Spatial Representativeness Error in the Ground-Level Observation Networks for...

Wang, R., E. Andrews, Y. Balkanski, O. Boucher, G. Myhre, B. H. Samset, M. Schulz, G. Schuster, M. Valari, and S. Tao (2018), Spatial Representativeness Error in the Ground-Level Observation Networks for Black Carbon Radiation Absorption, Geophys. Res. Lett., 45, 2106-2114, doi:10.1002/2017GL076817.

There is high uncertainty in the direct radiative forcing of black carbon (BC), an aerosol that strongly absorbs solar radiation. The observation-constrained estimate, which is several times larger than the bottom-up estimate, is influenced by the spatial representativeness error due to the mesoscale inhomogeneity of the aerosol fields and the relatively low resolution of global chemistry-transport models. Here we evaluated the spatial representativeness error for two widely used observational networks (AErosol RObotic NETwork and Global Atmosphere Watch) by downscaling the geospatial grid in a global model of BC aerosol absorption optical depth to 0.1° × 0.1°. Comparing the models at a spatial resolution of 2° × 2° with BC aerosol absorption at AErosol RObotic NETwork sites (which are commonly located near emission hot spots) tends to cause a global spatial representativeness error of 30%, as a positive bias for the current top-down estimate of global BC direct radiative forcing. By contrast, the global spatial representativeness error will be 7% for the Global Atmosphere Watch network, because the sites are located in such a way that there are almost an equal number of sites with positive or negative representativeness error. Plain Language Summary When comparing the black carbon model at a resolution of 2° × 2° with local measurements, the global representativeness error is 30% for AErosol RObotic NETwork sites, compared to 7% for Global Atmosphere Watch sites. It demonstrates that, in absence of high-resolution models, the current top-down estimate of black carbon direct radiative forcing is overestimated by 30%.

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Atmospheric Composition
Atmospheric Composition Modeling and Analysis Program (ACMAP)
Radiation Science Program (RSP)