The Arctic Methane, Carbon Aerosols, and Tracers Study

 

Authors

Ray P. Bambha — Sandia National Laboratories

Daniel A Lucero — Sandia National Laboratories
Mark D. Ivey — Sandia National Laboratories
Walter Scott Brower — UIC Science Division/ARM - N
James L. Ivanoff — UIC Science LLC ARM/NSA/OPS
Anne Jefferson — NOAA- Earth System Research Laboratory
Ross Burgener — NOAA Global Monitoring Division
Bryan D Thomas — NOAA Global Monitoring Laboratory
Hope A. Michelsen — Sandia National Laboratories

Category

High-latitude clouds and aerosols

Description

Black carbon and methane are believed to be significant climate forcers, but the sizes of their various sources and sinks remain highly uncertain. The Arctic region is known to have large reservoirs of carbon that can potentially be released as methane as the region warms. Black carbon (BC) may have a strong influence on snow and ice albedo. The sensitivities of emissions of CH4 and BC to changes in climate are also poorly understood and, therefore, cannot be included with confidence in climate models. Multiple sources of methane and black carbon contribute to concentrations of these species in the Arctic, which complicates the source attribution problem. Measurements of co-emitted species that act as natural tracers for sources of CH4 and BC can help to connect atmospheric concentrations to source emission rates. The Arctic Methane, Carbon Aerosols, and Tracers Study is a new measurement campaign at the DOE-ARM North Slope of Alaska site in Barrow that involves the deployment of instruments to measure CH4, BC, and source tracers. We have deployed an in situ instrument to measure methane, the ratio of its isotopologues 13CH4/12CH4, and ethane. A second instrument is measuring carbon monoxide, carbonyl sulfide, carbon dioxide and water vapor. We have also deployed an in situ instrument to measure BC, a Single-Particle Soot Photometer, at the nearby NOAA Barrow Observatory, co-located with the other aerosol instruments. Changes in emissions across different regions and seasons will be inferred using atmospheric transport and inverse modeling techniques.