Institution
Appalachian State University
Education•Boone, North Carolina, United States•
About: Appalachian State University is a education organization based out in Boone, North Carolina, United States. It is known for research contribution in the topics: Population & Context (language use). The organization has 2956 authors who have published 6762 publications receiving 169736 citations. The organization is also known as: ASU.
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University of California, Los Angeles1, United States Department of Energy2, University of Paris3, Duke University4, University of Massachusetts Medical School5, University of California, Berkeley6, Centre national de la recherche scientifique7, University of California, San Francisco8, Sun Yat-sen University9, University of Tennessee Health Science Center10, University of Minnesota11, Iowa State University12, Genetic Information Research Institute13, Salk Institute for Biological Studies14, Stanford University15, University of Liège16, University of Nebraska–Lincoln17, University of Cambridge18, Washington University in St. Louis19, University of Córdoba (Spain)20, Kyoto University21, Carnegie Institution for Science22, National Autonomous University of Mexico23, University of Münster24, École Normale Supérieure25, University of Melbourne26, University of Paris-Sud27, University of Mainz28, Scripps Research Institute29, Ohio State University30, University of Chicago31, University of Jena32, University of Arizona33, Louisiana State University34, University of New Brunswick35, University College London36, University of Potsdam37, Delaware Biotechnology Institute38, Boyce Thompson Institute for Plant Research39, Macquarie University40, Oklahoma State University Center for Health Sciences41, İzmir University of Economics42, Academy of Sciences of the Czech Republic43, Charles University in Prague44, St. Edward's University45, University of Puget Sound46, Hokkaido University47, Tsinghua University48, Washington State University49, Appalachian State University50, Marquette University51
TL;DR: Analyses of the Chlamydomonas genome advance the understanding of the ancestral eukaryotic cell, reveal previously unknown genes associated with photosynthetic and flagellar functions, and establish links between ciliopathy and the composition and function of flagella.
Abstract: Chlamydomonas reinhardtii is a unicellular green alga whose lineage diverged from land plants over 1 billion years ago. It is a model system for studying chloroplast-based photosynthesis, as well as the structure, assembly, and function of eukaryotic flagella (cilia), which were inherited from the common ancestor of plants and animals, but lost in land plants. We sequenced the approximately 120-megabase nuclear genome of Chlamydomonas and performed comparative phylogenomic analyses, identifying genes encoding uncharacterized proteins that are likely associated with the function and biogenesis of chloroplasts or eukaryotic flagella. Analyses of the Chlamydomonas genome advance our understanding of the ancestral eukaryotic cell, reveal previously unknown genes associated with photosynthetic and flagellar functions, and establish links between ciliopathy and the composition and function of flagella.
2,554 citations
22 Sep 1997
TL;DR: The goals of this article are to elaborate on the reasons for choosing qualitative methodologies, and to provide a basic introduction to the features of this type of research.
Abstract: A number of writers have commented on the dearth of substantive research within the field of technology education, and point to the expansion of its research agenda as a means of strengthening the discipline. Waetjen, in his call for good research in technology education, states that “the plea is to use experimental type research as much as possible” (1992, p. 30). Interestingly, the three areas of research need outlined in his essay would all lend themselves to alternative methodologies, including qualitative methodologies.
1,864 citations
École Normale Supérieure1, University of Exeter2, Norwich Research Park3, University of Groningen4, Wageningen University and Research Centre5, Ludwig Maximilian University of Munich6, Max Planck Society7, Commonwealth Scientific and Industrial Research Organisation8, Université Paris-Saclay9, Stanford University10, National Oceanic and Atmospheric Administration11, National Institute for Space Research12, Bermuda Institute of Ocean Sciences13, University of Southampton14, PSL Research University15, National Institute for Environmental Studies16, Japan Agency for Marine-Earth Science and Technology17, University of Maryland, College Park18, University of Leeds19, International Institute of Minnesota20, Flanders Marine Institute21, ETH Zurich22, University of East Anglia23, German Aerospace Center24, Woods Hole Research Center25, University of Illinois at Urbana–Champaign26, University of Toulouse27, Japan Meteorological Agency28, Plymouth Marine Laboratory29, University of Paris30, Hobart Corporation31, Oeschger Centre for Climate Change Research32, Tsinghua University33, National Center for Atmospheric Research34, Appalachian State University35, University of Colorado Boulder36, University of Washington37, Atlantic Oceanographic and Meteorological Laboratory38, Princeton University39, Met Office40, Leibniz Institute of Marine Sciences41, Auburn University42, University of Tasmania43, VU University Amsterdam44, Oak Ridge National Laboratory45, Sun Yat-sen University46, Nanjing University47
TL;DR: In this paper, the authors describe and synthesize data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties, including emissions from land use and land-use change data and bookkeeping models.
Abstract: Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) and terrestrial CO2 sink (SLAND) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2010–2019), EFOS was 9.6 ± 0.5 GtC yr−1 excluding the cement carbonation sink (9.4 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 1.6 ± 0.7 GtC yr−1. For the same decade, GATM was 5.1 ± 0.02 GtC yr−1 (2.4 ± 0.01 ppm yr−1), SOCEAN 2.5 ± 0.6 GtC yr−1, and SLAND 3.4 ± 0.9 GtC yr−1, with a budget imbalance BIM of −0.1 GtC yr−1 indicating a near balance between estimated sources and sinks over the last decade. For the year 2019 alone, the growth in EFOS was only about 0.1 % with fossil emissions increasing to 9.9 ± 0.5 GtC yr−1 excluding the cement carbonation sink (9.7 ± 0.5 GtC yr−1 when cement carbonation sink is included), and ELUC was 1.8 ± 0.7 GtC yr−1, for total anthropogenic CO2 emissions of 11.5 ± 0.9 GtC yr−1 (42.2 ± 3.3 GtCO2). Also for 2019, GATM was 5.4 ± 0.2 GtC yr−1 (2.5 ± 0.1 ppm yr−1), SOCEAN was 2.6 ± 0.6 GtC yr−1, and SLAND was 3.1 ± 1.2 GtC yr−1, with a BIM of 0.3 GtC. The global atmospheric CO2 concentration reached 409.85 ± 0.1 ppm averaged over 2019. Preliminary data for 2020, accounting for the COVID-19-induced changes in emissions, suggest a decrease in EFOS relative to 2019 of about −7 % (median estimate) based on individual estimates from four studies of −6 %, −7 %, −7 % (−3 % to −11 %), and −13 %. Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2019, but discrepancies of up to 1 GtC yr−1 persist for the representation of semi-decadal variability in CO2 fluxes. Comparison of estimates from diverse approaches and observations shows (1) no consensus in the mean and trend in land-use change emissions over the last decade, (2) a persistent low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) an apparent discrepancy between the different methods for the ocean sink outside the tropics, particularly in the Southern Ocean. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set (Friedlingstein et al., 2019; Le Quere et al., 2018b, a, 2016, 2015b, a, 2014, 2013). The data presented in this work are available at https://doi.org/10.18160/gcp-2020 (Friedlingstein et al., 2020).
1,764 citations
Harvard University1, Tsinghua University2, Chinese Academy of Sciences3, Centre national de la recherche scientifique4, University of Maryland, College Park5, Appalachian State University6, Oak Ridge National Laboratory7, University of Cambridge8, Peking University9, University of Oslo10, Beijing Forestry University11, University of East Anglia12, Nanjing University13
TL;DR: China’s carbon emissions are re-evaluated using updated and harmonized energy consumption and clinker production data and two new and comprehensive sets of measured emission factors for Chinese coal, finding that total energy consumption in China was 10 per cent higher in 2000–2012 than the value reported by China's national statistics, and that emission factors are on average 40 per cent lower than the default values recommended by the Intergovernmental Panel on Climate Change.
Abstract: Nearly three-quarters of the growth in global carbon emissions from the burning of fossil fuels and cement production between 2010 and 2012 occurred in China. Yet estimates of Chinese emissions remain subject to large uncertainty; inventories of China's total fossil fuel carbon emissions in 2008 differ by 0.3 gigatonnes of carbon, or 15 per cent. The primary sources of this uncertainty are conflicting estimates of energy consumption and emission factors, the latter being uncertain because of very few actual measurements representative of the mix of Chinese fuels. Here we re-evaluate China's carbon emissions using updated and harmonized energy consumption and clinker production data and two new and comprehensive sets of measured emission factors for Chinese coal. We find that total energy consumption in China was 10 per cent higher in 2000-2012 than the value reported by China's national statistics, that emission factors for Chinese coal are on average 40 per cent lower than the default values recommended by the Intergovernmental Panel on Climate Change, and that emissions from China's cement production are 45 per cent less than recent estimates. Altogether, our revised estimate of China's CO2 emissions from fossil fuel combustion and cement production is 2.49 gigatonnes of carbon (2 standard deviations = ±7.3 per cent) in 2013, which is 14 per cent lower than the emissions reported by other prominent inventories. Over the full period 2000 to 2013, our revised estimates are 2.9 gigatonnes of carbon less than previous estimates of China's cumulative carbon emissions. Our findings suggest that overestimation of China's emissions in 2000-2013 may be larger than China's estimated total forest sink in 1990-2007 (2.66 gigatonnes of carbon) or China's land carbon sink in 2000-2009 (2.6 gigatonnes of carbon).
1,075 citations
École Normale Supérieure1, University of Exeter2, Norwich Research Park3, Alfred Wegener Institute for Polar and Marine Research4, University of Groningen5, Wageningen University and Research Centre6, Ludwig Maximilian University of Munich7, Max Planck Society8, Commonwealth Scientific and Industrial Research Organisation9, Centre national de la recherche scientifique10, Stanford University11, Karlsruhe Institute of Technology12, Atlantic Oceanographic and Meteorological Laboratory13, Cooperative Institute for Marine and Atmospheric Studies14, Bjerknes Centre for Climate Research15, Geophysical Institute, University of Bergen16, Japan Agency for Marine-Earth Science and Technology17, University of Maryland, College Park18, National Institute of Water and Atmospheric Research19, National Oceanic and Atmospheric Administration20, Appalachian State University21, Flanders Marine Institute22, Augsburg College23, ETH Zurich24, Leibniz Institute of Marine Sciences25, University of East Anglia26, Woods Hole Research Center27, University of Illinois at Urbana–Champaign28, University of Hong Kong29, Netherlands Environmental Assessment Agency30, Utrecht University31, University of Paris32, University of Tasmania33, Hobart Corporation34, University of Bern35, National Center for Atmospheric Research36, University of Reading37, Cooperative Institute for Research in Environmental Sciences38, National Institute for Environmental Studies39, Russian Academy of Sciences40, Goddard Space Flight Center41, Leibniz Institute for Baltic Sea Research42, Princeton University43, Met Office44, Lund University45, Auburn University46, Food and Agriculture Organization47, VU University Amsterdam48
TL;DR: In this article, the authors describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties, including emissions from land use and land use change, and show that the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere is a measure of imperfect data and understanding of the contemporary carbon cycle.
Abstract: . Accurate assessment of anthropogenic carbon dioxide ( CO2 ) emissions and
their redistribution among the atmosphere, ocean, and terrestrial biosphere
– the “global carbon budget” – is important to better understand the
global carbon cycle, support the development of climate policies, and
project future climate change. Here we describe data sets and methodology to
quantify the five major components of the global carbon budget and their
uncertainties. Fossil CO2 emissions ( EFF ) are based on energy
statistics and cement production data, while emissions from land use change
( ELUC ), mainly deforestation, are based on land use and land use change
data and bookkeeping models. Atmospheric CO2 concentration is measured
directly and its growth rate ( GATM ) is computed from the annual changes
in concentration. The ocean CO2 sink ( SOCEAN ) and terrestrial
CO2 sink ( SLAND ) are estimated with global process models
constrained by observations. The resulting carbon budget imbalance
( BIM ), the difference between the estimated total emissions and the
estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a
measure of imperfect data and understanding of the contemporary carbon
cycle. All uncertainties are reported as ±1σ . For the last
decade available (2009–2018), EFF was 9.5±0.5 GtC yr −1 ,
ELUC 1.5±0.7 GtC yr −1 , GATM 4.9±0.02 GtC yr −1 ( 2.3±0.01 ppm yr −1 ), SOCEAN 2.5±0.6 GtC yr −1 , and SLAND 3.2±0.6 GtC yr −1 , with a budget
imbalance BIM of 0.4 GtC yr −1 indicating overestimated emissions
and/or underestimated sinks. For the year 2018 alone, the growth in EFF was
about 2.1 % and fossil emissions increased to 10.0±0.5 GtC yr −1 , reaching 10 GtC yr −1 for the first time in history,
ELUC was 1.5±0.7 GtC yr −1 , for total anthropogenic
CO2 emissions of 11.5±0.9 GtC yr −1 ( 42.5±3.3 GtCO2 ). Also for 2018, GATM was 5.1±0.2 GtC yr −1 ( 2.4±0.1 ppm yr −1 ), SOCEAN was 2.6±0.6 GtC yr −1 , and SLAND was 3.5±0.7 GtC yr −1 , with a BIM of 0.3 GtC. The global atmospheric CO2 concentration reached 407.38±0.1 ppm averaged over 2018. For 2019, preliminary data for the first 6–10 months indicate a reduced growth in EFF of +0.6 % (range of
−0.2 % to 1.5 %) based on national emissions projections for China, the
USA, the EU, and India and projections of gross domestic product corrected
for recent changes in the carbon intensity of the economy for the rest of
the world. Overall, the mean and trend in the five components of the global
carbon budget are consistently estimated over the period 1959–2018, but
discrepancies of up to 1 GtC yr −1 persist for the representation of
semi-decadal variability in CO2 fluxes. A detailed comparison among
individual estimates and the introduction of a broad range of observations
shows (1) no consensus in the mean and trend in land use change emissions
over the last decade, (2) a persistent low agreement between the different
methods on the magnitude of the land CO2 flux in the northern
extra-tropics, and (3) an apparent underestimation of the CO2
variability by ocean models outside the tropics. This living data update
documents changes in the methods and data sets used in this new global
carbon budget and the progress in understanding of the global carbon cycle
compared with previous publications of this data set (Le Quere et
al., 2018a, b, 2016, 2015a, b, 2014, 2013). The data generated by
this work are available at https://doi.org/10.18160/gcp-2019 (Friedlingstein
et al., 2019).
981 citations
Authors
Showing all 3034 results
| Name | H-index | Papers | Citations |
|---|---|---|---|
| Bente Klarlund Pedersen | 134 | 689 | 72177 |
| William J. Kraemer | 123 | 755 | 54774 |
| David C. Nieman | 86 | 394 | 29712 |
| Robert W. Talbot | 77 | 297 | 19783 |
| Jason F. Shogren | 74 | 527 | 21003 |
| William B. Stiles | 73 | 294 | 18118 |
| Michael H. Stone | 65 | 370 | 16355 |
| Caroline A. Macera | 65 | 215 | 35914 |
| David White | 61 | 369 | 12255 |
| Dru A. Henson | 58 | 119 | 9030 |
| Mark L. Wilson | 58 | 197 | 10689 |
| Gregory W. Heath | 57 | 147 | 32935 |
| Steven J. Fleck | 56 | 136 | 14500 |
| Michael McKee | 56 | 246 | 12692 |
| John Suppe | 55 | 139 | 15835 |