Joint Global Change Research Institute

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.

Analysis of Agricultural Greenhouse Gas Mitigation Options Within a Multi-Sector Economic Framework

Abstract: Publisher Summary National-scale analysis of greenhouse gas mitigation options is generally carried out using topdown economic models with moderate energy detail but very limited detail in most other sectors, including agriculture and forestry. However, a complete analysis of greenhouse gas mitigation options including sequestration requires an improved representation of agriculture and forestry within the models used. A full analysis of greenhouse gas mitigation options should include activities that reduce emissions of carbon dioxide (CO2) and other greenhouse gases, and activities that sequester carbon. Analysis of greenhouse gas mitigation policies typically starts with an economic model that simulates national or global energy consumption in response to a carbon price, then appends marginal abatement cost curves for non-CO2 greenhouse gases, and perhaps includes simple assumptions on carbon sinks. No single model can adequately simulate all the activities and processes that might contribute to reductions in net greenhouse gas emissions. However, detailed process models for various activities, including agriculture and forestry, can be used to inform national and global economic models. In particular, greenhouse gas mitigation options within the agricultural and forestry sectors include changes in afforestation of agricultural lands, altered crop and livestock management practices, harvesting of biomass crops for fuel, and the sequestration of carbon in agricultural soils. Analysis of such options is usually carried out in a detailed sectoral model.

HIRM v1.0: a hybrid impulse response model for climate modeling and uncertainty analyses

Abstract: . Simple climate models (SCMs) are frequently used in research and decision-making communities because of their flexibility, tractability, and low computational cost. SCMs can be idealized, flexibly representing major climate dynamics as impulse response functions, or process-based, using explicit equations to model possibly nonlinear climate and earth system dynamics. Each of these approaches has strengths and limitations. Here we present and test a hybrid impulse response modeling framework (HIRM) that combines the strengths of process-based SCMs in an idealized impulse response model, with HIRM’s input derived from the output of a process-based model. This structure allows it to capture the crucial nonlinear dynamics frequently encountered in going from greenhouse gas emissions to atmospheric concentration to radiative forcing to climate change. As a test, the HIRM framework was configured to emulate total temperature of the simple climate model Hector 2.0 under the four Representative Concentration Pathways and the temperature response of an abrupt four times CO2 concentration step. HIRM was able to reproduce near-term and long-term Hector global temperature with a high degree of fidelity. Additionally, we conducted two case studies to demonstrate potential applications for this hybrid model: examining the effect of aerosol forcing uncertainty on global temperature, and incorporating more process-based representations of black carbon into a SCM. The open-source HIRM framework has a range of applications including complex climate model emulation, uncertainty analyses of radiative forcing, attribution studies, and climate model development.

Measuring income and projecting energy use

Abstract: Energy is a key requirement for a healthy, productive life and a major driver of the emissions leading to an increasingly warm planet. The implications of a doubling and redoubling of per capita incomes over the remainder of this century for energy use are a critical input into understanding the magnitude of the carbon management problem. A substantial controversy about how the Special Report on Emissions Scenarios (SRES) measured income and the potential implications of how income was measured for long term levels of energy use is revisited again in the McKibbin, Pearce and Stegman article appearing elsewhere in this issue. The recent release of a new set of purchasing power estimates of national income, and the preparations for creating new scenarios to support the IPCC’s fifth assessment highlight the importance of the issues which have arisen surrounding income and energy use. Comparing the 1993 and 2005 ICP results on Purchasing Power Parity (PPP) based measures of income reveals that not only do the 2005 ICP estimates share the same issue of common growth rates for real income as measured by PPP and US $, but the lack of coherence in the estimates of PPP incomes, especially for developing countries raises yet another obstacle to resolving the best way to measure income. Further, the common use of an income term to mediate energy demand (as in the Kaya identity) obscures an underlying reality about per capita energy demands, leading to unreasonable estimates of the impact of changing income measures and of the recent high GDP growth rates in India and China. Significant new research is required to create both a reasonable set of GDP growth rates and long term levels of energy use.

Case study of visualizing global user download patterns using Google Earth and NASA World Wind

Abstract: Geo-visualization is significantly changing the way we view spatial data and discover information. On the one hand, a large number of spatial data are generated every day. On the other hand, these data are not well utilized due to the lack of free and easily used data-visualiza- tion tools. This becomes even worse when most of the spatial data remains in the form of plain text such as log files. This paper describes a way of visualizing massive plain-text spatial data at no cost by utilizing Google Earth and NASA World Wind. We illustrate our methods by visua- lizing over 170,000 global download requests for satellite images maintained by the Earth Resources Observation and Science (EROS) Center of U.S. Geological Survey (USGS). Our visualization results identify the most popular satellite images around the world and discover the global user download patterns. The benefits of this research are: 1. assisting in improving the satellite image downloading services provided by USGS, and 2. providing a proxy for analyzing the "hot spot" areas of research. Most importantly, our methods demonstrate an easy way to geo- visualize massive textual spatial data, which is highly applicable to mining spatially referenced data and information on a wide variety of research domains (e.g., hydrology, agriculture, atmospheric science, natural hazard, and global climate change). © 2012 Society of Photo-Optical Instrumentation Engineers (SPIE). (DOI: 10.1117/1.JRS.6.061703)