Oscar Rojas-Munoz

The knowledge of land-surface variables (LSVs) is essential for an accurate description of the carbon cycle and, hence, for deriving the fraction of anthropogenic CO ₂ emissions contributing to global warming. For a better representation of LSVs like the leaf-area index (LAI), we assimilate the observations of the satellite microwave sensors SMAP and AMSR2 with our land-data assimilation system (LDAS) on global cropland. Dedicated observation operators using a neural network (NN) are developed to enable a direct assimilation of the measured brightness temperatures. Direct assimilation is not yet an established method due to the incomplete representation of physical emission processes and the associated computational constraints of physical observation operators. We derive an optimal set of predictors for both instruments resulting in a good match between model equivalent and the observations in the testing period, with correlations up to 0.87/0.93 and a total RMSE of 8.2/3.6 K for SMAP/AMSR2, respectively. The implementation of the derived weights into the LDAS is straightforward and is found to lead to a reasonably good performance of the assimilation system, with a stronger improvement of the departures for SMAP than for AMSR2, which can be attributed to the characteristics of the microwave bands of the observations. For the best assimilation experiments, the verification against LAI observations shows an improvement compared to the open loop on a global scale. In agricultural regions of the world that are particularly susceptible to drought, AMSR2 experiments outperform SMAP experiments in terms of LAI in most cases. We also investigated the impact on the Gross Primary Productivity (GPP) and soil-water index (SWI) anomalies and found relatively little impact with mixed results. Nevertheless, the seasonal and sub-seasonal variability is still not well represented due to systematic errors in our land surface model.

This work integrates new satellite observations into the Interactions between Soil, Biosphere and Atmosphere (ISBA) land surface model via data assimilation. This improves our understanding of the impact of droughts on forests and agricultural land at global and regional scales. First, innovative artificial intelligence methods are employed to generate observation operators, enabling ISBA to simulate satellite microwave radiances ranging from the L to X bands, as well as sun‐induced fluorescence (SIF) observations. Second, these observations are assimilated into ISBA sequentially. Examples of sequential data assimilation are presented for SIF and C‐band backscatter observations derived from the tropospheric monitoring instrument and the advanced scatterometer on board the Sentinel‐5P and MetOp satellites respectively. We also demonstrate how machine learning can be used to derive new variables from those calculated by the land surface model in order to monitor natural disasters related to drought, as well as to characterise the regional exposure to drought‐related hazards. We present a prototype regional application for monitoring and forecasting clay shrink–swell and wildfire danger in France.

Accurate representations of the surface and vegetation are critical for simulating the terrestrial CO ₂ cycle in response to climate and meteorological conditions. To meet this challenge, an increasing number of satellite missions are being launched which can monitor vegetation conditions and biomass. One is the Copernicus Sentinel-5P mission, which carries the TROPOMI instrument and retrieves Sun-Induced Chlorophyll Fluorescence (SIF). As an indicator of plant photosynthetic activity, SIF can provide critical information for evaluating and parameterising gross carbon flux dynamics in surface models. This study aims to assimilate TROPOMI SIF data into the ISBA (Interactions between Soil, Biosphere and Atmosphere) land surface model developed by Meteo-France, with the objective of directly correcting the representation of Leaf Area Index (LAI) and Gross Primary Production (GPP). To achieve this, we have developed a dedicated observation operator that links the modelled LAI to the TROPOMI SIF daily product. This neural network (NN) operator was developed using deep learning and was trained using observations over Europe. This operator achieved good accuracy, was implemented in a land data assimilation system (LDAS), and was used to assimilate TROPOMI SIF in ISBA using a sequential simplified extended Kalman filter. Specific experiments were conducted to study the assimilation process over the Ebro basin in Spain. This area is known for its irrigated croplands, which are not well represented by ISBA. Some experiments assimilate TROPOMI SIF, some assimilate a Copernicus Land Monitoring Service (CLMS) LAI 10 d product, and some assimilate both. This provided us with a useful point of reference for improving the vegetation simulation. Following SIF assimilation, LAI representation improved across the domain, highlighting heavily irrigated croplands. The gross primary production (GPP) derived from the analysis is closing the gap between the simulated and observed values, though a marked difference remains. When compared with other assimilation experiments, assimilating SIF alone provides a similar benefit to standard assimilation of a CLMS LAI product on LAI and GPP. The best improvements to the LAI and GPP results come from co-assimilating TROPOMI SIF with the CLMS LAI product, which combines the advantages of high-frequency SIF observations and robust 10 d LAI assimilation.

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.

Surface reanalyses are an important tool for examining past natural hazards, such as wildfires, sudden floods and droughts, in great detail and on a consistent scale. With the surge in deep learning, these reanalyses could contribute to the development of new tools for identifying, characterizing and anticipating such hazards. Météo-France has just conducted a 60-year reanalysis of Western Europe using the AROME numerical weather prediction model, known as ARRA. Here, we use these atmospheric conditions to reanalyse the continental surface of the same area using the Land Data Assimilation System (LDAS-Monde). ARRA reanalyses are used to drive the Interaction Surface Biosphere Atmosphere (ISBA) land surface model via the SURFEX platform, incorporating prognostic soil moisture, leaf area index and woody biomass at kilometer scale. These modeled variables are then corrected by assimilating Copernicus Land Monitoring Service LAI satellite data and ESA-CCI above-ground biomass satellite data using a simplified extended Kalman filter. The reanalysis period will range between 2000 and 2025 to coincide with CLMS LAI observation availability. Incorporating LAI into ISBA enhances the model's representation of the carbon and water cycles, as well as addressing agricultural practices not currently modeled by ISBA. We will additionally evaluate the impact on river discharge using CTRIP river routing.The additional focus on biomass in the reanalyses stems from the need to improve the representation of living fuel moisture content and dead biomass in our model, as these are two critical variables for providing an early warning of fire ignition. To this end, above-ground biomass will be assimilated to adjust the distribution of biomass between leaves, wood, and litter.

It is now widely recognized that assimilating consolidated satellite-derived vegetation products improves the representation of land surface variables and fluxes. This raises the question of their suitability for both reanalysis products and near-real-time (NRT) applications. Among these variables, Leaf Area Index (LAI) plays a key role in controlling exchanges of water, energy, and carbon between the land surface and the atmosphere. While reprocessed and consolidated products are well suited for retrospective reanalyses, NRT applications require observational streams with low latency and sufficient consistency with delayed, higher-quality products.In this study, we assess the potential of the Copernicus Land Monitoring Service (CLMS) 300 m LAI products for land surface analysis and NRT applications within the LDAS-Monde system coupled to the ISBA land surface model for the year 2021 in a global scale. We investigate the assimilation of the different CLMS processing streams, namely RT0 and RT1, available in NRT, and RT6, which is provided as a consolidated product with a 60-day latency. A set of global offline experiments forced by ERA5 is conducted to evaluate their consistency and suitability for land data assimilation.The results show that RT1 captures the main temporal variability of the consolidated RT6 product while benefiting from a substantially reduced latency. Compared with RT0, RT1 provides a more stable and consistent signal, offering a robust compromise between timeliness and data quality. Assimilation experiments within LDAS-Monde indicate that analyses driven by RT1 are consistent with those obtained using RT6, supporting its use in systems targeting NRT land monitoring. In addition, an in situ evaluation based on observations from the ICOS FR-Tou site is conducted for selected surface variables, providing an independent benchmark to further assess whether the use of RT1 leads to comparable performance to RT6.

The knowledge of land-surface variables (LSVs) is essential for an accurate description of the carbon cycle and, hence, for deriving the fraction of anthropogenic CO₂ emissions contributing to global warming. For a better representation of LSVs like the leaf-area index (LAI), we assimilate the observations of the satellite microwave sensors SMAP and AMSR2 with our land-data assimilation system (LDAS). Dedicated observation operators using a neural network (NN) are developed to enable a direct assimilation of the measured brightness temperatures. Direct assimilation is not yet an established method due to the incomplete representation of physical emission processes and the associated computational constraints of physical observation operators. We derive an optimal set of predictors for both instruments resulting in a good match between model equivalent and the observations in the testing period, with correlations up to 0.87/0.93 and a total RMSE of 8.2/3.6 K for SMAP/AMSR2, respectively. The implementation of the derived weights into the LDAS is straightforward and is found to lead to a reasonably good performance of the assimilation system, with a stronger improvement of the departures for SMAP than for AMSR2, which can be attributed to the characteristics of the microwave bands of the observations. For the best assimilation experiments, the verification against LAI observations shows an improvement compared to the open loop on a global scale. For different global cropland regions especially prone to droughts, AMSR2 outperforms SMAP in most cases. Nevertheless, the seasonal and subseasonal variability is still not well represented though due to unsolved issues in our 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.

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.