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1. X-BASE: the first terrestrial carbon and water flux products from an extended data-driven scaling framework, FLUXCOM-X

   Jacob, A Nelson (February 2024, EGU Sphere)

   Abstract. Mapping in-situ eddy covariance measurements of terrestrial land-atmosphere fluxes to the globe is a key method for diagnosing the Earth system from a data-driven perspective. We describe the first global products (called X-BASE) from a newly implemented up-scaling framework, FLUXCOM-X. The X-BASE products comprise of estimates of CO2 net ecosys- tem exchange (NEE), gross primary productivity (GPP) as well as evapotranspiration (ET) and, for the first time, a novel fully data-driven global transpiration product (ETT ), at high spatial (0.05°) and temporal (hourly) resolution. X-BASE es- timates the global NEE at -5.75 ± 0.33 PgC·yr−1 for the period 2001-2020, showing a much higher consistency with independent atmospheric carbon cycle constraints compared to the previous versions of FLUXCOM. The improvement of global NEE was likely only possible thanks to the international effort to increase the precision and consistency of eddy co- variance collection and processing pipelines, as well as to the extension of the measurements to more site-years resulting in a wider coverage of bio-climatic conditions. However, X-BASE global net ecosystem exchange shows a very low inter-annual variability, which is common to state-of-the-art data-driven flux products and remains a scientific challenge. With 125 ± 2.1 P gC · yr−1 for the same period, X-BASE GP P is slightly higher than previous FLUXCOM estimates, mostly in temperate and boreal areas. X-BASE evapotranspiration amounts to 74.7x103 ± 0.9x103 km3 globally for the years 2001-2020, but ex- ceeds precipitation in many dry areas likely indicating overestimation in these regions. On average 57% of evapotranspiration are estimated to be transpiration, in good agreement with isotope-based approaches, but higher than estimates from many land surface models. Despite considerable improvements to the previous up-scaling products, many further opportunities for devel- opment exist. Pathways of exploration include methodological choices in the selection and processing of eddy-covariance and satellite observations, their ingestion into the framework, and the configuration of machine learning methods. For this the new FLUXCOM-X framework was specifically designed to have the necessary flexibility to experiment, diagnose, and converge to more accurate global flux estimates. 

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2. Constrained Carbon Partitioning: A Self‐Trained Physics‐Informed Machine Learning Model Refines GPP Estimates From Eddy Covariance Measurements

   https://doi.org/10.1111/gcb.70886  

   Ranjbar, Sadegh; Desai, Ankur R; Hoffman, Sophie; Zahn, Einara; Bou‐Zeid, Elie; Stoy, Paul C (May 2026, Global Change Biology)

   Gross primary productivity (GPP) is the largest term in the global carbon budget but cannot be directly observed. We present a knowledge‐guided machine learning (KGML) framework that partitions eddy covariance‐measured net ecosystem exchange (NEE) into gross primary production (GPP) and ecosystem respiration (RECO) with partitioned water vapor fluxes and CO₂flux source areas from 36 U.S. National Ecological Observatory Network (NEON) towers. The KGML is guided by hard physical constraints that enforce mass balance and ‘soft’ theoretical expectations including optimal stomatal response to vapor pressure deficit (VPD) and links between GPP and transpiration (T) through stomatal function. The model achieves strong physical consistency (NEER² = 0.99) while capturing expected ecophysiological relationships including GPP‐T coupling (R² = 0.58) and stomatal responses to light and VPD. Compared to conventional partitioning methods, KGML infers lower GPP and RECO estimates on average, with the largest negative biases occurring at low light levels (0–200 μmol photons m⁻² s⁻¹). These differences likely reflect a combination of mechanisms including light‐induced respiration suppression consistent with the Kok effect, stomatal‐transpiration coupling constraints, and dynamic allocation between respiration components. The flux differences vary across plant functional types (PFTs), where forested ecosystems (deciduous broadleaf, evergreen needleleaf, and mixed), savannas and grasslands show the largest negative annual GPP deviations (−10% to −18% versus nighttime partitioning), while croplands and open shrublands show moderate negative deviations (−5% to −10%). The lower GPP estimates by PFT are closer to those inferred by Keenan et al. that explicitly considers limitations on RECO from the Kok effect. We discuss implications for our understanding of ecosystem and global carbon cycle processes, as well as ways to further benefit from the full information content of eddy covariance observations by combining physics with knowledge of biological processes. 

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3. CEDAR-GPP: spatiotemporally upscaled estimates of gross primary productivity incorporating CO ₂ fertilization

   https://doi.org/10.5194/essd-17-3009-2025  

   Kang, Yanghui; Bassiouni, Maoya; Gaber, Max; Lu, Xinchen; Keenan, Trevor F (January 2025, Earth System Science Data)

   Abstract. Gross primary productivity (GPP) is the largest carbon flux in the Earth system, playing a crucial role in removing atmospheric carbon dioxide and providing carbohydrates needed for ecosystem metabolism. Despite the importance of GPP, however, existing estimates present significant uncertainties and discrepancies. A key issue is the underrepresentation of the CO2 fertilization effect, a major factor contributing to the increased terrestrial carbon sink over recent decades. This omission could potentially bias our understanding of ecosystem responses to climate change. Here, we introduce CEDAR-GPP, the first global machine-learning-upscaled GPP product that incorporates the direct CO2 fertilization effect on photosynthesis. Our product is comprised of monthly GPP estimates and their uncertainty at 0.05° resolution from 1982 to 2020, generated using a comprehensive set of eddy covariance measurements, multi-source satellite observations, climate variables, and machine learning models. Importantly, we used both theoretical and data-driven approaches to incorporate the direct CO2 effects. Our machine learning models effectively predict monthly GPP (R2 ∼ 0.72), the mean seasonal cycles (R2 ∼ 0.77), and spatial variabilities (R2 ∼ 0.63) based on cross-validation at flux sites. After incorporating the direct CO2 effects, the predicted long-term GPP trend across global flux towers substantially increases from 3.1 to 4.5–5.4 gC m−2 yr−1, which aligns more closely with the 7.7 gC m−2 yr−1 trend detected from eddy covariance data. While the global patterns of annual mean GPP, seasonality, and interannual variability generally align with existing satellite-based products, CEDAR-GPP demonstrates higher long-term trends globally after incorporating CO2 fertilization and reflected a strong temperature control on direct CO2 effects. The estimated global GPP trend is 0.57–0.76 PgC yr−1 from 2001 to 2018 and 0.32–0.34 PgC yr−1 from 1982 to 2018. Estimating and validating GPP trends in data-scarce regions, such as the tropics, remains challenging, underscoring the importance of ongoing ground-based monitoring and advancements in modeling techniques. CEDAR-GPP offers a comprehensive representation of GPP temporal and spatial dynamics, providing valuable insights into ecosystem–climate interactions. The CEDAR-GPP product is available at https://doi.org/10.5281/zenodo.8212706 (Kang et al., 2024). 

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4. Historically inconsistent productivity and respiration fluxes in the global terrestrial carbon cycle

   https://doi.org/10.1038/s41467-022-29391-5  

   Jian, Jinshi; Bailey, Vanessa; Dorheim, Kalyn; Konings, Alexandra G.; Hao, Dalei; Shiklomanov, Alexey N.; Snyder, Abigail; Steele, Meredith; Teramoto, Munemasa; Vargas, Rodrigo; et al (December 2022, Nature Communications)

   Abstract The terrestrial carbon cycle is a major source of uncertainty in climate projections. Its dominant fluxes, gross primary productivity (GPP), and respiration (in particular soil respiration, R S ), are typically estimated from independent satellite-driven models and upscaled in situ measurements, respectively. We combine carbon-cycle flux estimates and partitioning coefficients to show that historical estimates of global GPP and R S are irreconcilable. When we estimate GPP based on R S measurements and some assumptions about R S :GPP ratios, we found the resulted global GPP values (bootstrap mean $${149}_{-23}^{+29}$$ 149 − 23 + 29 Pg C yr −1 ) are significantly higher than most GPP estimates reported in the literature ( $${113}_{-18}^{+18}$$ 113 − 18 + 18 Pg C yr −1 ). Similarly, historical GPP estimates imply a soil respiration flux (Rs GPP , bootstrap mean of $${68}_{-8}^{+10}$$ 68 − 8 + 10 Pg C yr −1 ) statistically inconsistent with most published R S values ( $${87}_{-8}^{+9}$$ 87 − 8 + 9 Pg C yr −1 ), although recent, higher, GPP estimates are narrowing this gap. Furthermore, global R S :GPP ratios are inconsistent with spatial averages of this ratio calculated from individual sites as well as CMIP6 model results. This discrepancy has implications for our understanding of carbon turnover times and the terrestrial sensitivity to climate change. Future efforts should reconcile the discrepancies associated with calculations for GPP and Rs to improve estimates of the global carbon budget. 

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5. Upscaling Wetland Methane Emissions From the FLUXNET‐CH4 Eddy Covariance Network (UpCH4 v1.0): Model Development, Network Assessment, and Budget Comparison

   https://doi.org/10.1029/2023AV000956  

   McNicol, Gavin; Fluet‐Chouinard, Etienne; Ouyang, Zutao; Knox, Sara; Zhang, Zhen; Aalto, Tuula; Bansal, Sheel; Chang, Kuang‐Yu; Chen, Min; Delwiche, Kyle; et al (October 2023, AGU Advances)

   Abstract Wetlands are responsible for 20%–31% of global methane (CH₄) emissions and account for a large source of uncertainty in the global CH₄budget. Data‐driven upscaling of CH₄fluxes from eddy covariance measurements can provide new and independent bottom‐up estimates of wetland CH₄emissions. Here, we develop a six‐predictor random forest upscaling model (UpCH4), trained on 119 site‐years of eddy covariance CH₄flux data from 43 freshwater wetland sites in the FLUXNET‐CH4 Community Product. Network patterns in site‐level annual means and mean seasonal cycles of CH₄fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash‐Sutcliffe Efficiency ∼0.52–0.63 and 0.53). UpCH4 estimated annual global wetland CH₄emissions of 146 ± 43 TgCH₄ y⁻¹for 2001–2018 which agrees closely with current bottom‐up land surface models (102–181 TgCH₄ y⁻¹) and overlaps with top‐down atmospheric inversion models (155–200 TgCH₄ y⁻¹). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH₄fluxes has the potential to produce realistic extra‐tropical wetland CH₄emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid‐to‐arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25° from UpCH4 are available via ORNL DAAC (https://doi.org/10.3334/ORNLDAAC/2253). 

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