08 Apr 2021

08 Apr 2021

Review status: a revised version of this preprint is currently under review for the journal ESD.

Parameter uncertainty dominates C cycle forecast errors over most of Brazil for the 21st Century

Thomas Luke Smallman1,2, David Thomas Milodowski1,2, Eráclito Sousa Neto3, Gerbrand Koren4, Jean Ometto3, and Mathew Williams1,2 Thomas Luke Smallman et al.
  • 1School of GeoSciences, University of Edinburgh, Edinburgh UK
  • 2National Centre for Earth Observations, University of Edinburgh, UK
  • 3INPE, Sao Jose dos Campos, Brazil
  • 4Meteorology and Air Quality, Wageningen University, Wageningen, the Netherlands

Abstract. Identification of terrestrial carbon (C) sources and sinks is critical for understanding the earth system and to mitigate and adapt to climate change results from greenhouse gas emissions. Predicting whether a given location will act as a C source or sink using terrestrial ecosystem models (TEMs) is challenging due to net flux being the difference between far larger, spatially and temporally variable fluxes with large uncertainties. Uncertainty in projections of future dynamics, critical for policy evaluation, has been determined using multi-TEM intercomparisons, for various emissions scenarios. This approach quantifies structural and forcing errors. However, the role of parameter error within models has not been determined. TEMs typically have defined parameters for specific plant functional types generated from the literature. To ascertain the importance of parameter error in forecasts we present a Bayesian analysis that uses data on historical and current C cycling for Brazil to parameterise five TEMs of varied complexity with a retrieval of model error covariance at 1 degree spatial resolution. After evaluation against data from 2001–2017, the parameterised models are simulated to 2100 under four climate change scenarios spanning the likely range of climate projections. Using multiple models, each with per pixel parameter ensembles, we partition forecast uncertainties. Parameter uncertainty dominates across most of Brazil when simulating future stock changes in biomass C and dead organic matter (DOM). Uncertainty of simulated biomass change is most strongly correlated with net primary productivity allocation to wood (NPPwood) and wood mean residence times (MRTwood). Uncertainty of simulated DOM change is most strongly correlated with MRTsoil and NPPwood. Due to the coupling between these variables and C stock dynamics being bi-directional we argue that using repeat estimates of woody biomass will provide a valuable constraint needed to refine predictions of the future carbon cycle. Finally, evaluation of our multi-model analysis shows that wood litter contributes substantially to fire emissions necessitating a greater understanding of wood litter C-cycling than is typically considered in large-scale TEMs.

Thomas Luke Smallman et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Defend the conclusions despite the large posterior uncertainty.', Thomas Wutzler, 31 May 2021
    • AC1: 'Reply on RC1', Thomas Smallman, 24 Aug 2021
  • RC2: 'Comment on esd-2021-17', Anonymous Referee #2, 21 Jun 2021
    • AC2: 'Reply on RC2', Thomas Smallman, 24 Aug 2021

Thomas Luke Smallman et al.

Data sets

CARDAMOM Brazil C-cycle multi-DALEC, multi-CMIP6 scenarios (1x1 degree; monthly; 2001-2017) Thomas Luke Smallman, Mathew Williams

Model code and software

CARDAMOM Thomas Luke Smallman, Mathew Williams

Thomas Luke Smallman et al.


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Short summary
Our study provides a novel assessment of model parameter, structure and climate change scenario uncertainty contribution to future predictions of the Brazilian terrestrial carbon stocks to 2100. We calibrated (2001–2017) 5 models of the terrestrial C-cycle of varied structures. The calibrated models were then projected to 2100 under multiple climate change scenarios. Parameter uncertainty dominates overall uncertainty being ~40 times that of either model structure or climate change scenario.