Abstract

Abstract. This study assesses the impact of different state of the art global biospheric CO2 flux models, when applied as prior information, on inverse model “top-down” estimates of terrestrial CO2 fluxes obtained when assimilating Orbiting Carbon Observatory 2 (OCO-2) observations. This is done with a series of observing system simulation experiments (OSSEs) using synthetic CO2 column-average dry air mole fraction (XCO2) retrievals sampled at the OCO-2 satellite spatiotemporal frequency. The OSSEs utilized a 4-D variational (4D-Var) assimilation system with the GEOS-Chem global chemical transport model (CTM) to estimate CO2 net ecosystem exchange (NEE) fluxes using synthetic OCO-2 observations. The impact of biosphere models in inverse model estimates of NEE is quantified by conducting OSSEs using the NASA-CASA, CASA-GFED, SiB-4, and LPJ models as prior estimates and using NEE from the multi-model ensemble mean of the Multiscale Synthesis and Terrestrial Model Intercomparison Project as the “truth”. Results show that the assimilation of simulated XCO2 retrievals at OCO-2 observing modes over land results in posterior NEE estimates which generally reproduce “true” NEE globally and over terrestrial TransCom-3 regions that are well-sampled. However, we find larger spread among posterior NEE estimates, when using different prior NEE fluxes, in regions and seasons that have limited OCO-2 observational coverage and a large range in “bottom-up” NEE fluxes. Seasonally averaged posterior NEE estimates had standard deviations (SD) of ∼10 % to ∼50 % of the multi-model-mean NEE for different TransCom-3 land regions with significant NEE fluxes (regions/seasons with a NEE flux ≥0.5 PgC yr−1). On a global average, the seasonally averaged residual impact of the prior model NEE assumption on the posterior NEE spread is ∼10 %–20 % of the posterior NEE mean. Additional OCO-2 OSSE simulations demonstrate that posterior NEE estimates are also sensitive to the assumed prior NEE flux uncertainty statistics, with spread in posterior NEE estimates similar to those when using variable prior model NEE fluxes. In fact, the sensitivity of posterior NEE estimates to prior error statistics was larger than prior flux values in some regions/times in the tropics and Southern Hemisphere where sufficient OCO-2 data were available and large differences between the prior and truth were evident. Overall, even with the availability of spatiotemporally dense OCO-2 data, noticeable residual differences (up to ∼20 %–30 % globally and 50 % regionally) in posterior NEE flux estimates remain that were caused by the choice of prior model flux values and the specification of prior flux uncertainties.

Highlights

  • Carbon dioxide (CO2) is the most important greenhouse gas (GHG) contributing to climate change on a global scale (IPCC, 2014)

  • The standard deviations (SD) values for prior net ecosystem exchange (NEE) fluxes range from ∼ 20 % to frequently > 100 % of the multimodel NEE mean for different regions/seasons with significant NEE fluxes

  • All regions/seasons tend to have at least an ∼ 1 PgC yr−1 range among the four prior models, indicating the large diversity in NEE predicted by current bottom-up biosphere models (Table 3)

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Summary

Introduction

Carbon dioxide (CO2) is the most important greenhouse gas (GHG) contributing to climate change on a global scale (IPCC, 2014). In addition to fossil fuel emissions, the processes involved in the exchange of carbon between the atmosphere and terrestrial biosphere are a major factor controlling atmospheric concentrations of CO2 (e.g., Schimel et al, 2001) with an estimated global biosphere sink of ∼ 3.0 PgC yr−1 (Le Quéré et al, 2018). Current estimates of regional-scale atmosphere–terrestrial biosphere CO2 exchange have large uncertainties (Schimel et al, 2015). Previous studies intercomparing several of the most commonly used biospheric flux models (Heimann et al, 1998, Huntzinger et al, 2012; Sitch et al, 2015; Ott et al, 2015; Ito et al, 2016) and multi-model ensemble integration projects (Schwalm et al, 2015) reveal a large spread among global/regional bottom-up terrestrial biospheric flux estimates and the subcomponents such as ecosystem primary production and respiration (Huntzinger et al, 2012)

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