Parameter uncertainty dominates C-cycle forecast errors over most of Brazil for the 21st century
<p>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 ecosy...
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2021
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oai:doaj.org-article:71a6cf9fc239449bbc1e7dcbdab79b3a2021-11-23T08:33:23ZParameter uncertainty dominates C-cycle forecast errors over most of Brazil for the 21st century10.5194/esd-12-1191-20212190-49792190-4987https://doaj.org/article/71a6cf9fc239449bbc1e7dcbdab79b3a2021-11-01T00:00:00Zhttps://esd.copernicus.org/articles/12/1191/2021/esd-12-1191-2021.pdfhttps://doaj.org/toc/2190-4979https://doaj.org/toc/2190-4987<p>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<span class="inline-formula"><sup>∘</sup></span> 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 (<span class="inline-formula">NPP<sub>wood</sub></span>) and mean residence time of wood (<span class="inline-formula">MRT<sub>wood</sub></span>). Uncertainty of simulated DOM change is most strongly correlated with <span class="inline-formula">MRT<sub>soil</sub></span> and <span class="inline-formula">NPP<sub>wood</sub></span>. 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.</p>T. L. SmallmanT. L. SmallmanD. T. MilodowskiD. T. MilodowskiE. S. NetoG. KorenG. KorenJ. OmettoM. WilliamsM. WilliamsCopernicus PublicationsarticleScienceQGeologyQE1-996.5Dynamic and structural geologyQE500-639.5ENEarth System Dynamics, Vol 12, Pp 1191-1237 (2021) |
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Science Q Geology QE1-996.5 Dynamic and structural geology QE500-639.5 |
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Science Q Geology QE1-996.5 Dynamic and structural geology QE500-639.5 T. L. Smallman T. L. Smallman D. T. Milodowski D. T. Milodowski E. S. Neto G. Koren G. Koren J. Ometto M. Williams M. Williams Parameter uncertainty dominates C-cycle forecast errors over most of Brazil for the 21st century |
| description |
<p>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<span class="inline-formula"><sup>∘</sup></span> 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 (<span class="inline-formula">NPP<sub>wood</sub></span>) and mean
residence time of wood (<span class="inline-formula">MRT<sub>wood</sub></span>). Uncertainty of simulated DOM change is most strongly correlated with <span class="inline-formula">MRT<sub>soil</sub></span> and
<span class="inline-formula">NPP<sub>wood</sub></span>. 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.</p> |
| format |
article |
| author |
T. L. Smallman T. L. Smallman D. T. Milodowski D. T. Milodowski E. S. Neto G. Koren G. Koren J. Ometto M. Williams M. Williams |
| author_facet |
T. L. Smallman T. L. Smallman D. T. Milodowski D. T. Milodowski E. S. Neto G. Koren G. Koren J. Ometto M. Williams M. Williams |
| author_sort |
T. L. Smallman |
| title |
Parameter uncertainty dominates C-cycle forecast errors over most of Brazil for the 21st century |
| title_short |
Parameter uncertainty dominates C-cycle forecast errors over most of Brazil for the 21st century |
| title_full |
Parameter uncertainty dominates C-cycle forecast errors over most of Brazil for the 21st century |
| title_fullStr |
Parameter uncertainty dominates C-cycle forecast errors over most of Brazil for the 21st century |
| title_full_unstemmed |
Parameter uncertainty dominates C-cycle forecast errors over most of Brazil for the 21st century |
| title_sort |
parameter uncertainty dominates c-cycle forecast errors over most of brazil for the 21st century |
| publisher |
Copernicus Publications |
| publishDate |
2021 |
| url |
https://doaj.org/article/71a6cf9fc239449bbc1e7dcbdab79b3a |
| work_keys_str_mv |
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