Constraining the exchange of carbon dioxide over the Amazon : New insights from stable isotopes, remote sensing and inverse modeling

Koren, Gerbrand

doi:10.18174/524771

Cited by 5 publications

(10 citation statements)

References 301 publications

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“…In addition to CTE's standard atmospheric measurements, CT-SAM includes airborne estimates focused over the Amazon forest (Gatti et al, 2014) and uses zoom regions over South America for improved atmospheric transport (van der Velde et al, 2015). The ensemble uses five net ecosystem exchange (NEE) priors and three fire emission drivers (combined to estimate NBE) but with a common set of atmospheric constraints and transport model (Schaefer et al, 2008;Bodesheim et al, 2018;Haynes et al, 2019;Koren, 2020). The mean pixel-level uncertainty between ensemble members is ∼ 0.5 g C m 2 d −1 which, due to the near-neutral estimates, is ∼ 50 times the mean value.…”

Section: Evaluation Of Models Against Independent Datamentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Evaluation Of Models Against Independent Datamentioning

confidence: 99%

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

et al. 2021

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“…Avitabile et al (2016) improved atmospheric transport (van der Laan-Luijkx et al, 2015). The ensemble uses five NEE priors and three fire emission drivers but with a common set of atmospheric constraints and transport model (Schaefer et al, 2008;Bodesheim et al, 2018;van Schaik et al, 2018;Haynes et al, 2019;Koren , 2020). By using a range of priors it covers the uncertainty in the seasonal variation of C fluxes in tropical regions (Saleska et al, 2003;Restrepo-Coupe et al, 2013;Koren et al, 2018;Mengistu et al, 2020).…”

Section: Wood Cmentioning

confidence: 99%

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

Smallman¹,

Milodowski²,

Sousa‐Neto³

et al. 2021

Preprint

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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.

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“…Locally, a source of carbon in our EC-NEE record, driven by a higher than normal R eco (FigureS5), can explain the ΔCO 2 obs 2016-ASO anomaly. Non-local drivers of this anomaly are attributed to a drought legacy effect(Kannenberg et al, 2020) that has been already characterized byKoren (2020) using atmospheric inverse modeling and remote sensing. Koren(2020) reported basin-wide positive anomalies in top-down-NEE and reductions in remote sensing proxies for GPP in the dry season of 2016.…”

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confidence: 98%

The CO₂ record at the Amazon Tall Tower Observatory: A new opportunity to study processes on seasonal and inter‐annual scales

Botía

Komiya

Marshall

et al. 2021

Global Change Biology

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Constraining the exchange of carbon dioxide over the Amazon : New insights from stable isotopes, remote sensing and inverse modeling

Cited by 5 publications

References 301 publications

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

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

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

The CO₂ record at the Amazon Tall Tower Observatory: A new opportunity to study processes on seasonal and inter‐annual scales

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Constraining the exchange of carbon dioxide over the Amazon : New insights from stable isotopes, remote sensing and inverse modeling

Cited by 5 publications

References 301 publications

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

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

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

The CO2 record at the Amazon Tall Tower Observatory: A new opportunity to study processes on seasonal and inter‐annual scales

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The CO₂ record at the Amazon Tall Tower Observatory: A new opportunity to study processes on seasonal and inter‐annual scales