09 Dec 2022
09 Dec 2022
Status: this preprint is open for discussion.

Improved process representation of leaf phenology significantly shifts climate sensitivity of ecosystem carbon balance

Alexander J. Norton1, A. Anthony Bloom1, Nicholas C. Parazoo1, Paul A. Levine1, Shuang Ma1, Renato K. Braghiere2,1, and Luke T. Smallman3 Alexander J. Norton et al.
  • 1Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91101, USA
  • 2Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena 91125, USA
  • 3School of GeoSciences and NCEO, University of Edinburgh, Edinburgh, UK

Abstract. Terrestrial carbon cycle models are routinely used to determine the response of the land carbon sink under expected future climate change, yet these predictions remain highly uncertain. Increasing the realism of processes in these models may help with predictive skill, but any such addition should be confronted with observations and evaluated in the context of the aggregate behavior of the carbon cycle. Here, two formulations for leaf area index (LAI) phenology are coupled to the same terrestrial biosphere model, one is climate agnostic and the other incorporates direct environmental controls on both timing and growth. Each model is calibrated simultaneously to observations of LAI, net ecosystem exchange (NEE), and biomass using the CARbon DAta-MOdel fraMework (CARDAMOM), and validated against withheld data including eddy covariance estimates of gross primary productivity (GPP) and ecosystem respiration (Re), across six ecosystems from the tropics to high-latitudes. Both model formulations show similar predictive skill for LAI and NEE. However, with the addition of direct environmental controls on LAI, the integrated model explains 22 % more variability in GPP and Re, and reduces biases in these fluxes by 58 % and 77 %, respectively, while also predicting more realistic annual litterfall rates, due to changes in carbon allocation and turnover. We extend this analysis to evaluate the inferred climate sensitivity of LAI and NEE with the new model, and show that the added complexity shifts the sign, magnitude, and seasonality of NEE sensitivity to precipitation and temperature. This highlights the benefit of process complexity when inferring underlying processes from Earth observations and in representing the climate response of the terrestrial carbon cycle.

Alexander J. Norton et al.

Status: open (until 28 Feb 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-1265', Anonymous Referee #1, 26 Jan 2023 reply

Alexander J. Norton et al.

Alexander J. Norton et al.


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Short summary
This study explores how the representation of leaf phenology affects our ability to predict changes to the carbon balance of land ecosystems. We calibrate a new leaf phenology model against a diverse range of observations at six forest sites, showing that it improves the predictive capability of the processes underlying the ecosystem carbon balance. We then show how changes in temperature and rainfall affect the ecosystem carbon balance with this new model.