CO₂ emissions from land‐use change affected more by nitrogen cycle, than by the choice of land‐cover data

Abstract

The high uncertainty in land-based CO2 fluxes estimates is thought to be mainly due to uncertainty in not only quantifying historical changes among forests, croplands, and grassland, but also due to different processes included in calculation methods. Inclusion of a nitrogen (N) cycle in models is fairly recent and strongly affects carbon (C) fluxes. In this study, for the first time, we use a model with C and N dynamics with three distinct historical reconstructions of land-use and land-use change (LULUC) to quantify LULUC emissions and uncertainty that includes the integrated effects of not only climate and CO2 but also N. The modeled global average emissions including N dynamics for the 1980s, 1990s, and 2000-2005 were 1.8 ± 0.2, 1.7 ± 0.2, and 1.4 ± 0.2 GtC yr(-1) , respectively, (mean and range across LULUC data sets). The emissions from tropics were 0.8 ± 0.2, 0.8 ± 0.2, and 0.7 ± 0.3 GtC yr(-1) , and the non tropics were 1.1 ± 0.5, 0.9 ± 0.2, and 0.7 ± 0.1 GtC yr(-1) . Compared to previous studies that did not include N dynamics, modeled net LULUC emissions were higher, particularly in the non tropics. In the model, N limitation reduces regrowth rates of vegetation in temperate areas resulting in higher net emissions. Our results indicate that exclusion of N dynamics leads to an underestimation of LULUC emissions by around 70% in the non tropics, 10% in the tropics, and 40% globally in the 1990s. The differences due to inclusion/exclusion of the N cycle of 0.1 GtC yr(-1) in the tropics, 0.6 GtC yr(-1) in the non tropics, and 0.7 GtC yr(-1) globally (mean across land-cover data sets) in the 1990s were greater than differences due to the land-cover data in the non tropics and globally (0.2 GtC yr(-1) ). While land-cover information is improving with satellite and inventory data, this study indicates the importance of accounting for different processes, in particular the N cycle.

Compensatory water effects link yearly global land CO2 sink changes to temperature

“…The TRENDY global carbon flux data set. We used simulations of seven dynamic global vegetation models from the TRENDY v3 ensemble 16,17 for the period 1980-2013, which have a spatial resolution of 0.5° (model simulations with coarser resolution were omitted): CABLE 39 , ISAM 40 , LPJ 41 , LPJ-GUESS 42 , ORCHIDEE 43 , VEGAS 14 , VISIT 44 . These models were forced by a common set of input data sets and experimental protocol (experiment 'S2') 16,17 .…”

The global carbon budget 1959–2011

“…Four different DGVM estimates are presented here and used to explore the uncertainty in LUC annual emissions (Jain et al, 2013;Kato et al, 2013;Poulter et al, 2010;Stocker et al, 2011). While many published DGVM LUC emissions estimates exist, these model runs were driven by a consistently updated HYDE LUC dataset up to year 2009.…”

Tropical nighttime warming as a dominant driver of variability in the terrestrial carbon sink

“…We used three estimates of E L : a bookkeeping method based on deforestation patterns and biomass data (40) and two vegetation model-based estimates [Land surface Processes and eXchanges model from the University of Bern (LPX-Bern) (41) and Integrated Science Assessment Model (ISAM) (42)] (data available at www.globalcarbonproject.org/carbonbudget/). E L s contain perhaps the largest uncertainty of any estimate in our analysis because they include a large number of poorly constrained processes, different combinations of which are included in each estimate.…”

“…The LPX-Bern model-based estimate is based on historical land-use maps and also includes the abandonment of land and regrowth of secondary forest (41). The ISAM model-based estimate includes historical land use and environmental responses such as nitrogen cycling, which influences secondary growth and uptake (42). Although other model-based datasets of E L exist, they rely on similar boundary conditions of historical land-use change maps; thus, we believe these three datasets likely encompass the entire range and uncertainty of CO 2 emissions estimates for scenarios of land-use and land-cover change.…”