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The availability of phosphorus (P) and nitrogen (N) constrains the ability of ecosystems to use resources such as light, water and carbon. In turn, nutrients impact the distribution of productivity, ecosystem carbon turnovers and their net exchange of CO2 with the atmosphere in response to variation of environmental conditions in both space and time. In this study, we evaluated the performance of the global version of the land surface model ORCHIDEE-CNP (v1.2), which explicitly simulates N and P biogeochemistry in terrestrial ecosystems coupled with carbon, water and energy transfers. We used data from remote sensing, ground-based measurement networks and ecological databases. Components of the N and P cycle at different levels of aggregation (from local to global) are in good agreement with datadriven estimates. When integrated for the period 1850 to 2017 forced with variable climate, rising CO2 and land use change, we show that ORCHIDEE-CNP underestimates the land carbon sink in the Northern Hemisphere (NH) during recent decades despite an a priori realistic gross primary productivity (GPP) response to rising CO2. This result suggests either that processes other than CO2 fertilization, which are omitted in ORCHIDEE-CNP such as changes in biomass turnover, are predominant drivers of the northern land sink and/or that the model parameterizations produce emerging nutrient limitations on biomass growth that are too strict in northern areas. In line with the latter, we identified biases in the simulated large-scale patterns of leaf and soil stoichiometry as well as plant P use efficiency, pointing towards P limitations that are too severe towards the poles. Based on our analysis of ecosystem resource use efficiencies and nutrient cycling, we propose ways to address the model biases by giving priority to better representing processes of soil organic P mineralization and soil inorganic P transformation, followed by refining the biomass production efficiency under increasing atmospheric CO2, phenology dynamics and canopy light absorption., SCOPUS: ar.j, info:eu-repo/semantics/published

International audience; Recent successes in passive remote sensing of far-red solar-induced chlorophyll fluorescence (SIF) have spurred the development and integration of canopy-level fluorescence models in global terrestrial biosphere models (TBMs) for climate and carbon cycle research. The interaction of fluorescence with photochemistry at the leaf and canopy scales provides opportunities to diagnose and constrain model simulations of photosynthesis and related processes , through direct comparison to and assimilation of tower, airborne, and satellite data. TBMs describe key processes related to the absorption of sunlight, leaf-level fluo-rescence emission, scattering, and reabsorption throughout the canopy. Here, we analyze simulations from an ensemble of process-based TBM-SIF models (SiB3-Simple Bio-sphere Model, SiB4, CLM4.5-Community Land Model, CLM5.0, BETHY-Biosphere Energy Transfer Hydrology, ORCHIDEE-Organizing Carbon and Hydrology In Dynamic Ecosystems, and BEPS-Boreal Ecosystems Productivity Simulator) and the SCOPE (Soil Canopy Observation Photosynthesis Energy) canopy radiation and vegetation model at a subalpine evergreen needleleaf forest near Niwot Ridge, Colorado. These models are forced with local meteorology and analyzed against tower-based continuous far-red SIF and gross-primary-productivity-partitioned (GPP) eddy covariance data at diurnal and synoptic scales during the growing season (July-August 2017). Our primary objective is to summarize the site-level state of the art in TBM-SIF modeling over a relatively short time period (sum-Published by Copernicus Publications on behalf of the European Geosciences Union. 3734 N. C. Parazoo et al.: Wide discrepancies in the magnitude and direction of modeled SIF mer) when light, canopy structure, and pigments are similar , setting the stage for regional-to global-scale analyses. We find that these models are generally well constrained in simulating photosynthetic yield but show strongly divergent patterns in the simulation of absorbed photosynthetic active radiation (PAR), absolute GPP and fluorescence, quantum yields, and light response at the leaf and canopy scales. This study highlights the need for mechanistic modeling of non-photochemical quenching in stressed and unstressed environments and improved the representation of light absorption (APAR), distribution of light across sunlit and shaded leaves, and radiative transfer from the leaf to the canopy scale.

The flow of carbon through terrestrial ecosystems and the response to climate are critical but highly uncertain processes in the global carbon cycle. However, with a rapidly expanding array of in situ and satellite data, there is an opportunity to improve our mechanistic understanding of the carbon (C) cycle's response to land use and climate change. Uncertainty in temperature limitation on productivity poses a significant challenge to predicting the response of ecosystem carbon fluxes to a changing climate. Here we diagnose and quantitatively resolve environmental limitations on the growing-season onset of gross primary production (GPP) using nearly 2 decades of meteorological and C flux data (2000–2018) at a subalpine evergreen forest in Colorado, USA. We implement the CARbon DAta-MOdel fraMework (CARDAMOM) model–data fusion network to resolve the temperature sensitivity of spring GPP. To capture a GPP temperature limitation – a critical component of the integrated sensitivity of GPP to temperature – we introduced a cold-temperature scaling function in CARDAMOM to regulate photosynthetic productivity. We found that GPP was gradually inhibited at temperatures below 6.0 ∘C (±2.6 ∘C) and completely inhibited below −7.1 ∘C (±1.1 ∘C). The addition of this scaling factor improved the model's ability to replicate spring GPP at interannual and decadal timescales (r=0.88), relative to the nominal CARDAMOM configuration (r=0.47), and improved spring GPP model predictability outside of the data assimilation training period (r=0.88). While cold-temperature limitation has an important influence on spring GPP, it does not have a significant impact on integrated growing-season GPP, revealing that other environmental controls, such as precipitation, play a more important role in annual productivity. This study highlights growing-season onset temperature as a key limiting factor for spring growth in winter-dormant evergreen forests, which is critical in understanding future responses to climate change.

International audience; Predicting terrestrial carbon, C, budgets and carbon-climate feedbacks strongly relies on our ability to accurately model interactions between vegetation, C and water cycles, and the atmosphere. However, C fluxes simulated by global, process-based terrestrial biosphere models (TBMs) remain subject to large uncertainties, partly due to unknown or poorly calibrated parameters. This is because TBMs have not routinely been confronted against C cycle related datasets within a statistical data assimilation (DA) system. In this review, we present 15 years' development of a C cycle DA system for optimizing C cycle parameters of the ORCHIDEE TBM. We analyze the impact of assimilating multiple different C cycle related datasets on regional to global-scale gross and net CO2 fluxes. We find that assimilating atmospheric CO2 data is crucial for improving (increasing) ORCHIDEE predictions of the terrestrial land C sink. The improvement is predominantly due to the global-scale constraint these data provide for optimizing initial soil C stocks, which are likely in error due to inaccurate assumptions about steady state spin-up and incomplete knowledge of land use change histories. When comparing the data-constrained ORCHIDEE land C sink estimates to the CAMS atmospheric inversion, we show that while the two approaches agree on the global C sink magnitude, they continue to differ in how the global C sink is partitioned between the northern hemisphere and tropics. We also discuss technical challenges faced in our C cycle DA studies, in particular the difficulty in characterizing the error covariance matrix due to unknown observation biases and/or model-data inconsistencies. We offer our perspectives on how to tackle these challenges that we hope can serve as a roadmap for other TBM groups wishing to develop C cycle DA systems.

Continuous atmospheric CO2 monitoring data indicate an increase in the amplitude of seasonal CO2-cycle exchange (SCANBP) in northern high latitudes. The major drivers of enhanced SCANBP remain unclear and intensely debated, with land-use change, CO2 fertilization and warming being identified as likely contributors. We integrated CO2-flux data from two atmospheric inversions (consistent with atmospheric records) and from 11 state-of-the-art land-surface models (LSMs) to evaluate the relative importance of individual contributors to trends and drivers of the SCANBP of CO2 fluxes for 1980–2015. The LSMs generally reproduce the latitudinal increase in SCANBP trends within the inversions range. Inversions and LSMs attribute SCANBP increase to boreal Asia and Europe due to enhanced vegetation productivity (in LSMs) and point to contrasting effects of CO2 fertilization (positive) and warming (negative) on SCANBP. Our results do not support land-use change as a key contributor to the increase in SCANBP. The sensitivity of simulated microbial respiration to temperature in LSMs explained biases in SCANBP trends, which suggests that SCANBP could help to constrain model turnover times.

Evaluating land surface models (LSMs) using available observations is important for understanding the potential and limitations of current Earth system models in simulating water- and carbon-related variables. To reveal the error sources of a LSM, five essential climate variables have been evaluated in this paper (i.e., surface soil moisture, evapotranspiration, leaf area index, surface albedo, and precipitation) via simulations with the IPSL (Institute Pierre Simon Laplace) LSM ORCHIDEE (Organizing Carbon and Hydrology in Dynamic Ecosystems) model, particularly focusing on the difference between (i) forced simulations with atmospheric forcing data (WATCH Forcing Data ERA-Interim – WFDEI) and (ii) coupled simulations with the IPSL atmospheric general circulation model. Results from statistical evaluation, using satellite- and ground-based reference data, show that ORCHIDEE is well equipped to represent spatiotemporal patterns of all variables in general. However, further analysis against various landscape and meteorological factors (e.g., plant functional type, slope, precipitation, and irrigation) suggests potential uncertainty relating to freezing and/or snowmelt, temperate plant phenology, irrigation, and contrasted responses between forced and coupled mode simulations. The biases in the simulated variables are amplified in the coupled mode via surface–atmosphere interactions, indicating a strong link between irrigation–precipitation and a relatively complex link between precipitation–evapotranspiration that reflects the hydrometeorological regime of the region (energy limited or water limited) and snow albedo feedback in mountainous and boreal regions. The different results between forced and coupled modes imply the importance of model evaluation under both modes to isolate potential sources of uncertainty in the model.

Aerosol- and cloud-induced changes in diffuselight have important impacts on the global land carbon cy-cle, as they alter light distribution and photosynthesis in veg-etation canopies. However, this effect remains poorly rep-resented or evaluated in current land surface models. Here,we add a light partitioning module and a new canopy lighttransmission module to the ORCHIDEE (Organising Car-bon and Hydrology In Dynamic Ecosystems) land surfacemodel (trunk version, v5453) and use the revised model, OR-CHIDEE_DF, to estimate the fraction of diffuse light andits effect on gross primary production (GPP) in a multilayercanopy. We evaluate the new parameterizations using fluxobservations from 159 eddy covariance sites over the globe.Our results show that, compared with the original model,ORCHIDEE_DF improves the GPP simulation under sunnyconditions and captures the observed higher photosynthe-sis under cloudier conditions in most plant functional types(PFTs). Our results also indicate that the larger GPP undercloudy conditions compared with sunny conditions is mainlydriven by increased diffuse light in the morning and in theafternoon as well as by a decreased vapor pressure deficit(VPD) and decreased air temperature at midday. The obser-vations show that the strongest positive effects of diffuse lighton photosynthesis are found in the range from 5 to 20◦C andat a VPD < 1 kPa. This effect is found to decrease when theVPD becomes too large or the temperature falls outside of theabovementioned range, which is likely due to the increasingstomatal resistance to leaf CO2uptake. ORCHIDEE_DF un-derestimates the diffuse light effect at low temperature in allPFTs and overestimates this effect at high temperature and ata high VPD in grasslands and croplands. The new model hasthe potential to better investigate the impact of large-scaleaerosol changes and long-term changes in cloudiness on theterrestrial carbon budget, both in the historical period and inthe context of future air quality policies and/or climate engi-neering This research has been supported by the Eu- ropean Research Council (grant no. SyG-2013-610028), the ANR CLAND Convergence Institute (grant no. 16-CONV-0003), the ANR China Trend Stream and the new 4C project. Bogdan Cho-jnicki was supported by the National Science Foundation, Poland (grant no. UMO-2017/27/B/ST10/02228), within the framework of the “Carbon dioxide uptake potential of sphagnum peatlands in the context of atmospheric optical parameters and climate changes” (KUSCO2) project.

The high-latitude regions of the Northern Hemisphere are a nexus for the interaction between land surface physical properties and their exchange of carbon and energy with the atmosphere. At these latitudes, two carbon pools of planetary significance – those of the permanently frozen soils (permafrost), and of the great expanse of boreal forest – are vulnerable to destabilization in the face of currently observed climatic warming, the speed and intensity of which are expected to increase with time. Improved projections of future Arctic and boreal ecosystem transformation require improved land surface models that integrate processes specific to these cold biomes. To this end, this study lays out relevant new parameterizations in the ORCHIDEE-MICT land surface model. These describe the interactions between soil carbon, soil temperature and hydrology, and their resulting feedbacks on water and CO2 fluxes, in addition to a recently developed fire module. Outputs from ORCHIDEE-MICT, when forced by two climate input datasets, are extensively evaluated against (i) temperature gradients between the atmosphere and deep soils, (ii) the hydrological components comprising the water balance of the largest high-latitude basins, and (iii) CO2 flux and carbon stock observations. The model performance is good with respect to empirical data, despite a simulated excessive plant water stress and a positive land surface temperature bias. In addition, acute model sensitivity to the choice of input forcing data suggests that the calibration of model parameters is strongly forcing-dependent. Overall, we suggest that this new model design is at the forefront of current efforts to reliably estimate future perturbations to the high-latitude terrestrial environment.

Land surface models rarely incorporate the terrestrial phosphorus cycle and its interactions with the carbon cycle, despite the extensive scientific debate about the importance of nitrogen and phosphorus supply for future land carbon uptake. We describe a representation of the terrestrial phosphorus cycle for the ORCHIDEE land surface model, and evaluate it with data from nutrient manipulation experiments along a soil formation chronosequence in Hawaii. ORCHIDEE accounts for the influence of the nutritional state of vegetation on tissue nutrient concentrations, photosynthesis, plant growth, biomass allocation, biochemical (phosphatase-mediated) mineralization, and biological nitrogen fixation. Changes in the nutrient content (quality) of litter affect the carbon use efficiency of decomposition and in return the nutrient availability to vegetation. The model explicitly accounts for root zone depletion of phosphorus as a function of root phosphorus uptake and phosphorus transport from the soil to the root surface. The model captures the observed differences in the foliage stoichiometry of vegetation between an early (300-year) and a late (4.1 Myr) stage of soil development. The contrasting sensitivities of net primary productivity to the addition of either nitrogen, phosphorus, or both among sites are in general reproduced by the model. As observed, the model simulates a preferential stimulation of leaf level productivity when nitrogen stress is alleviated, while leaf level productivity and leaf area index are stimulated equally when phosphorus stress is alleviated. The nutrient use efficiencies in the model are lower than observed primarily due to biases in the nutrient content and turnover of woody biomass. We conclude that ORCHIDEE is able to reproduce the shift from nitrogen to phosphorus limited net primary productivity along the soil development chronosequence, as well as the contrasting responses of net primary productivity to nutrient addition.

The SEVIRI instrument on board Meteosat Second Generation (MSG) offers new capabilities to monitor aerosol transport over the Atlantic and the Mediterranean at high temporal and spatial resolutions, in particular, Saharan dust from North Africa, biomass-burning aerosols from subtropical Africa and pollution from Europe. An inversion technique was developed to estimate both aerosol optical thickness and Angström coefficients from SEVIRI measurements at 0.63 and 0.81 µm. This method relies on an optimized set of aerosol models to ensure a fast processing of full-resolution MSG images and to allow the processing of long time series. SEVIRI images for slots 45, 49 and 53 (11:15, 12:15, 13:15 UT) were processed for June 2003. The retrieved optical thicknesses and Angström coefficients are in good agreement with AERONET in-situ measurements in the Atlantic and in the Mediterranean. Monthly mean maps of both parameters are compared to that obtained with the polar orbiting sensor POLDER for June 2003. There is a good consistency between the two monthly means in terms of optical thickness, but the Angström coefficients show significant differences in the Atlantic zone which is affected by dust transport. These differences may be explained by the lack of specific non-spherical dust models within the inversion. The preliminary results presented in this paper demonstrate, nevertheless, the potential of MSG/SEVIRI for the monitoring of aerosol optical properties at high frequencies over the Atlantic and the Mediterranean.

and other China-Trend-Stream project members; International audience; Soil moisture is a key variable of land surface hydrology, and its correct representation in land surface models is crucial for local to global climate predictions. The errors may come from the model itself (structure and parameterization) but also from the meteorological forcing used. In order to separate the two source of errors, four atmospheric forcing datasets, GSWP3 (Global Soil Wetness Project Phase 3), PGF (Princeton Global meteorological Forcing), CRU-NCEP (Climatic Research Unit-National Center for Environmental Prediction), and WFDEI (WATCH Forcing Data methodology applied to ERA-Interim reanalysis data), were used to drive simulations in China by the land surface model ORCHIDEE-MICT(ORganizing Carbon and Hydrology in Dynamic EcosystEms: aMeliorated Interactions between Carbon and Temperature). Simulated soil moisture was compared with in situ and satellite datasets at different spatial and temporal scales in order to (1) estimate the ability of ORCHIDEE-MICT to represent soil moisture dynamics in China; (2) demonstrate the most suitable forcing dataset for further hydrological studies in Yangtze and Yellow River basins; and (3) understand the discrepancies of simulated soil moisture among simulations. Results showed that ORCHIDEE-MICT can simulate reasonable soil moisture dynamics in China, but the quality varies with forcing data. Simulated soil moisture driven by GSWP3 and WFDEI shows the best performance according to the root mean square error (RMSE) and correlation coefficient, respectively, suggesting that both GSWP3 and WFDEI are good choices for further hydrological studies in the two catchments. The mismatch between simulated and observed soil moisture is mainly explained by the bias of magnitude, suggesting that the parameterization in ORCHIDEE-MICT should be revised for further simulations in China. Underestimated soil moisture in the North China Plain demonstrates possible significant impacts of human activities like irrigation on soil moisture variation, which was not considered in our simulations. Finally, the discrepancies of meteorological variables and simulated soil moisture among the four simulations are analyzed. The result shows that the discrepancy of soil moisture is mainly explained by differences in precipitation frequency and air humidity rather than differences in precipitation amount.

International audience; Assigning proper prior uncertainties for inverse modelling of CO2 is of high importance, both to regularise the otherwise ill-constrained inverse problem and to quantitatively characterise the magnitude and structure of the error between prior and "true" flux. We use surface fluxes derived from three biosphere models – VPRM, ORCHIDEE, and 5PM – and compare them against daily averaged fluxes from 53 eddy covariance sites across Europe for the year 2007 and against repeated aircraft flux measurements encompassing spatial transects. In addition we create synthetic observations using modelled fluxes instead of the observed ones to explore the potential to infer prior uncertainties from model–model residuals. To ensure the realism of the synthetic data analysis, a random measurement noise was added to the modelled tower fluxes which were used as reference. The temporal autocorrelation time for tower model–data residuals was found to be around 30 days for both VPRM and ORCHIDEE but significantly different for the 5PM model with 70 days. This difference is caused by a few sites with large biases between the data and the 5PM model. The spatial correlation of the model–data residuals for all models was found to be very short, up to few tens of kilometres but with uncertainties up to 100 % of this estimation. Propagating this error structure to annual continental scale yields an uncertainty of 0.06 Gt C and strongly underestimates uncertainties typically used from atmospheric inversion systems, revealing another potential source of errors. Long spatial e-folding correlation lengths up to several hundreds of kilometres were determined when synthetic data were used. Results from repeated aircraft transects in south-western France are consistent with those obtained from the tower sites in terms of spatial autocorrelation (35 km on average) while temporal autocorrelation is markedly lower (13 days). Our findings suggest that the different prior models have a common temporal error structure. Separating the analysis of the statistics for the model data residuals by seasons did not result in any significant differences of the spatial e-folding correlation lengths.

This study uses a variational data assimilation framework to simultaneously constrain a global ecosystem model with eddy covariance measurements of daily net carbon (NEE) and latent heat (LE) fluxes from a large number of sites grouped in seven plant functional types (PFTs). It is an attempt to bridge the gap between the numerous site-specific parameter optimization works found in the literature and the generic parameterization used by most land surface models within each PFT. The present multi-site approach allows deriving PFT-generic sets of optimized parameters enhancing the agreement between measured and simulated fluxes at most of the sites considered, with performances often comparable to those of the corresponding site-specific optimizations. Besides reducing the PFT-averaged model–data root-mean-square difference (RMSD) and the associated daily output uncertainty, the optimization improves the simulated CO2 balance at tropical and temperate forests sites. The major site-level NEE adjustments at the seasonal scale are: reduced amplitude in C3 grasslands and boreal forests, increased seasonality in temperate evergreen forests, and better model–data phasing in temperate deciduous broadleaf forests. Conversely, the poorer performances in tropical evergreen broadleaf forests points to deficiencies regarding the modeling of phenology and soil water stress for this PFT. An evaluation with data-oriented estimates of photosynthesis (GPP) and ecosystem respiration (Reco) rates indicates distinctively improved simulations of both gross fluxes. The multi-site parameter sets are then tested against CO2 concentrations measured at 53 locations around the globe, showing significant adjustments of the modeled seasonality of atmospheric CO2 concentration, whose relevance seems PFT-dependent, along with an improved interannual variability. Lastly, a global scale evaluation with remote sensing NDVI measurements indicates an improvement of the simulated seasonal variations of the foliar cover for all considered PFTs.

Fire is an important global ecological process that determines the distribution of biomes, with consequences for carbon, water, and energy budgets. The modelling of fire is critical for understanding its role in both historical and future changes in terrestrial ecosystems and the climate system. This study incorporates the process-based prognostic fire module SPITFIRE into the global vegetation model ORCHIDEE, which was then used to simulate the historical burned area and the fire regime for the 20th century. For 2001–2006, the simulated global spatial extent of fire occurrence agrees well with that given by the satellite-derived burned area datasets (L3JRC, GLOBCARBON, GFED3.1) and captures 78–92% of global total burned area depending on which dataset is used for comparison. The simulated global annual burned area is 329 Mha yr−1, which falls within the range of 287–384 Mha yr−1 given by the three global observation datasets and is close to the 344 Mha yr−1 given by GFED3.1 data when crop fires are excluded. The simulated long-term trends of burned area agree best with the observation data in regions where fire is mainly driven by the climate variation, such as boreal Russia (1920–2009), and the US state of Alaska and Canada (1950–2009). At the global scale, the simulated decadal fire trend over the 20th century is in moderate agreement with the historical reconstruction, possibly because of the uncertainties of past estimates, and because land-use change fires and fire suppression are not explicitly included in the model. Over the globe, the size of large fires (the 95th quantile fire size) is systematically underestimated by the model compared with the fire patch data as reconstructed from MODIS 500 m burned area data. Two case studies of fire size distribution in boreal North America and southern Africa indicate that both the number and the size of big fires are underestimated, which could be related with too low fire spread rate (in the case of static vegetation) and fire duration time. Future efforts should be directed towards building consistent spatial observation datasets for key parameters of the model in order to constrain the model error at each key step of the fire modelling.

This paper describes a new model for the calculation of daily, high-resolution (up to 1 km) fire emissions, developed in the framework of the APIFLAME project (Analysis and Prediction of the Impact of Fires on Air quality ModEling). The methodology relies on the classical approach, multiplying the burned area by the fuel load and the emission factors specific to the vegetation burned. Emissions can be calculated on any user-specified domain, horizontal grid, and list of trace gases and aerosols, providing input information on the burned area (location, extent) and emission factors of the targeted species are available. The strength of the proposed algorithm is its high resolution and its flexibility in terms of domain and input data (including the vegetation classification). The modification of the default values and databases proposed does not require changes in the core of the model. The code may be used for the calculation of global or regional inventories. However, it has been developed and tested more specifically for Europe and the Mediterranean area. In this region, the burning season extends from June to October in most regions, with generally small but frequent fires in Eastern Europe, Western Russia, Ukraine and Turkey, and large events in the Mediterranean area. The resulting emissions represents a significant fraction of the total yearly emissions (on average amounting to ~30% of anthropogenic emissions for PM2.5, ~20% for CO). The uncertainty on the daily carbon emissions was estimated to ~100% based on an ensemble analysis. Considering the large uncertainties on emission factors, the potential error on the emissions for the various pollutants is even larger. Comparisons to other widely used emission inventories shows good correlations but discrepancies of a factor of 2–4 on the amplitude of the emissions, our results being generally on the higher end.

Understanding and simulating land biosphere processes happening at the interface between plants and atmosphere are important research activities with operational applications for monitoring and predicting seasonal and inter-annual variability of terrestrial carbon fluxes in connection to a changing climate. This paper reports a comparison between three different Land Surface Models (LSMs), ORCHIDEE, ISBA-A-gs and CTESSEL used in the Copernicus-Land project precursor, forced with the same meteorological data, and compared with the carbon fluxes measured at 32 Eddy Covariance (EC) flux tower sites in Europe. The results show that the three models have the best performance for forest sites and the poorest performance for cropland and grassland sites. In addition, the three models have difficulties capturing the seasonality of Mediterranean and Sub-tropical biomes, characterized by dry summers. This reduced simulation performance is also reflected in deficiencies in diagnosed Light Use Efficiency (LUE) and Vapour Pressure Deficit (VPD) dependencies compared to observations. Shortcomings in the forcing data may also play a role. These results indicate that more research is needed on the LUE and VPD functions for Mediterranean and Sub-tropical biomes. Finally, this study highlights the importance well representing phenology (i.e. Leaf Area evolution) and management (i.e. rotation/irrigation for cropland, and grazing/harvesting for grassland) to simulate the carbon dynamics of European ecosystems and the importance of ecosystem level observation in models development and validation.

International audience; Land evapotranspiration (ET) estimates are available from several global datasets. Here, monthly global land ET synthesis products, merged from these individual datasets over the time periods 1989-1995 (7 yr) and 1989-2005 (17 yr), are presented. The merged synthesis products over the shorter period are based on a total of 40 distinct datasets while those over the longer period are based on a total of 14 datasets. In the individual datasets, ET is derived from satellite and/or in-situ observations (diagnostic datasets) or calculated via land-surface models (LSMs) driven with observations-based forcing and atmospheric reanalyses. Statistics for four merged synthesis products are provided, one including all datasets and three including only datasets from one category each (diagnostic, LSMs, and reanalyses). The multi-annual variations of ET in the merged synthesis products display realistic responses. They are also consistent with previous findings of a global increase in ET between 1989 and 1997 (1.15 mm yr-2 in our merged product) followed by a decrease in this trend (-1.40 mm yr-2), although these trends are relatively small compared to the uncertainty of absolute ET values. The global mean ET from the merged synthesis products (based on all datasets) is 1.35 mm per day for both the 1989-1995 and 1989-2005 products, which is relatively low compared to previously published estimates. We estimate global runoff (precipitation minus ET) to 34 406 km3 per year for a total land area of 130 922 km2. Precipitation, being an important driving factor and input to most simulated ET datasets, presents uncertainties between single datasets as large as those in the ET estimates. In order to reduce uncertainties in current ET products, improving the accuracy of the input variables, especially precipitation, as well as the parameterizations of ET are crucial.

The influence of seasonal phenology in tropical humid forests on canopy photosynthesis remains poorly understood and its representation in global vegetation models highly simplified, typically with no seasonal variability of canopy leaf area properties taken into account. However, recent flux tower and remote sensing studies suggest that seasonal phenology in tropical rainforests exerts a large influence over carbon and water fluxes, with feedbacks that can significantly influence climate dynamics. A more realistic description of the underlying mechanisms that drive seasonal tropical forest photosynthesis and phenology could improve the correspondence of global vegetation model outputs with the wet-dry season biogeochemical patterns measured at flux tower sites. Here, we introduce a leaf Net Primary Production (NPP) based canopy dynamics scheme for evergreen tropical forests in the global terrestrial ecosystem model ORCHIDEE and validated the new scheme against in-situ carbon flux measurements. Modelled Gross Primary Productivity (GPP) patterns are analyzed in details for a flux tower site in French Guiana, in a forest where the dry season is short and where the vegetation is considered to have developed adaptive mechanisms against drought stress. By including leaf litterfall seasonality and a coincident light driven leaf flush and seasonal change in photosynthetic capacity in ORCHIDEE, modelled carbon and water fluxes more accurately represent the observations. The fit to GPP flux data was substantially improved and the results confirmed that by modifying canopy dynamics to benefit from increased light conditions, a better representation of the seasonal carbon flux patterns was made.

Atmospheric CO2 drives most of the greenhouse effect increase and one major uncertainty on the future rate of increase of CO2 in the atmosphere is the impact of the anticipated climate change on the vegetation. Dynamic Global Vegetation Models (DGVM) are used to address this question. ORCHIDEE is such a DGVM that has proven useful for climate change studies. However, there is no objective and methodological way to accurately assess each new available version on the global scale. In this paper, we submit a methodological evaluation of ORCHIDEE by correlating satellite-derived Vegetation Index time series against those of the modeled Fraction of absorbed Photosynthetically Active Radiation (FPAR). A perfect correlation between the two is not expected, however an improvement of the model should lead to an increase of the median correlation. We detail two case studies in which model improvements are demonstrated, using our methodology. In the first one, a new phenology version in ORCHIDEE is shown to bring a significant impact on the simulated annual cycles, in particular for C3 Grasses and C3 Crops. In the second case study, we compare the simulations when using two different weather fields to drive ORCHIDEE. The ERA-Interim forcing leads to a better description of the FPAR interannual anomalies than the simulation forced by a mixed CRU-NCEP dataset. This work shows that long time series of satellite observations, despite their uncertainties, can identify weaknesses in global vegetation models, a necessary first step to improving them.

Winter CO2 fluxes represent an important component of the annual carbon budget in northern ecosystems. Understanding winter respiration processes and their responses to climate change is also central to our ability to assess terrestrial carbon cycle and climate feedbacks in the future. The factors influencing the spatial and temporal pattern of winter respiration (RECO) of northern ecosystems are poorly understood. For this reason, we analyzed eddy covariance flux data sets from 57 ecosystem sites ranging from ~35° N to ~70° N. Deciduous forests carry the highest winter RECO ratios (9.7–10.5 g C m−2 d−1), when winter is defined as the period during which air temperature remained below 0 °C. By contrast, wetland ecosystems had the lowest winter RECO (2.1–2.3 g C m−2 d−1). Evergreen needle-leaved forests, grasslands and croplands were characterized by intermediate winter RECO values of 7.4–7.9 g C m−2 d−1, 5.8–6.0 g C m−2 d−1, and 5.2–5.3 g C m−2 d−1, respectively. Cross site analysis showed that winter air or soil temperature, and the seasonal amplitude of the leaf area index inferred from satellite observation, which is a proxy for the amount of litter available for RECO in the subsequent winter, are the two main factors determining spatial pattern of daily mean winter RECO. Together, these two factors can explain 71% (Tair, ΔLAI) or 69% (Tsoil, ΔLAI) of the spatial variance of winter RECO across the 57 sites. The spatial temperature sensitivity of daily winter RECO was determined empirically by fitting an Arrhenius relationship to the data. The activation energy parameter of this relationship was found to decrease at increasing soil temperature at a rate of 83.1 KJ ° C-1 (r = −0.32, p < 0.05), which implies a possible dampening of the increase in winter RECO due to global warming. The interannual variability of winter RECO is better explained by soil temperature than by air temperature, likely due to the insulating effects of snow cover. The increase in winter RECO with a 1 °C warming based calculated from the spatial analysis was almost that double that calculated from the temporal analysis. Thus, models that calculate the effects of warming on RECO based only on spatial analyses could be over-estimating the impact.

This work presents a brief review of spatial data analysis methods and their application to radioactive contamination of territories. Two methods are described in the paper and applied to real data on soil contamination with strontium 90 (Sr90) and cesium 137 (Cs137) — cokriging model and general regression neural networks. Cokriging is a geostatistical predicator based on multivariate linear regression, which allows inclusion of data on correlated variables in the joint estimation procedure, in order to improve the prediction quality and to reduce estimation errors. General regression neural network is a nonparametric estimator, which is fast and produces high quality results on extremely variable Chernobyl data. Such research with adaptation of these methods to the characterization of radionuclides data has been under progress for 5 years by the senior authors group.

Isolated rat hepatocytes were incubated with carbon tetrachloride (CCl4) at a concentration of 0.2 mol CCl4/ml of incubation medium. The ultrastructural alterations and release of lactate dehydrogenase (LDH) and glutamate-oxaloacetate transaminase (GOT), were recorded after different periods of incubation. After 5 min incubation with CCl4, morphological changes observed by electron microscopy, involved the plasma membrane. The endoplasmic reticulum and mitochondria were altered later. These morphological alterations were accompanied by an early release of LDH and GOT into the incubation medium. It is concluded that, in contrast with its in vivo effects, in vitro CCl4 can induced an early morphological alteration of the hepatocyte plasma membrane before damaging the endoplasmic reticulum.

The influence of malotilate was tested in a model of galactosamine (GalN) intoxication in the rat; two different experiments were carried out: (1) an acute intoxication (GalN at a single dose of 500 mg/kg s.c.): after pretreatment with malotilate (100 and 200 mg/kg p.o.) a marked reduction of hepatocellular injury, as shown by decrease of ASAT, ALAT and GlDH, was observed; (2) a subacute intoxication, GalN at 3 x 300 mg/kg s.c. for 3 consecutive days, which induced a more moderate hepatic necrosis: malotilate, 200 mg/kg/day p.o. for 4 days beginning the day before the first GalN injection, decreased significantly the disturbances of the biochemical measured parameters (plasma total proteins, prothrombin time, fibrinogen level, BSP clearance and hepatic total lipids) and accelerates the return to normal. Pathology studies revealed that pretreatment with malotilate increased markedly the number of mitoses observed in the hepatic cells after intoxication. In addition, the time of appearance of these mitoses was earlier in malotilate treated animals. From these studies, it can be concluded that malotilate protects against hepatocellular injury induced by GalN in the rat and that it stimulates the regenerative capacity of the liver after intoxication.

Endothelial cells of porcine thoracic aorta were chosen as in vitro model to study the reaction of endothelial cells to injury induced by carbon tetrachloride (CCl4 0.2 microliter/ml). Lactate dehydrogenase (LDH) was used as the biochemical marker of cell injury. The changes in the morphology of the surface endothelial cells were observed by scanning electron microscopy. In addition, cell survival rate after replating cultures was evaluated. The pretreatment with Bencianol (10(-10) M-10(-7) M) produced cytoprotective effects against CCl4 induced cell injury on the above three parameters.

— The antinecrotic potential of a new drug, 3-palmitoyl-(+)-catechin (PC), which is a derivative of (+)-cyanidanol-3, was studied in two different experimental models of necrosis of the liver in the rat: acute hepatitis induced by galactosamine and liver damage induced by a combination of chronic ethanol feeding and hypoxia. The extent of liver damage was assessed by histological examination and by measuring blood hepatic enzyme and bilirubin levels. Intraperitoneal and oral treatments with PC were carried out at different dosages and times, before acute galactosamine intoxication. PC treatment decreased the extent of diffuse necrosis and inflammation in liver tissue and reduced galactosamine-induced biochemical deterioration. The lowest active doses of PC were 25 mg/kg, i.p. and 500 mg/kg by mouth. In experiments with ethanol, we confirmed that a 6-h period of hypoxia produced necrosis of the liver in rats undergoing chronic treatment with ethanol. Simultaneous treatment with PC (80 mg/kg/day) for 21 days during ethanol feeding gave significant protection against histological and biochemical deterioration induced by ethanol and hypoxia. The antinecrotic effect of PC in two models, which are recognized as producing part of the biochemical and/or histological deterioration induced by viruses and ethanol in man, indicates that it is a potentially useful agent for the treatment of necrosis of the human liver.

Adriamycin (Adriablastine), administered weekly at the dose of 5 mg/kg i.p. for 3 weeks in rats, produced a general decrease of vitality associated with a decrease of body weight, hypothermia, decreases of stroke volume and cardiac output. Hematocrit was decreased. Renal blood flow decreased whereas pulmonary blood flow increased. Mean blood pressure and heart rate remained unaffected. Biochemical evaluations revealed a decrease of blood urea and serum creatinine, which might be related to decreased food intake and protein metabolism. Morphological changes in the heart tissue could not be appreciated. Venoruton (HR), administered at the dose of 300 mg/kg p.o. daily for 28 days (5 days before and 23 days after the first injection of adriamycin), improved adriamycin-induced clinical signs and symptoms (loss of body weight, hypothermia and decreased general vitality). It tended to increase cardiac output and stroke volume.

Isolated rat hepatocytes were incubated with carbon tetrachloride (CCl4) at a concentration of 0.2 mol CCl4/ml of incubation medium. The ultrastructural alterations and release of lactate dehydrogenase (LDH) and glutamate-oxaloacetate transaminase (GOT), were recorded after different periods of incubation. After 5 min incubation with CCl4, morphological changes observed by electron microscopy, involved the plasma membrane. The endoplasmic reticulum and mitochondria were altered later. These morphological alterations were accompanied by an early release of LDH and GOT into the incubation medium. It is concluded that, in contrast with its in vivo effects, in vitro CCl4 can induced an early morphological alteration of the hepatocyte plasma membrane before damaging the endoplasmic reticulum.

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