CCE banner
 
Funded Research

Understanding user needs for carbon monitoring information

Duren, Riley: Carbon Mapper/U. Arizona (Project Lead)
Cooke, Roger: Resources For The Future, Inc. (Co-Investigator)
Crichton, Daniel: JPL (Co-Investigator)
Gurney, Kevin: Northern Arizona University (Co-Investigator)
Saatchi, Sassan: Jet Propulsion Laboratory / Caltech (Co-Investigator)
Woodall, Christopher (Chris): USDA Forest Service (Co-Investigator)
Croes, Bart: California Energy Commission / California Air Resources Board (retired) / CIRES at University of Colorado-Boulder (Participant)
Hockstad, Leif: Environmental Protection Agency (Participant)
Larsen, Kate: Rhodium Group (Participant)
Reidmiller, David: USGS (Participant)

Project Funding: 2013 - 2016

Funded by NASA

Abstract:
The objectives of the proposed work are to: 1) engage the user community and identify needs for policy-relevant carbon monitoring information, 2) evaluate current and planned NASA Carbon Monitoring System data products with regard to their value for decision making, and 3) explore alternative methods for visualizing and communicating carbon monitoring information and associated uncertainties to decision makers and other stakeholders. We will establish a framework that facilitates frequent and sustained engagement of carbon policy and management stakeholders to define requirements for CMS data products. Our team will work with the CMS science team to acquire prototype data products and help stakeholders evaluate the utility and relevance for policy planning and decision support. We will develop a Carbon Calculator and Data Portal that integrates multiple CMS products to enable those evaluation efforts. Where necessary we will explore new approaches for presenting the results of CMS data products and their uncertainties to decision-makers, again with the intent of helping to inform future CMS requirements and improve relevance of the ultimate data products. Our team combines experts in carbon management and policy from a representative cross-section of stakeholders in the US government (including the State Department's Bureau of Oceans and International Environment and Scientific Affairs (OES), the Environmental Protection Agency (EPA), and the White House Council on Environmental Quality (CEQ) with other experts working at the interface of science and policy for carbon monitoring (co-investigators from JPL, RFF, ASU, and USFS). The team will meet regularly and share information through a flexible web portal that leverages emerging tools for visualizing data. We will apply the above process to study a range of representative policy scenarios. Examples of topics that may be explored include but are not limited to: policies and management efforts focused on: 1) Land Use, Land Use Change, and Forestry (LULUCF) fluxes for the United States and/or selected developing countries (e.g., Indonesia), 2) Forest carbon stocks and disturbances for the US and/or tropical countries or sub-national projects therein, 3) methane (CH4) emissions from major shale gas basins in the US, and 4) fossil fuel CO2 and CH4 emissions from cities and industrialized states and provinces (including potential linked sub-national carbon emissions trading systems).

Publications:

Blackman, A., Veit, P. 2018. Titled Amazon Indigenous Communities Cut Forest Carbon Emissions. Ecological Economics. 153, 56-67. DOI: 10.1016/j.ecolecon.2018.06.016

Gurney, K. R., Liang, J., Patarasuk, R., Song, Y., Huang, J., Roest, G. 2020. The Vulcan Version 3.0 High-Resolution Fossil Fuel CO 2 Emissions for the United States. Journal of Geophysical Research: Atmospheres. 125(19). DOI: 10.1029/2020JD032974

Gurney, K. R., Patarasuk, R., Liang, J., Song, Y., O'Keeffe, D., Rao, P., Whetstone, J. R., Duren, R. M., Eldering, A., Miller, C. 2019. The Hestia fossil fuel CO<sub>2</sub> emissions data product for the Los Angeles megacity (Hestia-LA). Earth System Science Data. 11(3), 1309-1335. DOI: 10.5194/essd-11-1309-2019

Cooke, R. M., Saatchi, S., Hagen, S. 2016. Global correlation and uncertainty accounting. Dependence Modeling. 4(1). DOI: 10.1515/demo-2016-0009


More details may be found in the following project profile(s):