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 indicated that PGPP is better than three
widely-used GPP products. R2 between PGPP and observed GPP equals
0.75, the corresponding root mean squared error (RMSE) and mean absolute
error (MAE) equal to 1.77 g C m−2 d−1 and 1.28 g C
m−2 d−1. During the period from 1981 to 2020, PGPP significantly
increased in 69.02% of global vegetated regions (p < 0.05).
Overall, PGPP provides a new GPP product choice for global ecology studies
and the comparison of various water stress factors provides a new idea for
the improvement of the GPP model in the future.