. The native resolution of the S1 product is 10 m, and the aggregated 1 km product only covers areas where radar signal interpretation is possible. The two areas of interest in France are the Toulouse and the Montpellier regions. In these two areas, in situ soil moisture measurements are available (SMOSMANIA network and Meteopole-Flux stations of Meteo-France). The area of interest in Spain is located between Salamanca and Valladolid, where the REMEDHUS network of in-situ soil moisture measurements is located. In situ SSM observations at a depth of 5 cm were gathered from all stations at an hourly temporal resolution. The S1 SSM shows a good agreement with the in situ observations, including over the Météopole-Flux site which is located in a semi-urban area.The impact of assimilating SSM products is evaluated over three surface variables: SSM at the 1 – 4 cm soil deph layer (WG2), at the root zone at 30 cm soil depth (WG5) and on the Leaf Area Index (LAI). Three experiments are then carried out over the three regions: assimilation of the S1 SSM product alone, assimilation of the LAI retrieved from the Copernicus Global land Service (CGLS), and one last experience where S1 SSM is jointly assimilated with LAI.The results of these experiments on one hand show that when SSM alone is assimilated, almost no improvement is observed on WG2 between the ISBA model outputs and the assimilation outputs when compared to in situ measurements. On the other hand, when SSM is jointly assimilated with LAI, there is a stronger impact on WG2 and thus the outputs are closer to the in situ observations. Concerning WG5, the impact of assimilating SSM and LAI is found to be even stronger.

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.