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

Abstract

Abstract. Identification of terrestrial carbon (C) sources and sinks is critical for understanding the Earth system as well as mitigating and adapting to climate change resulting 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∘ 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 mean residence time of wood (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.

Resolving scale-variance in the carbon dynamics of fragmented, mixed-use landscapes estimated using Model-Data Fusion

“…Terrestrial biosphere models provide a means of quantifying the land carbon balance in a systemic, ecologically coherent way (Bonan et al, 2018). However, the current and future dynamics of terrestrial C exchange are highly uncertain, largely due to uncertainties in the structure and parameter constraints of the biosphere models themselves (Lovenduski and Bonan , 2017;Smallman et al, 2021).…”

“…Model-Data Fusion (MDF) frameworks provide the means to integrate EO observations with spatially explicit processbased ecosystem models that encapsulate our understanding of how C flows through ecosystems (Luo et al, 2011), thereby providing key, mass-balanced, constraints on the fluxes of C between the atmosphere and land surface alongside their associated uncertainties (Niu et al, 2014;Bloom et al, 2016;Peylin et al, 2016;MacBean et al, 2018;Smallman et al, 2021). MDF frameworks that exploit intermediate complexity models of the terrestrial C cycle, such as CARDAMOM (Bloom et al, 2016;Exbrayat et al, 2018;Lopez-Blanco et al, 2019;Smallman et al, 2021), are able to generate "local" calibrations based on pixel-level inversions of EO and auxiliary data streams.…”

“…Model-Data Fusion (MDF) frameworks provide the means to integrate EO observations with spatially explicit processbased ecosystem models that encapsulate our understanding of how C flows through ecosystems (Luo et al, 2011), thereby providing key, mass-balanced, constraints on the fluxes of C between the atmosphere and land surface alongside their associated uncertainties (Niu et al, 2014;Bloom et al, 2016;Peylin et al, 2016;MacBean et al, 2018;Smallman et al, 2021). MDF frameworks that exploit intermediate complexity models of the terrestrial C cycle, such as CARDAMOM (Bloom et al, 2016;Exbrayat et al, 2018;Lopez-Blanco et al, 2019;Smallman et al, 2021), are able to generate "local" calibrations based on pixel-level inversions of EO and auxiliary data streams. Calibrating ecosystem models to local data is important, because the functional traits of ecosystems vary in space (Smith et al, 2013;Reich et al, 2014;Butler et al, 2017;Exbrayat et al, 2018;Lopez-Blanco et al, 2019;Smallman et al, 2021), with trait differences within biomes often exceeding differences between biomes (Van Bodegom et al, 2012;Butler et al, 2017); failure to account for such variations may lead to biases in the estimated dynamics (Scheiter et al, 2013;Exbrayat et al, 2018).…”

“…MDF frameworks that exploit intermediate complexity models of the terrestrial C cycle, such as CARDAMOM (Bloom et al, 2016;Exbrayat et al, 2018;Lopez-Blanco et al, 2019;Smallman et al, 2021), are able to generate "local" calibrations based on pixel-level inversions of EO and auxiliary data streams. Calibrating ecosystem models to local data is important, because the functional traits of ecosystems vary in space (Smith et al, 2013;Reich et al, 2014;Butler et al, 2017;Exbrayat et al, 2018;Lopez-Blanco et al, 2019;Smallman et al, 2021), with trait differences within biomes often exceeding differences between biomes (Van Bodegom et al, 2012;Butler et al, 2017); failure to account for such variations may lead to biases in the estimated dynamics (Scheiter et al, 2013;Exbrayat et al, 2018). However, the computational intensity of large-scale MDF frameworks limits their spatial resolutions to 10-100 km, several orders of magnitude greater than the length scales relevant to differentiating the ecosystems within landscape mosaics (e.g.…”

“…However, the computational intensity of large-scale MDF frameworks limits their spatial resolutions to 10-100 km, several orders of magnitude greater than the length scales relevant to differentiating the ecosystems within landscape mosaics (e.g. Kaminski et al, 2012;Smith et al, 2013;Kuppel et al, 2014;Bloom et al, 2016;Peylin et al, 2016;Yin et al, 2020;Smallman et al, 2021).…”