Abstract

Long-term global monitoring of terrestrial Gross Primary Production (GPP) is crucial for assessing ecosystem response to global climate change. In recent years and decades, great advances in estimating GPP on a global level have been made and many global GPP datasets have been published. These global data records are either based on observations from optical remote sensing, are upscaled from in situ measurements, or rely on process-based models. The different estimation approaches are well established within the scientific community but also exhibit significant discrepancies among each other. Here, we introduce the new VODCA2GPP dataset, which utilizes microwave remote sensing estimates of Vegetation Optical Depth (VOD) to estimate GPP on a global scale. VODCA2GPP is able to complement existing products with long-term GPP estimates covering the period 1988–2020. VODCA2GPP applies a previously developed carbon sink-driven approach (Teubner et al., 2019, 2021) to estimate GPP from the Vegetation Optical Depth Climate Archive (Zotta et al., in prep.; Moesinger et al., 2020), which merges VOD observations from multiple sensors into one long-running, coherent data record. VODCA2GPP was trained and evaluated against FLUXNET in situ observations of GPP and assessed against largely independent state-of-the art GPP datasets (MODIS GPP, FLUXCOM GPP, and GPP estimates from the TRENDY-v7 model ensemble). These assessments show that VODCA2GPP exhibits very similar spatial patterns compared to existing GPP datasets across all biomes but with a consistent positive bias. In terms of temporal dynamics, a high agreement was found for regions outside the humid tropics, with median correlations around 0.75. Concerning anomalies from the long-term climatology, VODCA2GPP correlates well with MODIS and TRENDY-v7 GPP (Pearson’s r: 0.53 and 0.61) but less with FLUXCOM GPP (Pearson’s r: 0.29). A trend analysis for the period 1988–2019 did not exhibit a significant trend in VODCA2GPP on a global scale but rather suggests regionally differing long-term changes in GPP. Significant similar increases of global GPP that were found for VODCA2GPP, MODIS GPP, and the TRENDY-v7 ensemble for the shorter overlapping observation period (2003–2015) supports the theory of elevated CO2 uptake potentially induced by increased atmospheric CO2 concentrations and the associated rising temperatures. The VODCA2GPP dataset is available at TU Data (https://doi.org/10.48436/1k7aj-bdz35; Wild et al., 2021).

Highlights

  • Gross Primary Production (GPP) describes vegetation’s conversion of atmospheric CO2 to carbohydrates through 40 photosynthesis and it is the largest CO2 flux in the carbon cycle (Beer et al, 2010)

  • The VOD2GPPmodel makes use of several Vegetation Optical Depth (VOD) variables to represent the sum of NPP and Ra: the original VOD time series (VOD) which 180 relates to maintenance respiration, temporal changes in VOD (∆(VOD)) which relate to both growth respiration and NPP and the temporal median of VOD (mdn(VOD)) derived from the complete time series which serves as a proxy for the landcover

  • The results from the uncertainty analysis and the comparison with in-situ GPP show that VODCA2GPP estimates can be viewed as very reliable across most biomes

Read more

Summary

Introduction

Gross Primary Production (GPP) describes vegetation’s conversion of atmospheric CO2 to carbohydrates through 40 photosynthesis and it is the largest CO2 flux in the carbon cycle (Beer et al, 2010). GPP is considered the primary driver of the terrestrial carbon sink which is in total estimated to absorb approximately 30% of anthropogenic CO2 emissions (Friedlingstein et al, 2020). Terrestrial vegetation productivity exhibits a high degree of (inter-)annual variability and evidence suggests that it 45 is strongly affected by increasing concentrations of CO2 in the atmosphere and the associated global climate change (Haverd et al, 2020; Schimel et al, 2015; Cox et al, 2000). Quantifying GPP is essential to understand the effect of climate variability and changes on the land carbon cycle (e.g., Baldocchi et al, 2016; Nemani et al, 2003)

Methods
Findings
Discussion
Conclusion
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.