NOAA's Global Network of N2O Observations

Friday, 19 December 2014
Edward J Dlugokencky1, Andrew M Crotwell1, Molly Crotwell1, Kenneth A Masarie1, Patricia M Lang2, Geoffrey S Dutton2 and Brad David Hall3, (1)NOAA, Boulder, CO, United States, (2)National Oceanic and Atmospheric Administration, Global Monitoring Division, Earth System Research Laboratory, Boulder, CO, United States, (3)NOAA/ESRL Global Monitoring Division, Boulder, CO, United States
Nitrous oxide has surpassed CFC-12 to become the third largest contributor to radiative forcing. When climate impacts for equal emitted masses of N2O and CO2 are integrated over 100 years, N2O impacts are about 300 times greater than those of CO2. Increasing the atmospheric burden of N2O also decreases the abundance of O3 in the stratosphere. With reductions in emissions of ODSs as a result of the Montreal Protocol, N2O now has the largest ODP-weighted emissions of all gases. Given its long lifetime of about 130 years, today's emissions will impact climate and stratospheric O3 for a long time. Because emission rates are very small and spread over enormous areas, the detailed N2O budget has large uncertainties. It also means measurement requirements on precision and accuracy are stringent, especially for the background atmosphere. The Carbon Cycle Group of NOAA ESRL's Global Monitoring Division began measuring N2O in discrete air samples collected as part of its global cooperative air sampling network in 1998. Data from about 60 air sampling sites provide important constraints on the large-scale budget of N2O and provide boundary conditions for continental and regional-scale studies. This presentation will briefly describe the procedures used to ensure the data are of sufficient quality to meet scientific demands, and describe remaining limitations. Although sampling is infrequent (weekly), the data are quite useful in N2O budget studies. Examples will be given of large scale constraints on N2O's budget, including the global burden, trends in the burden, global emissions, spatial distributions, vertical gradients, and seasonal patterns.