Modelled land use and land cover change emissions – a spatio-temporal comparison of different approaches

To estimate the LASC, the f(LULCC) from pre-industrial DGVM simulations, which is independent of changing environmental conditions, can be used. Additionally, DGVMs using constant present-day environmental forcing enable an approximation of bookkeeping estimates. Here, we analyse these three DGVM-derived f(LULCC) estimations (under transient, pre-industrial, and present-day forcing) for 12 models within 18 regions and quantify their differences as well as climate- and CO2-induced components and compare them to bookkeeping estimates.

Averaged across the models, we find a global f(LULCC) (under transient conditions) of 2.0 +/- 0.6 PgC yr(-1) for 2009-2018, of which similar to 40% are attributable to the LASC (0.8 +/- 0.3 PgC yr(-1)). From 1850 onward, the f(LULCC) accumulated to 189 +/- 56 PgC with 40 +/- 15 PgC from the LASC. Around 1960, the accumulating nature of the LASC causes global transient f(LULCC) estimates to exceed estimates under present-day conditions, despite generally increased carbon stocks in the latter.

Regional hotspots of high cumulative and annual LASC values are found in the USA, China, Brazil, equatorial Africa, and Southeast Asia, mainly due to deforestation for cropland. Distinct negative LASC estimates in Europe (early reforestation) and from 2000 onward in the Ukraine (recultivation of post-Soviet abandoned agricultural land), indicate that f(LULCC) estimates in these regions are lower in transient DGVM compared to bookkeeping approaches.

Our study unravels the strong dependence of f(LULCC) estimates on the time a certain land use and land cover change event happened to occur and on the chosen time period for the forcing of environmental conditions in the underlying simulations. We argue for an approach that provides an accounting of the f(LULCC) that is more robust against these choices, for example by estimating a mean DGVM ensemble f(LULCC) and LASC for a defined reference period and homogeneous environmental changes (CO2 only).