Improved estimation of global gross primary productivity during 1981–2020 using the optimized P model

Accurate estimation of terrestrial gross primary productivity (GPP) is essential for quantifying the net carbon exchange between the atmosphere and biosphere. Light use efficiency (LUE) models are widely used to estimate GPP at different spatial scales. However, difficulties in proper determination of maximum LUE (LUEmax) and downregulation of LUEmax into actual LUE result in uncertainties in GPP estimated by LUE models. The recently developed P model, as a LUE-like model, captures the deep mechanism of photosynthesis and simplifies parameterization. Site-level studies have proved the outperformance of P model over LUE models. However, the global application of the P model is still lacking. Thus, the effectiveness of 5 water stress factors integrated into the P model was compared. The optimal P model was used to generate a new long-term (1981–2020) global monthly GPP dataset at a spatial resolution of 0.1° × 0.1°, called PGPP. Validation at globally distributed 109 FLUXNET sites indic..., , , # Improved estimation of global gross primary productivity during 1981–2020 using the optimized P model

A new monthly terrestrial GPP product (PGPP) derived from the P model with optimized water stress parameters.

Spatial resolution: about 0.1deg (corresponding to the original resolution of ERA-5 LAND dataset).

Time range: 1981-2020 (We will update it from time to time).