3 research outputs found
The Impact of Recent European Droughts and Heatwaves on Trace Gas Surface Fluxes: Insights from Land Surface Data Assimilation
Heatwave and drought extremes can have significant impacts on vegetation, which can in turn lead to important effects on reactive trace gas fluxes at the land-atmosphere interface that can ultimately alter atmospheric composition. We present results from the EU-funded Sentinel EObased Emission and Deposition Service (SEEDS) project, which aimed at developing upgrades to the existing Copernicus Atmospheric Monitoring Service (CAMS) component on European air quality. In this work, we used land surface modelling (SURFEX – Surface Externalisée) combined with data assimilation (Extended Kalman Filter - EKF) of satellite leaf area index (LAI) to deliver improved estimation of the land surface state. The land surface model is coupled with an online model for dry deposition and an offline model (MEGANv3.1) for biogenic volatile organic compounds (BVOCs) to estimate trace gas losses and emissions, respectively. This approach exploits methods at the forefront of land surface modelling (dynamic vegetation simulation and data assimilation) and combines them with the latest algorithms to estimate trace gas fluxes at the surface. We present findings from two extreme events in Europe: the 2018 drought and the 2019 June/July heat waves. SURFEX was forced using ECMWF meteorology at 0.1° × 0.1° resolution that captured both events. Both extreme events provoked strong responses in the models for dry deposition velocity and BVOC emissions. The 2018 drought began in spring and endured through summer, during which dry deposition velocities declined steadily beyond seasonal norms due to increased stomatal resistance forced by the vegetation response to drought. Over continental Europe, BVOCs initially increased in the early phase of the drought, but then sharply declined into July in the worst-affected regions in Germany, Denmark, and Poland. Meanwhile, BVOCs increased in Scandinavia relative to seasonal norms due to the warmer-than-average conditions. The first episode of severe heat in 2019 arrived in late June, which initially caused a large increase in BVOC emissions compared to seasonal norms. Then drought set in during July and despite a second large heat wave BVOC emissions were lower overall compared to seasonal norms. In fact, the European-wide BVOC emissions were higher in June compared to July due to the drought effects that commenced later in the heat wave cycle. This reverses the normal seasonal cycle in BVOC emissions, and drought impacts on vegetation were the primary driver behind this. Dry deposition velocities are reduced during both heat waves, but we see a larger decline in the second heat wave in July when drought conditions are more severe. Our findings suggest that these impacts on trace gas surface fluxes would have a strong effect on atmospheric composition, and on photochemical ozone formation. We, therefore, conclude that these effects likely played a contributory role to the ozone pollution episodes that occurred coincidentally in time with the heat wave events in both 2018 and 2019. The project aim within SEEDS is to eventually test the BVOC emissions and dry deposition velocities within a chemical transport model participating within the CAMS regional ensemble (MOCAGE) and to therefore evaluate the impact on ozone
Analysis of current validation practices in Europe for space-based climate data records of essential climate variables
Sequential assimilation of satellite-derived vegetation and soil moisture products using SURFEX_v8.0: LDAS-Monde assessment over the Euro-Mediterranean area
International audienceIn this study, a global land data assimilation system (LDAS-Monde) is applied over Europe and the Mediter-ranean basin to increase monitoring accuracy for land surface variables. LDAS-Monde is able to ingest information from satellite-derived surface soil moisture (SSM) and leaf area index (LAI) observations to constrain the interactions between soil-biosphere-atmosphere (ISBA, Interactions between Soil, Biosphere and Atmosphere) land surface model (LSM) coupled with the CNRM (Centre National de Recherches Météorologiques) version of the Total Runoff Integrating Pathways (ISBA-CTRIP) continental hydrological system. It makes use of the CO 2-responsive version of ISBA which models leaf-scale physiological processes and plant growth. Transfer of water and heat in the soil rely on a multilayer diffusion scheme. SSM and LAI observations are assimilated using a simplified extended Kalman filter (SEKF), which uses finite differences from perturbed simulations to generate flow dependence between the observations and the model control variables. The latter include LAI and seven layers of soil (from 1 to 100 cm depth). A sensitivity test of the Jacobians over 2000-2012 exhibits effects related to both depth and season. It also suggests that observations of both LAI and SSM have an impact on the different control variables. From the assimilation of SSM, the LDAS is more effective in modifying soil moisture (SM) from the top layers of soil, as model sensitivity to SSM decreases with depth and has almost no impact from 60 cm downwards. From the assimilation of LAI, a strong impact on LAI itself is found. The LAI assimilation impact is more pronounced in SM layers that contain the highest fraction of roots (from 10 to 60 cm). The assimilation is more efficient in summer and autumn than in winter and spring. Results shows that the LDAS works well constraining the model to the observations and that stronger corrections are applied to LAI than to SM. A comprehensive evaluation of the assimilation impact is conducted using (i) agricultural statistics over France, (ii) river discharge observations , (iii) satellite-derived estimates of land evapotranspira-tion from the Global Land Evaporation Amsterdam Model (GLEAM) project and (iv) spatially gridded observation-based estimates of upscaled gross primary production and evapotranspiration from the FLUXNET network. Comparisons with those four datasets highlight neutral to highly positive improvement