Abstract. We present observing system simulation experiments (OSSEs)
to evaluate the impact of a proposed network of ground-based
miniaturized laser heterodyne radiometer (mini-LHR) instruments that measure
atmospheric column-averaged carbon dioxide (XCO2) with a 1 ppm
precision. A particular strength of this passive measurement approach is its
insensitivity to clouds and aerosols due to its direct sun pointing and
narrow field of view (0.2∘). Developed at the NASA Goddard Space Flight
Center (GSFC), these portable, low-cost mini-LHR instruments were designed
to operate in tandem with the sun photometers used by the AErosol RObotic
NETwork (AERONET). This partnership allows us to leverage the existing
framework of AERONET's global ground network of more than 500 sites as well as
providing
simultaneous measurements of aerosols that are known to be a major source of
error in retrievals of XCO2 from passive nadir-viewing satellite
observations. We show, using the global 3-D GEOS-Chem chemistry transport
model, that a deployment of 50 mini-LHRs at strategic (but not optimized)
AERONET sites significantly improves our knowledge of global and regional
land-based CO2 fluxes. This improvement varies seasonally and ranges
58 %–81 % over southern lands, 47 %–76 % over tropical lands, 71 %–92 %
over northern lands, and 64 %–91 % globally. We also show significant added
value from combining mini-LHR instruments with the existing ground-based
NOAA flask network. Collectively, these data result in improved a posteriori
CO2 flux estimates on spatial scales of ∼10 km2,
especially over North America and Europe, where the ground-based networks are
densest. Our studies suggest that the mini-LHR network could also play a
substantive role in reducing carbon flux uncertainty in Arctic and tropical
systems by filling in geographical gaps in measurements left by ground-based
networks and space-based observations. A realized network would also provide
necessary data for the quinquennial global stocktakes that form part of the
Paris Agreement.