Global Gross Primary Production Research Articles

Mutual inhibition effects of elevated CO2 and climate change on global forest GPP

The impact of CO2 fertilization on enhancing global forest gross primary productivity (GPP) is acknowledged, but its interaction with climate factors—air temperature (Tem), precipitation (Pre), vapor pressure deficit (VPD), and radiation (Rad)—remains unclear. In this study, global forest GPP trends from 1982 to 2018 were examined using BEPS, NIRv, FLUXCOM, and revised EC-LUE datasets, with interannual trends of 5.618 (p < 0.01), 5.831 (p < 0.01), 0.227, and 6.566 g C m−2 yr−1 (p < 0.01), respectively. Elevated CO2 was identified as the primary driver of GPP trends, with the dominant area ranging from 51.11% to 90.37% across different GPP datasets. In the NIRv and revised EC-LUE datasets, the positive impact of CO2 on GPP showed a decrease of 0.222 g C m−2 yr−1, while the negative impact of Rad increased by 0.007 g C m−2 yr−1. An inhibitory relationship was found between the actual effects of elevated CO2 and climate change on GPP in most forest types. At lower latitudes, Tem primarily constrained CO2 fertilization, while at higher latitudes, VPD emerged as the key limiting factor. This was mainly attributed to the potential trade-off or competition between elevated CO2 and climate change in influencing GPP, with strategic resource allocation varying across different forest ecosystems. This study highlights the significant inhibitory effects of elevated CO2 and climate change on global forest GPP, providing insights into the dynamic responses of forest ecosystems to changing environments.

Terrestrial Ecosystem Model in R (TEMIR) version 1.0: simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes

Abstract. The newly developed offline land ecosystem model Terrestrial Ecosystem Model in R (TEMIR) version 1.0 is described here. This version of the model simulates plant ecophysiological (e.g., photosynthetic and stomatal) responses to varying meteorological conditions and concentrations of CO2 and ground-level ozone (O3) based on prescribed meteorological and atmospheric chemical inputs from various sources. Driven by the same meteorological data used in the GEOS-Chem chemical transport model, this allows asynchronously coupled experiments with GEOS-Chem simulations with unique coherency for investigating biosphere–atmosphere chemical interactions. TEMIR agrees well with FLUXNET site-level gross primary productivity (GPP) in terms of both the diurnal and monthly cycles (correlation coefficients R2&gt;0.85 and R2&gt;0.8, respectively) for most plant functional types (PFTs). Grass and shrub PFTs have larger biases due to generic model representations. The model performs best when driven by local site-level meteorology rather than reanalyzed gridded meteorology. Simulation using gridded meteorology agrees well for annual GPP in seasonality and spatial distribution with a global average of 134 Pg C yr−1. Application of Monin–Obukhov similarity theory to infer canopy conditions from gridded meteorology does not improve model performance, predicting an increase of +7 % in global GPP. Present-day O3 concentrations simulated by GEOS-Chem and an O3 damage scheme at high sensitivity show a 2 % reduction in global GPP with prominent reductions of up to 15 % in eastern China and the eastern USA. Regional correlations are generally unchanged when O3 is present and biases are reduced, especially for regions with high O3 damage. An increase in atmospheric CO2 concentration of 20 ppmv from the level in 2000 to the level in 2010 modestly decreases O3 damage due to reduced stomatal uptake, consistent with ecophysiological understanding. Our work showcases the utility of this version of TEMIR for evaluating biogeophysical responses of vegetation to changes in atmospheric composition and meteorological conditions.

Estimating global annual gross primary production based on satellite-derived phenology and maximal carbon uptake capacity

The high uncertainty regarding global gross primary production (GPP) remains unresolved. This study explored the relationships between phenology, physiology, and annual GPP to provide viable alternatives for accurate estimation. A statistical model of integrated phenology and physiology (SMIPP) was developed using GPP data from 145 FLUXNET sites to estimate the annual GPP for various vegetation types. By employing the SMIPP model driven by satellite-derived datasets of the global carbon uptake period (CUP) and maximal carbon uptake capacity (GPPmax), the global annual GPP was estimated for the period from 2001 to 2018. The results demonstrated that the SMIPP model accurately predicted annual GPP, with relative root mean square error values ranging from 11.20 to 19.29% for forest types and 20.49–35.71% for non-forest types. However, wetlands, shrublands, and evergreen forests exhibited relatively low accuracies. The average, trend, and interannual variation of global GPP during 2001–2018 were 132.6 Pg C yr−1, 0.25 Pg C yr−2, and 1.57 Pg C yr−1, respectively. They were within the ranges estimated in other global GPP products. Sensitivity analysis revealed that GPPmax had comparable effects to CUP in high-latitude regions but significantly greater impacts at the global scale, with sensitivity coefficients of 0.85 ± 0.23 for GPPmax and 0.46 ± 0.28 for CUP. This study provides a simple and practical method for estimating global annual GPP and highlights the influence of GPPmax and CUP on global-scale annual GPP.

Progress and challenges in remotely sensed terrestrial carbon fluxes

ABSTRACT Accurate evaluation of terrestrial carbon balance is essential for designing climate change mitigation policies, and capabilities of remote sensing techniques in monitoring carbon fluxes are widely recognized for their great contributions to regional and global carbon budget accounting. In this review, we synthesized satellite-based data and methodologies to estimate the main flux components of terrestrial carbon balance and their uncertainties over the past two decades. The global gross primary production (GPP) during the period 2001–2022 is 134 ± 14 PgC yr−1, and nearly half of them occurs in tropical forest regions such as South America and Africa. Less than 2% of global GPP is converted into a net carbon sink of 2.28 ± 1.12 PgC yr−1 using satellite-based atmospheric inversion during 2015–2020, and this sink is comparable to the stock change-based estimate (2.49 PgC yr−1) but twice as large as model-based estimate (1.08 ± 0.78 PgC yr−1). By decomposing satellite-derived net carbon balance into different terms including satellite-derived carbon emissions from land-use change and wildfires (3.55 PgC yr−1), we inferred that ~ 43% of global GPP would be respired through soil microbes (57.1 PgC yr−1), but which is higher than the previous bottom-up estimate (39–46 PgC yr−1). We then propose that an accurate remote sensing of terrestrial carbon balance requires to enhance representations of photosynthetic responses to rising CO2 and disturbances, develop satellite-constrained belowground carbon dynamics and separate natural fluxes from anthropogenic CO2 emissions, by integrating multi-source satellite sensors in orbit, revolutionized remote sensing capabilities with focused field campaigns in data-scarce regions.

Global datasets of hourly carbon and water fluxes simulated using a satellite-based process model with dynamic parameterizations

Abstract. Diagnostic terrestrial biosphere models (TBMs) forced by remote sensing observations have been a principal tool for providing benchmarks on global gross primary productivity (GPP) and evapotranspiration (ET). However, these models often estimate GPP and ET at coarse daily or monthly steps, hindering analysis of ecosystem dynamics at the diurnal (hourly) scales, and prescribe some essential parameters (i.e., the Ball–Berry slope (m) and the maximum carboxylation rate at 25 °C (Vcmax25)) as constant, inducing uncertainties in the estimates of GPP and ET. In this study, we present hourly estimations of global GPP and ET datasets at a 0.25° resolution from 2001 to 2020 simulated with a widely used diagnostic TBM – the Biosphere–atmosphere Exchange Process Simulator (BEPS). We employed eddy covariance observations and machine learning approaches to derive and upscale the seasonally varied m and Vcmax25 for carbon and water fluxes. The estimated hourly GPP and ET are validated against flux observations, remote sensing, and machine learning-based estimates across multiple spatial and temporal scales. The correlation coefficients (R2) and slopes between hourly tower-measured and modeled fluxes are R2=0.83, regression slope =0.92 for GPP, and R2=0.72, regression slope =1.04 for ET. At the global scale, we estimated a global mean GPP of 137.78±3.22 Pg C yr−1 (mean ± 1 SD) with a positive trend of 0.53 Pg C yr−2 (p&lt;0.001), and an ET of 89.03±0.82×103 km3 yr−1 with a slight positive trend of 0.10×103 km3 yr−2 (p&lt;0.001) from 2001 to 2020. The spatial pattern of our estimates agrees well with other products, with R2=0.77–0.85 and R2=0.74–0.90 for GPP and ET, respectively. Overall, this new global hourly dataset serves as a “handshake” among process-based models, remote sensing, and the eddy covariance flux network, providing a reliable long-term estimate of global GPP and ET with diurnal patterns and facilitating studies related to ecosystem functional properties, global carbon, and water cycles. The hourly GPP and ET estimates are available at https://doi.org/10.57760/sciencedb.ecodb.00163 (Leng et al., 2023a) and the accumulated daily datasets are available at https://doi.org/10.57760/sciencedb.ecodb.00165 (Leng et al., 2023b).

Comparative assessment of leaf photosynthetic capacity datasets for estimating terrestrial gross primary productivity

The maximum Rubisco carboxylation rate normalized to 25 °C (Vcmax25) is a key parameter in terrestrial biosphere models for simulating carbon cycling. Recently, global distributions of Vcmax25 have been derived through various methods and different data, including field measurements, ecological optimality theory (EOT), leaf chlorophyll content (LCC), and solar-induced chlorophyll fluorescence (SIF). However, direct validation poses challenges due to high uncertainty arising from limited ground-based observations. This study conducted an indirect evaluation of four Vcmax25 datasets by assessing the accuracy of gross primary productivity (GPP) simulated using the Biosphere-atmosphere Exchange Process Simulator (BEPS) at both site and global scales. Results indicate that, compared to utilizing Vcmax25 fixed by plant functional types (PFT) derived from field measurements, incorporating Vcmax25 derived from SIF and LCC (SIF + LCC), or solely LCC, into BEPS significantly reduces simulated errors in the annual total GPP, with a 23.2 %–25.1 % decrease in the average absolute bias across 196 FLUXNET2015 sites. Daily GPP for evergreen needleleaf forests, deciduous broadleaf forests, shrublands, grasslands, and croplands shows a 7.8 %–27.6 % decrease in absolute bias, primarily attributed to reduced simulation errors during off-peak seasons of vegetation growth. Conversely, the annual total GPP error simulated using EOT-derived Vcmax25 increases slightly (2.2 %) compared to that simulated using PFT-fixed Vcmax25. This is primarily due to a significant overestimation in evergreen broadleaf forests and underestimation in croplands, despite slight increased accuracy for other PFTs. The global annual GPP simulated using Vcmax25 with seasonal variations (i.e., LCC Vcmax25 and SIF + LCC Vcmax25) yields a 4.3 %–7.3 % decrease compared to that simulated using PFT-fixed Vcmax25. Compared to FLUXCOM and GOSIF GPP products, the GPP simulated based on SIF + LCC Vcmax25 and LCC Vcmax25 demonstrates better consistency (R2 = 0.91–0.93, RMSE = 314.2–376.6 g C m−2 yr−1). This study underscores the importance of accurately characterizing the spatiotemporal variations in Vcmax25 for the accurate simulation of global vegetation productivity.

A novel hybrid modelling framework for GPP estimation: Integrating a multispectral surface reflectance based [formula omitted] simulator into the process-based model

Terrestrial gross primary productivity (GPP) is the key element in the carbon cycle process. Accurate GPP estimation hinges on the maximum carboxylation rate (Vcmax,025). The high uncertainty in deriving ecosystem-level Vcmax,025 has long hampered efforts toward the performance of the GPP model. Recently studies suggest the strong relationship between spectral reflectance and Vcmax,025. We proposed the multispectral surface reflectance-driven Vcmax,025 simulator using the fully connected deep neural network and built the hybrid modelling framework for GPP estimation by integrating the data-driven Vcmax,025 simulator in the process-based model. The performance of hybrid GPP model was evaluated at 95 flux sites. The result shows that the multispectral surface reflectance-driven Vcmax,025 simulator acquires the satisfactory estimation, with correlation coefficient (R), root mean square error (RMSE) and median absolute percentage error (MdAPE) ranging from 0.34 to 0.80, 14 to 43 μmol m−2 s−1 and 21 % to 66 % across different land cover types, respectively. The hybrid framework generates good GPP estimates with R, RMSE and MdAPE varying from 0.76 to 0.89, 1.79 to 6.16 μmol m−2 s−1 and 27 % to 90 %, respectively. Compared with EVI-driven method, the multispectral surface reflectance significantly improves the Vcmax,025 and GPP estimates, with MdAPE declining by 0.6 %–18 % and 1 % to 21 %, respectively. The Shapley value analysis reveals that red (620–670 nm), near-infrared (841–876 nm) and shortwave infrared (1628–1652 nm and 2105–2155 nm) are the key bands for Vcmax,025 estimation. This study highlights the potential of multispectral surface reflectance for quantifying ecosystem-level Vcmax,025. The new hybrid framework fully extracts the information of all available spectral bands using deep learning to reduce parameter uncertainty while maintains the description of photosynthetic process to ensure its physical reasonability. It can serve as a powerful tool for accurate global GPP estimation.

Divergent trajectories of future global gross primary productivity and evapotranspiration of terrestrial vegetation in Shared Socioeconomic Pathways

Understanding the future trends of carbon and water fluxes between terrestrial ecosystems and the atmosphere is crucial for predicting Earth's climate dynamics. This study employs an advanced numerical approach to project global gross primary productivity (GPP) and evapotranspiration (ET) from 2001 to 2100 under various climate scenarios based on Shared Socioeconomic Pathways (SSPs). To improve predictions of vegetation dynamics, we introduce a novel model (CoLM-PVPM), an enhancement of the Common Land Model version 2014 (CoLM2014), incorporating a prognostic vegetation phenology model (PVPM). Compared to CoLM2014 that relies on satellite-based leaf area index (LAI) inputs, CoLM-PVPM predicts LAI time series using climate variables. Model validation using historical data from 2001 to 2010 demonstrates PVPM in capturing spatiotemporal variations in satellite LAI. Our modeling results indicate that annual averaged LAI and total GPP increase under SSP1–2.6 but decrease under SSP2–4.5, SSP3–7.0, and SSP5–8.5 by 2100. By comparison, annual total ET consistently increases under all SSP scenarios by 2100. Global annual averaged LAI is highly correlated with annual total GPP in all scenarios, while its correlation with annual total ET weakens in SSP2–4.5, SSP3–7.0, and SSP5–8.5. Global annual total vapor pressure deficit (VPD) and precipitation are highly correlated with annual total ET in all scenarios. As emission levels increase, the negative correlation between annual total VPD and GPP strengthens, while the correlation between annual total precipitation and GPP weakens. This research presents an improved model for predicting terrestrial vegetation processes and underscores the importance of low carbon emission scenarios in maintaining carbon-water balances in specific regions.

Measuring stomatal and guard cell metrics for plant physiology and growth using StoManager1.

Automated guard cell detection and measurement are vital for understanding plant physiological performance and ecological functioning in global water and carbon cycles. Most current methods for measuring guard cells and stomata are laborious, time-consuming, prone to bias, and limited in scale. We developed StoManager1, a high-throughput tool utilizing geometrical, mathematical algorithms, and convolutional neural networks to automatically detect, count, and measure over 30 guard cell and stomatal metrics, including guard cell and stomatal area, length, width, stomatal aperture area/guard cell area, orientation, stomatal evenness, divergence, and aggregation index. Combined with leaf functional traits, some of these StoManager1-measured guard cell and stomatal metrics explained 90% and 82% of tree biomass and intrinsic water use efficiency (iWUE) variances in hardwoods, making them substantial factors in leaf physiology and tree growth. StoManager1 demonstrated exceptional precision and recall (mAP@0.5 over 0.96), effectively capturing diverse stomatal properties across over 100 species. StoManager1 facilitates the automation of measuring leaf stomatal and guard cells, enabling broader exploration of stomatal control in plant growth and adaptation to environmental stress and climate change. This has implications for global gross primary productivity (GPP) modeling and estimation, as integrating stomatal metrics can enhance predictions of plant growth and resource usage worldwide. Easily accessible open-source code and standalone Windows executable applications are available on a GitHub repository (https://github.com/JiaxinWang123/StoManager1) and Zenodo (https://doi.org/10.5281/zenodo.7686022).

Gross primary production of Mediterranean watersheds: Using isotope mass balance approach to improve estimations

AbstractGlobal‐scale estimates of carbon fluxes from satellite data‐driven models are constrained by considerable uncertainties regarding Gross Primary Production (GPP) and the lack of the watershed‐scale measurements required for model calibration. Recently conducted global modelling efforts indicate that semiarid ecosystems dominate the increasing trends and inter‐annual variation of net CO2 exchange with the atmosphere, but semi‐arid regions have received little attention with regard to GPP estimation. In this study, we used the distinct isotope effect of transpiration and evaporation to calculate transpiration losses and subsequently CO2 uptake by terrestrial vegetation through the water and carbon cycle using the water use efficiency of plants. By studying two Mediterranean watersheds with contrasted environmental conditions over several hydrological years, we found a strong dependence of GPP on annual and seasonal water availability. The results demonstrated that when compared to GPP values obtained in worldwide biomes using biological methods, our isotope approach was validated, highlighting the limitations of satellite‐data‐driven models like MODIS in capturing the impact of water stress on photosynthesis and GPP estimates. These results encourage investigation of GPP by the isotope mass balance approach where direct carbon flux measurements are rare or absent in order to help to substantiate, modify or shed doubt on interpolated GPP for those regions and achieve consensus on global GPP estimates. Given the relevant role of semi‐arid ecosystems in the global carbon balance as well as the limitation of existing data sets, our improved method based on the isotope mass balance approach helps to obtain rapid and affordable estimates of GPP for semi‐arid ecosystems.

Estimation of Daily Maize Gross Primary Productivity by Considering Specific Leaf Nitrogen and Phenology via Machine Learning Methods

Maize gross primary productivity (GPP) contributes the most to the global cropland GPP, making it crucial to accurately estimate maize GPP for the global carbon cycle. Previous research validated machine learning (ML) methods using remote sensing and meteorological data to estimate plant GPP, yet they disregard vegetation physiological dynamics driven by phenology. Leaf nitrogen content per unit leaf area (i.e., specific leaf nitrogen (SLN)) greatly affects photosynthesis. Its maximum allowable value correlates with a phenological factor conceptualized as normalized maize phenology (NMP). This study aims to validate SLN and NMP for maize GPP estimation using four ML methods (random forest (RF), support vector machine (SVM), convolutional neutral network (CNN), and extreme learning machine (ELM)). Inputs consist of vegetation index (NDVI), air temperature, solar radiation (SSR), NMP, and SLN. Data from four American maize flux sites (NE1, NE2, and NE3 sites in Nebraska and RO1 site in Minnesota) were gathered. Using data from three NE sites to validate the effect of SLN and MMP shows that the accuracy of four ML methods notably increased after adding SLN and MMP. Among these methods, RF and SVM achieved the best performance of Nash–Sutcliffe efficiency coefficient (NSE) = 0.9703 and 0.9706, root mean square error (RMSE) = 1.5596 and 1.5509 gC·m−2·d−1, and coefficient of variance (CV) = 0.1508 and 0.1470, respectively. When evaluating the best ML models from three NE sites at the RO1 site, only RF and CNN could effectively incorporate the impact of SLN and NMP. But, in terms of unbiased estimation results, the four ML models were comprehensively enhanced by adding SLN and NMP. Due to their fixed relationship, introducing SLN or NMP alone might be more effective than introducing both simultaneously, considering the data redundancy for methods like CNN and ELM. This study supports the integration of phenology and leaf-level photosynthetic factors in plant GPP estimation via ML methods and provides a reference for similar research.

Joint improvement on absorbed photosynthetically active radiation and intrinsic quantum yield efficiency algorithms in the P model betters the estimate of terrestrial gross primary productivity

Terrestrial gross primary productivity (GPP) plays a crucial role in global carbon cycle and budget. A range of light use efficiency (LUE) models have been developed to estimate GPP at different spatial scales. However, large uncertainties still remain in GPP output from such models, mainly owing to the difficulty in the proper determination of maximum light use efficiency (LUEmax). The recently developed P model directly quantifies actual LUE based on environmental and physiological factors, reducing the uncertainty in GPP estimation caused by the ambiguous LUEmax. However, the existing P models still suffer from a potential underestimation in global validation, which might be related to the calculation of absorbed photosynthetically active radiation (APAR) and intrinsic quantum yield efficiency (φ0) in the P model. In this study, we improved the P model by differentiating sunlit and shaded leaves for APAR calculation and by considering the spatial variability of the optimal temperature for φ0 (Topt_phi0). The roles of modified APAR and φ0 calculations in reducing the uncertainty of simulated GPP were assessed through model simulation experiments with monthly tower-based GPP from 120 flux sites globally distributed as the benchmark. The validation indicates that simultaneous improvements on APAR and φ0 algorithms are able to better the performance of the P model. Monthly GPP simulated with this improved P model (i.e., IP model) is in good agreement with tower-based values, with a linear R2, root mean squared error, mean error, mean absolute error, and DISO of 0.76, 1.88 g C m−2d−1, -0.14 g C m−2d−1, 1.77 g C m−2d−1 and 0.60. The total global terrestrial GPP output from the IP model (IPGPP) averaged 134.3 Pg C yr−1 during 1981—2021, in the upper range of existing GPP products.

Responses of Ecosystem Productivity to Anthropogenic Ozone and Aerosols at the 2060

AbstractTerrestrial ecosystems help mitigate global warming by sequestering atmospheric carbon dioxide (CO2) through plant photosynthesis, the rate of which is affected by surface ozone (O3) and aerosols under simultaneous impacts of climate change and rising CO2. While the changes in anthropogenic emissions perturb atmospheric components, their consequent impacts on ecosystem productivity in the future climate remain unclear. Here, we apply a fully coupled climate‐chemistry‐vegetation model, ModelE2‐YIBs, to explore the effects of O3 and aerosols from anthropogenic emissions on global gross primary productivity (GPP) under different emission scenarios at 2060. At the present day, anthropogenic air pollutants induce a GPP loss of −1.67 Pg[C] (−4%) in boreal summer with the contributions of −2.18 Pg[C] by O3 and +0.52 Pg[C] by aerosols. At 2060, the detrimental effect of air pollutants on GPP is exacerbated to −1.85 Pg[C] under a high emissions scenario but alleviated to −0.59 Pg[C] under a low emission scenario. The mitigated GPP loss in the latter scenario is owing to the effective control of anthropogenic emissions that on average reduces surface O3 concentrations by 8.14 ppbv globally relative to 2010. Although the CO2 fertilization effect is weaker in the low emission scenario, the strong decline in air pollutants brings additional GPP gains compared to the high scenario. Regionally, such GPP amelioration is close to or even overweighs the CO2 fertilization effect in eastern China and United States, suggesting that the deep cut of anthropogenic emissions can effectively promote future ecosystem productivity over the nowadays polluted regions.

CMLR: A Mechanistic Global GPP Dataset Derived from TROPOMIS SIF Observations

Solar-induced chlorophyll fluorescence (SIF) has shown promise in estimating gross primary production (GPP); however, there is a lack of global GPP datasets directly utilizing SIF with models possessing clear expression of the biophysical and biological processes in photosynthesis. This study introduces a new global 0.05° SIF-based GPP dataset (CMLR GPP, based on Canopy-scale Mechanistic Light Reaction model) using TROPOMI observations. A modified mechanistic light response model was employed at the canopy scale to generate this dataset. The canopy q L (opened fraction of photosynthesis II reaction centers), required by the CMLR model, was parameterized using a random forest model. The CMLR GPP estimates showed a strong correlation with tower-based GPP ( R 2 = 0.72) in the validation dataset, and it showed comparable performance with other global datasets such as Boreal Ecosystem Productivity Simulator (BEPS) GPP, FluxSat GPP, and GOSIF (global, OCO-2-based SIF product) GPP at a global scale. The high accuracy of CMLR GPP was consistent across various normalized difference vegetation index, vapor pressure deficit, and temperature conditions, as well as different plant functional types and most months of the year. In conclusion, CMLR GPP is a novel global GPP dataset based on mechanistic frameworks, whose availability is expected to contribute to future research in ecological and geobiological regions.

A new global time-series GPP production: DFRF-GPP

Understanding the global ecosystem carbon cycle requires a quantitative estimation of terrestrial gross primary production (GPP). Although most existing GPP estimates can accurately reflect the spatial distribution, interannual trends and even seasonal cycles of GPP under average conditions to some extent, the GPP estimates of different models under extreme climate conditions are still unsatisfactory. In this study, based on the random forest algorithm, we integrated the multimodel GPP simulation results published by the Multiscale Synthesis and Terrestrial Model Intercomparison Project, the FLUXNET flux-site-observed GPP, the standardized precipitation index (SPI) and the standardized temperature index (STI) to generate a set of global GPP time-series data products from 2001 to 2010. The new GPP product was named DFRF-GPP, referring to the GPP generated by data fusion based on random forest. The quality of DFRF-GPP was evaluated by comparison with current commonly used GPP datasets and GPP observations at FLUXNET flux sites. The results showed that DFRF-GPP is in great agreement with other GPP products and can reproduce the spatial and temporal patterns and interannual trends of global GPP, with optimal performance in regions such as those north of 30° in the Northern Hemisphere where there are more FLUXNET flux sites. DFRF-GPP has higher accuracy and excellent performance on drought and high-temperature samples, with higher sensitivity to drought and high temperature. This study not only provides a set of time-series products of global gross primary production but also provides a convenient and practical solution for enhancing the applicability of GPP estimation products in large-scale extreme climate studies.

A dynamic-leaf light use efficiency model for improving gross primary production estimation

Accurate quantification of terrestrial gross primary production (GPP) is integral for enhancing our understanding of the global carbon budget and climate change. The light use efficiency (LUE) model is undoubtedly the most extensively applied method for GPP estimation. However, the two-leaf (TL)-LUE model using a ‘potential’ sunlit leaf area index (LAIsu) can separate a portion of LAIsu even when the canopy does not receive any direct radiation, leading to the underestimation of GPP under cloudy and overcast days. Here, we developed a dynamic-leaf (DL) LUE model by introducing an ‘effective’ LAIsu to improve GPP estimation, which considers the comprehensive contribution of LAIsu when the canopy does and does not receive direct radiation. In particular, the new model decreases LAIsu to zero when direct radiation reaches zero. Our evaluation at eight ChinaFLUX sites showed that (1) the DL-LUE model outperformed the most well-known BL-LUE (namely, the MOD17 GPP algorithm) and TL-LUE models in reproducing the daily in situ GPP, especially at four forest sites [reducing the root mean square error (RMSE) from 1.74 g C m−2 d−1 and 1.53 g C m−2 d−1 to 1.36 g C m−2 d−1 and increasing the coefficient of determination (R 2) from 0.74 and 0.79–0.82, respectively]. Moreover, the improvements were particularly pronounced at longer temporal scales, as indicated by the RMSE decreasing from 29.32 g C m−2 month−1 and28.11 g C m−2 month−1 to 25.81 g C m−2 month−1 at a monthly scale and from 231.82 g C m−2 yr−1 and 221.60 g C m−2 yr−1–200.00 g C m−2 yr−1 at a yearly scale; (2) the DL-LUE model mitigated the systematic underestimation of the in situ GPP by both the TL-LUE and BL-LUE models when the clearness index (CI) was below 0.5, as indicated by the Bias reductions of 0.25 g C m−2 d−1 and 0.46 g C m−2 d−1, respectively; and (3) the contributions of the shaded GPP to the total GPP from the DL-LUE model were higher by 0.07–0.16 than those from the TL-LUE model across the eight ChinaFLUX sites. The proposed parsimonious and effective DL-LUE model not only has great potential for improving global GPP estimations but also provides a more mechanism-based approach for partitioning the total GPP into its shaded and sunlit components.

The direct and indirect effects of the environmental factors on global terrestrial gross primary productivity over the past four decades

Gross primary productivity (GPP) is jointly controlled by the structural and physiological properties of the vegetation canopy and the changing environment. Recent studies showed notable changes in global GPP during recent decades and attributed it to dramatic environmental changes. Environmental changes can affect GPP by altering not only the biogeochemical characteristics of the photosynthesis system (direct effects) but also the structure of the vegetation canopy (indirect effects). However, comprehensively quantifying the multi-pathway effects of environmental change on GPP is currently challenging. We proposed a framework to analyse the changes in global GPP by combining a nested machine-learning model and a theoretical photosynthesis model. We quantified the direct and indirect effects of changes in key environmental factors (atmospheric CO2 concentration, temperature, solar radiation, vapour pressure deficit (VPD), and soil moisture (SM)) on global GPP from 1982 to 2020. The results showed that direct and indirect absolute contributions of environmental changes on global GPP were 0.2819 Pg C yr−2 and 0.1078 Pg C yr−2. Direct and indirect effects for single environmental factors accounted for 1.36%–51.96% and 0.56%–18.37% of the total environmental effect. Among the direct effects, the positive contribution of elevated CO2 concentration on GPP was the highest; and warming-induced GPP increase counteracted the negative effects. There was also a notable indirect effect, mainly through the influence of the leaf area index. In particular, the rising VPD and declining SM negatively impacted GPP more through the indirect pathway rather than the direct pathway, but not sufficient to offset the boost of warming over the past four decades. We provide new insights for understanding the effects of environmental changes on vegetation photosynthesis, which could help modelling and projection of the global carbon cycle in the context of dramatic global environmental change.

Global prediction of gross primary productivity under future climate change

The ecosystem gross primary productivity (GPP) is crucial to land-atmosphere carbon exchanges, and changes in global GPP as well as its influencing factors have been well studied in recent years. However, identifying the spatio-temporal variations of global GPP under future climate changes is still a challenging issue. This study aims to develop data-driven approach for predicting the global GPP as well as its monthly and annual variations up to the year 2100 under changing climate. Specifically, Catboost was employed to examine the potential relationship between the GPP and environmental factors, with climate variables, CO2 concentration and terrain attributes being selected as environmental factors. The predicted monthly and annual GPP from Coupled Model Intercomparison Project phase 6 (CMIP6) under future SSP1–2.6, SSP2–4.5, SSP3–7.0 and SSP5–8.5 scenarios were analyzed. The results indicate that the global GPP is predicted to increase under the future climate change in the 21st century. The annual GPP is expected to be 115.122 Pg C, 116.537 Pg C, 117.626 Pg C, and 120.097 Pg C in 2100 under four future scenarios, and the predicted monthly GPP shows seasonal difference. Meanwhile, GPP tends to increase in the northern mid–high latitude regions and decrease in the equatorial regions. For the climate zones form Köppen-Geiger classification, the arid, cold, and polar zones present increased GPP, while GPP in the tropical zone will decrease in the future. Moreover, the high importance of climate variables in GPP prediction illustrates that the future climate change is the main driver of the global GPP dynamics. This study provides a basis for predicting how global GPP responds to future climate change in the coming decades, which contribute to understanding the interactions between vegetation and climate.

A novel approach for retrieving GPP of evergreen forest regions of India using random forest regression

Gross Primary Productivity (GPP) is a crucial variable of global carbon cycle for determining the ecosystem’s health. Various methods are devised to quantify GPP and upscale it in both time and space. The most common methods are physical model and eddy covariance-based estimation, which are very restricted to surrounding area of study, only. The alternative methods are empirical (e.g., LUE, CASA, SCARF, and MODIS) and Machine learning (ML) models that employ remote sensing satellite data and geographical factors. However, for using ML models, ground-based measurements of GPP is a very important factor, which is not available in most places. We propose an alternative and effective way of estimating the GPP using the ML model and data from various flux sites around the globe for a particular plant functional type (PFT). In the present study, RF is used as ML model, which is trained on global GPP data from evergreen forest and implemented in Indian region. The key findings indicated that ML-based GPP is highly accurate and hence, we generated 20 years of time series GPP dataset (2001–2020). We validated with ground-based flux tower observations during 2016–2018 for three sites (very limited datasets) in India and compared them with MODIS GPP. The coefficient of determination (R2) value of the ML-based model was 0.84 with root mean square error (RMSE) of 1.45 gC m−2 Day−1 and mean absolute error (MAE) of 0.838 gC m−2 Day−1. The proposed approach is highly accurate and far better than the MODIS-based GPP. Therefore, it can be further extended to other forest types for a holistic assessment of the carbon cycle of a region.

Higher global gross primary productivity under future climate with more advanced representations of photosynthesis.

Gross primary productivity (GPP) is the key determinant of land carbon uptake, but its representation in terrestrial biosphere models (TBMs) does not reflect our latest physiological understanding. We implemented three empirically well supported but often omitted mechanisms into the TBM CABLE-POP: photosynthetic temperature acclimation, explicit mesophyll conductance, and photosynthetic optimization through redistribution of leaf nitrogen. We used the RCP8.5 climate scenario to conduct factorial model simulations characterizing the individual and combined effects of the three mechanisms on projections of GPP. Simulated global GPP increased more strongly (up to 20% by 2070-2099) in more comprehensive representations of photosynthesis compared to the model lacking the three mechanisms. The experiments revealed non-additive interactions among the mechanisms as combined effects were stronger than the sum of the individual effects. The modeled responses are explained by changes in the photosynthetic sensitivity to temperature and CO2 caused by the added mechanisms. Our results suggest that current TBMs underestimate GPP responses to future CO2 and climate conditions.