Preprints
https://doi.org/10.5194/acp-2020-496
https://doi.org/10.5194/acp-2020-496

  06 Aug 2020

06 Aug 2020

Review status: a revised version of this preprint was accepted for the journal ACP and is expected to appear here in due course.

Calibrating satellite-derived carbon fluxes for retrospective and near real-time assimilation systems

Brad Weir1,2, Lesley E. Ott2, George J. Collatz2, Stephan R. Kawa2, Benjamin Poulter2, Abhishek Chatterjee1,2, Tomohiro Oda1,2, and Steven Pawson1,2 Brad Weir et al.
  • 1Universities Space Research Association, Columbia, MD, USA
  • 2NASA Goddard Space Flight Center, Greenbelt, MD, USA

Abstract. The ability to monitor and understand natural and anthropogenic variability in atmospheric carbon dioxide (CO2) is a growing need of many stakeholders across the world. Systems that assimilate satellite observations, given their short latency and dense spatial coverage, into high-resolution global models are valuable, if not essential, tools for addressing this need. A notable drawback of modern assimilation systems is the long latency of many vital input datasets, e.g., inventories, in situ measurements, and reprocessed remote-sensing data can trail the current date by months to years. This paper describes techniques for calibrating surface fluxes derived from satellite observations of the Earth's surface to be consistent with constraints from inventories and in situ CO2 datasets. The techniques are applicable in both short-term forecasts and retrospective simulations, thus taking advantage of the coverage and short latency of satellite data while reproducing the major features of long-term inventory and in situ records. Our approach begins with a standard collection of diagnostic fluxes which incorporate a variety of remote-sensing driver data, viz. vegetation indices, fire radiative power, and nighttime lights. We then apply an empirical sink to calibrate the diagnostic fluxes to match given atmospheric and oceanic growth rates for each year. This step removes coherent, systematic flux errors that produce biases in CO2 which mask the signals an assimilation system hopes to capture. Depending on the simulation mode, the empirical sink uses different choices of atmospheric growth rates: estimates based on observations in retrospective mode and projections based on seasonal forecasts of sea surface temperature in forecasting mode. The retrospective fluxes, when used in simulations with NASA's Goddard Earth Observing System (GEOS), reproduce marine boundary layer measurements with comparable skill to those using fluxes from a modern inversion system. The forecasted fluxes show promising accuracy in their application to the analysis of changes in the carbon cycle as they occur.

Brad Weir et al.

 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Brad Weir et al.

Brad Weir et al.

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Short summary
We present a collection of carbon surface fluxes, the Low-order Flux Inversion (LoFI), derived from satellite observations of the Earth's surface and calibrated to match long-term inventories and atmospheric and oceanic records. Simulations using LoFI reproduce background atmospheric carbon dioxide measurements with comparable skill to the leading surface flux products. Available both retrospectively and as a forecast, LoFI enables the study of the carbon cycle as it occurs.
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