POPSnet-SGP: A Pilot Aerosol Microphysics Network for Targeting Climate Model Uncertainty
1 September 2019 - 1 September 2021
Lead Scientist: Allison McComiskey
A spatially dense network of relatively low-cost aerosol microphysical property measurements can target persistent model error that hampers our ability to project climate with useful accuracy. Aerosols play a significant role in the radiative forcing of climate change, either through their direct radiative effects or cloud-nucleating properties. Estimates of this forcing, however, carry a large uncertainty that has not been significantly reduced despite years of directed research. Model intercomparison projects have shown that model diversity for simulated aerosol microphysical properties is much larger than for simulated optical properties (Mann et al. 2014; Myhre et al. 2009). This is due in part to the greater spatial variability in microphysical properties and in part to the availability of a relatively dense network of aerosol optical depth measurements that has been used to effectively constrain models. However, aerosol microphysical properties are the fundamental pieces of information required to convert aerosol emissions information to radiative and cloud-nucleating properties that drive radiative effects and forcing.
We will deploy a 13-site network of aerosol microphysical property measurements (number concentration and size distribution) across the ARM Southern Great Plains domain using low-cost, simple, and robust instruments. The measurement strategy is designed to minimize a major source of uncertainty associated with comparing global models with point measurements—the so-called ‘representation error’ (Schutgens et al. 2017.) The representation error arises because aerosol concentrations at a single measurement site can be persistently higher or lower than the average across a large model box (i.e., the measurement location is not representative of the area.) While low-cost instruments may lack some features of conventional instruments, when deployed in such a way as to target the representation error, they are more likely to provide the specific information needed to reduce the large uncertainty range in global climate model simulations.