A Bayesian ensemble data assimilation to constrain model parameters and land-use carbon emissions
A dynamic global vegetation model (DGVM) is applied in a probabilistic framework and benchmarking system to constrain uncertain model parameters by observations and to quantify carbon emissions from land-use and land-cover change (LULCC). Processes featured in DGVMs include parameters which are...
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doaj-819189beda1d4a0ba6818be3281b28ed2020-11-24T22:39:49ZengCopernicus PublicationsBiogeosciences1726-41701726-41892018-05-01152909293010.5194/bg-15-2909-2018A Bayesian ensemble data assimilation to constrain model parameters and land-use carbon emissionsS. Lienert0S. Lienert1F. Joos2F. Joos3Climate and Environmental Physics, Physics Institute, University of Bern, Bern, SwitzerlandOeschger Centre for Climate Change Research, University of Bern, Bern, SwitzerlandClimate and Environmental Physics, Physics Institute, University of Bern, Bern, SwitzerlandOeschger Centre for Climate Change Research, University of Bern, Bern, SwitzerlandA dynamic global vegetation model (DGVM) is applied in a probabilistic framework and benchmarking system to constrain uncertain model parameters by observations and to quantify carbon emissions from land-use and land-cover change (LULCC). Processes featured in DGVMs include parameters which are prone to substantial uncertainty. To cope with these uncertainties Latin hypercube sampling (LHS) is used to create a 1000-member perturbed parameter ensemble, which is then evaluated with a diverse set of global and spatiotemporally resolved observational constraints. We discuss the performance of the constrained ensemble and use it to formulate a new best-guess version of the model (LPX-Bern v1.4). The observationally constrained ensemble is used to investigate historical emissions due to LULCC (<i>E</i><sub>LUC</sub>) and their sensitivity to model parametrization. We find a global <i>E</i><sub>LUC</sub> estimate of 158 (108, 211) PgC (median and 90 % confidence interval) between 1800 and 2016. We compare <i>E</i><sub>LUC</sub> to other estimates both globally and regionally. Spatial patterns are investigated and estimates of <i>E</i><sub>LUC</sub> of the 10 countries with the largest contribution to the flux over the historical period are reported. We consider model versions with and without additional land-use processes (shifting cultivation and wood harvest) and find that the difference in global <i>E</i><sub>LUC</sub> is on the same order of magnitude as parameter-induced uncertainty and in some cases could potentially even be offset with appropriate parameter choice.https://www.biogeosciences.net/15/2909/2018/bg-15-2909-2018.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
S. Lienert S. Lienert F. Joos F. Joos |
spellingShingle |
S. Lienert S. Lienert F. Joos F. Joos A Bayesian ensemble data assimilation to constrain model parameters and land-use carbon emissions Biogeosciences |
author_facet |
S. Lienert S. Lienert F. Joos F. Joos |
author_sort |
S. Lienert |
title |
A Bayesian ensemble data assimilation to constrain model parameters and land-use carbon emissions |
title_short |
A Bayesian ensemble data assimilation to constrain model parameters and land-use carbon emissions |
title_full |
A Bayesian ensemble data assimilation to constrain model parameters and land-use carbon emissions |
title_fullStr |
A Bayesian ensemble data assimilation to constrain model parameters and land-use carbon emissions |
title_full_unstemmed |
A Bayesian ensemble data assimilation to constrain model parameters and land-use carbon emissions |
title_sort |
bayesian ensemble data assimilation to constrain model parameters and land-use carbon emissions |
publisher |
Copernicus Publications |
series |
Biogeosciences |
issn |
1726-4170 1726-4189 |
publishDate |
2018-05-01 |
description |
A dynamic global vegetation model (DGVM) is applied in a probabilistic
framework and benchmarking system to constrain uncertain model parameters by
observations and to quantify carbon emissions from land-use and land-cover
change (LULCC). Processes featured in DGVMs include parameters which are
prone to substantial uncertainty. To cope with these uncertainties Latin
hypercube sampling (LHS) is used to create a 1000-member perturbed parameter
ensemble, which is then evaluated with a diverse set of global and
spatiotemporally resolved observational constraints. We discuss the
performance of the constrained ensemble and use it to formulate a new
best-guess version of the model (LPX-Bern v1.4). The observationally
constrained ensemble is used to investigate historical emissions due to LULCC
(<i>E</i><sub>LUC</sub>) and their sensitivity to model parametrization. We find
a global <i>E</i><sub>LUC</sub> estimate of 158 (108, 211) PgC (median and
90 % confidence interval) between 1800 and 2016. We compare
<i>E</i><sub>LUC</sub> to other estimates both globally and regionally. Spatial
patterns are investigated and estimates of <i>E</i><sub>LUC</sub> of the 10
countries with the largest contribution to the flux over the historical
period are reported. We consider model versions with and without additional
land-use processes (shifting cultivation and wood harvest) and find that the
difference in global <i>E</i><sub>LUC</sub> is on the same order of magnitude as
parameter-induced uncertainty and in some cases could potentially even be
offset with appropriate parameter choice. |
url |
https://www.biogeosciences.net/15/2909/2018/bg-15-2909-2018.pdf |
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