A Novel Network‐Based Approach to Determining Measurement Representation Error for Model Evaluation of Aerosol Microphysical Properties

Atmospheric aerosol size and abundance influence radiative effects and climate change. To date, efforts to constrain global climate models' radiative forcing with in situ aerosol observations have been hamstrung by uncertainty. One source of error, the regional “representation error,” arises when accurate but sparse single‐point measurements of atmospheric aerosol distributions are compared with a model value, assuming that the single‐point measurement is representative of the model domain. The Portable Optical Particle Spectrometer network in the Southern Great Plains (POPSnet‐SGP) campaign has demonstrated that a network of nearly autonomous aerosol instruments operating at ambient temperature and relative humidity (with low measurement error) may be used to quantify measurement representation error and investigate the factors introducing heterogeneity in aerosol distributions across a rural, continental background region. Measurements were made using Portable Optical Particle Spectrometer (POPS) instruments at several sites for five months across the Department of Energy's Aerosol Radiation Measurement Southern Great Plains (ARM‐SGP) User Facility in the central USA. Measurement representation error decreased with longer averaging periods (20%–40% between 1 s and 1 day), varied between sites by 10%–20% for aerosol concentration 140–2,500 nm in diameter (N_140), and was higher for aerosols >400 nm in diameter (N_400). Our measurements also show the influence of local meteorology on aerosol surface area (A_140) and size distributions: A_140 is positively correlated with wind speed and relative humidity, negatively correlated with precipitation, and lower given westerly winds. We conclude that the POPSnet approach provides considerably more insight into the spatial variability in the aerosol population that can be used to constrain climate models than would be available from similar networks of PM 2.5 monitors. Plain Language Summary Global climate models have long struggled to predict the radiative effects of aerosols on climate. In addition to measurement error and model error, model‐observation comparisons may suffer from error related to a sample's representativeness of the aerosol population. This study outlines an approach to quantify the representation error, using the Southern Great Plains (SGP) as a testbed, and find that to an extent, observed differences are related to local meteorology. Key Points Measurement networks can quantify regional repr