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Institution

Joint Global Change Research Institute

FacilityRiverdale 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.


Papers
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Journal ArticleDOI
TL;DR: In this paper, climate change is increasingly recognized as having national security implications, which has prompted dialogue between the climate change and national security communities, with resultant advantages and differences, and has shown that changes such as increased heat, more intense storms, longer periods without rain and earlier spring onset call for building climate resilience as part of building stability.
Abstract: Climate change is increasingly recognized as having national security implications, which has prompted dialogue between the climate change and national security communities—with resultant advantages and differences. Climate change research has proven useful to the national security community sponsors in several ways. It has opened security discussions to consider climate as well as political factors in studies of the future. It has encouraged factoring in the stresses placed on societies by climate changes (of any kind) to help assess the potential for state stability. And it has shown that changes such as increased heat, more intense storms, longer periods without rain, and earlier spring onset call for building climate resilience as part of building stability. For the climate change research community, studies from a national security point of view have revealed research lacunae, such as the lack of usable migration studies. This has also pushed the research community to consider second- and thi...

2 citations

Journal ArticleDOI
TL;DR: The Capacity Expansion Regional Feasibility model is an open-source geospatial model that is designed to determine the on-the-ground feasibility of achieving a projected energy technology expansion plan and can provide insight into factors that influence energy system resilience under a variety of future scenarios.
Abstract: The Capacity Expansion Regional Feasibility (CERF) model is an open-source geospatial model, written in Python and C++, that is designed to determine the on-the-ground feasibility of achieving a projected energy technology expansion plan. Integrated human-Earth systems models and grid expansion models typically do not have sufficient spatial, temporal, or process-level resolution to account for technology-specific siting considerations—for example, the value or costs of connecting a new power plant to the electric grid at a particular location or whether there is sufficient cooling water to support the installation of thermal power plants in a certain region. CERF was developed to specifically examine where power plant locations can be feasibly sited when considering high spatial resolution siting suitability data as well as the net locational costs (i.e., considering both net operating value and interconnection costs), at a spatial resolution of 1 km2. The outputs from CERF can provide insight into factors that influence energy system resilience under a variety of future scenarios can be used to refine model-based projections and be useful for capacity expansion planning exercises. CERF is open-source and publicly available via GitHub. Funding Statement: The original development of the CERF model was conducted under the Laboratory Directed Research and Development Program at Pacific Northwest National Laboratory, a multi-program national laboratory operated by Battelle for the U.S. Department of Energy under Contract DE-AC05-76RL01830. Further development and ongoing demonstration of CERF is supported by the U.S. Department of Energy, Office of Science, as part of research in Multi-Sector Dynamics, Earth and Environmental System Modeling Program.

2 citations

Journal ArticleDOI
TL;DR: In this article, a Random Forest algorithm was used to predict the spatio-temporal patterns of RAsoil from 1981 to 2017 based on the most updated Global Soil Respiration Database (v5) with global environmental variables; calculated carbon allocation from photosynthesis to RAsoils (CAB) as a fraction of gross primary production; and assessed its temporal and spatial patterns in global forest ecosystems.

2 citations

Posted ContentDOI
TL;DR: In this paper, past and future carbon emissions from global croplands, considering land-use change, changes in crop productivity, tillage practices, and residue removal, were examined, and they found that emissions over the historical period are sensitive to the assumed productivity of arable land that is not planted in a given year and the assumed fraction of soil carbon released during land conversion.
Abstract: . We examine past and future carbon emissions from global croplands, considering land-use change, changes in crop productivity, tillage practices, and residue removal. We find that emissions over the historical period are sensitive to the assumed productivity of arable land that is not planted in a given year and the assumed fraction of soil carbon that is released during land conversion. The role of this "other" arable land, both at present and over the historical period, is not well understood and should be examined further. The carbon balance of croplands over 21st century depends on changes in management practices, particularly the adoption of conservation tillage and the potential removal of residue for use as energy feedstocks. We find that croplands will not become large carbon sinks in the future, however, unless most crop residue is left on fields. Given the relatively low carbon "penalty" incurred by removal, residue use for energy feedstocks may be the preferred option.

2 citations


Authors

Showing all 213 results

NameH-indexPapersCitations
Katherine Calvin5818114764
Steven J. Smith5819036110
George C. Hurtt5715924734
Brian C. O'Neill5717414636
Leon Clarke5318110770
James A. Edmonds5117510494
Claudia Tebaldi5010021389
Roberto C. Izaurralde481429790
Ghassem R. Asrar4614112280
Yuyu Zhou461696578
Ben Bond-Lamberty431447732
Marshall Wise401107074
William K. M. Lau401547095
Allison M. Thomson399122037
Ben Kravitz371274256
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202310
202218
2021106
2020112
201973
201878