dataesr/scientific-paragraphs-categorization huggingface dataset:
jgi. doe. gov/Synps1; QUVV00000000) and M. bicuspidata single cell (https://genome. jgi. doe. gov/Metbi_SCcomb; QUVR00000000). The whole-genome sequence for the non-single-cell isolate C. protostelioides ATCC 52028 is available through MycoCosm (https://genome. jgi. doe. gov/Caupr1) and Genbank (QAJV00000000). The whole-genome sequences for the non-single-cell isolate of R. allomycis CSF55 was not determined in this study and is available through MycoCosm (https://genome. jgi. doe. gov/Rozal1_1) and Genbank (ATJD00000000). The genome sequence for the non-single-cell M. bicuspidata NRRL YB-4993 was also not determined in this study and is available through MycoCosm (https://genome. jgi. doe. gov/Metbi1) and Genbank (LXTC00000000).
Dataset and data selection Our interest is to calibrate the ensemble over the Midwest U. S. using the meteorological observations available over this region. The calibration of the ensemble will be done only within the inner domain. To perform the calibration, we used balloon soundings collected over the Midwest region (Figure  1 ). Meteorological data were obtained from the University of Wyoming's 10 online data archive (http://weather. uwyo. edu/upperair/sounding. html) for 14 rawinsonde stations over the U. S. Midwest region (Figure  1 ). To evaluate how the new calibrated ensemble impacts CO 2 mole fractions we will use in-situ atmospheric CO 2 mole fraction data provided by seven communication towers (Figure  1 ). Five of these towers were part of a Penn State experimental network, deployed from 2007 to 2009  (Richardson et al.
, 2012; Miles et al. , 2012 Miles et al. , , 2013;;  http://dx. doi. org/10. 3334/ORNLDAAC/1202). The other two towers (Park Falls-WLEF and West Branch-WBI) are part of 15 the Earth System Research Laboratory/Global Monitoring Division (ESRL/GMD) tall tower network  (Andrews et al. , 2014) , managed by NOAA. Each of these towers sampled air at multiple heights, ranging from 11 m AGL to 396 m AGL. The ensemble will be calibrated for three different meteorological variables: PBL wind speed, PBL wind direction and planetary boundary layer height (PBLH). We will calibrate the ensemble with the late afternoon data (i. e. , 0000 UTC) from the different rawinsondes. In this study, we use only daytime data, because we want to calibrate and evaluate the ensemble 20 under the same well mixed conditions that are used to perform atmospheric inversions.
The CO 2 differences between the models and the observations may be caused by CO 2 flux or boundary condition errors, the two components impacting the modeled CO 2 mole fractions in addition to atmospheric transport. The cause of the total difference cannot be determined from the CO 2 data alone. The increased daily variance in CO 2 resulting from the ensemble calibration process is shown in Figure  14. The 8-member ensemble often has the maximum CO 2 variance. Table  5  shows the spread 5 (model-ensemble mean) and RMSE (model-data) ratio of the CO 2 mole fraction for the full and calibrated 10-member ensemble at each in-situ CO 2 observation tower. The ratio of the variances is an estimate of the contribution of the transport errors to the CO 2 model data mismatch for the summer of 2008. This table shows that the transport errors represent about 20% to 40% of the CO 2 model-data mismatch.
Code availability. The code is accessible under request by contacting the corresponding author (lzd120@psu. edu). Data availability. Meteorological data were obtained from the University of Wyoming's online data archive (http://weather. uwyo. edu/upperair/sounding. html) for the 14 rawinsonde stations. Tower Atmospheric CO2 Concentration data set is available on-line [http://daac. ornl. gov] from Oak Ridge National Laboratory Distributed Active 25.
Knowledge transformation Changing the knowledge towards a different state or condition through its internalization as social constructions. (Fazey et al. 2013; Abson et al. 2017)  decisions, emphasizing the need for proper problem consideration and formulation and including steps for monitoring and evaluation  (Bower et al. 2017 ) (see Fig. 2 ). Recently,  Bower et al. (2017)  recommended the implementation of clear and documented structured decisionmaking processes and archiving results in a global database to support environmental professionals in making future evidence-based decisions. This solution aims to improve knowledge-action outcomes (see Table  2 ), enhancing transparency and information sharing. Systematic mapping is a rigorous technique used to synthesize the state of knowledge for a question or topic, giving a reliable overview of the breadth of science often needed for policy-based questions.
Acknowledgements We would like to thank  Georgina Mace  for her insightful comments and suggestions that largely improved the paper. We would like to thank  Diego Carrillo Sandrock  for his help improving the figures. We thank the 18 researchers from across the sciences gathered in a scoping ''natural assets'' workshop in September 2017 at the  University of Bern, Switzerland , organized by bioDISCOVERY, GMBA, GLP, and  PAGES  under the auspices of  Future Earth , for their great discussions that informed this paper. We thank the  University of Bern  for hosting the meeting, and  Future Earth  and  SCNAT  for providing financial support. MMH and SG are supported by the  Centre of Applied Ecology and Sustainability CAPES   FB 0002-2014. MMH is supported by  Fondo Nacional de Desarrollo Cientı ´fico y Tecnologico FONDECYT 318011.
3). Health institutions, including the American Public Health Association, Medibank Australia, and the Hospitals Contribution Fund of Australia, announced their commitment to divest from fossil fuels, with funds totalling 33. 6 billion USD (Indicator 4. 5). The United States' announcement of its intention to withdraw from the Paris Agreement contrasted with the formation of a new alliance of US medical associations (including the American Medical Association, the American College of Physicians, and the American Academy of Paediatrics) representing 500,000 clinicians, dedicated to tackling climate change. 10  data presented in the Lancet Countdown's 2018 report provides ongoing reason for cautious optimism, with the continuation of important trends signalling the beginning of a broader transition.
In all regions of the world, the proportion of populations vulnerable to heat exposure is rising. Europe and the Eastern Mediterranean demonstrate markedly higher vulnerability as compared to Africa and South East Asia, a finding that is most likely the result of a more elderly population living in urban areas. In addition, demographic transitions in low-and middle-income countries (LMICs) show accelerating trends in non-communicable diseases, especially in South East Asia, where vulnerability has increased by 3. 5% since 1990 (see Appendix 2 for figure and further details). This heat vulnerability index was compiled using data from the Global Burden of Disease (GBD) for trends in disease prevalence, and the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), for GDP, population densities and demographics.
al. 17  This method assigns work fraction loss functions to different activity sectors in accordance to the power typically expended by a worker performing that activity; labour loss is calculated as a function of the Wet Bulb Globe Temperature (WBGT). Total global hours of labour loss are calculated by factoring in the working population distribution and distribution of activities across sectors in different countries. Labour is divided into three sectors: service (metabolic rate of 200W), industry (300W), and agriculture (400W), all assumed to be working in the shade. As with Indicators 1. 2 and 1. 3, weather data was obtained from ECMWF; details of the method and datasets used can be found in the Appendix 2. 16  In total, 153 billion hours of labour were lost in 2017, an increase of 62 billion hours relative to the year 2000; notably, 80% of these losses were in the agricultural sector (see Appendix 2 for figure and further details).
5. 20, 21 A full description of methods and other data sources (ECMWF) can be found in Appendix 2. 16  Floods and extreme precipitation also have severe health implications, and 15% of all deaths related to natural disasters are due to floods. 23 In addition to immediate injury and death from flood water, longer-term impacts on health include spread of infectious disease and mental illness, both of which are exacerbated by the destruction of infrastructure, homes and livelihoods. 24, 25  second component of this new Indicator maps extreme rainfall events, as a proxy indicator of flood risk. In Watts et. al 2015, flood risk was estimated for 2090 by defining a flood event as a 5-day precipitation total exceeding the 10-year return level (a level of rainfall only expected once every ten years) in the reference period.
1  This method has been adapted here to produce extreme rainfall trends from 2000 to 2016. An extreme rainfall event is defined as commencing when the 5-day rolling sum of daily total precipitation exceeds the 10-year return level in the 1986-2005 reference period, and ending when it drops below this value. The return values and events were calculated using the ERA-Interim daily precipitation dataset from ECMWF. 16 Exposures were calculated as the sum of people at a location multiplied by the number of events at that location, measured in personevents. A full account of the methods and data can be found in Appendix 2. As with drought, changes in extreme heavy rain vary regionally, with particularly significant increases in extreme heavy rainfall events evident in South America and South East Asia(see Appendix 2 for figure and further details).
Whilst there are important trends within regions and income-groups in the lethality of weather-related disasters, no clear trend is seen at the global level, with the exception of a slight decline in the absolute numbers of those affected by floods. As section 2 of this report describes, governments and national health services are increasingly adapting to extreme weather events and climate change, with impressive results. These adaptation interventions and broader development initiatives present a plausible explanation for the results identified here. Crucially, Indicator 4. 1 makes clear that health and human wellbeing is impacted indirectly through the economic and social losses that result from such events. Indicator 1. 6 makes use of the same methods and data sources (the Emergency Events Database (EM-DAT)) described in the 2017 Lancet Countdown report, and in Appendix 2.
2: Marine food security and under-nutrition Headline finding: Sea surface temperatures (SST) have risen significantly in 16 of the 21 key fishing basins analysed, resulting in coral bleaching in many of these basins and threats to marine primary productivity expected to follow. This indicator on marine food security has been further developed since the 2017 Lancet Countdown report. Here, 21 basins have been analysed, selected for their geographical coverage and importance to marine food security. 36 A multi-layered indicator is tracked for each basin, monitoring changes in SST and subsequent coral bleaching from thermal stress (abiotic indicators), alongside per-capita capture-based fish consumption (biotic indicator). The data presented is sourced from NASA and the US Environmental Protection Agency, with all methods described in full in Appendix 2.
01%). Globally, relative health adaptation spending has grown slightly from 4. 6% of all adaptation spending estimated by the A&RCC dataset in 2015/16, to 4. 8% of all spending in 2016/17 (a percentage change of 3. 1%). For the wider health-related values, relative spending increased from 13. 5% to 15. 2% of total A&RCC spending Grouped by World Bank Income Group, the highest percentage change in health adaptation spending was in lower middleincome countries, followed by low-income countries, although the differences at this level of aggregation are small (figure  13 ). Grouped by WHO Region, the highest percentage change is observed in in Europe and South East Asia. However, it is important to note the much lower spending in LICs and so despite large percentage changes, the total spending in LICs is still far too low to meet needs in these countries.
5) were analysed from the Data Integration Model for Air Quality (DIMAQ) for 308 globallyrepresentative cities of the Sustainable Healthy Urban Environments (SHUE) database. 78, 79 nual average levels of PM2. 5 increased in 208 (67. 5%) of these cities and decreased in 100 (32. 5%), with an average increase of 3. 6 µg. m -3 a year (unweighted by population) (figure  17 ). The number above the WHO's annual guideline of 10 μg. m -3 increased from 254 1604   82  Emissions and concentrations correspond to the year 2015 and are calculated from updated statistics of the World Energy Outlook 2017. The geographical coverage has been expanded since the 2017 report to global coverage, and the breakdown refined to quantify contributions from coal combustion in all sectors (figure  18 ). Although the analysis is performed by country, results are aggregated to regions for clarity.
However, there are also health and economic benefits to be gained from tackling climate change, beyond avoiding the potential costs of inaction. Markandya et al   (2018) estimate that the global cost of reducing GHG emissions in line with the aims of the Paris Agreement could be offset by the economic value of improved health associated with the co-benefit of reduced air pollution alone, by a ratio of 2:1. 97  eight indicators in this section fall into four broad themes: the economic costs of climate change; investing in a low-carbon economy; economic benefits of tackling climate change; and pricing GHG emissions from fossil fuels. The methodologies and datasets used here closely mirror those from the 2017 Lancet Countdown report, with no significant changes to the indicators in this year's report.
Mean of annual average PM2. 5 concentrations over the period 2010-2016 for 1608 SHUE cities by WHO region (blue lines) estimated using DIMAQ. 78 Also shown are the range 1609 for all cities (light blue shaded area) and cities with the largest decrease (green dotted lines) 1610 and increase (red dotted lines) over the period based on linear trends. 1611 1612 1613 These estimates are consistent with those in 4,000 cities covered by the most recent update 1614 of the WHO's air pollution database. 80 Concentrations in the majority of cities remain well 1615 above recommended targets, especially in low-and middle-income settings, 81 and in part 1616 reflect the slow pace of change towards a low carbon world. Premature mortality from ambient air pollution by sector Headline Finding: Ambient air pollution resulted in more than 2 million premature deaths globally in 2015 from fine particulates alone.
54is indicator has been enhanced since 2017, with the original survey now replaced by the WMO Country Profile Database Integrated questionnaire. 54Not only does this provide greater insights into the nature of the provision of climate services to the heath sector, but it also allows for continuous updating of this indicator. A snapshot of responses as of May 2018 were used; the methods and data for this indicator are presented in full in Appendix 3, with a full list of reporting countries described to provide climate services to the health sector included. Of the 55 national meteorological and hydrological services of WMO Member States providing climate services to the health sector, 14 were from Africa, 4 from the Eastern Mediterranean, 18 from Europe, 11 from the Americas, 3 from South East Asia, and 5 from the Western Pacific.
This paper describes an integrated, high-resolution dataset of hydro-meteorological variables (rainfall and discharge) concerning a number of high-intensity flash floods that occurred in Europe and in the Mediterranean region from 1991 to 2015. This type of dataset is rare in the scientific literature because flash floods are typically poorly observed hydrological extremes. Valuable features of the dataset (hereinafter referred to as the EuroMedeFF database) include (i) its coverage of varied hydro-climatic regions, ranging from Continental Europe through the Mediterranean to Arid climates, (ii) the high space-time resolution radar rainfall estimates, and (iii) the dense spatial sampling of the flood response, by observed hydrographs and/or flood peak estimates from post-flood surveys. Flash floods included in the database are selected based on the limited upstream catchment areas (up to 3000 km 2 ), the limited storm durations (up to 2 days), and the unit peak flood magnitude.
The EuroMedeFF database comprises 49 events that occurred in France, Israel, Italy, Romania, Germany and Slovenia, and constitutes a sample of rainfall and flood discharge extremes in different climates. The dataset may be of help to hydrologists as well as other scientific communities because it offers benchmark data for the identification and analysis of the hydro-meteorological causative processes, evaluation of flash flood hydrological models and for hydro-meteorological forecast systems. The dataset also provides a template for the analysis of the space-time variability of flash flood triggering rainfall fields and of the effects of their estimation on the flood response modelling.
In the few cases where a stream gauge is in place, streamflow monitoring is affected by major limitations. For instance, peak water levels may exceed the range of available direct discharge measurements in rating curves, causing major uncertainties in the conversion of flood stage data to discharge data. In other cases, stream gauges are damaged or even wiped out by the flood current: in these cases, only part of the hydrograph (usually a segment of the rising limb) is recorded. The call for better observations of flash flood response has stimulated the development of a focused monitoring methodology in the last 15 years over Europe and the Mediterranean region  (Gaume et al. , 2004; Marchi et al. , 2009; Bouilloud et al. , 2009; Calianno et al. , 2013; Amponsah et al. , 2016). This methodology is built on the use of post-flood surveys, where observations of traces left by water and sediments during a flood are combined with accurate topographic river section survey to provide spatially detailed estimates of peak discharges along the stream network.
The aim of this paper is to outline the development of the EuroMedeFF dataset, which organises flash flood hydrometeorological and geographical data from 49 high-intensity flash floods, whose location stretches from the western and central Mediterranean, through the Alps and into Continental Europe. The database includes high-resolution radar rainfall estimates, flood hydrographs and/or flood peak estimates through IPEC, and digital terrain models (DTMs) of the concerned catchments. Collation of the EuroMedeFF dataset is a challenging task  (Borga et al. , 2014) , due (i) to the lack of conventional hydro-meteorological data which characterises these events (owing to the small spatio-temporal scales at which these events occur), and (ii) to the fact that extreme events are, by definition, rare.
Collecting rainfall and flood data by means of opportunistic post-flood surveys required the mobilisation of a group of researchers (ranging in size from 5 to more than 20 persons) for an extended period of time (ranging from a few days to some weeks). In addition to this, high-quality weather radar estimates of extreme events such as the ones triggering flash floods are not easy to gather, due to the number of sources of error affecting radar estimation under heavy precipitation and in rough topography environments  (Germann et al. , 2006; Villarini and Krajevski, 2010). Owing to these reasons, the EuroMedeFF dataset of 49 flash flood events comprising high-quality radar rainfall estimates, flood hydrographs, surveyed flood peaks at ungauged sites, and digital terrain models is simply unprecedented in size in Europe and in the Mediterranean in terms of (i) number of events, (ii) variety of provided data, and (iii) the degree of integration.
Given the quality and resolution of the rainfall input, the archive provides unprecedented data to examine the impact of space-time resolution in the modelling of high-intensity flash floods under different climate and environmental controls. Since results from previous modelling studies are quite mixed, much of the knowledge being either site-specific or expressed qualitatively, the availability of the EuroMedeFF data archive may open new avenues to synthesise this knowledge and transfer it to new situations. The criteria for the EuroMedeFF database development and a summary table and spatial locations of the collected flash floods are presented in Sect. 2. Section 3 describes the components of the flash flood datasets, whereas the methods used to generate the rainfall and discharge datasets are presented in Sect.
Criteria for EuroMedeFF database development The EuroMedeFF database includes data from high-intensity flash flood events from different hydro-climatic regions in the Euro-Mediterranean area. To be included in the dataset, the following data availability was ensured: (i) digital terrain model (DTM) of resolutions 5-90 m of the impacted region/catchment; (ii) weather radar rainfall estimation with high spatial and temporal resolutions, and (iii) discharge data from stream gauges and/or post-flood analyses. Rainfall data are provided at a time resolution of 60 min or less and as "best available rainfall products" (i. e. estimates which include the merging of radar and raingauge estimates). Three criteria have been considered for the development of the EuroMedeFF database. i. Flood magnitude.
A unit peak discharge of 0. 5 m 3 s -1 km -2 (this parameter is termed F th ) is considered as the lowest value for defining a flash flood event. This means that, for an event to be included in the database, at least one measured flood peak should exceed the value of F th. The authors are aware that, depending on climate and catchment size, a unit peak discharge of 0. 5 m 3 s -1 km -2 can correspond to a severe flash flood (for instance, in the inner sector of the alpine range) or a moderate flash flood (for instance, in many Mediterranean basins). A value of 0. 5 m 3 s -1 km -2 can be considered as a lower threshold for flash floods across a variety of climates and studies  (Gaume et al. , 2009; Marchi et al. , 2010; Tarolli et al. , 2012; Braud et al. , 2014). For the sake of simplicity, we adopted the same value of F th in all the studied regions.
Since the identification of the flash floods included in the database is primarily driven by the local observed impact, for most floods the lowest unit peak discharge is much higher than F th. ii. Spatial extent. The upper limit for a catchment impacted by the flood is 3000 km 2 (this parameter is termed A th ). The same meteorological event may have triggered multiple floods (e. g. September and October 2014 floods in France which have affected several catchments of about 2000 km 2 -Ardèche, Cèze, Gard, and Hérault). In this case, we report several events for the same date, corre-sponding to different specific catchments with areas less than A th. iii. Storm duration. The upper limit for the duration of the flood-triggering storm is up to 48 h (this parameter is termed D th ). The rainfall duration is identified by defining a minimum period duration with basin-averaged hourly rainfall intensity less than 1 mm h -1 over the impacted catchment to separate the time series in consistent events.