Parton distributions with LHC data

Abstract. We used a global long-term (1995–2015) data set of total column water vapour (TCWV) derived from satellite observations to quantify to which extent the temporal patterns of various teleconnections can be identified in this data set. To our knowledge, such a comprehensive global TCWV data set was rarely used for teleconnection studies. One important property of the TCWV data set is that it is purely based on observational data. We developed a new empirical method to decide whether a teleconnection index is significantly detected in the global data set. We compared our new method to well-established hypothesis tests and found good agreement with the results of our approach. Based on our empirical method more than 40 teleconnection indices were significantly detected in the global TCWV data set derived from satellite observations. In addition to the satellite data we also applied our method to other global data sets derived from ERA-Interim. One important finding is that the spatial patterns obtained for the ERA TCWV data are very similar to the observational TCWV data set indicating a high consistency between the satellite and ERA data. Moreover, similar results are also found for two selections of ERA data (either all data or mainly clear-sky data). This finding indicates that the clear-sky bias of the satellite data set is negligible for the results of this study. However, for some indices, also systematic differences in the spatial patterns between the satellite and model data set were found probably indicating possible shortcomings in the model data. For most “traditional” teleconnection data sets (surface temperature, surface pressure, geopotential heights and meridional winds at different altitudes) a smaller number of significant teleconnection indices was found than for the TCWV data sets, while for zonal winds at different altitudes, the number of significant teleconnection indices (up to > 50) was higher. The strongest teleconnection signals were found in the data sets of tropospheric geopotential heights and surface pressure. In all global data sets, no “other indices” (solar variability, stratospheric AOD or hurricane frequency) were significantly detected. Since many teleconnection indices are strongly correlated, we also applied our method to a set of orthogonalised indices, which represent the dominant independent temporal teleconnection patterns. The number of significantly detected orthogonalised indices (20) was found to be much smaller than for the original indices (42). Based on the orthogonalised indices we derived the global spatial distribution of the cumulative effect of teleconnections. The strongest effect on the TCWV is found in the tropics and high latitudes.

The full exploitation of the increasingly precise LHC measurements is essential in order to reduce the uncertainty of theoretical predictions at hadron colliders. The NNPDF2.3 fit was the first PDF determination including the effect of the early LHC data. Here the new NNPDF3.0 PDF set is announced and its main features are presented. The novel NNPDF analysis is based on an improved fitting methodology, statistically validated by closure tests. Over a thousand new data points are included, both the recent HERA II measurements and a wide set of new LHC data. In this contribution details on the experimental data are given and their impact on PDF uncertainties is displayed.

In this study we propose a new methodology for pluvial flood risk estimation, combining stochastic rainfall modelling, climate projection based adaptations of the rainfall frequency-intensity relations and DEM data sets, along with hydrodynamic modelling. New global precipitation datasets, such as CMORPH, GSMaP or MERRA2 offer an affordable and accessible solution for water resource and water-hazard risk management in data-scarce regions and enable comprehensive global comparative studies. However, these datasets, often derived from satellite observations and coarse-scale climate modelling, consistently underestimate short-duration, high-intensity rainfall events, particularly those lasting one hour or less, that belong to the tails of the distributions (i.e., return levels higher than 30-year). This underestimation goes beyond spatial scale considerations, commonly addressed by areal reduction factors. Consequently, utilizing these global datasets for pluvial flood risk analysis results in conservative flood risk estimates. The availability of global terrain models and mapped man-made structures like buildings, channels, and roads enables the generation of wide-coverage digital surface models. These can be used for flood inundation modelling in combination with corrected extremes of the global precipitation data sets, allowing near-global rough flood risk estimates. In this study, we introduce a methodology for estimating pluvial flood risk using openly available global datasets. To achieve this, we derive hourly-scale Intensity-Duration-Frequency (IDF) curves suitable for pluvial flood inundation modeling in ungauged areas using global precipitation datasets. The first step uses high temporal resolution satellite remote sensing rainfall data (GSMaP) to train a stochastic rainfall generator model - the point process Bartlet-Lewis model. Subsequently, the weather generator is used to disaggregate daily global precipitation data (GPCC) through stochastic ensemble simulation. The resulting disaggregated ensemble data is then utilized to generate more accurate IDF curves including uncertainty, forming the basis for pluvial flooding risk assessments. Our approach integrates the openly available FabDEM terrain model with OpenStreetMap to generate digital surface models for flood risk modeling analysis. Discrepancies in flood inundation risk estimates in urban environments, attributable to underestimated rainfall intensity, are demonstrated using CADDIES, a 2-dimensional hydrodynamic model. The workflow allows the IDF curves for the current climate to be adapted based on climate model projections of temperatures using the Clausius–Clapeyron relation, and to study their impact on future flood risk. A comparative risk analysis is presented for several tropical coastal cities, including future pluvial risk projections. All analytical steps adhere to FAIR principles, utilizing publicly available datasets. The proposed workflow provides globally applicable first order estimates of pluvial flood risk, especially in data-poor areas, with better quality than existing global IDF studies or IDF curves derived directly from global precipitation datasets.

[1] A new publicly available daily gridded precipitation data set over mainland Portugal is presented. This data set is also combined with a recent Spanish data set to obtain a high resolution (0.2° × 0.2°) Iberian data set, labeled IB02. This data set covers the period from 1950 to 2003 and is based on a dense network, with more than 2000 and 400 quality-controlled stations over Spain and Portugal, respectively. The ordinary kriging method, applied over Portugal for consistency with the Spanish data set, performs slightly better than simpler interpolation techniques tested over Portugal. Additionally, this paper evaluates four global gridded data sets: two based on rain gauges (Climate Research Unit (CRU) and Global Precipitation Climate Center (GPCC)) and two European Centre for Medium-Range Weather Forecasts (ECMWF) reanalyses (ERA-40 and ERA-Interim), comparing them with the IB02 data set. The main features of the spatial distribution of IB02 mean annual precipitation are reasonably captured by the global data sets, despite their dry biases, mostly in mountainous regions. The four data sets perform better in western Iberia and are able to identify the major drought spells at the Iberian scale. Despite these similarities, GPCC outperforms CRU and ERA-Interim is superior to ERA-40 with respect to several aspects, such as annual cycle and drought detection. The performance of CRU is similar to that of ERA-Interim. The frequency of wet days is overestimated by reanalyses, mainly by ERA-Interim, while heavy precipitation events are underestimated, mostly by ERA-40. At 5 day scales, ECMWF reanalyses reveal difficulties in predicting the magnitude of precipitation, despite their greater ability to estimate the peak locations.

Discrepancies in various land cover and land use (LCLU) data sets could induce big uncertainties in assessing interactions among human activities, climate, and ecosystem. In this study, we analyzed inventory LCLU records from Department of Economics and Statistics (DES) and remote-sensing-based data sets obtained from Resourcesat-1, MODIS, Globcover, and HYDE 3.1 for 2005 in India. Based on the DES and Resourcesat-1 data sets, the best estimate of agricultural area was 143 million ha which was lower than MODIS and Globcover data sets (158–167 million ha). Global data sets have underestimated forest area by 20–30 million ha, comparing to DES and Resourcesat-1 data sets (63–66 million ha). All the remote sensing data sets showed a wide range for urban area (1.5–8.4 million ha) which was lower than inventory data sets. Our results caution scientific community and policymakers to carefully use global LCLU data sets that are significantly different from DES records in India.

Abstract. Although wetlands are well known as one of the most important ecosystems in the world, there are still few global wetland mapping efforts at present. To evaluate the wetland-related types of data accurately for both the Global Land Cover 2000 (GLC2000) data set and MODIS land cover data set (MOD12Q1), we used the China wetland map of 2000, which was interpreted manually based on Landsat TM images, to examine the precision of these global land cover data sets from two aspects (class area accuracy, and spatial agreement) across China. The results show that the area consistency coefficients of wetland-related types between the two global data sets and the reference data are 77.27% and 56.85%, respectively. However, the overall accuracy of relevant wetland types from GLC2000 is only 19.81% based on results of confusion matrix of spatial consistency, and similarly, MOD12Q1 is merely 18.91%. Furthermore, the accuracy of the peatlands is much lower than that of the water bodies according to the results of per-pixel comparison. The categories where errors occurred frequently mainly include grasslands, croplands, bare lands and part of woodland (deciduous coniferous forest, deciduous broadleaf forest and open shrubland). The possible reasons for the low precision of wetland-related land cover types include (1)the different aims of various products and therefore the inconsistent wetland definitions in their systems; (2) the coarse spatial resolution of satellite images used in global data; (3) Discrepancies in dates when images were acquired between the global data set and the reference data. Overall, the unsatisfactory results highlight that more attention should be paid to the application of these two global data products, especially in wetland-relevant types across China.

The remote sensing‐based continental to global scale land cover data sets provide several land cover depictions over the circumpolar tundra–taiga transition zone. The aim of this study was to evaluate three data sets in northernmost Finland: the Global Land Cover 2000 Northern Eurasia map (GLC2000‐NE), the MODIS global land cover map (MODIS‐IGBP) and the tree cover layer of the MODIS vegetation continuous fields product (MODIS‐VCF). The data sets were first compared both visually and statistically to biotope inventory data including tree cover, height, species composition and shrub cover information as continuous variables. The agreement with reference data was poor because the classifications do not correspond to the class descriptions. The MODIS‐VCF tree cover overestimates the tree cover in the low values and underestimates it in the high values. The agreement was relatively good when the global data sets were aggregated to a forest–non‐forest level and compared to the Finnish CORINE Land Cover 2000 map over a larger area. However, the inaccurate mapping of the deciduous broadleaf forests and mires reduced the agreement at the forest–non‐forest level. The vegetation transitions are difficult to map using low‐resolution satellite data and further improvements to the land cover characterization over the tundra–taiga transition zone are required.

Over the last two decades, several data sets have been developed to assess flood risk at the global scale. In recent years, some of these data sets have become detailed enough to be informative at national scales. The use of these data sets nationally could have enormous benefits in areas lacking existing flood risk information and allow better flood management decisions and disaster response. In this study, we evaluate the usefulness of global data for assessing flood risk in five countries: Colombia, England, Ethiopia, India, and Malaysia. National flood risk assessments are carried out for each of the five countries using six data sets of global flood hazard, seven data sets of global population, and three different methods for calculating vulnerability. We also conduct interviews with key water experts in each country to explore what capacity there is to use these global data sets nationally. We find that the data sets differ substantially at the national level, and this is reflected in the national flood risk estimates. While some global data sets could be of significant value for national flood risk management, others are either not detailed enough, or too outdated to be relevant at this scale. For the relevant global data sets to be used most effectively for national flood risk management, a country needs a functioning, institutional framework with capability to support their use and implementation.

We update the phenomenological parameters of the Transverse Momentum Dependent (TMD, or unintegrated) gluon density in a proton proposed in our previous studies. This analysis is based on the analytical expression for starting gluon distribution which provides a self-consistent simultaneous description of HERA data on proton structure function \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{F}_{2}}(x,{{Q}^{2}})$$\\end{document}, reduced cross section for the electron-proton deep inelastic scattering at low \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{Q}^{2}}$$\\end{document} and soft hadron production in \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$pp$$\\end{document} collisions at the LHC conditions. We extend it to the whole kinematical region using the Catani–Ciafaloni–Fiorani–Marchesini (CCFM) evolution equation. Exploiting our previous results on a number of semihard QCD processes, we performed a combined fit to an extended set of LHC and HERA data, comprising a total of 509 points from 16 data sets. We illustrate our fit by applying the derived TMD gluon density in a proton to inclusive prompt photon photoproduction at HERA.

The photosynthetic composition (C3 or C4) of vegetation on the land surface is essential for accurate simulations of biosphere-atmosphere exchanges of carbon, water, and energy. C3 and C4 plants have different responses to light, temperature, CO2, and nitrogen; they also differ in physiological functions like stomatal conductance and isotope fractionation. A fine-scale distribution of these plant types is essential for earth science modeling. The C4 percentage is determined from data sets that describe the continuous distribution of plant growth forms (i.e., the percent of a grid cell covered by herbaceous or woody vegetation), climate classifications, the fraction of a grid cell covered in croplands, and national crop type harvest area statistics. The staff from the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II have made the original data set consistent with the ISLSCP-2 land/water mask. This data set contains a single file in ArcInfo ASCIIGRID format. This data set is one of the products of the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP II) data collection which contains 50 global time series data sets for the ten-year period 1986 to 1995. Selected data sets span even longer periods. ISLSCP II is a consistent collection of data sets that were compiled from existing data sources and algorithms, and were designed to satisfy the needs of modelers and investigators of the global carbon, water and energy cycle. The data were acquired from a number of U.S. and international agencies, universities, and institutions. The global data sets were mapped at consistent spatial (1, 0.5 and 0.25 degrees) and temporal (monthly, with meteorological data at finer (e.g., 3-hour)) resolutions and reformatted into a common ASCII format. The data and documentation have undergone two peer reviews. ISLSCP is one of several projects of Global Energy and Water Cycle Experiment (GEWEX) [http://www.gewex.org/] and has the lead role in addressing land-atmosphere interactions -- process modeling, data retrieval algorithms, field experiment design and execution, and the development of global data sets.

A global data set on forest cover change was recently published and made freely available for use (Hansenet al. 2013.Science342: 850–853). Although this data set has been criticized for inaccuracies in distinguishing vegetation types at the local scale, it remains a valuable source of forest cover information for areas where local data is severely lacking. Masoala National Park, in northeastern Madagascar, is an example of a region for which very little spatially explicit forest cover information is available. Yet, this extremely diverse tropical humid forest is undergoing a dramatic rate of forest degradation and deforestation through illegal selective logging of rosewood and ebony, slash‐and‐burn agriculture, and damage due to cyclones. All of these processes result in relatively diffuse and small‐scale changes in forest cover. In this paper, we examine to what extent Hansenet al.'s global forest change data set captures forest loss within Masoala National Park by comparing its performance to a locally calibrated, object‐oriented classification approach. We verify both types of classification with substantial ground truthing. We find that both the global and local classifications perform reasonably well in detecting small‐scale slash‐and‐burn agriculture, but neither performs adequately in detecting selective logging. We conclude that since the use of the global forest change data set requires very little technical and financial investment, and performs almost as well as the more resource‐demanding, locally calibrated classification, it may be advantageous to use the global forest change data set even for local conservation purposes.

Examination of the scientific priorities for the International Geosphere Biosphere Programme (IGBP) reveals a requirement for global land data sets in several of its Core Projects. These data sets need to be at several space and time scales. Requirements are demonstrated for the regular acquisition of data at spatial resolutions of 1 km and finer and at high temporal frequencies. Global daily data at a resolution of approximately 1 km are sensed by the Advanced Very High Resolution Radiometer (AVHRR), but they have not been available in a single archive. It is proposed, that a global data set of the land surface is created from remotely sensed data from the AVHRR to support a number of IGBP's projects. This data set should have a spatial resolution of 1 km and should be generated at least once every 10 days for the entire globe. The minimum length of record should be a year, and ideally a system should be put in place which leads to the continuous acquisition of 1 km data to provide a base line data set prior to the Earth Observing System (EOS) towards the end of the decade. Because of the high cloud cover in many parts of the world, it is necessary to plan for the collection of data from every orbit. Substantial effort will be required in the preprocessing of the data set involving radiometric calibration, atmospheric correction, geometric correction and temporal compositing, to make it suitable for the extraction of information.

Land use and land cover change is an important driver of global change (Turner et al., 1993). It is recognized that land use change has important consequences for global and regional climates, the global biogeochemical cycles such as carbon, nitrogen, and water, biodiversity, etc. Nevertheless, there have been relatively few comprehensive studies of global, long-term historical changes in land cover due to land use. In this paper, we review the development of global scale data sets of land use and land cover change. Furthermore, we assess the differences between two recently developed global data sets of historical land cover change due to land use. Based on historical statistical inventories (e.g. census data, tax records, land surveys, historical geography estimates, etc) and applying different spatial analysis techniques, changes in agricultural land cover (croplands, pastures) were reconstructed for the last 300 years. The two data sets indicate that cropland areas expanded from 3–4 million km2 in 1700 to 15–18 million km2 in 1990 (mostly at the expense of forests), while grazing land area expanded from 5 million km2 in 1700 to 31 million km2 in 1990 (mostly at the expense of natural grasslands). The data sets disagree most over Latin America and Oceania, and agree best over North America. Major differences in the two data sets can be explained by the use of a fractional versus Boolean approach, different modelling assumptions, and inventory data sets.

LHC data have the potential to provide constraints on the gluon distribution, especially at high x, with both ATLAS and CMS performing differential measurements. Recently, CMS has measured double-differential distributions at 8 TeV. In this paper we examine the impact of this data set on the gluon distribution. To that end we develop novel, double-differential NNLO predictions for that data. No significant impact is found when the CMS data is added to the CT14HERA2 global PDF fit, due to the larger impact of the inclusive jet data from both the Tevatron and the LHC. If the jet data are removed from the fit, then an impact is observed. If the CMS data is scaled by a larger weight, representing the greater statistical power of the jet data, a roughly equal impact on the gluon distribution is observed for the as for the inclusive jet data. For data samples with higher integrated luminosity at 13 TeV, a more significant impact of the double-differential data may be observed.

The landscape of W± and Z bosons produced in pp collisions up to LHC energies