Challenges for a Greenhouse Gas Information System (Invited)

Coping with global climate change requires both adaptation and mitigation. The former requires improved prognoses of likely climate futures, including natural emissions of greenhouse gases (GHG). The latter includes bringing down net emissions of greenhouse gases in order to first slow down, halt, and then decrease the forcing of climate change. The task implies a complete reorganization of society's energy system, which will require investments of the order of one to several percent of global gross domestic product for several decades. The political and economic stakes are very large, which is likely to put much pressure on self-reported emissions inventories to show success. There need to be observation based tools that can provide credible and objective evidence of the degree of success of emissions reduction policies and carbon sequestration efforts at scales that are relevant to policy makers. The atmosphere itself can provide such evidence, but several developments are necessary. The current international GHG observing system of in-situ chemical measurements needs to be enhanced by intensification as well as placement near large source regions. National inventories need to become specific in where and when emissions take place, so that the expected enhancements of the GHG mole fractions can be tested against observations. Atmospheric chemical transport models, that translate emissions into expected atmospheric GHG patterns, need to be improved, especially in the area of boundary layer mixing and dynamics. The GHG abundances in the atmosphere reflect the accumulation of many years of sources and surface exchange. Therefore the signals produced by recent emissions are relatively small compared to a common global background, which translates into a requirement for very accurate measurements. The integration of satellite observations, which provide global coverage, into the GHG observing system demands very careful and ongoing comparison with independent measurements to diagnose and correct for systematic biases down to very small levels. Other challenges are complete and prompt transparency of all data and methods, without which there would be no credibility. An outline of how we might bring together these elements will be presented.