Assessing the impact of cloud slicing techniques on estimates of surface CO2 exchange using atmospheric inversions

Abstract:

Typically more than half of the earth’s surface is cloudy at any one point in time. Passive CO2 satellite instruments such as GOSAT and OCO-2 have historically filtered out these scenes, as being too difficult to interpret. However, with the advent of active sensing technologies coupled with ranging capabilities, many of these limitations are being lifted. While, the remote sensing community continues to grapple with the radiative-transfer aspects of the cloud-top CO2 retrieval problem, the carbon cycling community has begun to consider what parts of the carbon cycle might be constrained with this new stream of data. Using cloud data derived from CALIPSO, a simulated carbon cycle, and state of the art atmospheric inversion models, we will investigate the impact of "above cloud" partial-column retrievals of CO2 upon estimates of surface CO2 flux. In particular, we will investigate (1) the general constraint imposed upon surface CO2 fluxes, by retrievals over spatially and time coherent cloud structures around the globe as well as (2) the partitioning of gross primary production and respiration CO2 flux terms by differencing full-column and above-cloud partial column CO2 over scenes with optically thick low clouds.