Accurate and precise, long-term measurements of greenhouse gas (GHG) concentrations were an original cause for concern linking human activities to rapid, and so far, unceasing rise in global GHG concentrations. The resulting increases in global temperatures, sea-level, glacial retreat, and other negative impacts are clear. In response to this evidence, nations, states, and cities, industries and individuals have been accelerating GHG emission reduction and other mitigation efforts while working towards equitable development and environmental justice. Research advances have shown that GHG measurements and analyses are much more than merely harbingers of global warming. The urgency, complexity, and economic implications of the needed GHG emission reductions and other climate action demand strategic investment in science-based information for planning, implementing, and tracking emission reduction policies and actions. Several national and international efforts seek to enhance the capacity of nations, states, cities, and industries to target emissions reduction opportunities and track progress towards their goals. Success depends on the availability of measurements of atmospheric composition, GHG fluxes, and emission activity data in key GHG emission source regions and relies on a multi-tiered observing strategy involving satellite, aircraft, and surface-based measurements, as well as innovative data mining and analysis methods.
Since EGU18, this session has been a showcase for how scientific data and analyses are transformed into actionable information services and successful climate solutions for a wide range of user-communities. These methodologies must have the required temporal and granular details to target and track explicit emission activity where climate action is achievable.
We seek presentations from researchers, inventory compilers, government decision and policy makers, non-government and private sector service providers showing the use and impact of science-based methods of detecting, quantifying, and tracking GHG emissions, and, where possible, the resulting climate mitigation. These methods can involve direct-detection, inverse-modeling, and AI/ML data fusion/mining of statistical and observational activity data, as well as hybrid combinations of all these approaches.