Institution
University of New Hampshire
Education•Durham, New Hampshire, United States•
About: University of New Hampshire is a education organization based out in Durham, New Hampshire, United States. It is known for research contribution in the topics: Population & Solar wind. The organization has 9379 authors who have published 24025 publications receiving 1020112 citations. The organization is also known as: UNH.
Topics: Population, Solar wind, Poison control, Magnetosphere, Heliosphere
Papers published on a yearly basis
Papers
More filters
••
TL;DR: A review of recent progress in understanding and predicting polymer drag reduction (DR) in turbulent wall-bounded shear flows is provided in this paper, where numerical simulations of viscoelastic turbulent flows and detailed turbulence measurements in flows of dilute polymer solutions using laser-based optical techniques.
Abstract: This article provides a review of recent progress in understanding and predicting polymer drag reduction (DR) in turbulent wall-bounded shear flows. The reduction in turbulent friction losses by the dilute addition of high–molecular weight polymers to flowing liquids has been extensively studied since the phenomenon was first observed over 60 years ago. Although it has long been reasoned that the dynamical interactions between polymers and turbulence are responsible for DR, it was not until recently that progress had been made to begin to elucidate these interactions in detail. These advancements come largely from numerical simulations of viscoelastic turbulent flows and detailed turbulence measurements in flows of dilute polymer solutions using laser-based optical techniques. This review presents a selective overview of the current state of the numerics and experimental techniques and their impact on understanding the mechanics and prediction of polymer DR. It includes a discussion of areas in which our ...
639 citations
••
University of East Anglia1, University of Oslo2, Centre national de la recherche scientifique3, University of Exeter4, National Oceanic and Atmospheric Administration5, Karlsruhe Institute of Technology6, Oak Ridge National Laboratory7, University of Paris8, Commonwealth Scientific and Industrial Research Organisation9, University of Maryland, College Park10, Alfred Wegener Institute for Polar and Marine Research11, Woods Hole Research Center12, University of Bristol13, University of Illinois at Urbana–Champaign14, Geophysical Institute, University of Bergen15, Bjerknes Centre for Climate Research16, National Institute for Environmental Studies17, University of California, San Diego18, Plymouth Marine Laboratory19, Netherlands Environmental Assessment Agency20, Lawrence Berkeley National Laboratory21, ETH Zurich22, Hobart Corporation23, Woods Hole Oceanographic Institution24, Appalachian State University25, Wageningen University and Research Centre26, Montana State University27, Australian National University28, Université libre de Bruxelles29, Max Planck Society30, Japan Meteorological Agency31, University of New Hampshire32, Leibniz Institute of Marine Sciences33, Imperial College London34, Oeschger Centre for Climate Change Research35, Joint Institute for the Study of the Atmosphere and Ocean36, Lamont–Doherty Earth Observatory37, VU University Amsterdam38, Atlantic Oceanographic and Meteorological Laboratory39, Met Office40
TL;DR: In this paper, the authors present a methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics, and model estimates and their interpretation by a broad scientific community.
Abstract: Accurate assessment of anthropogenic carbon dioxide (CO 2 ) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere 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 a methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics, and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates, consistency within and among components, alongside methodology and data limitations. CO 2 emissions from fossil fuel combustion and cement production (E FF ) are based on energy statistics and cement production data, respectively, while emissions from land-use change (E LUC ), mainly deforestation, are based on combined evidence from land-cover-change data, fire activity associated with deforestation, and models. The global atmospheric CO 2 concentration is measured directly and its rate of growth (G ATM ) is computed from the annual changes in concentration. The mean ocean CO 2 sink (S OCEAN ) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in S OCEAN is evaluated with data products based on surveys of ocean CO 2 measurements. The global residual terrestrial CO 2 sink (S LAND ) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models forced by observed climate, CO 2 , and land-cover-change (some including nitrogen–carbon interactions). We compare the mean land and ocean fluxes and their variability to estimates from three atmospheric inverse methods for three broad latitude bands. All uncertainties are reported as ±1σ, reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2004–2013), E FF was 8.9 ± 0.4 GtC yr −1 , E LUC 0.9 ± 0.5 GtC yr −1 , G ATM 4.3 ± 0.1 GtC yr −1 , S OCEAN 2.6 ± 0.5 GtC yr −1 , and S LAND 2.9 ± 0.8 GtC yr −1 . For year 2013 alone, E FF grew to 9.9 ± 0.5 GtC yr −1 , 2.3% above 2012, continuing the growth trend in these emissions, E LUC was 0.9 ± 0.5 GtC yr −1 , G ATM was 5.4 ± 0.2 GtC yr −1 , S OCEAN was 2.9 ± 0.5 GtC yr −1 and S LAND was 2.5 ± 0.9 GtC yr −1 . G ATM was high in 2013, reflecting a steady increase in E FF and smaller and opposite changes between S OCEAN and S LAND compared to the past decade (2004–2013). The global atmospheric CO 2 concentration reached 395.31 ± 0.10 ppm averaged over 2013. We estimate that E FF will increase by 2.5% (1.3–3.5%) to 10.1 ± 0.6 GtC in 2014 (37.0 ± 2.2 GtCO 2 yr −1 ), 65% above emissions in 1990, based on projections of world gross domestic product and recent changes in the carbon intensity of the global economy. From this projection of E FF and assumed constant E LUC for 2014, cumulative emissions of CO 2 will reach about 545 ± 55 GtC (2000 ± 200 GtCO 2 ) for 1870–2014, about 75% from EF FF and 25% from E LUC . This paper documents changes in the methods and data sets used in this new carbon budget compared with previous publications of this living data set (Le Quere et al., 2013, 2014). All observations presented here can be downloaded from the Carbon Dioxide Information Analysis Center (doi:10.3334/CDIAC/GCP_2014).
639 citations
••
TL;DR: This work compared chemical extraction and mathematical correction methods for freshwater and marine fishes and aquatic invertebrates to better understand impacts of correction approaches on carbon (delta(13)C) and nitrogen (d delta(15)N) stable isotope data.
Abstract: Summary 1. Lipids have more negative δ 13 C values relative to other major biochemical compounds in plant and animal tissues. Although variable lipid content in biological tissues alters results and conclusions of δ 13 C analyses in aquatic food web and migration studies, no standard correction protocol exists. 2. We compared chemical extraction and mathematical correction methods for freshwater and marine fishes and aquatic invertebrates to better understand impacts of correction approaches on carbon ( δ 13 C) and nitrogen ( δ 15 N) stable isotope data. 3. Fish and aquatic invertebrate tissue δ 13 C values increased significantly following extraction for almost all species and tissue types relative to nonextracted samples. In contrast, δ 15 N was affected for muscle and whole body samples from only a few freshwater and marine species and had a limited effect for the entire data set. 4. Lipid normalization models, using C : N as a proxy for lipid content, predicted lipid-corrected δ 13 C for paired data sets more closely with parameters specific to the tissue type and species to which they were applied. 5. We present species- and tissue-specific models based on bulk C : N as a reliable alternative to chemical extraction corrections. By analysing a subset of samples before and after lipid extraction, models can be applied to the species and tissues of interest that will improve estimates of dietary sources using stable isotopes.
639 citations
••
TL;DR: The authors found that people who are good at connecting thoughts to feelings may better "hear" the emotional implications of their own thoughts, as well as understand the feelings of others from what they say.
638 citations
••
TL;DR: In this paper, a satellite-based Vegetation Photosynthesis Model (VPM) was developed and validated using site-specific CO2 flux and climate data from a temperate deciduous broadleaf forest at Harvard Forest, Massachusetts, USA.
636 citations
Authors
Showing all 9489 results
Name | H-index | Papers | Citations |
---|---|---|---|
Derek R. Lovley | 168 | 582 | 95315 |
Peter B. Reich | 159 | 790 | 110377 |
Jerry M. Melillo | 134 | 383 | 68894 |
Katja Klein | 129 | 1499 | 87817 |
David Finkelhor | 117 | 382 | 58094 |
Howard A. Stone | 114 | 1033 | 64855 |
James O. Hill | 113 | 532 | 69636 |
Tadayuki Takahashi | 112 | 932 | 57501 |
Howard Eichenbaum | 108 | 279 | 44172 |
John D. Aber | 107 | 204 | 48500 |
Andrew W. Strong | 99 | 563 | 42475 |
Charles T. Driscoll | 97 | 554 | 37355 |
Andrew D. Richardson | 94 | 282 | 32850 |
Colin A. Chapman | 92 | 491 | 28217 |
Nicholas W. Lukacs | 91 | 367 | 34057 |