Abstract

Abstract. Several modelling studies reported elevated carbon emissions from historical land use change (ELUC) by including bidirectional transitions on the sub-grid scale (termed gross land use change), dominated by shifting cultivation and other land turnover processes. However, most dynamic global vegetation models (DGVMs) that have implemented gross land use change either do not account for sub-grid secondary lands, or often have only one single secondary land tile over a model grid cell and thus cannot account for various rotation lengths in shifting cultivation and associated secondary forest age dynamics. Therefore, it remains uncertain how realistic the past ELUC estimations are and how estimated ELUC will differ between the two modelling approaches with and without multiple sub-grid secondary land cohorts – in particular secondary forest cohorts. Here we investigated historical ELUC over 1501–2005 by including sub-grid forest age dynamics in a DGVM. We run two simulations, one with no secondary forests (Sageless) and the other with sub-grid secondary forests of six age classes whose demography is driven by historical land use change (Sage). Estimated global ELUC for 1501–2005 is 176 Pg C in Sage compared to 197 Pg C in Sageless. The lower ELUC values in Sage arise mainly from shifting cultivation in the tropics under an assumed constant rotation length of 15 years, being 27 Pg C in Sage in contrast to 46 Pg C in Sageless. Estimated cumulative ELUC values from wood harvest in the Sage simulation (31 Pg C) are however slightly higher than Sageless (27 Pg C) when the model is forced by reconstructed harvested areas because secondary forests targeted in Sage for harvest priority are insufficient to meet the prescribed harvest area, leading to wood harvest being dominated by old primary forests. An alternative approach to quantify wood harvest ELUC, i.e. always harvesting the close-to-mature forests in both Sageless and Sage, yields similar values of 33 Pg C by both simulations. The lower ELUC from shifting cultivation in Sage simulations depends on the predefined forest clearing priority rules in the model and the assumed rotation length. A set of sensitivity model runs over Africa reveal that a longer rotation length over the historical period likely results in higher emissions. Our results highlight that although gross land use change as a former missing emission component is included by a growing number of DGVMs, its contribution to overall ELUC remains uncertain and tends to be overestimated when models ignore sub-grid secondary forests.

Highlights

  • Historical land use change (LUC), such as the permanent establishment of agricultural land over forests, shifting cultivation, and wood harvest, has contributed significantly to the atmospheric CO2 increase, in particular since industrialization (Houghton, 2003; Le Quéré et al, 2016; Pongratz et al, 2009)

  • BLUE Lower C (Hansis et al, 2015) Gasser & Ciais (2013) Shevliakova et al (2013) Stocker et al (2014) Wilkenskjeld et al (2014) This study This study the cumulative emissions from land use change (ELUC) turnover as 45.4 Pg C and ELUC harvest as 27.4 Pg C in Sageless simulations

  • Accounting for age dynamics, in contrast, generates a ELUC turnover of 27.3 Pg C, 40 % lower than that obtained by the Sageless simulation

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Summary

Introduction

Historical land use change (LUC), such as the permanent establishment of agricultural land over forests (deforestation), shifting cultivation, and wood harvest, has contributed significantly to the atmospheric CO2 increase, in particular since industrialization (Houghton, 2003; Le Quéré et al, 2016; Pongratz et al, 2009). C. Yue et al.: Land use carbon emissions with sub-grid land cohorts precludes any direct measurements of global or regional ELUC, modelling turned out to be the only approach to its quantification (Gasser and Ciais, 2013; Hansis et al, 2015; Houghton, 1999, 2003; Piao et al, 2009b). Methods to quantify ELUC could fall broadly into three categories, namely bookkeeping models (Gasser and Ciais, 2013; Hansis et al, 2015; Houghton, 2003), dynamic global vegetation models (DGVMs; Shevliakova et al, 2009; Stocker et al, 2014; Wilkenskjeld et al, 2014; Yang et al, 2010), and satellitebased estimates of deforestation fluxes (Baccini et al, 2012; van der Werf et al, 2010)

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