CCIWG Carbon Cycle Predictions Workshop report published

November 2, 2016

A report on the CCIWG sponsored Carbon Cycle Predictions worshop has been published by AGU EOS here.

An excerpt from the article 'Improving Carbon Cycle Projections for Better Carbon Management' is below.

'...One important step toward carbon management is developing the science that predicts carbon cycles. Over the past 10 years, the North American Carbon Program (NACP) has helped to significantly advance observation and monitoring systems in carbon cycle research. Observation and monitoring programs such as AmeriFlux and the National Ecological Observatory Network (NEON) are essential to improving understanding of the carbon cycle through diagnosing the magnitude, spatial patterns, and temporal variability of carbon fluxes and stocks. However, the value of these programs would be greatly increased if their results could be used to constrain future projections of carbon cycle dynamics in response to climate change and human activities, including carbon management efforts. To examine the state of predictive carbon cycle research and to advance model capabilities, scientists met in College Park, Md., with members of the Carbon Cycle Interagency Working Group (CCIWG) in March 2016....'

A PDF of the full article is available here.

Map of AmeriFlux and NEON sites in North America.

Figure: AmeriFlux sites and NEON sites in North America measure the exchange of carbon dioxide between ecosystems and the atmosphere. This land cover map is based on the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on NASA’s Terra satellite. The land cover types include evergreen needleleaf forests (ENF), evergreen broadleaf forests (EBF), deciduous needleleaf forests (DNF), deciduous broadleaf forests (DBF), mixed forests (MF), closed shrublands (CSH), open shrublands (OSH), woody savannas (WSA), savannas (SAV), grasslands (GRA), croplands (CRO), urban, and barren.


Citation: Xiao, J., Y. Luo, and G. Shrestha (2016), Improving carbon cycle projections for better carbon management, Eos, 97,doi:10.1029/2016EO062341. Published on 02 November 2016.