B066-0010
Land-Use Harmonization Datasets for Global Carbon Budget 2019 and Beyond

Friday, 11 December 2020
Poster
Louise P Chini1, George C Hurtt2, Ritvik Sahajpal3, Steve E Frolking4, Lei Ma3, Kees Goldewijk5, Stephen Sitch6, Benjamin Poulter7 and Julia Pongratz8, (1)University of Maryland College Park, Department of Geographical Sciences, College Park, MD, United States, (2)University of Maryland, Department of Geographical Sciences, College Park, MD, United States, (3)University of Maryland, College Park, MD, United States, (4)University of New Hampshire, Institute for the Study of Earth, Oceans, and Space, Durham, NH, United States, (5)San Antonio, TX, United States, (6)University of Exeter, College of Life and Environmental Sciences, Exeter, United Kingdom, (7)NASA GSFC, Biospheric Science, Greenbelt, MD, United States, (8)Max Planck Institute for Meteorology, Hamburg, Germany
Abstract:
Land-use change was the dominant source of historical anthropogenic carbon emissions until around 1950, and is currently one of the largest and most uncertain components of global and regional carbon cycles, while also having the potential to play an important role in future carbon mitigation strategies. Advancing the scientific understanding on this topic requires the best data be used as input to the best models in well-organized scientific assessments. To this end the Land-Use Harmonization dataset (LUH2) was developed and used as input for CMIP6 simulations, providing global, annual gridded land-use and land-use change data relating to agricultural expansion, deforestation, wood harvesting, shifting cultivation, afforestation, crop rotations, and cropland management. Over the last 8 years the LUH2 dataset was also updated annually to provide required input to the Global Carbon Budget (GCB) for use by both book-keeping models and Dynamic Global Vegetation Models (DGVMs). Each year this process involves incorporating new FAO wood harvest data and new HYDE cropland and pasture data (based on the latest FAO agricultural updates) for several recent years, while also extrapolating the time-series for years without data. The latencies involved with ingesting these data updates, along with unexpected changes to previous years of FAO data, can pose challenges for consistent and timely updates to the GCB land-use inputs. For GCB 2019 a more significant update to LUH2 was produced to correct errors found in the underlying input datasets for the globally important region of Brazil. The improvements in LUH2-GCB2019 cause it to gradually diverge from the LUH2 v2h dataset over the years 1951-2012, with peak differences in Brazil in the year 2000 for grazing land (maximum difference 100,000 km2) and in the year 2009 for cropland (maximum difference of 77,000 km2), along with significant sub-national reorganization of agricultural land-use patterns. These LUH2-GCB2019 corrections for Brazil provide the base for future LUH2-GCB updates including the recent LUH2-GCB2020 dataset.
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