dataesr/scientific-paragraphs-categorization huggingface dataset:
This is chosen to be large enough to include information from the lower resolution models (Table  1 ) (e. g. , SPCAM3 and CanCM4) and capture the evolution of the MJO while small enough to isolate the convectively active and suppressed regions. The longitudinal extent of these boxes are marked in Figure  1   vertical profiles are converted to a common pressure coordinate determined by the average pressure variations during each forecast at model levels. All temporal means used in this analysis are derived from the time step data. Model precipitation is compared with the TRMM 3B42 3-hourly rainfall data  [Kummerow et al. , 2000]. Temperature, humidity, and winds are taken from the ERA YoTC analysis for evaluation of model biases. Other variables such as cloud properties, mass flux, heat, and moisture tendencies are obtained from the short (3-24 h) forecasts using the ECMWF model used to generate the analysis.
These variables are linked to somewhat better constrained dynamics than the models used for the experiments but are still strongly a function of the parametrizations in the ECMWF model. They are thus treated as an additional piece of information rather than the truth. Quality controlled model outputs from all three experiments are archived at Earth System Grid (https://earthsystemcog. org/projects/gass-yotc-mip/) and are available for download. All data analysis and visualization in this study are done using NCAR Command Language  [2014]. All models (other than CanCM4) are initialized using the ERA YoTC analysis. It is expected that the large-scale dynamics of the model within the first 48 h is constrained by the analysis-which remains our best estimate of the state of the real atmosphere.
The purpose of this paper has been to provide a basic reference and summary of the 2 day hindcast component of the GASS-YoTC project on the vertical structure and diabatic processes of the MJO. It has merely scratched the surface of a vast resource which is available to the model development community. This resource should be a very valuable tool in evaluating their modeling systems with a level of detail which can allow them to understand and improve the behavior of their physical parametrization schemes. Quality-controlled model data from all three experiments are archived at Earth System Grid (https:// earthsystemcog. org/projects/gass-yotc-mip/) and are available for download.
The maturing of Earth system science as a discipline has underpinned the development of concepts such as the Anthropocene and planetary boundaries. The International Geosphere-Biosphere Programme's (IGBP) scientific and institutional history is deeply intertwined with the development of the concept of the Earth as a system as well as the discipline of Earth system science. Here we frame the broader programme of IGBP through its core projects and programme-level activities and illustrate this coevolution. We identify and discuss three phases in the programme's history. In its first phase beginning in 1986, IGBP focused on building international networks and global databases that were key to understanding Earth system component processes. In the early 2000s IGBP's first major synthesis and associated activities promoted a more integrated view of the Earth system informed by greater emphasis on interdisciplinarity.
UNEP director Mustafa Tolba invited Bolin to chair the panel, which he did from 1988 to 1997-the period of the first two assessments. Close ties between IPCC and IGBP have continued. IGBP's long-term planning and organization hinged around process studies, observations, global models, past global change and, finally, global data and communications systems  (IGBP, 1986). By 1991, five international projects were underway in IGBP (Fig. 2  and Box 1). Some of these were bottom-up projects developed by various disciplinary communities that were then absorbed by IGBP's growing interdisciplinary network. By 1995, ten projects were active (Fig. 2 ).
Science Understanding the Earth system needs a global perspective of the processes and interactions within and among the Earth's atmosphere, oceans and land linked to regional and local scales  (Andreae et al. , 2004; Steffen et al. , 2004). Fundamental to this was the development of global databases to record the spatial and Fig. 2. Timeline of some significant events in the history of IGBP and the global environmental change programs. Box 1. IGBP projects and their acronyms. temporal variation of many key system components. For example, researchers working on the atmosphere in the IGAC core project (Box 1) compiled some of the first global databases of the distribution and emissions of reactive trace species (e. g. , N 2 O, NO, CH 4 , DMS) and aerosols in the troposphere  (Brasseur et al.
, 2003). This was supported by in-situ measurements and air-borne campaigns around the world  (Melamed et al. , 2015). Meanwhile, using advances in remote sensing technologies (Advanced Very High Resolution Radiometer, AVHRR), IGBP-DIScover developed a global land-cover classification scheme with 17 classes, which were identified on the basis of requirements of the IGBP core projects, and was used to develop global 1 km land cover databases  (Belward et al. , 1999; Loveland et al. , 2000). IGBP-DIS collaborated actively with the United States Geological Survey, NASA, National Oceanic and Atmospheric Administration and the European Space Agency  (Belward et al. , 1999). The classification scheme continues to be used widely today to assess changes at local to global scales in deforestation, cropland, urbanization and climate change, for example  (Goldewijk 2001; McGuire et al.
, 2002)  which were critical for understanding the role of the ocean in climate change. Contemporary conditions were not the only perspective in database development. The land community (LUCC) reconstructed land cover for the past 300 years, motivated in part by the need to contextualize present-day tropical deforestation  (Ramankutty and Foley 1999; Ramankutty et al. , 2006). New work on ice cores led to major advances in documenting past climate, especially for the late Quaternary Period. For example, ice cores from Antarctica and Greenland led to a detailed record of atmospheric composition (in particular CO 2 , N 2 O, CH 4 ,)  (Petit et al. , 1999; Raynaud et al. , 2003)  (Fig. 3 ). For the first time, we had an insight into long-term forcings and response of the climate system, thus allowing us to put the recent, anthropogenic changes in context.
PAGES undertook a major synthesis to document the temperature and precipitation history of various regions of the world during the past two millennia  (PAGES-2k Consortium, 2013). SOLAS, along with the International Ocean Carbon Coordination Project (IOCCP), continued development of global databases for surface-water CO 2 distribution and DMS emissions: these were used for model-data comparison and for better quantification of the ocean carbon sink and to understand how it varies with location and in time  (Bakker et al. , 2014). Given the many different climate and Earth system models being developed, a critical activity in preparation for IPCC AR5 was the intercomparison of models (MIPs) that increased understanding of uncertainty across the range of climate and Earth System models  (Brovkin et al.
Throughout its three decades IGBP built new international networks, engaging thousands of scientists from developed and developing countries. Beyond the global reach, a key aspect of these networks was that they brought together disciplines that traditionally did not work together (e. g. , atmospheric chemistry with biology, ecology, and biogeochemistry) leading to the development of a more integrative understanding of the Earth system, including past and potential future changes. Creation of new global databases, process studies and advances in Earth system modeling from IGBP projects were a foundation upon which new knowledge of the dynamics of the Earth system components and their interactions were built. One indication of the impact of IGBP is that in the last five years at least 144 papers were published in the Nature group alone from core project and programme level initiatives  (IGBP Annual Reports 2010 -2015; www.
Acknowledgements We would like to acknowledge the contributions of individuals, organizations, and countries throughout the three decades of IGBP whose scientific and/or financial contributions have supported the goals of IGBP. A number of IGBP core projects were co-sponsored by other programmes and are acknowledged in the individual core project papers in this volume. Financial support for preparing this paper was provided by Grant # GEO-1247560  from the  US National Science Foundation  (to SPS). Ninad Bondre made important contributions to this paper at many stages in its development. Additional valuable input to the text were made by  Eileen Hoffman  on marine components and  H-C. Hansson  on land-atmosphere interactions. Hilarie Cutler  provided graphics assistance. All authors on this paper had substantial involvement in IGBP at one time or other.
Ecology, 97 (4), 1023-1037. , DOI : 10. 1890/15-1263. 1 18 is more subtle when the initial soil variation among ecosystem types is considered (Table  1 ). In this latter model, the litter species explained 94% of the average variance of carbon mineralisation independently of the sampling date (Table  1 ). Among the different litter traits, the lignin:N and C:N ratios were poor predictors of the mineralisation rates (p > 0. 05, Appendix F). By contrast, the lignocellulose index (LCI) showed strong negative correlations with the carbon mineralisation rates for each litter type individually but also globally using the entire dataset (r 2 = 0. 47, p < 0. 05, Appendix F). Finally, when the effect of each litter type was investigated separately over the course of the incubation, the dynamics of the net carbon mineralisation rates of the cropland, forest and plantation litter types were influenced by the soil type and by a soil type × time interaction that decreased from 0-27 to 98-202 days (Table  2 ).
of h (from home combinations 1 to K), and Home h = Litter l * Soil s when l and s are home-field pairings. The parameters to be estimated are β l , γ s and η h. The intercept term is defined by α and represents the average carbon mineralisation rate for all observations in the dataset after controlling for litter, soil and home-field pairings. Negative parameter estimates indicate lower carbon mineralisation than the average rate observed across all samples in the device. The error term is defined by ε. Using this model, we calculated a quality index (LQ), the ability of soil Comment citer ce document : Fanin, N. , Fromin, N. , Bertrand, I. (2016). Functional breadth and home-field advantage generate functional differences among soil microbial decomposers. Ecology, 97 (4), 1023-1037. , DOI : 10.
Acknowledgments We thank  Gonzague Alavoine  for the management of the incubation experiment;  Pascal Thiebeau  for his help with litter sampling;  Sylvie Millon ,  Manon Gaddi ,  Antoine Portelette , and  Jorge Lebrato Mejijas  for their highly appreciated laboratory assistance;  Olivier Delfosse  and  Marie Sauvadet  for their help during microcosm destruction; Sylvie Recous and Gwenaëlle.
Fish (n = 229) were then sampled for fin rays by excising fin rays 5 and 6 from their second dorsal fin, as close as possible to their articulation point. Recreational fishermen of the Association des Plaisanciers et Pêcheurs de Guyane (APPG) were also instructed on how to sample fish and contributed to the data collection. When tagged fish were recaptured, the adjoining fin rays (7 and 8) were removed to re-age the fish. Fin rays were put on ice in the field and then transferred to a freezer in the laboratory until processed for aging. Fin ray aging of goliath grouper followed the protocol of  Murie et al. (2009)  with modifications specific to goliath grouper in French Guiana. Thawed fin rays were cleaned by immersing them in simmering water for ~2 min and then mechanically removing the re maining skin and muscle tissue.
Growth A von Bertalanffy growth curve was fitted to the age and length data using a non-linear regression with least-squares estimation as the loss function (R software) as: L t = L ∞ (1 -e -k (t -t 0 ) ) (2) where L t is the total length (cm) at age t; L ∞ is asymptotic length (cm); k is the Brody growth coefficient (yr -1 ); t is the age (yr); and t 0 is the theoretical age when length is zero. Ages of fish used in the model were based on the age class of each fish, which is the observed age corrected for time of capture relative to an international birthdate of 1 January. Assigning fish into age classes ensured that cohorts, or fish born in the same year, were not split between years. Fractional ages, or ages that take into account the capture date of the fish relative to its actual birthdate, could not be calculated because it is currently unknown when (or if) goliath grouper spawn in French Guiana.
Acknowledgements. This study was conducted in partial fulfillment of the degree of Doctor of  Marine Biology  at the  University of French West Indies and French Guiana by C. A. We thank the  Grand Connectable Reserve agents ,  A. Alcide  and  A. Hauselmann , for providing access to the field and the reserve in their research vessel, the  Association des plainsanciers et pêcheurs de Guyane (APPG) , especially  E. Ribas  for his involvement and support in the grouper project, and  J. Hargrove  for his hospitality in Gainesville (C. A. ). We also thank the  Direction de l'Environnement, de l'Amenagement et du Logement (DEAL)  for supporting the research and the  Office National de la Chasse et de la Faune Sauvage  for the administrative and financial management of the grouper project.
The performance of the prototype simulator based on state estimation (SE) or parameter estimation (PE) is then evaluated by comparison with data taken from a reduced-scale controlled grassland fire experiment. Results indicate that data-driven simulations are capable of correcting inaccurate predictions of the fire front location and of subsequently providing an optimized forecast of the wildfire behavior at future lead times. The complementary benefits of both PE and SE approaches, in terms of analysis and forecast performance, are also emphasized. In particular, it is found that the size of the assimilation window must be specified adequately with the persistence of the model initial condition and/or with the temporal and spatial variability of the environmental conditions in order to track sudden changes in wildfire behavior.
, 2013)  is used (the original formulation includes a revised wind speed limit function to correct the rate of spread dependence on strong wind speeds). Since the focus is on the DA strategy, the complex description of the biomass fuel that can be used by forest service experts  (Sandberg et al. , 2007)  is not addressed in this work. The default values given in  Rothermel (1972)  such as the moisture content at extinction (M v,ext = 30 %) are therefore used. Still, the PE and SE approaches presented in the following are valid for any fire spread model. While beyond the scope of this work, they could be extended to more complex description of the input data (biomass fuel, surface wind conditions, terrain topography) without changing the estimation algorithm. Furthermore, FIREFLY could readily be replaced by any other front-tracking simulator of wildfire spread, for instance FARSITE, FOREFIRE, PROMETHEUS or PHOENIX RapidFire.
Application to a controlled grassland fire experiment The EnKF-FIREFLY data-driven simulator is now evaluated in a validation study corresponding to a controlled grassland fire experiment (a case in which the true control vector is not known and may not exist if the model is not sufficiently representative). The experimental configuration corresponds to a small (4 m × 4 m), flat and horizontal, open-field grassland lot burning under moderate wind conditions in which the ROS takes values on the order of 1 cm s -1 (with a maximum value reaching 5 cm s -1 in the wind direction). The properties of the grass are (approximately) known: δ v = 8 cm (field measurement), M v = 22 % (field measurement) and v = 11 500 m -1 (Rothermel's fuel database for short grass); the wind conditions are also approximately known: the magnitude and direction angle of the wind are constant and equal to u * w = 1 m s -1 and α * w = 307 •.
Conclusions This study is the second part in a series of two articles, in which we propose and explore a new paradigm for producing optimized forecasts of the wildfire behavior based on observations of the fire front location. The prototype data-driven simulator combines a regional-scale wildfire spread model, FIREFLY, with a data assimilation (DA) algorithm based on an ensemble Kalman filter (EnKF); it features a choice between a parameter estimation (PE) approach (in which the estimation targets are the parameters of the rate of spread (ROS) model) and a state estimation (SE) approach (in which the estimation targets are the time-evolving spatial coordinates of the fire front). The cornerstone of this prototype data-driven simulator is to do the following: 1. explicitly account for the effects of both measurement and modeling errors and thereby, overcome some of the current limitations of regional-scale wildfire modeling; 2.
The duration of the assimilation cycle (i. e. , a user-defined variable) is therefore of primary importance in the success of the proposed DA approaches; it must be specified consistently with the persistence of the initial condition and/or with the temporal and spatial variability of the errors in the environmental conditions. This series of two articles emphasizes the potential of DA to dramatically increase wildfire simulation accuracy. In particular, the present original comparison between different data assimilation strategies show that the future data-driven wildfire spread strategy will be a blend of the PE and SE strategies in order to address all possible sources of uncertainties in the fire representation, consistently with the available amount of information on the fire event. While wildfire spread forecast capabilities are still at an early stage of development, it is envisioned that they will be similar to current weather forecast capabilities and that they will provide real-time fire forecasts using thermal-infrared imaging including a description of both wildfire dynamics and plume emissions such as FOREFIRE-MESONH  (Filippi et al.
Acknowledgements. The financial support provided by the  Agence Nationale de la Recherche  under the  IDEA 2 project  grant  ANR-09- COSI-006  ( 2010COSI-006 ( -2013 ) ) and by the  LEFE-MANU grant  ( INSU-CNRS program , 2011-2013) was greatly appreciated. The authors also gratefully acknowledge  Ronan Paugam  and  Martin Wooster  ( King's College London ) for sharing the experimental data,  Florent Duchaine  and  Thierry Morel  ( CERFACS ) for support on OpenPALM,  Albert Simeoni  ( University of Edinburgh ) for clarifications on Rothermel's model and helpful discussions on wildfire spread modeling, as well as  Jonathan Beezley  for helpful discussions on state estimation problems for wildfire spread.
The experimental data of six independent injections per concentration were overlaid and analyzed using the BIAevaluation 4. 1 software (GE Healthcare) considering a 1:1 Langmuir binding model. The experimental SPR signals were normalized against the fitted maximum binding signal (Rmax), and the figures thus show the proportion of LepB molecules that are complexed on the y axis ("LepB site occupancy %"). Between binding assays, the surface was regenerated by successive injections of EDTA 0. 35 M (2 min), NaOH 50 mM (1 min) and SDS 0. 1% (1 min) followed by washing with the running buffer. To test the interaction of the N-terminally His-tagged CD of colicin D with the His-tagged LepB, the latter was covalently immobilized in an oriented fashion onto a nitrilotriacetic acidderivatized sensorchip according to Jomain et al.
In Silico Identification of Conserved Amino Acid Motifs at and Near to the LepB Binding Sites (LBS) Located in the Central Domain of E. coli Colicin D The plasmid-encoded colicin D (pColD CA23) protein originating from E. coli, the colicin D central domain (Glu-314 -Met-590), and a shorter peptide segment centered on the LBS were used independently as bait to detect homologous ORFs. The 3241 completely sequenced bacterial chromosomes provided by the NCBI repository were analyzed locally with a C# script using TBLASTN on the SYNTTAX genomic database  (27).
5D ). No PF of these two mutated colicins was detected in the cytoplasm. The processing defect thus appeared to be the direct consequence of the absence of their productive interaction with LepB at the inner membrane. All these results indicated that the structural role of LepB is a key player in the process of colicin D uptake. Involvement of the Box E Residues of LepB in the Interaction with Colicin D-Four strictly conserved residues of the LepB Box E (positions 272-282) are critical for maintaining a functional enzyme (32) (Fig. 7A ). The analysis of the solvent accessibility using the ESPript program  (33)  showed that most Box E residues are buried near the catalytic center, whereas only Asp-276, Asn-277, and Ala-279 have intermediate or full accessibility to water molecules. In addition to the N274K mutation of strain A38, we have constructed several mutants at (and in the vicinity of) Asn-274.
coli LepB (PDB code 1KN9; Ref. 34) mutated residues Gly-272, Asn-274, Asn-277, and Ala-279 are shown in sticks and are indicated in blue (PyMOL Molecular Graphic System). LepB residues within interacting distance (Ͻ3. 6 Å) of the mutated residues are indicated in black. Some relevant hydrogen bonds are shown as dotted yellow lines. Residues of the catalytic dyad (Ser-90, Lys-145) are represented in pink sticks.
Location and depth of the active zones are shown in Fig. 3. Table  1  summarizes the 3 He fluxes used for our simulations. For the Aeolian and Aegean volcanic arcs, 3 He fluxes were determined by simple scaling to the global 3 He flux from arc volcanism, which can be estimated (to within a factor of 2) to be ∼ 4 × 10 -3 mol of 3 He per kilometre of arc based on the assumption that the magma production rate of arcs is ∼ 20 % of that of mid-ocean ridges  (Torgersen, 1989; Hilton et al. , 2002)  and the total length of subduction zones. For the Marsili seamount, the 3 He flux was estimated from 3 He fluxes at nearby subaerial volcanoes  (Allard, 1992a, b). 3 He / 4 He isotopic ratios were chosen according to available in situ data (when available) or to 3 He / 4 He data from nearby subaerial volcanoes.
Acknowledgements. We are grateful to  M. Hecht  (topical editor) for his careful reading of the manuscript. We thank  W. Roether  and the anonymous referee for their constructive comments and suggestions. We thank  J. Palmieri  for his help with technical aspects, and  Y. Donnadieu  for computing support. Edited by:  M. Hecht  www. ocean-sci. net/11/965/2015/Ocean Sci. , 11,  965-978 , 2015.
Data Sources Total mortality for all age groups were obtained from the French National Institute for Statistics and Economics Studies (INSEE) for Paris, Lyon and Marseille for the period 1971-2007. Mortality data for 36 US cities (listed in supplemental Table 1) were obtained from the U. S. National Center for Health Statistics, and cover all non-accidental deaths (consistent with most previous studies). For French cities, daily mean temperature data were obtained from Météo-France for a reference station (near city centre in Paris and Lyon, and at the airport in Marseilles) for the period 1971-2007. U. S. daily temperature data covering the years 1985-2006 were obtained from the National Climatic Data Center.