Verification of Land-Atmosphere Coupling in Forecast Models, Reanalyses and Land Surface Models Using Flux Site Observations

We confront four model systems in three configurations (LSM, LSM+GCM, and reanalysis) with global flux tower observations to validate states, surface fluxes, and coupling indices between land and atmosphere. Models clearly under-represent the feedback of surface fluxes on boundary layer properties (the atmospheric leg of land-atmosphere coupling), and may over-represent the connection between soil moisture and surface fluxes (the terrestrial leg). Models generally under-represent spatial and temporal variability relative to observations, which is at least partially an artifact of the differences in spatial scale between model grid boxes and flux tower footprints. All models bias high in near-surface humidity and downward shortwave radiation, struggle to represent precipitation accurately, and show serious problems in reproducing surface albedos. These errors create challenges for models to partition surface energy properly and errors are traceable through the surface energy and water cycles. The spatial distribution of the amplitude and phase of annual cycles (first harmonic) are generally well reproduced, but the biases in means tend to reflect in these amplitudes. Interannual variability is also a challenge for models to reproduce. Our analysis illuminates targets for coupled land-atmosphere model development, as well as the value of long-term globally-distributed observational monitoring

A carbon sink-driven approach to estimate gross primary production from microwave satellite observations

Global estimation of Gross Primary Production (GPP) - the uptake of atmospheric carbon dioxide by plants through photosynthesis - is commonly based on optical satellite remote sensing data. This presents a source-driven approach since it uses the amount of absorbed light, the main driver of photosynthesis, as a proxy for GPP. Vegetation Optical Depth (VOD) estimates obtained from microwave sensors provide an alternative and independent data source to estimate GPP on a global scale, which may complement existing GPP products. Recent studies have shown that VOD is related to aboveground biomass, and that both VOD and temporal changes in VOD relate to GPP. In this study, we build upon this concept and propose a model for estimating GPP from VOD. Since the model is driven by vegetation biomass, as observed through VOD, it presents a carbon sink-driven approach to quantify GPP and, therefore, is conceptually different from common source-driven approaches. The model developed in this study uses single frequencies from active or passive microwave VOD retrievals from C-, X- and Ku-band (Advanced Scatterometer (ASCAT) and Advanced Microwave Scanning Radiometer for Earth Observation (AMSR-E)) to estimate GPP at the global scale. We assessed the ability for temporal and spatial extrapolation of the model using global GPP from FLUXCOM and in situ GPP from FLUXNET. We further performed upscaling of in situ GPP based on different VOD data sets and compared these estimates with the FLUXCOM and MODerate-resolution Imaging Spectroradiometer (MODIS) GPP products. Our results show that the model developed for individual grid cells using VOD and change in VOD as input performs well in predicting temporal patterns in GPP for all VOD data sets. For spatial extrapolation of the model, however, additional input variables are needed to represent the spatial variability of the VOD-GPP relationship due to differences in vegetation type. As additional input variable, we included the grid cell median VOD (as a proxy for vegetation cover), which increased the model performance during cross validation. Mean annual GPP obtained for AMSR-E X-band data tends to overestimate mean annual GPP for FLUXCOM and MODIS but shows comparable latitudinal patterns. Overall, our findings demonstrate the potential of VOD for estimating GPP. The sink-driven approach provides additional information about GPP independent of optical data, which may contribute to our knowledge about the carbon source-sink balance in different ecosystems

Soil Moisture Encodes Plant Water Use Strategies

Resilient water, food, and energy management strategies for an ever-growing population and changing environment depends on our understanding of water and carbon cycles from local to global scales. Fluxes of water and carbon are coupled by photosynthesis and plant transpiration cycles the largest fraction of terrestrial water from the land back to the atmosphere. Our limited ability to characterize interactions between hydrology and climate, regulated by plants’ response to stress, contributes to the greatest source of uncertainty in climate and carbon projections. Parameterized models need to represent the complexity and diversity of plant water use strategies, but hydrologically relevant model inputs are difficult to measure at ecosystem scales. Soil moisture integrates landscape fluxes and the spatial and temporal variability in soil moisture reflects dynamics of dominant land-surface processes. Diagnosing variability in soil moisture observations from point to landscape scales can thus quantify characteristics which are not measured directly. The central hypothesis of this dissertation is: soil moisture observations encode valuable ecohydrological information, and this information can be extracted to quantify plant water use strategies. This dissertation develops: (1) an inverse modeling framework to estimate scale-specific ecohydrological thresholds from probability distributions of soil moisture observations; (2) a global dataset of thresholds of soil water uptake, which are consistent with satellite soil moisture; and (3) relations between evapotranspiration and soil moisture at a range of biomes, based on the energy spectrum and probability distribution of soil moisture and information theory metrics. This work provides data driven methods that leverage new global observations and quantify ecohydrological relations which are critical to a variety of open climate, water, and ecosystem research questions and modeling endeavors

Impact of climate and anthropogenic effects on the energy, water, and carbon budgets of monitored agrosystems: multi-site analysis combining modelling and experimentation

Les terres cultivées représentent une unité importante dans le climat mondial, et en réponse à la population, elles sont en expansion. Il est crucial de comprendre et de quantifier les interactions terre-atmosphère via les échanges d'eau, d'énergie et de carbone. Dans ce contexte, cette thèse a consisté à étudier la variabilité du bilan énergétique en fonction de différentes cultures, phénologies et pratiques agricoles via système Eddy-Covariance. En réponse au manque d'eau dans le sud-ouest de la France, deux modèles de surface (ISBA et ISBA-MEB) ont été évalués sur deux cultures (blé et maïs) pour évaluer leur capacité à estimer les flux d'énergie et d'eau. Enfin, en réponse à la contribution des terres cultivées à l'augmentation du dioxyde de carbone atmosphérique, la capacité du modèle ISBA-MEB à simuler correctement les principaux composants du carbone a été testée sur 11 saisons de maïs et de blé.Croplands represent an important unit within the global climate, and in response to population, they are expanding. Hence, understanding and quantifying the land-atmosphere interactions via water, energy and carbon exchanges is crucial. In this context, the first objective of this thesis studied the variability of the energy balance over different crops, phenologies, and farm practices at Lamasquère and Auradé. Secondly, in response to water scarcity and increasing drought in southwestern France, two land surface models (ISBA and ISBA-MEB) of different configurations were evaluated over some wheat and maize years to test their ability to estimate energy and water fluxes using measurements from an eddy covariance system as reference. Finally, in response to the contribution of croplands to increasing atmospheric carbon dioxide, the capability of the ISBA-MEB model to correctly simulate the major carbon components was tested over 11 seasons of maize and wheat