Overview:
\n----------
\nCEDAR-GPP is a global Gross Primary Productivity (GPP) product, including monthly GPP estimates at 0.05º spatial resolution. These datasets were generated via upscaling eddy covariance measurements with machine learning and satellite datasets. GPP estimates were produced from ten different model setups that vary by temporal span, direct CO2 fertilization effect (CFE) incorporation method, and GPP partitioning approaches. CEDAR stands for upsCaling Ecosystem Dynamics with ARtificial inteliggence (CEDAR).
\nAuthors:
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\nYanghui Kang, Max Gaber, Maoya Bassiouni, Xinchen Lu, Trevor Keenan
\nUniversity of California, Berkeley
\n\n
File Structure:
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\nEach zip file contains GPP data from a CEDAR model setup.
\nFile Naming Convention:
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\nAll netCDF files follow this naming convention:
\nCEDAR-GPP_<version>_<model-setup>_<YYYYMM>.nc
Where:
\n<model-setup> comprises of <temporal_span>_<CFE_option>_<GPP_partitioning>
\n<temporal_span>: ST denotes short-term (2001 to 2020); LT denotes long-term (1982 to 2020)
\n<CFE_option>: 'Baseline' indicates no direct CO2 fertilization effect, 'CFE-ML' represents direct CO2 fertilization incorporated by ML, 'CFE-Hybrid' implies direct CO2 fertilization incorporated by theory
\n<GPP_partitioning>: 'NT' for night-time GPP partitioning method, 'DT' for day-time GPP partitioning method
\nnetCDF characteristics:
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\n- Spatial Resolution: 0.05 degree
\n- Temporal Resolution: Monthly
\n- Temporal Coverage: Short-term (ST): 2001-2020; Long-term (LT): 1982 - 2020
\n- Image Dimension: Rows: 3600, Columns: 7200
\n- Units: gCm^-2day^-1
\n- Fill Value: -9999
\n- Multiply By Scale Factor: 0.01
\n- Data Type: uint16
\n- File Size: Approximately 99 MB per file
\nData variables:
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\n- GPP_mean: monthly gross primary productivity (gCm^-2day^-1), mean from 30 model ensemble
\n- GPP_std: standard deviation of 30 model ensemble
\nSupport Contact:
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\nFor any queries related to this dataset, please contact:
Name: Yanghui Kang
\nEmail: yanghuikang@berkeley.edu
\n\n
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