Climate-Induced Uncertainty in Modeling Gross Primary Productivity From the Light Use Efficiency Approach

Abstract

Light use efficiency (LUE) models, along with satellite-based vegetation maps and climatic reanalysis products as drivers, are effective tools for estimating large-scale gross primary productivity (GPP). However, the climate-induced uncertainty in LUE-based GPP remains poorly understood, particularly the temporal scaling uncertainty from ignored climatic fluctuations. Here, two 1-hourly reanalysis products, along with site-based observations, were employed to drive a two-leaf LUE model at 194 eddy-covariance (EC) sites. The observation-driven and reanalysis-driven GPP at the 1-hourly resolution were evaluated against EC GPP to illustrate the uncertainty from reanalysis products, with mean absolute deviation (MAD) and Nash-Sutcliffe efficiency (NSE) as criterion. Moreover, the climate-induced temporal scaling uncertainty was characterized by comparing distributed GPP (modeled with 1-hourly resolution climatic drivers) and lumped GPP (modeled with 6-hourly resolution climatic drivers). At the 1-hourly resolution, results demonstrated that the reanalysis-driven GPP showed a weaker relationship with EC GPP (MAD = 0.14 gC m⁻²h⁻¹, NSE = 0.48) than the observation-driven GPP (MAD = 0.12 gC m⁻²h⁻¹, NSE = 0.60), confirming the nonnegligible climate-induced uncertainty from reanalysis products. Additionally, the climate-induced uncertainty arising from gridded radiation was found to be significantly larger than that associated with temperature and vapor pressure deficit (VPD). At the 6-hourly resolution, both the observation-driven and reanalysis-driven lumped GPP exhibited a lower relationship with EC GPP (MAD = 0.63 gC m⁻²6h⁻¹, NSE = 0.54) than the corresponding distributed GPP (MAD = 0.57 gC m⁻²6h⁻¹, NSE = 0.59), demonstrating that the climate-induced temporal scaling uncertainty in 6-hourly GPP estimates was significantly apparent. This study emphasizes the imperative to refine reanalysis products for more precise capture of short-term fluctuations and to reduce scaling uncertainties in coarse temporal resolution GPP estimates.