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Published August 3, 2023 | Version v1.0

CEDAR-GPP: A Spatiotemporally Upscaled Dataset of Gross Primary Productivity Incorporating CO2 Fertilization

  • 1. University of California, Berkeley
  • 2. University of Copenhagen

Description

Overview:
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CEDAR-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).


Authors:
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Yanghui Kang, Max Gaber, Maoya Bassiouni, Xinchen Lu, Trevor Keenan
University of California, Berkeley

 

File Structure:
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Each zip file contains GPP data from a CEDAR model setup.


File Naming Convention:
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All netCDF files follow this naming convention:
CEDAR-GPP_<version>_<model-setup>_<YYYYMM>.nc

Where:
<model-setup> comprises of <temporal_span>_<CFE_option>_<GPP_partitioning>
<temporal_span>: ST denotes short-term (2001 to 2020); LT denotes long-term (1982 to 2020)
<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
<GPP_partitioning>: 'NT' for night-time GPP partitioning method, 'DT' for day-time GPP partitioning method


netCDF characteristics:
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- Spatial Resolution: 0.05 degree
- Temporal Resolution: Monthly
- Temporal Coverage: Short-term (ST): 2001-2020; Long-term (LT): 1982 - 2020
- Image Dimension: Rows: 3600, Columns: 7200
- Units: gCm^-2day^-1
- Fill Value: -9999
- Multiply By Scale Factor: 0.01
- Data Type: uint16
- File Size: Approximately 99 MB per file


Data variables:
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- GPP_mean: monthly gross primary productivity (gCm^-2day^-1), mean from 30 model ensemble
- GPP_std: standard deviation of 30 model ensemble


Support Contact:
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For any queries related to this dataset, please contact:

Name: Yanghui Kang
Email: yanghuikang@berkeley.edu

 

 

Files

LT_Baseline_DT.zip

Files (34.3 GB)

Name Size
md5:79a2a09ba4021e6ac86310b2cd62f4df
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