Towards reducing uncertainty in land carbon predictions through the Global Matrix MIP traceability framework
Towards reducing uncertainty in land carbon predictions through the Global Matrix MIP traceability framework
Kostiantyn Viatkin1, Markus Müller2, Yu Zhou1, Aneesh Chandel2, Lifen Jiang3, Jon Wells4, Feng Tao5, Cuijuan Liao5, Yuanyuan Huang6, Song Wang7, Chenyu Bian8, Junyi Liang9, Chengcheng Gang10, Alison Bennett11, Enqing Hou12 and Yiqi Luo2, (1)Cornell University, School of Integrative Plant Science, Ithaca, NY, United States, (2)Cornell University, School of Integrative Plant Science, Ithaca, United States, (3)Northern Arizona University, Center for Ecosystem Science and Society, Flagstaff, AZ, United States, (4)Northern Arizona University, Center for Ecosystem Science and Society, Flagstaff, United States, (5)Tsinghua University, Department of Earth System Science, Beijing, China, (6)CSIRO Oceans and Atmosphere, Aspendale, Victoria, Australia, (7)IGSNRR Institute of Geographic Sciences and Natural Resources Research, CAS, Key Laboratory of Ecosystem Network Observation and Modeling, Beijing, China, (8)East China Normal University, School of Ecological and Environmental Sciences, Shanghai, China, (9)CAU China Agricultural University, Department of Grassland Resource and Ecology, Beijing, China, (10)Northwest A&F University, Institute of Soil and Water Conservation, Yangling, China, (11)University of Melbourne, School of Ecosystem and Forest Science, Parkville, VIC, Australia, (12)South China Botanical Garden, Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, Guangzhou, China