Abstract
Abstract. The synthesis of model and observational information using data assimilation can improve our understanding of the terrestrial carbon cycle, a key component of the Earth's climate–carbon system. Here we provide a data assimilation framework for combining observations of solar-induced chlorophyll fluorescence (SIF) and a process-based model to improve estimates of terrestrial carbon uptake or gross primary production (GPP). We then quantify and assess the constraint SIF provides on the uncertainty in global GPP through model process parameters in an error propagation study. By incorporating 1 year of SIF observations from the GOSAT satellite, we find that the parametric uncertainty in global annual GPP is reduced by 73 % from ±19.0 to ±5.2 Pg C yr−1. This improvement is achieved through strong constraint of leaf growth processes and weak to moderate constraint of physiological parameters. We also find that the inclusion of uncertainty in shortwave down-radiation forcing has a net-zero effect on uncertainty in GPP when incorporated into the SIF assimilation framework. This study demonstrates the powerful capacity of SIF to reduce uncertainties in process-based model estimates of GPP and the potential for improving our predictive capability of this uncertain carbon flux.
Highlights
The productivity of the terrestrial biosphere forms a key component of Earth’s climate–carbon system
The results presented show that with 1 year of satellite solarinduced chlorophyll fluorescence (SIF) data observed at the GOSAT and OCO-2 satellite overpass time and SIF retrieval wavelength, we can constrain a large portion of the BETHY-SCOPE parameter space and yield a parametric uncertainty in global annual Gross primary production (GPP) of ±5.2 Pg C yr−1
We assessed the ability of satellite SIF observations to constrain uncertainty in model parameters and uncertainty in spatiotemporal patterns of simulated GPP using a processbased terrestrial biosphere model
Summary
The productivity of the terrestrial biosphere forms a key component of Earth’s climate–carbon system. Much of the interannual variability in atmospheric CO2 concentration is driven by terrestrial productivity. Despite this significance, an understanding of the underlying mechanisms of terrestrial productivity is still lacking. Estimating spatiotemporal patterns of GPP at the scales required for global change and modelling studies has proven difficult. This is primarily for two reasons: the complexity of the processes involved and the difficulty in observing those processes (Baldocchi et al, 2016; Schimel et al, 2015).
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