Tracking Seasonal and Interannual Variability in Photosynthetic Downregulation in Response to Water Stress at a Temperate Deciduous Forest

The understanding and modeling of photosynthetic dynamics affected by climate variability can be highly uncertain. In this paper, we examined a well‐characterized eddy covariance site in a drought‐prone temperate deciduous broadleaf forest combining tower measurements and satellite observations. We find that an increase in spring temperature usually leads to enhanced spring gross primary production (GPP), but a GPP reduction in late growing season due to water limitation. We evaluated how well a coupled fluorescence‐photosynthesis model (SCOPE) and satellite data sets track the interannual and seasonal variations of tower GPP from 2007 to 2016. In SCOPE, a simple stress factor scaling of Vcmax as a linear function of observed predawn leaf water potential (ψ_(pd)) shows a good agreement between modeled and measured interannual variations in both GPP and solar‐induced chlorophyll fluorescence (SIF) from the Global Ozone Monitoring Experiment‐2 (GOME‐2). The modeled and satellite‐observed changes in SIF_(yield) are ~30% smaller than corresponding changes in light use efficiency (LUE) under severe stress, for which a common linear SIF to GPP scaling would underestimate the stress reduction in GPP. Overall, GOME‐2 SIF tracks interannual tower GPP variations better than satellite vegetations indices (VIs) representing canopy “greenness.” However, it is still challenging to attribute observed SIF variations unequivocally to greenness or physiological changes due to large GOME‐2 footprint. Higher‐resolution SIF data sets (e.g., TROPOMI) already show the potential to well capture the downregulation of late‐season GPP and could pave the way to better disentangle canopy structural and physiological changes in the future

Evaluation of climate-related carbon turnover processes in global vegetation models for boreal and temperate forests.

Turnover concepts in state-of-the-art global vegetation models (GVMs) account for various processes, but are often highly simplified and may not include an adequate representation of the dominant processes that shape vegetation carbon turnover rates in real forest ecosystems at a large spatial scale. Here, we evaluate vegetation carbon turnover processes in GVMs participating in the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP, including HYBRID4, JeDi, JULES, LPJml, ORCHIDEE, SDGVM, and VISIT) using estimates of vegetation carbon turnover rate (k) derived from a combination of remote sensing based products of biomass and net primary production (NPP). We find that current model limitations lead to considerable biases in the simulated biomass and in k (severe underestimations by all models except JeDi and VISIT compared to observation-based average k), likely contributing to underestimation of positive feedbacks of the northern forest carbon balance to climate change caused by changes in forest mortality. A need for improved turnover concepts related to frost damage, drought, and insect outbreaks to better reproduce observation-based spatial patterns in k is identified. As direct frost damage effects on mortality are usually not accounted for in these GVMs, simulated relationships between k and winter length in boreal forests are not consistent between different regions and strongly biased compared to the observation-based relationships. Some models show a response of k to drought in temperate forests as a result of impacts of water availability on NPP, growth efficiency or carbon balance dependent mortality as well as soil or litter moisture effects on leaf turnover or fire. However, further direct drought effects such as carbon starvation (only in HYBRID4) or hydraulic failure are usually not taken into account by the investigated GVMs. While they are considered dominant large-scale mortality agents, mortality mechanisms related to insects and pathogens are not explicitly treated in these models.Vetenskapsradet, Grant/Award Number: 621- 2014-4266; NOVA, Grant/Award Number: UID/AMB/04085/2013; GlobBiomass Project, Grant/Award Number: 4000113100/ 14/I-NB; Joint UK DECC/Defra Met Office Hadley Centre Climate Programme, Grant/ Award Number: GA0110

Atmospheric inverse modeling to constrain regional‐scale CO 2 budgets at high spatial and temporal resolution

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95480/1/jgrd15697.pd

Atmospheric inverse modeling to constrain regional-scale CO₂ budgets at high spatial and temporal resolution

We present an inverse modeling framework designed to constrain CO2 budgets at regional scales. The approach captures atmospheric transport processes in high spatiotemporal resolution by coupling a mesoscale model with Lagrangian Stochastic backward trajectories. Terrestrial biosphere CO₂ emissions are generated through a simple diagnostic flux model that splits the net ecosystem exchange into its major components of gross primary productivity and autotrophic and heterotrophic respirations. The modeling framework assimilates state-of-the-art data sets for advected background CO₂ and anthropogenic fossil fuel emissions as well as highly resolved remote sensing products. We introduce a Bayesian inversion setup, optimizing a posteriori flux base rates for surface types that are defined through remote sensing information. This strategy significantly reduces the number of parameters to be optimized compared with solving fluxes for each individual grid cell, thus permitting description of the surface in a very high resolution. The model is tested using CO₂ concentrations measured in the fall and winter of 2006 at two AmeriFlux sites in Oregon. Because this database does not cover a full seasonal cycle, we focus on conducting model sensitivity tests rather than producing quantitative CO₂ flux estimates. Sensitivity tests on the influence of spatial and temporal resolution indicate that optimum results can be obtained using 4 h time steps and grid sizes of 6 km or less. Further tests demonstrate the importance of dividing biome types by ecoregions to capture their different biogeochemical responses to external forcings across climatic gradients. Detailed stand age information was shown to have a positive effect on model performance

Global Forest Monitoring from Earth Observation

Covering recent developments in satellite observation data undertaken for monitoring forest areas from global to national levels, this book highlights operational tools and systems for monitoring forest ecosystems. It also tackles the technical issues surrounding the ability to produce accurate and consistent estimates of forest area changes, which are needed to report greenhouse gas emissions and removals from land use changes. Written by leading global experts in the field, this book offers a launch point for future advances in satellite-based monitoring of global forest resources. It gives readers a deeper understanding of monitoring methods and shows how state-of-art technologies may soon provide key data for creating more balanced policies

The Community Land Model version 5 : description of new features, benchmarking, and impact of forcing uncertainty

The Community Land Model (CLM) is the land component of the Community Earth System Model (CESM) and is used in several global and regional modeling systems. In this paper, we introduce model developments included in CLM version 5 (CLM5), which is the default land component for CESM2. We assess an ensemble of simulations, including prescribed and prognostic vegetation state, multiple forcing data sets, and CLM4, CLM4.5, and CLM5, against a range of metrics including from the International Land Model Benchmarking (ILAMBv2) package. CLM5 includes new and updated processes and parameterizations: (1) dynamic land units, (2) updated parameterizations and structure for hydrology and snow (spatially explicit soil depth, dry surface layer, revised groundwater scheme, revised canopy interception and canopy snow processes, updated fresh snow density, simple firn model, and Model for Scale Adaptive River Transport), (3) plant hydraulics and hydraulic redistribution, (4) revised nitrogen cycling (flexible leaf stoichiometry, leaf N optimization for photosynthesis, and carbon costs for plant nitrogen uptake), (5) global crop model with six crop types and time‐evolving irrigated areas and fertilization rates, (6) updated urban building energy, (7) carbon isotopes, and (8) updated stomatal physiology. New optional features include demographically structured dynamic vegetation model (Functionally Assembled Terrestrial Ecosystem Simulator), ozone damage to plants, and fire trace gas emissions coupling to the atmosphere. Conclusive establishment of improvement or degradation of individual variables or metrics is challenged by forcing uncertainty, parametric uncertainty, and model structural complexity, but the multivariate metrics presented here suggest a general broad improvement from CLM4 to CLM5

Drought tolerance and implications for vegetation-climate interactions in the Amazon forest

2012 Spring.Includes bibliographical references.On seasonal and annual timescales, the Amazon forest is resistant to drought, but more severe droughts can have profound effects on ecosystem productivity and tree mortality. The majority of climate models predict decreased rainfall in tropical South America over this century. Until recently, land surface models have not included mechanisms of forest resistance to seasonal drought. In some coupled climate models, the inability of tropical forest to withstand warming and drying leads to replacement of forest by savanna by 2050. The main questions of this research are: What factors affect forest drought tolerance, and what are the implications of drought tolerance mechanisms for climate? Forest adaptations to drought, such as development of deep roots, enable Amazon forests to withstand seasonal droughts, and the maintenance of transpiration during dry periods can affect regional climate. At high levels of water stress, such as those imposed during a multiyear rainfall exclusion experiment or during interannual drought, trees prevent water loss by closing their stomata. We examine forest response to drought in an ecosystem model (SiB3 - the Simple Biosphere model) compared to two rainfall exclusion experiments in the Amazon. SiB3 best reproduces the observed drought response using realistic soil parameters and annual LAI, and by adjusting soil depth. SiB3's optimal soil depth at each site serves as a proxy for forest drought resistance. Based on the results at the exclusion sites, we form the hypothesis that forests with periodic dry conditions are more adapted to drought. We parameterize stress resistance as a function of precipitation climatology, soil texture, and percent forest cover. The parameterization impacts carbon and moisture fluxes during extreme drought events. The loss of productivity is of similar magnitude as plot-based measurements of biomass loss during the 2005 drought. Changing stress resistance in SiB3 also affects surface evapotranspiration during dry periods, which has the potential to affect climate through changing sensible and latent heat fluxes. We examine the effects of forest stress resistance on climate through coupled experiments of SiB3 in a GCM. In a single column model, we find evidence for a more active hydrologic cycle due to increased stress resistance. The boundary layer responds through changes in its depth, relative humidity, and turbulent kinetic energy, and the changes feed back to influence wet season onset and intensity. In a full global GCM, increased stress resistance often decreases drought intensity through enhanced ET and changes to circulation. The circulation responds to changes in atmospheric latent heating and can affect precipitation in the South Atlantic Convergence Zone

Mapping and Monitoring Forest Cover

This book is a compilation of six papers that provide some valuable information about mapping and monitoring forest cover using remotely sensed imagery. Examples include mapping large areas of forest, evaluating forest change over time, combining remotely sensed imagery with ground inventory information, and mapping forest characteristics from very high spatial resolution data. Together, these results demonstrate effective techniques for effectively learning more about our very important forest resources

The global tree carrying capacity (keynote)

editorial reviewe

Développement et validation d’un indice de production des prairies basé sur l’utilisation de séries temporelles de données satellitaires : application à un produit d’assurance en France

Une assurance indicielle est proposée en réponse à l'augmentation des sécheresses impactant les prairies. Elle se base sur un indice de production fourragère (IPF) obtenu à partir d'images satellitaires de moyenne résolution spatiale pour estimer l'impact de l'aléa dans une zone géographique définie. Le principal enjeu lié à la mise en place d'une telle assurance réside dans la bonne estimation des pertes subies. Les travaux de thèse s’articulent autour de deux objectifs : la validation de l'IPF et la proposition d'amélioration de cet indice. Un protocole de validation est construit pour limiter les problèmes liés à l'utilisation de produit de moyenne résolution et au changement d’échelle. L'IPF, confronté à des données de référence de différentes natures, montre de bonnes performances : des mesures de production in situ (R² = 0,81; R² = 0,71), des images satellitaires haute résolution spatiale (R² = 0,78 - 0,84) et des données issues de modélisation (R² = 0,68). Les travaux permettent également d'identifier des pistes d'amélioration pour la chaîne de traitement de l'IPF. Un nouvel indice, basé sur une modélisation semiempirique combinant les données satellitaires avec des données exogènes relatives aux conditions climatiques et à la phénologie des prairies, permet d'améliorer la précision des estimations de production de 18,6 %. L’ensemble des résultats obtenus ouvrent de nombreuses perspectives de recherche sur le développement de l'IPF et ses potentiels d'application dans le domaine assurantiel