dataesr/scientific-paragraphs-categorization huggingface dataset:
This was extended with 2. 4 million additional measurements from 2012 and 2013 from all basins (see data attribution table in Appendix A), submitted to SOCAT but not yet fully quality-controlled following standard SOCAT procedures. Revisions and corrections to measurements from before 2012 were also included where they were available. All new data were subjected to an automated quality control system to detect and remove the most obvious errors (e. g. incorrect reporting of metadata such as position, wrong units, clearly unrealistic data). The combined SOCAT v2 and preliminary 2012-2013 data were implemented in an inversion method  (Rödenbeck et al. , 2013)  and a combined self-organising map and feed-forward neural network  (Landschützer et al. , 2014). The observationbased estimates were corrected to remove a background (not part of the anthropogenic ocean flux) ocean source of CO 2 to the atmosphere of 0.
Emissions Fossil fuel combustion and cement production (E FF ) 3. 1 ± 0. 2 4. 7 ± 0. 2 5. 5 ± 0. 3 6. 4 ± 0. 3 7. 8 ± 0. 4 8. 9 ± 0. 4 9. 9 ± 0. 5 Land-use-change emissions (E LUC ) 1. 5 ± 0. 5 1. 3 ± 0. 5 1. 4 ± 0. 5 1. 6 ± 0. 5 1. 0 ± 0. 5 0. 9 ± 0. 5 0. 9 ± 0. 5 We use the data-based product of  Khatiwala et al. (2009) , updated by  Khatiwala et al. (2013) , to estimate the anthropogenic carbon accumulated in the ocean during  1765 -1958 (60. 2 GtC) and 1870 -1958 (47. 5 GtC) , and assume an oceanic uptake of 0. 4 GtC for 1750-1765, for which time no data are available, based on the mean uptake during 1765-1770. The estimate of  Khatiwala et al. (2009)  is based on regional disequilibrium between surface pCO 2 and atmospheric CO 2 , and a Green's function utilising transient ocean tracers like CFCs and 14 C to ascribe changes through time.
The atmospheric perspective The worldwide network of atmospheric measurements can be used with atmospheric inversion methods to constrain the location of the combined total surface CO 2 fluxes from all sources, including fossil and LUC emissions and land and ocean CO 2 fluxes. As the geographical distribution of fossil fuel emissions is already known, it can be subtracted from the total surface CO 2 flux to provide CO 2 fluxes over land and over the ocean. Here we used preliminary atmospheric CO 2 data to the end of 2013, and three atmospheric CO 2 inversions (Table  6 ) to infer the total CO 2 flux over land regions, and the distribution of the total land and ocean CO 2 fluxes for the mid-to high-latitude Northern Hemisphere (30-90 • N), tropics (30 • S-30 • N) and mid-to high-latitude region of the Southern Hemisphere (30-90 • S).
2. 7. 2). during 2004-2013, with interannual variations of the order of a few tenths of GtC yr -1 generally showing an increased ocean sink during El Niño events  (i. e. 1982 (i. e. -1983 (i. e. , 1991 (i. e. -1993 (i. e. , 1997 (i. e. -1998) (Fig. 7;) (Fig. 7; Rödenbeck et al. , 2014). Although there is some coherence between the ocean models and data www. earth-syst-sci-data. net/7/47/2015/ Earth Syst. Sci. Data, 7, 47-85, 2015  products and among data products, their mutual correlation is weak and highlights disagreement on the exact amplitude of the interannual variability, as well as on the relative importance of the trend versus the variability (Sect. 2. 4. 3 and Fig. 7 ). Most estimates produce a mean CO 2 sink for the 1990s that is below the mean assessed by the IPCC from indirect (but arguably more reliable) observations  (Denman et al.
Discussion Each year when the global carbon budget is published, each component for all previous years is updated to take into account corrections that are due to further scrutiny and verification of the underlying data in the primary input data sets. The updates have generally been relatively small and focused on the most recent years, except for LUC, where they are more significant but still generally within the provided uncertainty range (Fig. 9 ). The difficulty in accessing land-cover-change data to estimate E LUC is the key problem to providing continuous records of emissions in this sector. Current FAO estimates are based on statistics reported at the country level and are not spatially explicit. Advances in satellite recovery of land-cover change could help to keep track of LUC through time  (Achard et al.
, 2014; Harris et al. , 2014). Revisions in E LUC for the 2008/2009 budget were the result of the release of FAO 2010, which contained a major update to forest-cover change for the period 2000-2005 and provided the data for the following 5 years to 2010 (Fig. 9b ). The differences in this year could be attributable to both the different data and the different methods. Updates to values for any give year in each component of the global carbon budget were highest at 0. 34 GtC yr -1 for the atmospheric growth rate, 0. 19 GtC yr -1 for the fossil fuel and cement emissions, and 0. 1 GtC yr -1 for the ocean CO 2 sink, all within the reported uncertainty. The update for the residual land CO 2 sink was also large (Fig. 9e ), with a maximum value of 0. 71 GtC yr -1 , directly reflecting revisions in other terms of the budget, but still within the reported uncertainty.
, 2012). Annual estimates of each component of the global carbon budgets have their limitations, some of which could be improved with better data and/or better understanding of carbon dynamics. The primary limitations involve resolving fluxes on annual timescales and providing updated estimates for recent years for which data-based estimates are not yet available or only beginning to emerge. Of the various terms in the global budget, only the burning of fossil fuels and at-mospheric growth rate terms are based primarily on empirical inputs supporting annual estimates in this carbon budget. The data on fossil fuel consumption and cement production are based on survey data in all countries. The other terms can be provided on an annual basis only through the use of models. While these models represent the current state of the art, they provide only simulated changes in primary carbon budget components.
The best resolved changes are in atmospheric growth (G ATM ), fossil fuel emissions (E FF ), and, by difference, the change in the sum of the remaining terms (S OCEAN + S LAND -E LUC ). The variations from year to year in these remaining terms are largely model-based at this time. Further efforts to increase the availability and use of annual data for estimating the remaining terms with annual to decadal resolution are especially needed. Our approach also depends on the reliability of the energy and land-cover-change statistics provided at the country level, and are thus potentially subject to biases. Thus it is critical to develop multiple ways to estimate the carbon balance Earth Syst. Sci. Data, 7, 47-85, 2015  www. earth-syst-sci-data. net/7/47/2015/ at the global and regional level, including estimates from the inversion of atmospheric CO 2 concentration used here for the first time, the use of other oceanic and atmospheric tracers, and the compilation of emissions using alternative statistics (e.
Conclusions The estimation of global CO 2 emissions and sinks is a major effort by the carbon cycle research community that requires a combination of measurements and compilation of statistical estimates and results from models. The delivery of an annual carbon budget serves two purposes. First, there is a large demand for up-to-date information on the state of the anthropogenic perturbation of the climate system and its underpinning causes. A broad stakeholder community relies on the data sets associated with the annual carbon budget, including scientists, policy makers, businesses, journalists, and the broader society increasingly engaged in adapting to and mitigating human-driven climate change. Second, over the last decade we have seen unprecedented changes in the human and biophysical environments (e.
This all requires more frequent, robust, and transparent data sets and methods that can be scrutinised and replicated. After nine annual releases from the GCP, the effort is growing and the traceability of the methods has become increasingly complex. Here, we have documented in detail the data sets and methods used to compile the annual updates of the global carbon budget, explained the rationale for the choices made and the limitations of the information, and finally highlighted the need for additional information where gaps exist. This paper, via "living data", will help to keep track of new budget updates. The evolution over time of the carbon budget is now a key indicator of the anthropogenic perturbation of the climate system, and its annual delivery joins a set of other climate indicators to monitor the evolution of humaninduced climate change, such as the annual updates on the global surface temperature, sea level rise, minimum Arctic sea ice extent, and others.
Data access The data presented here are made available in the belief that their wide dissemination will lead to greater understanding and new scientific insights into how the carbon cycle works, how humans are altering it, and how we can mitigate the resulting human-driven climate change. The free availability of these data does not constitute permission for publication of the data. For research projects, if the data are essential to the work, or if an important result or conclusion depends on the data, co-authorship may need to be considered. Full contact details and information on how to cite the data are given at the top of each page in the accompanying database and summarised in Table  2. The accompanying database includes an Excel file organised in the following spreadsheets (accessible with the free viewer http://www.
The naming convention of the budgets has changed. Up to and including 2010, the budget year (Carbon Budget 2010) represented the latest year of the data. From 2012, the budget year (Carbon Budget 2012) refers to the initial publication year. bThe CDIAC database has about 250 countries, but we show data for about 216 countries since we aggregate and disaggregate some countries to be consistent with current country definitions (see Sect. 2. 1. 1 for more details).
The naming convention of the budgets has changed. Up to and including 2010, the budget year (Carbon Budget 2010) represented the latest year of the data. From 2012, the budget year (Carbon Budget 2012) refers to the initial publication year. bThe CDIAC database has about 250 countries, but we show data for about 216 countries since we aggregate and disaggregate some countries to be consistent with current country definitions (see Sect. 2. 1. 1 for more details). Publication Fossil fuel emissions LUC Reservoirs Uncertainty & year a emissions other changes Global Country (territorial) Country Atmosphere Ocean Land (consumption) 2006 Split in regions Raupach et al. (2007) 2007 E LUC based on FAO- 1959-1979 data from Based on one ocean ±1σ provided for all Canadell et FRA 2005; constant Mauna Loa; data after model tuned to repro- components al.
Methods We assembled a comprehensive database of all confi rmed and probable cases from public sources and estimated the incubation period and generation time from case cluster data. Using data of numbers of visitors to the Middle East and their duration of stay, we estimated the number of symptomatic cases in the Middle East. We did independent analyses, looking at the growth in incident clusters, the growth in viral population, the reproduction number of cluster index cases, and cluster sizes to characterise the dynamical properties of the epidemic and the transmission scenario. Findings The estimated number of symptomatic cases up to Aug 8, 2013, is 940 (95% CI 290-2200), indicating that at least 62% of human symptomatic cases have not been detected. We fi nd that the case-fatality ratio of primary cases detected via routine surveillance (74%; 95% CI 49-91) is biased upwards because of detection bias; the case-fatality ratio of secondary cases was 20% (7-42).
13 In the absence of robust community-based serological surveys for MERS-CoV, assessment of the extent of undetected infection must rely on epidemiological inference. Another essential aspect of risk assessment is the characterisation of transmissibility, and a study  14  Lancet Infect Dis 2014; 14: 50-56 concluded that transmission levels were under the epidemic threshold in detected clusters. 14 However, since stringent control measures were implemented in many of those clusters, the transmissibility in the absence of such controls is still unknown. Here, we undertook analyses of epidemiological and genetic data to assess the number of cases missed by surveillance, the detection bias towards severe cases, and the transmission potential of MERS-CoV with and without control measures. We also estimated risk factors for fatal disease outcomes.
Data, incubation times, and incidence calculations The appendix shows further details of all methods used. We assembled a comprehensive database of all confi rmed and probable cases up to Aug 8, 2013, from public sources (appendix). We classifi ed cases with known epidemiological links as a cluster, with singleton cases being viewed as independent clusters of size one. We estimated the incubation period from travel-related clusters. In the absence of other potential exposures, we extracted detailed information on the exposure of secondary cases to the index case from the medical literature (appendix). A lognormal distribution was fi tted to the data. We obtained an approximate lower bound for the generation time T G (mean time from symptom onset of a case to symptom onset of their secondary cases) by considering the average delay between onset of fi rst and second cases in clusters with more than one case.
A Gamma distribution was fi tted to the data. Since discovery of an index case prompts enhanced surveillance in contacts of that case, the occurrence of detected clusters is likely to represent a more reliable indication of changes in the underlying population incidence than the incidence of cases. We therefore used observed cluster incidence over time to study growth in incidence over time. An exponential growth model was fi tted to these data and the doubling time of the epidemic was estimated (appendix). We obtained an independent estimate of doubling time by phylogenetic analysis of seven sequences from diff erent MERS-CoV case clusters (appendix). We applied Bayesian phylogenetic and coalescent models assuming a strict molecular clock and exponential growth of the viral population (appendix).
Using data of numbers of visitors to Jordan, Qatar, Saudi Arabia, and United Arab Emirates who are resident outside the Middle East (denoted returning non-resident travellers) and their duration of stays, we estimated the expected number of symptomatic cases among residents in those countries from the incidence in travellers under the assumption that residents and visitors had an equal per-day risk of infection (appendix). From estimates of the growth rate of the viral population derived from analysis of available MERS-CoV genetic sequences, we also estimated the total number of infections that occurred between the time to most recent common ancestor (TMRCA) and Aug 8, 2013.
Discussion In this study, we analysed publicly available epidemiological and genetic data to evaluate the extent of human infection, the performance of case detection, and the transmission potential of MERS-CoV with and without control measures (panel). We used data on returning non-resident traveller cases to estimate the total number of resident severe cases in the Middle East. By comparing this number to the number of reported cases in the Middle East, we conclude that at least 62% of clinically apparent cases have been missed. This conclusion is indirectly supported by our independent fi nding that detection of milder cases seems to have improved in recent months. Since the four returning non-resident traveller cases were clinically severe, this fi rst analysis refl ects missed severe clinical cases.
Our analysis is most directly similar to their fi gure 3, an analysis of 11 representative sequences. We further removed two sequences (Al Hasa 8, so that the outbreak is only represented once, and Bisha-1 because of the controversy surrounding it and the encephalomyocarditis virus), and added the new Paris sequence. Based on this new dataset, we estimated the molecular clock rate at 1•0 × 10 -³ substitutions per site per year with 95% credibility interval (6•8 × 10 -⁴-1•3 × 10 -³) that overlaps the estimates of Cotten and colleagues. 18 Based on this updated analysis, the updated TMRCA for all sequences was Oct 23, 2010 (95% CI Nov 28, 2008-July 3, 2011), whereas that for the recent clade of now We conclude that a slowly growing epidemic is underway, but current epidemiological data do not allow us to determine whether transmission is self-sustaining in man.
Acknowledgments This study was funded by the  Medical Research Council , the  Bill & Melinda Gates Foundation ,  EU   FP7  (grants  278433-PREDEMICS  and  223498-EMPERIE ), and the  National Institute of General Medical Sciences through its Models of Infectious Disease Agent Study (MIDAS) initiative. We thank  Paul Kellam ,  Jeremy Farrar , and  David Heymann  for useful discussions. We thank  David Briand  for technical help with sequencing. This paper was only possible thanks to the countries and institutions who have shared epidemiological and sequence data on MERS-CoV.
google. com/p/ea-utils) was used to control the sequencing data for general quality features. The resulting 4 scaffolds of the genome, with 161ϫ average coverage, were annotated using Prokka 1. 7 (http://www. vicbioinformatics. com /software. prokka. shtml). The sequenced draft genome of strain MOLA455 consists of 4 contigs, totaling 2,605,026 bp in size, and it has a GϩC content of 50. 02%. It was found to encode 2,331 coding sequences, 3 rRNAs, and 35 tRNAs. Genome analysis revealed the presence of a green-lightabsorbing proteorhodopsin-encoding sequence (PR) of 229 amino acid residues  (4) (5) (6). It shows sequence features suggestive of proton pump activity from the inside to the outside of the bacterial cell, leading to a proton motive force (pmf) across the cell membrane (4, 6, 7). In addition, genes associated with a retinalproducing pathway were detected  (7) (8) (9)  BLAST analysis  (10)  showed the highest PR protein sequence identity to the PR sequence of the marine gammaproteobacterium HTCC 2207, which was reported to reach up to 10% of the total bacterioplankton in surface waters close to the coast of Oregon  (11).
The presence of a PR gene sequence, together with gene sequences putatively associated with retinal biosynthesis in the genome sequence of strain MOLA455, suggest a putative photoheterotrophic lifestyle that generates energy from light. Nucleotide sequence accession numbers. The whole-genome shotgun project has been deposited at DDBJ/EMBL/GenBank under the accession no. AZIN00000000. The version described in this paper is version AZIN01000000.
Magnetic clouds (MCs) are large-scale magnetic flux ropes ejected from the Sun into the interplanetary space. They play a central role in solar-terrestrial relations as they can efficiently drive magnetic activity in the near-Earth environment. Their impact on the Earth's magnetosphere is often attributed to the presence of southward magnetic fields inside the MC, as observed in the upstream solar wind. However, when they arrive in the vicinity of the Earth, MCs first encounter the bow shock, which is expected to modify their properties, including their magnetic field strength and direction. If these changes are significant, they can in turn affect the interaction of the MC with the magnetosphere. In this paper, we use data from the Cluster and Geotail spacecraft inside the magnetosheath and from the Advanced Composition Explorer (ACE) upstream of the Earth's environment to investigate the impact of the bow shock's crossing on the magnetic structure of MCs.
Data sets To investigate how the structure of MCs evolves from the solar wind to the magnetosheath, we need simultaneous observations in these two regions. The solar wind upstream of the Earth is continuously monitored by the Advanced Composition Explorer (ACE)  (Stone et al. , 1998)  spacecraft, located near L1. Other data sets, such as the measurements from the Wind spacecraft  (Acuña et al. , 1995)  or the OMNI data set propagated to Earth's bow shock nose, can also be used as solar wind monitors. However, during some of the events presented in the next sections, the Wind spacecraft was not located upstream of Earth's environment, and in one of the studied intervals the OMNI propagated data set contains a large data gap. Therefore, we use ACE Magnetic Field Experiment (MAG)  (Smith et al.
, 1998)  data as a monitor of the magnetic structure of MCs in the unperturbed solar wind. We assume that all the large-scale structures observed in L1 actually reach the Earth's environment. As ACE is located close to the Sun-Earth line, this is generally true. The data are obtained through the CDAWeb service (http://cdaweb. gsfc. nasa. gov/ spphys/). They are shifted in time in order to account for the propagation from L1 to the magnetosheath. The delay is obtained by dividing the distance between ACE and the spacecraft inside the magnetosheath in the x direction by the solar wind speed along x. Using a constant time lag throughout the studied events is sufficient for the purpose of our study, as we focus on long-lasting and large-scale structures. In the following, we will only display time-shifted ACE data, and the reference time will be given by Cluster's observations.
In the magnetosheath, we use data from the Cluster and the Geotail spacecraft when available in this region. However, since the spacecraft generally do not remain in the magnetosheath during the entire MC events, observations in the solar wind and/or the magnetosphere are also displayed. The magnetic field measurements are obtained from the FluxGate Magnetometer (FGM)  (Balogh et al. , 1997)  aboard Cluster and the Magnetic Field (MGF) instrument aboard Geotail  (Kokubun et al. , 1994). All data from ACE, Cluster and.
Description of the events In this section, we present four MCs observed simultaneously in the solar wind and the magnetosheath. They have been selected as the most representative ones from a database of about 30 MC events during which spacecraft observations are available inside the magnetosheath. These four events comprise rather long intervals of magnetosheath measurements, and generally also bow shock crossings. Each of them illustrates a different behaviour of the MC's structure inside the magnetosheath. The orbits of Cluster and Geotail during the studied intervals are displayed in Fig. 1 , in the GSE x-y (left panel) and the x-z (right panel) planes. The diamonds indicate the position of the spacecraft at the beginning of the events. As a reference, the dotted lines indicate the Jeřáb et al.
We study four events as four typical and different examples of magnetosheath observations during MCs. In the first case, the MC's structure observed in the magnetosheath is roughly the same as that in the solar wind. In contrast, the second event highlights that the MC's smooth rotation sometimes differs largely from that observed upstream of the bow shock. Moreover, the alteration of the MC's structure across the bow shock can vary with time as the MC passes by the Earth. In the third example, data from two different spacecraft are available simultaneously at two different locations inside the magnetosheath. The combined observations of Cluster and Geotail show that the structure of the MC differs from one region of the magnetosheath to another during this event. In the dawnside magnetosheath, the magnetic field orientation is the same as in the solar wind, whereas in the duskside it follows the smooth rotation observed upstream, but at a different angle.