Search NASA⌕ Search

SEARCH · Search NASA

Results for “gross primary production”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Improving E3SM Land Model Photosynthesis Parameterization via Satellite SIF, Machine Learning, and Surrogate Modeling

The parameterization of key photosynthesis parameters is one of the key uncertain sources in modeling ecosystem gross primary productivity (GPP). Solar-induced chlorophyll fluorescence (SIF) offers a good proxy for GPP since it marks the actual process of photosynthesis; while machine learning (ML) provides a robust approach to model the GPP-SIF relationship. Here, we trained the boosted regressing tree (BRT) and the Random Forest ML models with Greenhouse Gases Observing Satellite SIF data and in situ GPP observations from 49 eddy covariance towers. These trained ML GPP-SIF models were fed into the Energy Exascale Earth System Model (E3SM) Land Model (ELM) to generate ELM-simulated global SIF estimates, which were then benchmarked against satellite SIF observations with a surrogate modeling approach. Our results indicated good modeling performance of the ML-based GPP-SIF relationship. The ELM model when fed with the ML GPP-SIF models also can well predict the spatial-temporal variations in SIF. We also found high model accuracy for the surrogate modeling. Model parameter sensitivity analysis suggested that the fraction of leaf nitrogen in RuBisCO (flnr) is the most sensitive parameter to the SIF; other sensitive parameters include the Ball-Berry stomatal conductance slope (mbbopt) and the vcmax entropy (vcmaxse). The posterior uncertainty in simulated GPP was greatly reduced after benchmarking, and the model produced improved spatial patterns of mean GPP relative to FLUXCOM GPP. Our integrated approach provides a new avenue for improving land models and using remote-sensing SIF, which can be further improved in the future with more ground- and satellite-based observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Systematic Underestimation of Canopy Conductance Sensitivity to Drought by Earth System Models

The response of vegetation canopy conductance (g c ) to changes in moisture availability ($γ^{m}_{gc}$) is a major source of uncertainty in climate projections. While vegetation typically reduces stomatal conductance during drought, accurately modeling how and to what degree stomata respond to changes in moisture availability at global scales is particularly challenging, because no global scale g c observations exist. Here, we leverage a collection of satellite, reanalysis and station-based near-surface air and surface temperature estimates, which are physically and statistically linked to $γ^{m}_{gc}$ due to the local cooling effect of g c through transpiration, to develop a novel emergent constraint of $γ^{m}_{gc}$ in an ensemble of Earth System Models (ESMs). We find that ESMs systematically underestimate $γ^{m}_{gc}$ by ~33%, particularly in grasslands, croplands, and savannas in semi-arid and bordering regions of the Central United States, Central Europe, Southeastern South America, Southern Africa, Eastern Australia, and parts of East Asia. We show that this underestimation occurs because ESMs inadequately reduce g c when soil moisture decreases. As g c controls carbon, water and energy fluxes, the misrepresentation of modeled $γ^{m}_{gc}$ contributes to biases in ESM projections of gross primary production, transpiration, and temperature during droughts. Our results suggest that the severity and duration of droughts may be misrepresented in ESMs due to the impact of sustained g c on both soil moisture dynamics and the biosphere-atmosphere feedbacks that affect local temperatures and regional weather patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Uncertain Spatial Pattern of Future Land Use and Land Cover Change and Its Impacts on Terrestrial Carbon Cycle Over the Arctic–Boreal Region of North America

Land use and land cover change (LULCC) represents a key process of human-Earth system interaction and has profound impacts on terrestrial ecosystem carbon cycling. As a key input for ecosystem models, future gridded LULCC data is typically spatially downscaled from regional LULCC projections by integrated assessment models, such as the Global Change Analysis Model (GCAM). The uncertainty associated with the different spatial downscaling methods and its impacts on the subsequent model projections have been historically ignored and rarely examined. This study investigated this problem using two representative spatial downscaling methods and focused on their impacts on the carbon cycle over the Arctic-Boreal Vulnerability Experiment (ABoVE) domain, where extensive LULCC is expected. Specifically, we used the Future Land Use Simulation model (FLUS) and the Demeter model to generate 0.25° gridded LULCC data (i.e., LULCC FLUS and LULCC Demeter , respectively) with the same input of regional LULCC projections from GCAM, under both the low (i.e., SSP126) and high (i.e., SSP585) greenhouse gas emission scenarios. The two sets of downscaled LULCC were used to drive the Community Land Model version 5 and prognostically simulate the terrestrial carbon cycle dynamics over the 21st century. The results suggest large spatial-temporal differences between LULCC FLUS and LULCC Demeter , and the spatial distributions of the needleleaf evergreen boreal tree, broadleaf deciduous boreal tree, broadleaf deciduous boreal shrub, and C3 arctic grass are particularly different under both SSP126 and SSP585. Additionally, the spatiotemporal differences are larger under SSP126 than SSP585, due to more intensive LULCC under SSP126 than SSP585 from GCAM projection. The differences in LULCC further lead to large discrepancies in the spatial patterns of projected gross primary productivity, ecosystem respiration, and net ecosystem exchange, which represent more than 79% of the contributions of future LULCC in 2100. Additionally, the difference in carbon flux under SSP126 is generally larger than those under SSP585. This study highlights the importance of considering the uncertainties induced by the spatial downscaling process in future LULCC projections and carbon cycle simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Carbon and Water Fluxes of the Boreal Evergreen Needleleaf Forest Biome Constrained by Assimilating Ecosystem Carbonyl Sulfide Flux Observations

Gross primary production (GPP) by boreal forests is highly sensitive to environmental changes. However, GPP simulated by land surface models (LSMs) remains highly uncertain due to the lack of direct photosynthesis observations at large scales. Carbonyl sulfide (COS) has emerged as a promising proxy to improve the representation of GPP in LSMs. Because COS is absorbed by vegetation following the same diffusion pathway as CO2 during photosynthesis and not emitted back to the atmosphere, incorporating a mechanistic representation of vegetation COS uptake in LSMs allows using COS observations to refine GPP representation. Here, we perform ecosystem COS flux and GPP data assimilations to constrain the COS- and GPP-related parameters in the ORCHIDEE LSM for boreal evergreen needleleaf forests (BorENF). Assimilating ecosystem COS fluxes at Hyytiälä forest increases the simulated net ecosystem COS uptake by 14%. This increase largely results from changes in the internal conductance to COS, highlighting the need to improve the representation of COS internal diffusion and consumption. Moreover, joint assimilation of ecosystem COS flux and GPP at Hyytiälä improves the simulated latent heat flux, contrary to the GPP-only data assimilation, which fails to do so. Finally, we scaled this assimilation framework up to the boreal region and find that the joint assimilation of COS at Hyytiälä and GPP fluxes at 10 BorENF sites increases the modeled vegetation COS uptake up to 18%, but not GPP. Therefore, this study encourages the use of COS flux observations to inform GPP and latent heat flux representations in LSMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Alternating Conditional Expectations: Introducing a Non‐Parametric Statistical Method to Interpret Long‐Term Greenhouse Gas Flux Measurements Over Semi‐Arid and Wetland Ecosystems

Abstract We explore the potential of using a non‐parametric statistical method called Alternating Conditional Expectations, ACE, to quantify functional relationships in biogeosciences. Here, ACE is used to quantify the non‐linear and multi‐faceted responses of greenhouse gas fluxes to a set of biophysical forcings, when the shapes of those response surfaces are unknown. We evaluated the statistical method over two contrasting ecosystems and two contrasting time steps. One case involved quantifying the biophysical controls of water vapor and carbon dioxide (CO 2 ) fluxes over a semi‐arid oak savanna using daily integrated fluxes. The other case evaluated the responses of CO 2 and methane (CH 4 ) flux measurements to a set of biophysical forcings at a restored tidal wetland using thirty‐minute averages. The statistical model, based on 4 independent variables, explained up over 90% of the variation in daily integrated flux densities of water vapor and net carbon dioxide exchange at the savanna site. This fit was defined by distinct non‐linear responses to such drivers as gross primary production, photosynthetically active radiation, air temperature, vapor pressure deficit and soil moisture. At the tidal wetland site, we evaluated net carbon dioxide and methane fluxes with short‐term measurements to capture the influence of rising and falling tides and seasonality in biological activity. The statistical model defined the shape of the forcing of fluxes due to the roles of carbon exudates, water table depth, oxygen level in the water column, temperature and vegetation status. The statistical fits of the greenhouse gas fluxes were less precise than the savanna case. The fetch varies on a run‐to‐run basis as it is comprised of a heterogeneous mosaic of open water and vegetation. Furthermore, it is difficult to monitor the environmental conditions of the archaea and bacteria in the sediments that produce methane and carbon dioxide.

Environmental Sciences & Ecology↗
DOE OSTI2024

A New Coupled Biogeochemical Modeling Approach Provides Accurate Predictions of Methane and Carbon Dioxide Fluxes Across Diverse Tidal Wetlands

Abstract Tidal wetlands provide valuable ecosystem services, including storing large amounts of carbon. However, the net exchanges of carbon dioxide (CO 2 ) and methane (CH 4 ) in tidal wetlands are highly uncertain. While several biogeochemical models can operate in tidal wetlands, they have yet to be parameterized and validated against high‐frequency, ecosystem‐scale CO 2 and CH 4 flux measurements across diverse sites. We paired the Cohort Marsh Equilibrium Model (CMEM) with a version of the PEPRMT model called PEPRMT‐Tidal, which considers the effects of water table height, sulfate, and nitrate availability on CO 2 and CH 4 emissions. Using a model‐data fusion approach, we parameterized the model with three sites and validated it with two independent sites, with representation from the three marine coasts of North America. Gross primary productivity (GPP) and ecosystem respiration (R eco ) modules explained, on average, 73% of the variation in CO 2 exchange with low model error (normalized root mean square error (nRMSE) <1). The CH 4 module also explained the majority of variance in CH 4 emissions in validation sites ( R 2 = 0.54; nRMSE = 1.15). The PEPRMT‐Tidal‐CMEM model coupling is a key advance toward constraining estimates of greenhouse gas emissions across diverse North American tidal wetlands. Further analyses of model error and case studies during changing salinity conditions guide future modeling efforts regarding four main processes: (a) the influence of salinity and nitrate on GPP, (b) the influence of laterally transported dissolved inorganic C on R eco , (c) heterogeneous sulfate availability and methylotrophic methanogenesis impacts on surface CH 4 emissions, and (d) CH 4 responses to non‐periodic changes in salinity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Enhanced Carbon Flux Response to Atmospheric Aridity and Water Storage Deficit During the 2015–2016 El Niño Compromised Carbon Balance Recovery in Tropical South America

During the 2015–2016 El Niño, the Amazon basin released almost one gigaton of carbon (GtC) into the atmosphere due to extreme temperatures and drought. The link between the drought impact and recovery of the total carbon pools and its biogeochemical drivers is still unknown. With satellite-constrained net carbon exchange and its component fluxes including gross primary production and fire emissions, we show that the total carbon loss caused by the 2015–2016 El Niño had not recovered by the end of 2018. Forest ecosystems over the Northeastern (NE) Amazon suffered a cumulative total carbon loss of ~0.6 GtC through December 2018, driven primarily by a suppression of photosynthesis whereas southeastern savannah carbon loss was driven in part by fire. We attribute the slow recovery to the unexpected large carbon loss caused by the severe atmospheric aridity coupled with a water storage deficit during drought. We show the attenuation of carbon uptake is three times higher than expected from the pre-drought sensitivity to atmospheric aridity and ground water supply. Our study fills an important knowledge gap in our understanding of the unexpectedly enhanced response of carbon fluxes to atmospheric aridity and water storage deficit and its impact on regional post-drought recovery as a function of the vegetation types and climate perturbations. Our results suggest that the disproportionate impact of water supply and demand could compromise resiliency of the Amazonian carbon balance to future increases in extreme events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Key Environmental and Ecological Variables of Wetland CH 4 and CO 2 Fluxes Change With Warming

Wetlands are important ecosystems for the global carbon cycle, impacting regional and global methane (CH 4 ) and carbon dioxide (CO 2 ) budgets. This study examines how environmental and ecological variables impact wetland CH 4 flux and net ecosystem exchange of CO 2 (NEE) across 17 sites globally. We also quantified the importance of variables for each wetland type and site at monthly scale under normal and warm temperatures using dominance analysis. We identified soil and air temperature (TS, TA, respectively) as key variables influencing wetland CH4, and latent heat (LE) and shortwave radiation (SW) for NEE under normal and warm conditions. However, the importance of some variables shifted with warming. For predicting the variability of wetland CH4 flux under warming, gross primary productivity (GPP) and LE, replacing wind direction (WD), were dominant variables for tropical swamps, while NEE was important for high-latitude fens and bogs under warm temperatures. For wetland NEE, the role of TA and TS decreased across all wetland types with warming, while vapor pressure deficit (VPD) became more important for mid and high-latitude wetlands. Our results reveal the complex responses of wetland carbon flux to environmental and ecological variables with warming and provide new insights into improving wetland models by incorporating additional variables and accounting for the changing roles of variables in carbon flux under warming.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Integrating State Data Assimilation and Innovative Model Parameterization Reduces Simulated Carbon Uptake in the Arctic and Boreal Region

Model representation of carbon uptake and storage is essential for accurate projection of the response of the arctic-boreal zone to a rapidly changing climate. Land model estimates of LAI and aboveground biomass that can have a marked influence on model projections of carbon uptake and storage vary substantially in the arctic and boreal zone, making it challenging to correctly evaluate model estimates of Gross Primary Productivity (GPP). To understand and correct bias of LAI and aboveground biomass in the Community Land Model (CLM), we assimilated the 8-day Moderate Resolution Imaging Spectroradiometer (MODIS) LAI observation and a machine learning product of annual aboveground biomass into CLM using an Ensemble Adjustment Kalman Filter (EAKF) in an experimental region including Alaska and Western Canada. Assimilating LAI and aboveground biomass reduced these model estimates by 58% and 72%, respectively. The change of aboveground biomass was consistent with independent estimates of canopy top height at both regional and site levels. The International Land Model Benchmarking system assessment showed that data assimilation significantly improved CLM's performance in simulating the carbon and hydrological cycles, as well as in representing the functional relationships between LAI and other variables. Here, to further reduce the remaining bias in GPP after LAI bias correction, we re-parameterized CLM to account for low temperature suppression of photosynthesis. The LAI bias corrected model that included the new parameterization showed the best agreement with model benchmarks. Combining data assimilation with model parameterization provides a useful framework to assess photosynthetic processes in LSMs.

58 GEOSCIENCES↗
DOE OSTI2025

Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics

Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics as well as limited data availability. We developed the Rangeland Carbon Tracking and Management (RCTM) system to track long-term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable data sets with algorithms representing terrestrial C-cycle processes. Bayesian calibration was conducted using quality-controlled C flux data sets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern US rangelands to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass-shrub mixture, and grass-tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) (R 2 > 0.6, RMSE <390 g C m -2 ) relative to net ecosystem exchange of CO 2 (NEE) (R 2 > 0.4, RMSE <180 g C m -2 ). Model performance in estimating rangeland C fluxes varied by season and vegetation type. The RCTM captured the spatial variability of SOC stocks with R 2 = 0.6 when validated against SOC measurements across 13 NEON sites. Model simulations indicated slightly enhanced SOC stocks for the flux tower sites during the past decade, which is mainly driven by an increase in precipitation. Future efforts to refine the RCTM system will benefit from long-term network-based monitoring of vegetation biomass, C fluxes, and SOC stocks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Incorporating the Acclimation of Photosynthesis and Leaf Respiration in the Noah‐MP Land Surface Model: Model Development and Evaluation

Abstract Realistic simulation of leaf photosynthetic and respiratory processes is needed for accurate prediction of the global carbon cycle. These two processes systematically acclimate to long‐term environmental changes by adjusting photosynthetic and respiratory traits (e.g., the maximum photosynthetic capacity at 25°C ( V cmax,25 ) and the leaf respiration rate at 25°C ( R 25 )) following increasingly well‐understood principles. While some land surface models (LSMs) now account for thermal acclimation, they do so by assigning empirical parameterizations for individual plant functional types (PFTs). Here, we have implemented an Eco‐Evolutionary Optimality (EEO)‐based scheme to represent the universal acclimation of photosynthesis and leaf respiration to multiple environmental effects, and that therefore requires no PFT‐specific parameterizations, in a standard version of the widely used LSM, Noah MP. We evaluated model performance with plant trait data from a 5‐year experiment and extensive global field measurements, and carbon flux measurements from FLUXNET2015. We show that observed R 25 and V cmax,25 vary substantially both temporally and spatially within the same PFT ( C.V. >20%). Our EEO‐based scheme captures 62% of the temporal and 70% of the spatial variations in V cmax,25 (73% and 54% of the variations in R 25 ). The standard scheme underestimates gross primary production by 10% versus 2% for the EEO‐based scheme and generates a larger spread in r (correlation coefficient) across flux sites (0.79 ± 0.16 vs. 0.84 ± 0.1, mean ± S.D.). The standard scheme greatly overestimates canopy respiration (bias: ∼200% vs. 8% for the EEO scheme), resulting in less CO 2 uptake by terrestrial ecosystems. Our approach thus simulates climate‐carbon coupling more realistically, with fewer parameters.

Ren, Yanghang [Department of Earth System Science ↗
DOE OSTI2025

Integrating Characteristic Arctic Vegetation in a Land Surface Model Improves Representation of Carbon Dynamics Across a Tundra Landscape

Arctic warming is altering vegetation and carbon dynamics with global implications, yet Earth System Model (ESM) predictions in the Arctic remain highly uncertain, in part due to historically limited data for model parameterization and validation. As such, ESMs typically represent Arctic ecosystems in an oversimplified manner. Recently, nine plant functional types (PFTs) designed to realistically represent tundra vegetation were integrated into the Energy Exascale Earth System Model (E3SM) Land Model (ELM) and parameterized using plot-scale observations from a single site. Additional evaluation was needed to determine their transferability across the Arctic. Here, in this study, we evaluated whether refined representation of tundra vegetation improved model accuracy by conducting spatially explicit 100 × 100 m resolution ELM simulations on Alaska's Seward Peninsula. Simulations with the default two-PFT configuration and with the nine Arctic-specific PFTs were benchmarked against observations of net ecosystem exchange, gross primary production, and aboveground biomass from multiple data streams including an eddy covariance flux tower, flux chambers, and aircraft and unoccupied aerial system hyperspectral remote sensing. Evaluation revealed that Arctic-specific PFT simulations produced more realistic landscape-level carbon exchanges, and better captured observed heterogeneity in biomass and productivity, explaining 60%–70% of spatial variance (R 2 = 0.6–0.7) compared to just 12%–18% (R 2 = 0.12–0.18) with the default configuration. However, the refined model failed to reproduce observed aboveground biomass for highly productive alder-willow communities, requiring further evaluation of carbon allocation parameterizations for tall shrubs that are increasingly expanding across tundra landscapes. Our results demonstrate that enhanced representation of vegetation heterogeneity boosts predictive understanding of tundra carbon dynamics, facilitating regional to pan-Arctic model and remote-sensing scaling.

Murphy, Bailey A. [Oak Ridge National Laboratory (↗
DOE OSTI2026

Contrasting Parametric Sensitivities in Two Global Vegetation Models Using Parameter Perturbation Ensembles

Uncertainty in land model projections remains high and the roles of parametric and structural uncertainty are difficult to disentangle. To compare parametric sensitivity across model structures we present two parameter perturbation ensembles using the Community Land Model (CLM) operating in satellite phenology mode. The ensembles contrast two vegetation modules: (a) the default CLM vegetation module and (b) the Functionally Assembled Terrestrial Ecosystem Simulator (CLM-FATES). We perturbed over 300 parameters and quantified their effects on biophysical fluxes globally and across biomes. Most parameters have minimal impact on biophysical fluxes, with only a few substantially influencing results. While both models exhibit similar parameter sensitivity for some fluxes, CLM-FATES shows larger spread in gross primary productivity (GPP), driven by strong sensitivity to carboxylation rate. CLM-FATES also shows a weaker GPP response to soil hydrology parameters and exhibits higher water use efficiency (WUE). Cross-model comparisons reveal similar sensitivities for some parameters (e.g., leaf dimension) but divergent responses to others (e.g., stomatal intercept), highlighting underlying structural differences. Differences in WUE and sensitivity to hydrology and stomatal conductance parameters underscore how model structure fundamentally alters parametric sensitivity. The data sets generated from these ensembles can be used to identify influential parameters and guide future calibration efforts.

Foster, A. C. [NSF National Center for Atmospheric↗
DOE OSTI2026

Hyporheic‐Zone Processes and Stream Oxygen Dynamics: Insights From a Multiscale Reactive Transport Model

Aquatic ecosystem metabolism encapsulates the daily fixation (gross primary production, GPP d ) and mineralization (ecosystem respiration, ER d ) of organic carbon. In fluvial systems, these are commonly estimated by inverse solutions to field observations using a model that describes oxygen concentrations varying in the water column in response to metabolic fluxes and air‐water gas exchange controlled by a rate coefficient (K 600 ). The most common conceptual model is the single‐station metabolism (SSM) model. The simplicity and flexibility of this conceptualization make it attractive; however, it implicitly assumes that all the processes that consume oxygen in fluvial systems can be lumped into a bulk estimate of respiration with poorly understood consequences for estimates of GPP d , ER d , and K 600 . Here, we focus on the implications of using SSM conceptualization when estimating metabolic fluxes from oxygen dynamics in channels where hyporheic exchange occurs. We use a new multiscale numerical model for reactive transport in streams that represents hyporheic exchange and streambed heterotrophic respiration. Nondimensionalization of this model reveals dimensionless groups that collectively control oxygen dynamics. Numerical experiments offer a mechanistic understanding of the impacts of hyporheic exchange on diel oxygen dynamics revealing that potential biases arise from neglecting mass transfer limitations. Specifically, we found that hyporheic exchange significantly affects diel oxygen dynamics, even for nonreactive streambed sediments. Moreover, while the SSM performs well in many situations, we find conditions where significant bias is produced by hyporheic exchange, even when oxygen data are well‐fitted. These situations pose a major challenge in the interpretation of metabolism assessment estimates.

Gomez‐Velez, Jesus D. [Oak Ridge National Laborato↗
DOE OSTI2023

Increased photosynthesis during spring drought in energy-limited ecosystems

Abstract Drought is often thought to reduce ecosystem photosynthesis. However, theory suggests there is potential for increased photosynthesis during meteorological drought, especially in energy-limited ecosystems. Here, we examine the response of photosynthesis (gross primary productivity, GPP) to meteorological drought across the water-energy limitation spectrum. We find a consistent increase in eddy covariance GPP during spring drought in energy-limited ecosystems (83% of the energy-limited sites). Half of spring GPP sensitivity to precipitation was predicted solely from the wetness index (R 2 = 0.47, p < 0.001), with weaker relationships in summer and fall. Our results suggest GPP increases during spring drought for 55% of vegetated Northern Hemisphere lands ( >30° N). We then compare these results to terrestrial biosphere model outputs and remote sensing products. In contrast to trends detected in eddy covariance data, model mean GPP always declined under spring precipitation deficits after controlling for air temperature and light availability. While remote sensing products captured the observed negative spring GPP sensitivity in energy-limited ecosystems, terrestrial biosphere models proved insufficiently sensitive to spring precipitation deficits.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Reduction of microbial diversity in grassland soil is driven by long-term climate warming

Anthropogenic climate change threatens ecosystem functioning. Soil biodiversity is essential for maintaining the health of terrestrial systems, but how climate change affects the richness and abundance of soil microbial communities remains unresolved. Here, we examined the effects of warming, altered precipitation and annual biomass removal on grassland soil bacterial, fungal and protistan communities over 7 years to determine how these representative climate changes impact microbial biodiversity and ecosystem functioning. We show that experimental warming and the concomitant reductions in soil moisture play a predominant role in shaping microbial biodiversity by decreasing the richness of bacteria (9.6%), fungi (14.5%) and protists (7.5%). Our results also show positive associations between microbial biodiversity and ecosystem functional processes, such as gross primary productivity and microbial biomass. We conclude that the detrimental effects of biodiversity loss might be more severe in a warmer world.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Terrestrial photosynthesis inferred from plant carbonyl sulfide uptake

Terrestrial photosynthesis, or gross primary production (GPP), is the largest carbon flux in the biosphere, but its global magnitude and spatiotemporal dynamics remain uncertain. The global annual mean GPP is historically thought to be around 120 PgC yr −1 , which is about 30–50 PgC yr −1 lower than GPP inferred from the oxygen-18 ( 18 O) isotope and soil respiration. This disparity is a source of uncertainty in predicting climate–carbon cycle feedbacks. Here, in this study, we infer GPP from carbonyl sulfide, an innovative tracer for CO 2 diffusion from ambient air to leaf chloroplasts through stomata and mesophyll layers. We demonstrate that explicitly representing mesophyll diffusion is important for accurately quantifying the spatiotemporal dynamics of carbonyl sulfide uptake by plants. From the estimate of carbonyl sulfide uptake by plants, we infer a global contemporary GPP of 157 (±8.5) PgC yr −1 , which is consistent with estimates from 18 O (150–175 PgC yr −1 ) and soil respiration ($149^{+29}_{-23}$) PgC yr −1 ), but with an improved confidence level. Our global GPP is higher than satellite optical observation-driven estimates (120–140 PgC yr –1 ) that are used for Earth system model benchmarking. This difference predominantly occurs in the pan-tropical rainforests and is corroborated by ground measurements, suggesting a more productive tropics than satellite-based GPP products indicated. As GPP is a primary determinant of terrestrial carbon sinks and may shape climate trajectories, our findings lay a physiological foundation on which the understanding and prediction of carbon–climate feedbacks can be advanced.

Lai, Jiameng↗
DOE OSTI2023

MetaFlux: Meta-learning global carbon fluxes from sparse spatiotemporal observations

We provide a global, long-term carbon flux dataset of gross primary production and ecosystem respiration generated using meta-learning, called MetaFlux. The idea behind meta-learning stems from the need to learn efficiently given sparse data by learning how to learn broad features across tasks to better infer other poorly sampled ones. Using meta-trained ensemble of deep models, we generate global carbon products on daily and monthly timescales at a 0.25-degree spatial resolution from 2001 to 2021, through a combination of reanalysis and remote-sensing products. Site-level validation finds that MetaFlux ensembles have lower validation error by 5–7% compared to their non-meta-trained counterparts. In addition, they are more robust to extreme observations, with 4–24% lower errors. We also checked for seasonality, interannual variability, and correlation to solar-induced fluorescence of the upscaled product and found that MetaFlux outperformed other machine-learning based carbon product, especially in the tropics and semi-arids by 10–40%. Overall, MetaFlux can be used to study a wide range of biogeochemical processes.

54 ENVIRONMENTAL SCIENCES↗