Institution
Joint Global Change Research Institute
Facility•Riverdale Park, Maryland, United States•
About: Joint Global Change Research Institute is a facility organization based out in Riverdale Park, Maryland, United States. It is known for research contribution in the topics: Greenhouse gas & Climate change. The organization has 197 authors who have published 934 publications receiving 62390 citations.
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University of Edinburgh1, University of Gothenburg2, Institute of Arctic and Alpine Research3, Max Planck Society4, National University of Cordoba5, National Scientific and Technical Research Council6, Université de Sherbrooke7, VU University Amsterdam8, University of Lapland9, University of British Columbia10, Grand Valley State University11, Aarhus University12, United States Geological Survey13, University of Zurich14, University of Greifswald15, Swiss National Park16, University of New South Wales17, Northern Arizona University18, Bigelow Laboratory For Ocean Sciences19, Smithsonian Tropical Research Institute20, Leiden University21, University of California, Riverside22, Florida State University College of Arts and Sciences23, Qatar University24, Mississippi State University25, University of Barcelona26, Umeå University27, Utrecht University28, Adam Mickiewicz University in Poznań29, University of Alaska Anchorage30, Technische Universität München31, University of Parma32, Alaska Department of Fish and Game33, St. John's University34, University of Copenhagen35, Helmholtz Centre for Environmental Research - UFZ36, University of Oulu37, Agro ParisTech38, Spanish National Research Council39, Queen's University40, Wageningen University and Research Centre41, University of Saskatchewan42, University of Vienna43, Oak Ridge National Laboratory44, Bjerknes Centre for Climate Research45, University of Aberdeen46, University of Helsinki47, Vrije Universiteit Brussel48, Université du Québec à Trois-Rivières49, University of Victoria50, Swiss Federal Institute of Aquatic Science and Technology51, Research Institute for Nature and Forest52, Florida International University53, University of Lausanne54, Norwegian University of Science and Technology55, University of Alaska Fairbanks56, University of Waterloo57, Deakin University58, University of Bonn59, University of Stirling60, University of Innsbruck61, Rocky Mountain Biological Laboratory62, Environmental Change Institute63, University of California, Berkeley64, Joint Global Change Research Institute65, University of Insubria66, University of Würzburg67, Harvard University68, University of Ulm69, University of Oldenburg70, Estonian University of Life Sciences71, Kyoto University72, University of Regensburg73, University of Sydney74, University of Minnesota75, Santa Clara University76, Algoma University77, Komarov Botanical Institute78, University of Amsterdam79
TL;DR: It is shown that known plant trait relationships extend to the tundra biomes and exhibit the same two dimensions of variation detected at the global scale, informing prediction of plant community change in a warming world.
Abstract: The majority of variation in six traits critical to the growth, survival and reproduction of plant species is thought to be organised along just two dimensions, corresponding to strategies of plant size and resource acquisition. However, it is unknown whether global plant trait relationships extend to climatic extremes, and if these interspecific relationships are confounded by trait variation within species. We test whether trait relationships extend to the cold extremes of life on Earth using the largest database of tundra plant traits yet compiled. We show that tundra plants demonstrate remarkably similar resource economic traits, but not size traits, compared to global distributions, and exhibit the same two dimensions of trait variation. Three quarters of trait variation occurs among species, mirroring global estimates of interspecific trait variation. Plant trait relationships are thus generalizable to the edge of global trait-space, informing prediction of plant community change in a warming world.
41 citations
TL;DR: In this paper, the authors analyzed the role of end use efficiency improvements for Indian climate change mitigation policy and the associated co-benefits, within the integrated assessment modeling framework of Global Change Assessment Model (GCAM).
Abstract: Addressing the challenges of global warming requires interventions on both the energy supply and demand side. With the supply side responses being thoroughly discussed in the literature, our paper focuses on analyzing the role of end use efficiency improvements for Indian climate change mitigation policy and the associated co-benefits, within the integrated assessment modeling framework of Global Change Assessment Model (GCAM). Six scenarios are analyzed here in total- one no climate policy and two climate policy cases, and within each of these one scenario with reference end use energy technology assumptions and another with advance end use energy technology assumptions has been analyzed. The paper has some important insights. Final energy demand and emissions in India are significantly reduced with energy efficiency improvements, and the role of this policy is important especially for the building and transportation sector under both reference and climate policy scenarios. Though energy efficiency policy should be an integral part of climate policy, by itself it is not sufficient for achieving mitigation targets, and a climate policy is necessary for achieving mitigation goals. There are significant co-benefits of energy efficiency improvements. Energy security for India is improved with reduced oil, coal and gas imports. Significant reduction in local pollutant gases is found which is important for local health concerns. Capital investment requirement for Indian electricity generation is reduced, more so for the climate policy scenarios, and finally there are significant savings in terms of reduced abatement cost for meeting climate change mitigation goals.
41 citations
TL;DR: In this paper, the authors combined an old paper-based US civil war map and modern aerial photos to derive land-use history and landscape dynamics at fine scales for a region near Chancellorsville, USA, from 1867 to 2014.
41 citations
41 citations
Goddard Space Flight Center1, University of Tokyo2, Kyoto University3, University of Maryland, Baltimore4, Joint Institute for Nuclear Research5, California Institute of Technology6, University of Miami7, Joint Global Change Research Institute8, Florida State University9, Morgan State University10, Colorado State University11, University of Washington12, NASA Headquarters13, Nagoya University14, Japan Aerospace Exploration Agency15
TL;DR: Yanai et al. as discussed by the authors used the meteorological data collected from a sounding network to present a pioneering work in 1973 on thermodynamic budgets, which are referred to as the apparent heat source (Q1) and apparent moisture sink (Q2).
Abstract: Yanai and coauthors utilized the meteorological data collected from a sounding network to present a pioneering work in 1973 on thermodynamic budgets, which are referred to as the apparent heat source (Q1) and apparent moisture sink (Q2). Latent heating (LH) is one of the most dominant terms in Q1. Yanai’s paper motivated the development of satellite-based LH algorithms and provided a theoretical background for imposing large-scale advective forcing into cloud-resolving models (CRMs). These CRM-simulated LH and Q1 data have been used to generate the look-up tables in Tropical Rainfall Measuring Mission (TRMM) LH algorithms. A set of algorithms developed for retrieving LH profiles from TRMM-based rainfall profiles is described and evaluated, including details concerning their intrinsic space–time resolutions. Included in the paper are results from a variety of validation analyses that define the uncertainty of the LH profile estimates. Also, examples of how TRMM-retrieved LH profiles have been used ...
40 citations
Authors
Showing all 213 results
| Name | H-index | Papers | Citations |
|---|---|---|---|
| Katherine Calvin | 58 | 181 | 14764 |
| Steven J. Smith | 58 | 190 | 36110 |
| George C. Hurtt | 57 | 159 | 24734 |
| Brian C. O'Neill | 57 | 174 | 14636 |
| Leon Clarke | 53 | 181 | 10770 |
| James A. Edmonds | 51 | 175 | 10494 |
| Claudia Tebaldi | 50 | 100 | 21389 |
| Roberto C. Izaurralde | 48 | 142 | 9790 |
| Ghassem R. Asrar | 46 | 141 | 12280 |
| Yuyu Zhou | 46 | 169 | 6578 |
| Ben Bond-Lamberty | 43 | 144 | 7732 |
| Marshall Wise | 40 | 110 | 7074 |
| William K. M. Lau | 40 | 154 | 7095 |
| Allison M. Thomson | 39 | 91 | 22037 |
| Ben Kravitz | 37 | 127 | 4256 |