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Diagnosing Linearity Along the Carbon Cascade in Terrestrial Biosphere Models and Observations

  • Huanyuan Zhang-Zheng
  • , Vivek K. Arora
  • , Peter Anthoni
  • , Thomas A.M. Pugh
  • , Atul K. Jain
  • , Wenping Yuan
  • , Yadvinder Malhi
  • , Julia Nabel
  • , Daniel S. Goll
  • , Julia Pongratz
  • , Benjamin Poulter
  • , Anthony P. Walker
  • , Sönke Zaehle
  • , Jürgen Knauer
  • , Etsushi Kato
  • , Ruijie Ding
  • , Minxue Tang
  • , Stephen Sitch
  • , Michael O'Sullivan
  • , César Terrer
  • Hanqin Tian, Naiqing Pan, Pierre Friedlingstein, Akihiko Ito, Qing Sun, Jeanne Decayeux, Benjamin D. Stocker
  • University of Oxford
  • Umeå University
  • Meteorological Research Branch
  • Institute of Meteorology and Climate Research
  • Lund University
  • University of Birmingham
  • University of Illinois at Urbana-Champaign
  • Tsinghua University
  • Max Planck Institute for Biogeochemistry
  • Max Planck Institute for Meteorology
  • Université Paris-Saclay
  • Universität München
  • Spark Climate Solutions
  • University of Maryland, College Park
  • Oak Ridge National Laboratory
  • University of Technology Sydney
  • Hawkesbury Institute for the Environment
  • Institute of Applied Energy (IAE)
  • Imperial College London
  • University of Exeter
  • Massachusetts Institute of Technology
  • Boston College
  • Graduate School of Agricultural and Life Sciences The University of Tokyo
  • University of Bern, Institute of Applied Physics
  • University of Bern
  • Université Paul Sabatier

Research output: Contribution to journalArticlepeer-review

Abstract

Elevated carbon dioxide (eCO2) acts as a fertiliser for photosynthesis, driving an increase in gross primary production (GPP). However, it is unclear how effectively increased GPP propagates along the ‘carbon (C) cascade’ to increase net primary production (NPP) and vegetation C stocks (Cveg) in different plant compartments. Vegetation models were criticised for being overly sensitive to photosynthesis (source-driven), neglecting sink-driven processes which may attenuate (or amplify) changes in NPP and vegetation C stocks. Here, we introduce an analytical framework to diagnose linearity (L) as ratios of relative changes in linked fluxes and pools. We then apply this framework to 16 models of the TRENDY v11 ensemble and to observation-based estimates of CO2 sensitivities. We found widely varying global patterns in L across models. Six models showed a majority of grid cells with larger relative changes in NPP than in GPP (LNPP:GPP > 1 for > 60% of gridcells), indicating increased vegetation carbon use efficiency under eCO2. Only three models had LNPP:GPP < 1 for > 60% of gridcells. Four models showed a majority of gridcells with larger relative changes in estimated steady-state Cveg than in NPP, while five models showed the opposite—in both cases with a large spread of LCveg*:NPP across grid cells within models. Observations-based analysis reveals median LCveg*:NPP < 1 overall but evidence is insufficient for a conclusion. Three models showed a larger relative increase in root C than in Cveg, (LCroot:Cveg > 1) while five models showed the opposite. Most field evidence shows LCroot:Cveg > 1. Widely differing distributions of L among models and links in the C cascade reveal a strong influence of nonlinear behaviour in individual models. However, due to the spread in L, across the whole models ensemble, L deviations from 1 were roughly balanced, leading to an overall linear behaviour of terrestrial C cycle representations in the multi-model-mean.

Original languageEnglish
Article numbere70982
JournalGlobal Change Biology
Volume32
Issue number7
DOIs
Publication statusPublished - 1 Jul 2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 2 - Zero Hunger
    SDG 2 Zero Hunger

Keywords

  • carbon allocation
  • carbon cycle
  • elevated CO
  • gross primary production
  • net primary production
  • vegetation models

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