Land-Use Harmonization Datasets for Annual Global Carbon Budgets

Abstract

Abstract. Land-use change has been the dominant source of anthropogenic carbon emissions for most of the historical period, and is currently one of the largest and most uncertain components of the global carbon cycle. Advancing the scientific understanding on this topic requires that the best data be used as input to state-of-the-art models in well-organized scientific assessments. The Land-Use Harmonization 2 dataset (LUH2), previously developed and used as input for CMIP6 simulations, has been updated annually to provide required input to land models in the annual Global Carbon Budget (GCB) assessments. Here we discuss the methodology for producing these annual LUH2-GCB updates and extensions which incorporate annual FAO wood harvest data updates for dataset years after 2015 and HYDE gridded cropland and grazing area data updates (based on annual FAO cropland and grazing area data updates) for dataset years after 2012, along with extrapolations to the current year due to a lag of one or more years in the FAO data releases. The resulting updated LUH2-GCB datasets have provided global, annual gridded land-use and land-use change data relating to agricultural expansion, deforestation, wood harvesting, shifting cultivation, regrowth and afforestation, crop rotations, and pasture management and are used by both bookkeeping models and Dynamic Global Vegetation Models (DGVMs) for the GCB. For GCB 2019, a more significant update to LUH2 was produced, LUH2-GCB2019 (https://doi.org/10.3334/ORNLDAAC/1851, Chini et al., 2020b), to take advantage of new data inputs that corrected cropland and grazing areas in the globally important region of Brazil, as far back as 1950. From 1951–2012 the LUH2-GCB2019 dataset begins to diverge from the version of LUH2 used for CMIP6, with peak differences in Brazil in the year 2000 for grazing land (difference of 100,000 km2) and in the year 2009 for cropland (difference of 77,000 km2), along with significant sub-national reorganization of agricultural land-use patterns within Brazil. The LUH2-GCB2019 dataset provides the base for future LUH2-GCB updates including the recent LUH2-GCB2020 dataset, and presents a starting point for operationalizing the creation of these datasets to reduce time-lags due to the multiple input dataset and model latencies.

Global Evaluation of the Ecosystem Demography Model (ED v3.0)

“…This feature however also offers the potential for direct connection to lidar observations on vegetation structure for the purpose of model validation and/or initialization. Numerous studies have been completed at local and regional scales by initializing the ED model with airborne lidar data, demonstrating the power of lidar technique in improving characterization of contemporary ecosystems conditions (Hurtt et al, 2004, 2016and Ma et al, 2021. The advent of GEDI (Dubayah et al, 2020a) and ICESat-2 (Markus et al, 2017) consider further.…”

“…One hypothesis is that this overestimation may result at least in part from the land-use forcing. The LUH2 has been shown to underestimate harvesting area on primary forest in southern China, and Southeast Asia for the period after 1950, and also underestimates total cropland area in Brazil (Chini et al, 2021). LUH2 is being continuously updated and improved through the contribution to the Global Carbon Budget project (Chini et al, 2021).…”

“…The LUH2 has been shown to underestimate harvesting area on primary forest in southern China, and Southeast Asia for the period after 1950, and also underestimates total cropland area in Brazil (Chini et al, 2021). LUH2 is being continuously updated and improved through the contribution to the Global Carbon Budget project (Chini et al, 2021). Second, while relative patterns for soil carbon showed close agreement at biome level for the majority of biomes, the absolute magnitude of soil carbon was much lower than reference for several biomes and thus globally.…”

“…Explicitly modelling vegetation height facilitates a potential connection to lidar data. The most advanced version of ED was used in this study and it has been recently calibrated and evaluated globally by various benchmarking datasets such as gross primary productivity (GPP), leaf area index (LAI), aboveground biomass (AGB), and net biome productivity (NBP) (Ma et al, 2021).…”

A comprehensive dataset for global, regional and national greenhouse gas emissions by sector 1970–2019

“…For all types of models, land-use forcing is a major determinant of emissions and removals, and its high uncertainty impacts CO2-LULUCF estimates (Hartung et al, 2021). The reconstruction of land-use change of the historical past, which has to cover decades to centuries of legacy LULUCF fluxes, is based on sparse data or proxies (Hurtt et al, 2020;Klein Goldewijk et al, 2017), while satellite-based products suffer from complications in distinguishing natural from anthropogenic drivers (Hansen et al, 2013;Li et al, 2018) or accounting for small-scale disturbances and degradation (Matricardi et al, 2020). Lastly, regional carbon budgets can be substantially over-or underestimated when carbon embodied in trade products is not accounted for (Ciais et al, 2021).…”