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Towards Low-Latency Estimation of Atmospheric CO2 Growth Rates using Satellite Observations: Evaluating Sampling Errors of Satellite and In Situ Observing Approaches
  • +9
  • Sudhanshu Pandey,
  • John B Miller,
  • Sourish Basu,
  • Junjie Liu,
  • Brad Weir,
  • Brendan Byrne,
  • Frédéric Chevallier,
  • Kevin W Bowman,
  • Zhiqiang Liu,
  • Feng Deng,
  • Christopher W O'dell,
  • Abhishek Chatterjee
Sudhanshu Pandey
Jet Propulsion Laboratory, California Institute of Technology

Corresponding Author:[email protected]

Author Profile
John B Miller
NOAA Global Monitoring Laboratory
Sourish Basu
Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Earth System Science Interdisciplinary Center
Junjie Liu
Jet Propulsion Laboratory, California Institute of Technology, Division of Geological and Planetary Sciences, California Institute of Technology
Brad Weir
Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Morgan State University
Brendan Byrne
Jet Propulsion Laboratory, California Institute of Technology
Frédéric Chevallier
LSCE/IPSL, Laboratoire des Sciences du Climat et de L'Environnement, CEA-CNRS-UVSQ, Université Paris-Saclay
Kevin W Bowman
Jet Propulsion Laboratory, California Institute of Technology, Joint Institute for Regional Earth System Science and Engineering, University of California
Zhiqiang Liu
CMA Key Open Laboratory of Transforming Climate Resources to Economy, Chongqing Institute of Meteorological Sciences
Feng Deng
Department of Physics, University of Toronto
Christopher W O'dell
Cooperative Institute for Research in the Atmosphere, Colorado State University
Abhishek Chatterjee
Jet Propulsion Laboratory, California Institute of Technology

Abstract

The atmospheric CO2 growth rate is a fundamental measure of climate forcing. NOAA's annual growth rate estimates, derived from in situ observations at the marine boundary layer (MBL), serve as the benchmark in policy and science. However, NOAA's MBL-based method encounters challenges in accurately estimating the whole-atmosphere CO2 growth rate at sub-annual scales. We introduce the Growth Rate from Satellite Observations (GRESO) method as a complementary approach to estimate the whole-atmosphere CO2 growth rate utilizing satellite data. Satellite CO2 observations offer extensive atmospheric coverage that extends the capability of the current NOAA benchmark. We assess the sampling errors of the GRESO and NOAA methods using ten atmospheric transport model simulations. The simulations generate synthetic OCO-2 satellite and NOAA MBL data for calculating CO2 growth rates, which are compared against the global sum of carbon fluxes used as model inputs. We find good performance for the NOAA method (R = 0.93, RMSE = 0.12 ppm/year or 0.25 PgC/year). GRESO demonstrates lower sampling errors (R = 1.00; RMSE = 0.04 ppm/year or 0.09 PgC/year). Additionally, GRESO shows better performance at monthly scales than NOAA (R = 0.77 vs 0.47, respectively). Due to CO2's atmospheric longevity, the NOAA method accurately captures growth rates over five-year intervals. GRESO's robustness across partial coverage configurations (ocean or land data) shows that satellites can be promising tools for low-latency CO2 growth rate information, provided the systematic biases are minimized using in situ observations. Along with accurate and calibrated NOAA in situ data, satellite-derived growth rates can provide information about the global carbon cycle at sub-annual scales.
15 Aug 2024Submitted to ESS Open Archive
15 Aug 2024Published in ESS Open Archive