A Bayesian ensemble data assimilation to constrain model parameters and land-use carbon emissions

Sebastian Lienert,Fortunat Joos

doi:10.5194/bg-15-2909-2018

Sebastian Lienert, Fortunat Joos

Open Access

https://doi.org/10.5194/bg-15-2909-2018

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Journal: Biogeosciences (Online)	Publication Date: May 16, 2018
Citations: 66	License type: CC BY 4.0

Affiliation: University of Bern

Abstract

Abstract. 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 (ELUC) and their sensitivity to model parametrization. We find a global ELUC estimate of 158 (108, 211) PgC (median and 90 % confidence interval) between 1800 and 2016. We compare ELUC to other estimates both globally and regionally. Spatial patterns are investigated and estimates of ELUC 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 ELUC is on the same order of magnitude as parameter-induced uncertainty and in some cases could potentially even be offset with appropriate parameter choice.

Highlights

Due to constraining atmospheric CO2 concentrations and the relatively well known CO2 sink in the ocean it follows that about a fifth of anthropogenic CO2 emissions is stored in the terrestrial biosphere (Ciais et al, 2013)
It is estimated that approximately a third of the cumulative anthropogenic CO2 emissions in the industrial period stem from the effects of land-use and land-cover change (LULCC) (Arneth et al, 2017; Brovkin et al, 2013; Gerber et al, 2013; Houghton and Nassikas, 2017; McGuire et al, 2001; Mahowald et al, 2017; Pongratz and Caldeira, 2012; Sitch et al, 2015; Strassmann et al, 2008; Stocker et al, 2017, 2014; Peng et al, 2017)
Following the procedure outlined in the method section, emissions due to LULCC (ELUC) is computed for every ensemble member

Summary

Introduction

Due to constraining atmospheric CO2 concentrations and the relatively well known CO2 sink in the ocean it follows that about a fifth of anthropogenic CO2 emissions is stored in the terrestrial biosphere (Ciais et al, 2013). It is estimated that approximately a third of the cumulative anthropogenic CO2 emissions in the industrial period stem from the effects of LULCC (Arneth et al, 2017; Brovkin et al, 2013; Gerber et al, 2013; Houghton and Nassikas, 2017; McGuire et al, 2001; Mahowald et al, 2017; Pongratz and Caldeira, 2012; Sitch et al, 2015; Strassmann et al, 2008; Stocker et al, 2017, 2014; Peng et al, 2017). A better understanding of the residual terrestrial sink can help to improve our understanding of the terrestrial carbon cycle of the past, unperturbed by human influence. Dynamic global vegetation models (DGVMs) are used to quantify the contribution of LULCC to the terrestrial carbon budget (Le Quéré et al, 2016).

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Results

Discussion

Conclusion