dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability Data used in the paper are either publicly available or made accessible in Supplement. The CO total column data from NDACC stations at Jungfraujoch and Wollongong are publicly accessible  (Mahieu, 2014; Griffith, 2015). Corresponding data from the MOPITT and IASI satellites are also freely available  (Deeter et al. , 2014; Clerbaux, 2014). The Swiss in situ data have been published by  Ballaman (2014)  and Wollongong surface measurements are publicly available via PANGAEA  (Buchholz et al. , 2016a). Paris station data are provided via the Supplement to this article. The Supplement related to this article is available online at doi:10. 5194/acp-16-10911-2016-supplement.
Acknowledgements. We are grateful to  Université Pierre et Marie Curie and Région Île-de-France  for their financial contributions as well as to  Institut Pierre-Simon Laplace  for support and facilities. We thank the  National Center for Atmospheric Research MOPITT  science team and  NASA  for producing and archiving the MOPITT CO product. Thanks are also due to the  Swiss National Air Pollution Monitoring Network (NABEL)  for delivering ground data around Switzerland. The  University of Liège contribution  to the present work has primarily been supported by the  F. R. S. -FNRS , the  Fédération Wallonie-Bruxelles  and MeteoSwiss (GAW-CH programme). We thank the  International Foundation High Altitude Research Stations Jungfraujoch and Gornergrat (HFSJG, Bern). We are grateful to all colleagues who contributed to the acquisition of the FTIR data.
The  Australian Research Council  has provided financial support over the years for the NDACC site at Wollongong, most recently as part of project  DP110101948. We also acknowledge the important contribution to the measurement programme at Wollongong made by researchers other than those listed as coauthors here, including  Voltaire Velazco  and  Nicholas Deutscher. IASI has been developed and built under the responsibility of the French space agency CNES. It is flown on board the MetOp satellite as part of the  EUMETSAT Polar System (EPS). The IASI L1 data are received through the Eumetcast near real time data distribution service. IASI data are stored in the French atmospheric database ETHER. The  National Center for Atmospheric Research (NCAR)  is sponsored by the  National Science Foundation.
Alternatively, traits may show no PS because they are under strong selective pressure and/or because they show phenotypic plasticity in response to environmental conditions  [20, 25]. Here, we use a large dataset of several hundred permanent forest plots that occur across a wide range of the environmental conditions from all nine Amazonian countries  [26] , to quantify key demographic traits of more than 300 lineages of tropical trees and explore the PS of these traits using recently published molecular genus-  [13]  and specieslevel phylogenies (  [27] , KG Dexter & RT Pennington 2013, unpublished data). By exploring how traits are correlated and the strength of PS, our goal is to address the fundamental question of whether repeated convergent and divergent evolution of life-history strategies has erased PS for life-historyrelated traits in tropical trees, or whether phylogenetic information can be used to understand ecosystem function in the world's most diverse and ecologically important forest.
net database which curates treeby-tree records from RAINFOR and other plot networks  [26, 30]. For productivity and mortality analyses, we used a subset of 257 repeated census plots with a minimum monitoring period of 2 years from 1962 to 2014. Mean census interval length is 4. 4 years and plot mean total monitoring period is 9. 9 years. During each census, all surviving trees and palms were measured, dead trees were documented and new trees with greater than or equal to 10 cm DBH were recorded. More detailed measurement methods and plot characteristics have been previously published (e. g. [31, 32] ). All recorded species and genus names were checked and standardized using the Taxonomic Name Resolution Service  [33]. We excluded all trees and palms not identified to genus level (7. 9% of stems).
(b) Trait data Trait mean values of potential tree size, mean and maximum growth rates, mortality rates and wood density were calculated at both the genus and species levels. Our main analyses were performed at the genus level and covered genera present in a recently published genus-level phylogeny for Amazonian trees  [13]. Species-level trait data for those clades where we had species-level phylogenies with sufficient sampling of species in our dataset (more than 20 species): Burseraceae  [27]  and Inga (KG Dexter & RT Pennington 2013, unpublished data) were used to investigate whether patterns of PS at the genus level were consistent with species-level patterns. Species-level trait data were also used to account for intrageneric variation in the genus-level analyses of PS: the species-level data were used to calculate the standard error of each trait within each genus, and these values were incorporated into the calculations of PS (described below)  [34].
In the methods below, all the details are rspb. royalsocietypublishing. org Proc. R. Soc. B 283: 20161587 given for trait values calculated at the genus level; similar calculations and methods were used at the species level. Potential tree size, mean and maximum growth rates were all calculated in terms of tree diameter, basal area and biomass for each genus with at least 20 individuals across multiple censuses. Potential tree size was estimated as the 95th percentile of the size distribution of all trees within each genus. For trees with multiple measurements, we selected the maximum size across different censuses to define these distributions. Tree aboveground biomass per stem was calculated using the pan-tropical, three parameter allometric equation (diameter, wood density and E) of Chave, Rejou-Mechain  [35] , which assumes that tree diameter-height relationships depend linearly on bioclimatic variables (E), where E is a measurement of environmental stress based on measures of temperature seasonality and precipitation seasonality derived from the WorldClim dataset  [28]  and a measure of Climatic Water Deficit extracted from a global gridded dataset  [35].
(d) Phylogenetic signal In order to estimate PS for traits, we used Blomberg's K  [1]. This metric quantifies the amount of variance in an observed trait in Map of location of 577 selected plots in lowland tropical South America over a backcloth of the precipitation gradient (annual precipitation, from the WorldClim dataset  [28] ). The map shows plots, with annual precipitation greater than 1300 mm yr 21 and altitude less than 500 m. Yellow circles: single census, plots used exclusively for estimating wood density and potential tree size; red circles: multi censuses, plots used for estimating wood density, potential tree size, growth and mortality rates. rspb. royalsocietypublishing. org Proc. R. Soc. B 283: 20161587 relation to the expected trait variance under a Brownian motion model of evolution  [1, 4].
(e) Sensitivity analysis To investigate whether our results were affected by the spatial scale of our study, we repeated our analyses using 26 plots within 55 km of each other near Manaus. Similarly, to verify whether our results were affected by our use of genus-level data, we conducted the same analyses at the species level for the genus Inga and the Protieae (Burseraceae). Likewise, to investigate whether the number of lineages included in the analyses affected the extent of PS, we repeated the calculations of PS with just the genera with a complete set of trait values (214). Statistical analyses were performed in the R v. 3. 1. 1 program  [47] , using ape  [48] , phytools  [44]  and data. table [49]  packages.
(a) Trait data All traits measured varied substantially among genera (table 1 and figure  2 ): wood density varied eightfold, potential size in tree diameter 12-fold, potential size in biomass 814-fold, maximum growth rates in tree diameter 23-fold, mean diameter growth rates 35-fold and mortality rates 275-fold. Overall, the trait values after correcting for environmental variation and those estimated directly from the database without accounting for variation among plots were highly correlated with each other ( p , 0. 001 in all cases and t ranging from 0. 59 to 0. 79).
(d) Sensitivity analyses Although using just the Manaus plot data significantly reduced the number of genera, species and individual trees included in the analyses, PS at smaller spatial scales showed similar patterns to PS calculated using the whole dataset (electronic supplementary material, S5). Similarly, reducing the number of lineages to genera we had all trait values showed congruent patterns of PS (electronic supplementary material, S5). In addition, all traits showed similar or slightly higher Blomberg's K values for just Inga or Protieae than for all taxa together (electronic supplementary material, S5).
Figure1. Map of location of 577 selected plots in lowland tropical South America over a backcloth of the precipitation gradient (annual precipitation, from the WorldClim dataset [28] ). The map shows plots, with annual precipitation greater than 1300 mm yr21 and altitude less than 500 m. Yellow circles: single census, plots used exclusively for estimating wood density and potential tree size; red circles: multi censuses, plots used for estimating wood density, potential tree size, growth and mortality rates.
Acknowledgements. We thank  Ademir Ruschel ,  Alvaro Cogollo ,  Anand Roopsind ,  Antonio Lola da Costa ,  Antonio S. Lima ,  Armando Torres-Lezama ,  Atila Oliveira ,  Carlos Ceron ,  Claudinei Santos ,  Eder das Neves ,  Eliana Jimenez-Rojas ,  Foster Brown ,  Freddy Ramirez Arevalo ,  Georgia Pickvance ,  Gloria Galeano ,  Guido Pardo ,  Ieda Amaral ,  Ima Vieira ,  James Singh ,  John Terborgh ,  Juan Licona ,  Luzmilla Arroyo ,  Marielos Pen ˜a-Claros ,  Marisol Toledo ,  Natalino Silva ,  Patricia Loayza  and  Wenderson Castro  for allowing use of their data and the many colleagues and field assistants who have contributed to the development of the RAINFOR network. This paper is 705 in the  Technical Series of the Biological Dynamics Fragments Project  ( BDFFP-INPA/STRI ).
Funding. The field data used in this study have been generated by the  RAINFOR network , which has been supported by a  Gordon and Betty Moore Foundation  grant, the  European Union 's  Seventh Framework Programme projects 283080 , 'GEOCARBON'; and  282664 , 'AMAZALERT';  ERC grant  ' Tropical Forests in the Changing Earth System '), and  Natural Environment Research Council (NERC) Urgency ,  Consortium and Standard Grants 'AMAZONICA'  ( NE/ F005806/1 ), ' TROBIT ' ( NE/D005590/1 ) and ' Niche Evolution of South American Trees'  ( NE/I028122/1 ). Additional data were included from the  Tropical Ecology Assessment and Monitoring (TEAM) Network -a collaboration between Conservation International , the  Missouri Botanical Garden , the  Smithsonian Institution  and the  Wildlife Conservation Society , and partly funded by these institutions, the  Gordon and Betty Moore Foundation , and other donors.
ice core reveals that atmospheric CO 2 concentration stalled during the 1940s (so-called CO 2 plateau). Since the fossilfuel emissions did not decrease during the period, this stalling implies the persistence of a strong sink, perhaps sustained for as long as a decade or more. Double-deconvolution analyses have attributed this sink to the ocean, conceivably as a response to the very strong El Niño event in  1940-1942. However, this explanation is questionable, as recent ocean CO 2 data indicate that the range of variability in the ocean sink has been rather modest in recent decades, and El Niño events have generally led to higher growth rates of atmospheric CO 2 due to the offsetting terrestrial response. Here, we use the most up-to-date information on the different terms of the carbon budget: fossil-fuel emissions, four estimates of land-use change (LUC) emissions, ocean uptake from two different reconstructions, and the terrestrial sink modelled by the TRENDY project to identify the most likely causes of the 1940s plateau.
We find that they greatly overestimate atmospheric CO 2 growth rate during the plateau period, as well as in the 1960s, in spite of giving a plausible explanation for most of the 20th century carbon budget, especially from 1970 onwards. The mismatch between reconstructions and observations during the CO 2 plateau epoch of  [1940] [1941] [1942] [1943] [1944] [1945] [1946] [1947] [1948] [1949] [1950]  ranges between 0. 9 and 2. 0 Pg C yr -1 , depending on the LUC dataset considered. This mismatch may be explained by (i) decadal variability in the ocean carbon sink not accounted for in the reconstructions we used, (ii) a further terrestrial sink currently missing in the estimates by landsurface models, or (iii) LUC processes not included in the current datasets. Ocean carbon models from CMIP5 indicate that natural variability in the ocean carbon sink could explain an additional 0.
Published by Copernicus Publications on behalf of the European Geosciences Union. A. Bastos et al. :  Re-evaluating the 1940s CO 2 plateau Figure  1. Atmospheric CO 2 concentration in the Law Dome ice core and firn record from  Rubino et al. (2013)  and respective uncertainties (markers and whiskers) as well as the spline fit applied to the data following  Enting et al. (2006) , which attenuates by 50 % variations of ca. 23 years. The period corresponding to the plateau is highlighted between vertical grey lines. The blue markers correspond to samples from Law Dome and red markers from South Pole; different symbols indicate the different ice cores (big markers) and firn samples (dots).
We first compare the atmospheric CO 2 growth rate reconstructed using these datasets with the ice-core record to test their ability to capture the plateau. Additionally, we evaluate whether the ocean response to the 1940-1942 El Niño may explain the atmospheric CO 2 stabilization. Finally, we analyse the response of the land sink to this event using land-surface process models and, given that land-use data are highly uncertain, test the possible contribution of LUC to explain the additional sink required to match observations.
Atmospheric CO 2 The Law Dome ice-core and firn air records of atmospheric composition were constructed by analysis of air trapped in impermeable ice cores or in firn layers at four sites on Law Dome in Antarctica (DE08, DE08-2, DSS, and DSSW20K). They extend back about 2000 years before present with very high air-age resolution and measurement precision. It is the only CO 2 record covering the 1940s and 1950s period and overlapping with the start of direct measurements  (Etheridge et al. , 1996; Francey et al. , 1999; MacFarling Meure et al. , 2006). Here, we use the atmospheric CO 2 concentration data from air samples from the DE08, DE08-2, and DSS ice cores and from firn air from the DSSW20K record  (Trudinger et al. , 2002b)  and from the South Pole from  Rubino et al. (2013 ), shown in Fig.
1. To compile this dataset, Rubino et al. (2013)  added new CO 2 and δ 13 C measurements to the record and revised sampling methods, uncertainty estimates, and gravitational and diffusive mixing corrections relative to older measurements (e. g. Etheridge et al. , 1996; Francey et al. , 1999; MacFarling Meure et al. , 2006). The enclosure of air pores in ice is a gradual process that occurs in the lock-in zone, the transition layer between firn and impermeable ice. Due to the porosity of firn, there is also mixing between different parcels of air in the overlying layers. Therefore, the composition of air in ice-core samples corresponds not to a single discrete year in the past but rather to a mix of air parcels with different ages. The air-age distribution, i. e. the temporal range of real world atmospheric composition sampled by each single icecore measurement, can be quantified by a model of the processes  (Trudinger et al.
, 2002b). Law Dome ice has an average spectral width (a measure of the spread of the distribution;  Trudinger et al. , 2002b)  of 4. 5 years in DE08 and DE08-2, 5. 8 years in DSS ice samples, and 7. 0 years in DSSW20K firn samples  (Trudinger et al. , 2013; Rubino et al. , 2013). More information about the characteristics of the original dataset may be found in  Rubino et al. (2013). In order to derive a continuous time series of annual values from the individual measurements, we fit a smoothing spline curve to the ice-core measurements following the procedure described by  Enting et al. (2006)  which allows estimation of uncertainties in the spline, as well as in its derivative. The derivative corresponds to the annual atmospheric CO 2 growth rate (hereafter AGR; Fig. 1 ). However, when fitting a spline to data, a set of parameters needs to be chosen (regularization parameter and smoothing weights), which may affect the resulting spline  (Enting et al.
Fossil-fuel combustion and cement production The Carbon Dioxide Information Analysis Center (CDIAC) provides annual estimates of CO 2 emissions from fossil-fuel burning, cement production, and gas flaring (E FF ) from 1751 to the present  (Boden et al. , 2009; Le Quéré et al. , 2013). Here we use their most recent global estimates between 1900 and 2000, which have an uncertainty of ±5 %. However, it should be noted that an uncertainty range of about 11 % may be more realistic when accounting for differences in the datasets and methods to estimate CO 2 emissions  (Mohr et al. , 2015; Quilcaille et al. , 2016).
5 Pg C yr -1  (Wilkenskjeld et al. , 2014). Here, we use three different E LUC datasets, the first two relying on gross landuse transitions: 1. The bookkeeping model of  Houghton (2003)  is used in the Global Carbon Budget assessment (Le  Quéré et al. , 2013)  and covers the period 1850-2005. It is mainly based on regional statistics from the Food and Agricultural Organization (FAO, 2010) and includes the effect of peat fires (from 1997 onwards) and fire suppression, the latter only for the USA. The model by  Houghton (2003)  allocates pasture preferentially to grassland, which may yield lower CO 2 emissions by reducing deforestation  (Reick et al. , 2010). 2. The "Bookkeeping of Land Use Emissions" (BLUE) model described by  Hansis et al. (2015)  relies on the land-use transitions from  Hurtt et al.
(2011)  (which is based on the HYDE 3. 1 database  (Klein Goldewijk et al. , 2011)  for cropland and pasture areas) to reconstruct fluxes between 1501 and 2012 in a spatially explicit way. New cropland and new grassland are both taken proportionally from natural vegetation types. Two subsets of E LUC are calculated, one using vegetation and soil carbon stocks from  Houghton et al. (1983)  and the other using the modifications proposed by  Reick et al. (2010) , that feature generally lower carbon densities for natural vegetation and lead to lower emissions. More details about the data sources and methods can be found in the original literature. 3. We also use LUC emissions estimated by a set of process-based models, described in Sect. 2. 3. 4. This is intended to account for the loss of additional sink capacity, as discussed by  Pongratz et al.
Their analysis relied on a previous dataset  (Etheridge et al. , 1996; Francey et al. , 1999)  of the same CO 2 ice-core record used here  (Rubino et al. , 2013)  to solve two mass-balance equations for atmospheric CO 2 and δ 13 C, assuming fixed ocean mixed-layer response. The method uses prescribed carbon fossil-fuel emissions and their δ 13 C signature with a box model to simulate isotopic disequilibrium fluxes between the atmosphere, ocean (O J ), and biosphere (B J ), as shown in Fig. 2  (top panel). Trudinger et al. (2002a)  used the same measurements in a Kalman filter double deconvolution. They came to the generally similar conclusion, namely that the oceans played a significant role in creating the 1940s plateau. These two double deconvolutions have some common weaknesses. Neither calculation considers climate-driven variations in terrestrial isotopic discrimination  (Randerson et al.
Closing the CO 2 budget As discussed above, these different sets of data for the carbon budget terms should, if correct, allow reconstruction of the Law Dome CO 2 record during the 20th century. The estimates of  Joos et al. (1999)  were originally calculated from earlier measurements of the Law Dome record, which were confirmed by new measurements. Therefore, atmospheric CO 2 growth rate calculated from fossil-fuel emissions and their ocean and biospheric fluxes (AGR J ), i. e. AGR J = E FF -O J -B J , (3) should be similar to the AGR record resulting from the value obtained from our spline fit on atmospheric CO 2 concentration. However, it should be noted that in  Joos et al. (1999)  the smoothing is stronger. The values of O J and B J are shown in Fig. 2  together with the resulting AGR J between 1900 and 1990 and the corresponding difference with the observations (i.
e. AGR J minus AGR, AGR J ). The other sets of data, being calculated using very different techniques, are largely independent of the CO 2 record and of each other. However, it should be noted that in reality these fluxes are not entirely independent of each other. For example, the emissions resulting from LUC will depend to a certain extent on the carbon stocks of the terrestrial ecosystems, i. e. in previous states of L. This is partially taken into account in DGVMs forced with LUC, but not in the other datasets. The resulting CO 2 budget using the different datasets for each term may be calculated using Eq. 1, E FF data and respective range, the ocean uptake reconstruction from  Khatiwala et al. (2009)  (O K ), the land sink from DGVMs (L DGVM ), and the four E LUC estimates, i. e. AGR i = E FF + E LUC-i -O K -L DGVM ; , (4) with i = (H ; B; Blc; DGVM), referring to each of the four datasets used to estimate emissions from LUC (Houghton, BLUE, BLUE with low C-stocks, and DGVMs, respectively) and LDGVM refers to the inter-model median of the global land sink from DGVMs (S2 experiment), shown in Table  2.
LUC information is aggregated in 10 different regions from the original dataset from  Hurtt et al. (2011). The variation in the stable carbon isotopic composition (δ 13 C) may be calculated from the balance of the different CO 2 fluxes  (Tans et al. , 1993; Hellevang and Aagaard, 2015) , which are simulated by OSCAR using δ 13 C t -δ 13 C t-1 = C t-1 × δ t f E t-1 FF + δ t-1 o OA t + δ t-1 lb F t B -(δ 13 C t-1 + o )AO -(δ 13 C t-1 + lb )NPP × t, (5) where t refers to time, C is the atmospheric CO 2 concentration, E FF denotes fossil-fuel emissions, OA and AO the gross ocean-atmosphere and atmosphere-ocean fluxes respectively, F B is the gross flux between terrestrial ecosystems and the atmosphere (i. e. emissions from heterotrophic respiration, mortality, fires and land-use change), and NPP is the global net primary production.
In the case of the two BLUE datasets, AGR is overestimated during most of the 20th century and by up to 1-2 Pg C yr -1 in two periods: the 1940s and 1950s-1960s. During the plateau period (Table  3 ), the values of AGR are, as expected, lower for AGR J than for the other datasets, although the absolute uncertainty of the reconstruction is 1 order of magnitude higher (1. 4 Pg C yr -1 ) than the estimated misfit ( AGR, 0. 1 Pg C yr -1 ), i. e. the extra sink required to match observed AGR. In AGR J , the uncertainty is likely overestimated because O J and B J have anticorrelated errors in the double deconvolution. If this had been taken into account, uncertainty would be similar to the one in their CO 2 observations, while it is generally of the same magnitude as the estimated values, still increasing from 1960 onwards.