Comment on esd-2021-69

doi:10.5194/esd-2021-69-rc2

Abstract

Abstract. Understanding historical changes in gross primary productivity (GPP) is essential for better predicting the future global carbon cycle. However, the historical trends of terrestrial GPP, due to the CO2 fertilization effect, climate, and land-use change, remain largely uncertain. Using long-term satellite-based near-infrared radiance of vegetation (NIRv), a proxy for GPP, and multiple GPP datasets derived from satellite-based products, dynamic global vegetation model (DGVM) simulations, and an upscaled product from eddy covariance (EC) measurements, here we comprehensively investigated their trends and analyzed the causes for any discrepancies during 1982â2015. Although spatial patterns of climatological annual GPP from all products and NIRv are highly correlated (r>0.84), the spatial correlation coefficients of trends between DGVM GPP and NIRv significantly decreased (with the ensemble mean of r=0.49) and even the spatial correlation coefficients of trends between other GPP products and NIRv became negative. By separating the global land into the tropics plus extratropical Southern Hemisphere (Trop+SH) and extratropical Northern Hemisphere (NH), we found that, during 1982â2015, simulated GPP from most of the models showed a stronger increasing trend over Trop+SH than NH. In contrast, the satellite-based GPP products indicated a substantial increase over NH. Mechanistically, model sensitivity experiments indicated that the increase of annual global total GPP was dominated by the CO2 fertilization effect (83.9â% contribution), however, with the largest uncertainty in magnitude in individual simulations among the three drivers of CO2 fertilization, climate, and land-use change. Interestingly, the spatial distribution of inter-model spreads of GPP trends resulted mainly from climate and land-use change rather than CO2 fertilization effect. After 2000, trends from satellite-based GPP products were different from the full time series, suggesting weakened rising trends over NH and even significantly decreasing trends over Trop+SH, while the trends from DGVMs and NIRv kept increasing. The inconsistencies of GPP trends are very likely caused by the contrasting performance between satellite-derived and DGVM simulated vegetation structure parameter (leaf area index, LAI). Therefore, the uncertainty in satellite-based GPP products induced by highly uncertain LAI data in the tropics undermines their roles in assessing the performance of DGVM simulations and understanding the changes of global carbon sinks. The higher consistency between DGVM GPP and NIRv suggests that the trends from a DGVM ensemble might even have better performance than satellite-based GPP products.

Highlights

45 The gross primary productivity (GPP) and the ecosystem respiration (ER) dominate carbon fluxes from terrestrial ecosystems.quantifying global terrestrial GPP is essential to understanding the global carbon cycle (Ryu et al, 2019)
The global distribution of the Dynamic Global Vegetation Models (DGVMs) ensemble GPP trends is generally consistent with satellitederived near-infrared radiance of vegetation (NIRv) with their spatial correlation coefficient (r) of 0.49
For NIRv, 88.2% of the global land had positive GPP trends (Table 3). 200 the DGVM ensemble GPP trends are close to those of NIRv than the other GPP products used here, inconsistencies exist in spatial distribution and magnitude of GPP trends among individual model simulations

Summary

Introduction

45 The gross primary productivity (GPP) and the ecosystem respiration (ER) dominate carbon fluxes from terrestrial ecosystems.quantifying global terrestrial GPP is essential to understanding the global carbon cycle (Ryu et al, 2019). Based on the LUE principle and derived from the Advanced Very High-Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) datasets, the satellite-based GPP estimates include MOD17, GLASS, GIMMS, FluxSat, WECANN, and revised EC_LUE GPP product (Running et al, 2004; Yuan et al, 2007; Smith et al, 2016; Alemohammad et 60 al., 2017; Joiner et al, 2018; Zheng et al, 2020). These GPP products capture the seasonal variation, spatial distribution, and interannual variation to a large extent (Wang et al, 2014), but do not always account for the CO2 fertilization effect (O'sullivan et al, 2020). Many efforts have been made to constrain the global GPP magnitude based on the satellite observations like solar-induced chlorophyll fluorescence (SIF) (Hashimoto et al, 2013; Macbean et al, 2018; Bacour et al, 2019; Norton et al, 2019; Wang et al, 2021a)

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Conclusion