Shahid (2026) R3GENESI5/biome-specific-forcing-coefficients: v1.0.0 — Initial release

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Open MIND
• Year: 2026
• Date: 2026-03-30
• Authors: Ali Bin Shahid
• DOI: 10.5281/zenodo.19328340

Research Groups

A. B. Shahid (Author of the manuscript)

Short Summary

This study develops an analysis pipeline to empirically investigate biome-specific radiative forcing coefficients, demonstrating that greenhouse gas forcing is non-uniformly modulated by ecosystem energy partitioning, leading to a significant reduction in error compared to uniform forcing assumptions.

Objective

• To empirically determine if radiative forcing coefficients are biome-specific and modulated by ecosystem energy partitioning.

Study Configuration

• Spatial Scale: 254 flux tower sites distributed across 7 distinct biomes globally.
• Temporal Scale: Multi-year observational data (inferred from FLUXNET data usage).

Methodology and Data

• Models used: α(β) sigmoid fitting model.
• Data sources: FLUXNET data, CERES data, FluxDataKit (Zenodo).

Main Results

• The α(β) model achieved an R² value of 0.769.
• The biome-specific approach resulted in a 53.7% error reduction compared to the assumption of uniform radiative forcing (α = 1).
• Analysis was conducted using data from 254 flux tower sites across 7 biomes.

Contributions

• Provides the first public release of an analysis pipeline for investigating biome-specific radiative forcing coefficients.
• Offers empirical evidence supporting non-uniform greenhouse gas forcing modulated by ecosystem energy partitioning.
• Demonstrates a substantial improvement in accuracy (53.7% error reduction) over traditional uniform forcing assumptions.

Funding

Not specified in the provided text.

Citation

@article{Shahid2026R3GENESI5biomespecificforcingcoefficients,
  author = {Shahid, Ali Bin},
  title = {R3GENESI5/biome-specific-forcing-coefficients: v1.0.0 — Initial release},
  journal = {Open MIND},
  year = {2026},
  doi = {10.5281/zenodo.19328340},
  url = {https://doi.org/10.5281/zenodo.19328340}
}