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Fires have influenced atmospheric composition and climate since the rise of vascular plants, and satellite data have shown the overall global extent of fires. Our knowledge of historic fire emissions has progressively improved over the past decades due mostly to the development of new proxies and the improvement of fire models. Currently, there is a suite of proxies including sedimentary charcoal records, measurements of fire-emitted trace gases and black carbon stored in ice and firn, and visibility observations. These proxies provide opportunities to extrapolate emission estimates back in time based on satellite data starting in 1997, but each proxy has strengths and weaknesses regarding, for example, the spatial and temporal extents over which they are representative. We developed a new historic biomass burning emissions dataset starting in 1750 that merges the satellite record with several existing proxies and uses the average of six models from the Fire Model Intercomparison Project (FireMIP) protocol to estimate emissions when the available proxies had limited coverage. According to our approach, global biomass burning emissions were relatively constant, with 10-year averages varying between 1.8 and 2.3 Pg C yr−1. Carbon emissions increased only slightly over the full time period and peaked during the 1990s after which they decreased gradually. There is substantial uncertainty in these estimates, and patterns varied depending on choices regarding data representation, especially on regional scales. The observed pattern in fire carbon emissions is for a large part driven by African fires, which accounted for 58 % of global fire carbon emissions. African fire emissions declined since about 1950 due to conversion of savanna to cropland, and this decrease is partially compensated for by increasing emissions in deforestation zones of South America and Asia. These global fire emission estimates are mostly suited for global analyses and will be used in the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations.

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Unlike fragmental rockfall runout assessments, there are only few robust methods to quantify rock-mass-failure susceptibilities at regional scale. A detailed slope angle analysis of recent Digital Elevation Models (DEM) can be used to detect potential rockfall source areas, thanks to the Slope Angle Distribution procedure. However, this method does not provide any information on block-release frequencies inside identified areas. The present paper adds to the Slope Angle Distribution of cliffs unit its normalized cumulative distribution function. This improvement is assimilated to a quantitative weighting of slope angles, introducing rock-mass-failure susceptibilities inside rockfall source areas previously detected. Then rockfall runout assessment is performed using the GIS- and process-based software Flow-R, providing relative frequencies for runout. Thus, taking into consideration both susceptibility results, this approach can be used to establish, after calibration, hazard and risk maps at regional scale. As an example, a risk analysis of vehicle traffic exposed to rockfalls is performed along the main roads of the Swiss alpine valley of Bagnes.

il s'agit d'un type de produit dont les métadonnées ne correspondent pas aux métadonnées attendues dans les autres types de produit : REPORT; absent

Année de la première version : 2014Interface utilisateur : interface WebMode de diffusion : service en ligne; Common european tree species sampled in Gascony hills in South-West of France.

This dataset contains Hg stable isotope and speciation measurements of seawater

Policy Paper prepared in the framework of HNV-Link (project funded by the H2020 Research and Innovation Programme under Grant Agreement no 696391)

Simulating ensembles of extreme events is a necessary task to evaluate their probability distribution and analyze their meteorological properties. Algorithms of importance sampling have provided a way to simulate trajectories of dynamical systems (like climate models) that yield extreme behavior, like heat waves. Such algorithms also give access to the return periods of such events. We present an adaptation based on circulation analogues of importance sampling to provide a data-based algorithm that simulates extreme events like heat waves in a realistic way. This algorithm is a modification of a stochastic weather generator, which gives more weight to trajectories with higher temperatures. This presentation outlines the methodology using European heat waves and illustrates the spatial and temporal properties of simulations.

The Eastern Boundary Upwelling Systems (EBUS) contribute to one fifth of the global catches in the ocean. Often associated with Oxygen Minimum Zones (OMZs), EBUS represent key regions for the oceanic nitrogen (N) cycle. Important bioavailable N loss due to denitrification and anammox processes as well as greenhouse gas emissions (e.g, N2O) occur also in these EBUS. However, their dynamics are currently crudely represented in global models. In the climate change context, improving our capability to properly represent these areas is crucial due to anticipated changes in the winds, productivity, and oxygen content. We developed a biogeochemical model (BioEBUS) taking into account the main processes linked with EBUS and associated OMZs. We implemented this model in a 3-D realistic coupled physical/biogeochemical configuration in the Namibian upwelling system (northern Benguela) using the high-resolution hydrodynamic ROMS model. We present here a validation using in situ and satellite data as well as diagnostic metrics and sensitivity analyses of key parameters and N2O parameterizations. The impact of parameter values on the OMZ off Namibia, on N loss, and on N2O concentrations and emissions is detailed. The model realistically reproduces the vertical distribution and seasonal cycle of observed oxygen, nitrate, and chlorophyll a concentrations, and the rates of microbial processes (e.g, NH4+ and NO2− oxidation, NO3− reduction, and anammox) as well. Based on our sensitivity analyses, biogeochemical parameter values associated with organic matter decomposition, vertical sinking, and nitrification play a key role for the low-oxygen water content, N loss, and N2O concentrations in the OMZ. Moreover, the explicit parameterization of both steps of nitrification, ammonium oxidation to nitrate with nitrite as an explicit intermediate, is necessary to improve the representation of microbial activity linked with the OMZ. The simulated minimum oxygen concentrations are driven by the poleward meridional advection of oxygen-depleted waters offshore of a 300 m isobath and by the biogeochemical activity inshore of this isobath, highlighting a spatial shift of dominant processes maintaining the minimum oxygen concentrations off Namibia. In the OMZ off Namibia, the magnitude of N2O outgassing and of N loss is comparable. Anammox contributes to about 20% of total N loss, an estimate lower than currently assumed (up to 50%) for the global ocean.

1. Plant functional traits, in particular specific leaf area (SLA), wood density and seed mass, are often good predictors of individual tree growth rates within communities. Individuals and species with high SLA, low wood density and small seeds tend to have faster growth rates. 2. If community-level relationships between traits and growth have general predictive value, then similar relationships should also be observed in analyses that integrate across taxa, biogeographic regions and environments. Such global consistency would imply that traits could serve as valuable proxies for the complex suite of factors that determine growth rate, and, therefore, could underpin a new generation of robust dynamic vegetation models. Alternatively, growth rates may depend more strongly on the local environment or growth–trait relationships may vary along environmental gradients. 3. We tested these alternative hypotheses using data on 27 352 juvenile trees, representing 278 species from 27 sites on all forested continents, and extensive functional trait data, 38% of which were obtained at the same sites at which growth was assessed. Data on potential evapotranspiration (PET), which summarizes the joint ecological effects of temperature and precipitation, were obtained from a global data base. 4. We estimated size-standardized relative height growth rates (SGR) for all species, then related them to functional traits and PET using mixed-effect models for the fastest growing species and for all species together. 5. Both the mean and 95th percentile SGR were more strongly associated with functional traits than with PET. PET was unrelated to SGR at the global scale. SGR increased with increasing SLA and decreased with increasing wood density and seed mass, but these traits explained only 3.1% of the variation in SGR. SGR–trait relationships were consistently weak across families and biogeographic zones, and over a range of tree statures. Thus, the most widely studied functional traits in plant ecology were poor predictors of tree growth over large scales. 6. Synthesis. We conclude that these functional traits alone may be unsuitable for predicting growth of trees over broad scales. Determining the functional traits that predict vital rates under specific environmental conditions may generate more insight than a monolithic global relationship can offer.