On the effect of spatial variability and support on validation of remote sensing observations of CO2

•Remote sensing CO2 data are often validated via intercomparison to other observations.•We assess impacts of spatial support and horizontal variability on data comparisons.•We find that these factors can lead to differences of >0.5 ppm in observed XCO2.•Uncertainties due to support and variability must be considered in validation studies.

Validation of ground-based and satellite remote sensing CO2 observations involves comparisons among platforms and with in situ airborne measurements. Several factors unrelated to observational errors can lead to mismatches between measurements, and must be assessed to avoid misinterpreting actual differences in observed values as errors. Here we explore the impact of CO2 horizontal variability and differences in the spatial support of measurements. Case studies based on flights over Walnut Grove and Petaluma, California, are used to compare hypothetical airborne, TCCON, GOSAT, and OCO-2 measurements. We find that high CO2 variability can lead to differences in inferred XCO2 (1) of over 0.5 ppm between airborne and remote sensing observations, due to the spatial mismatch between spiral flight trajectories and atmospheric columns, and (2) of up to 0.3 ppm among remote sensing platforms, due to differences in the spatial support of observations. Horizontal CO2 variability must therefore be considered in intercomparisons aimed at validation of remote sensing observations.