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Among the natural ecosystems, forests and wetlands emit a sizable amount of carbon dioxide (CO2) and methane (CH4) through autotrophic and heterotrophic respiration and bacterial activities. Interestingly, some evidence suggests that a significant amount of CH4 is generated by the trees in forested ecosystems. The net ecosystem exchange (NEE), measured by the eddy covariance (EC) method, typically represents the net CO2 flux arising from the photosynthetic and respiration processes in the biosphere. This flux is subsequently partitioned into two components, the respired carbon and the assimilated carbon. However, the usual method of partitioning introduces significant errors in each of these fluxes. The present study was undertaken to address this issue where the NEE partitioning was constrained by using the carbon isotopic ratios of CO2. We used a real‐time in situ analyzer in a tropical forest in northeast India, the Kaziranga National Park. The greenhouse gas analyzer provided CO2 and CH4 concentrations, as well as their carbon isotopic ratios. The isotopic data were used to partition the EC‐derived NEE values and derive the isoflux values. Additionally, the isotopic data provided evidence of plant‐generated CH4 in conformity with some recent studies, which requires further investigation. Plain Language Summary The terrestrial vegetation absorbs a vast amount of carbon in the photosynthetic process. They also emit a sizeable amount of carbon through respiration. Eddy‐covariance‐based technique is often employed to quantify these two opposing fluxes. An eddy‐covariance‐based system measures carbon dioxide (CO2) concentrations along with wind speed, and covariance of their fluctuations is used to calculate the net ecosystem exchange (NEE). The NEE is then partitioned into the photosynthetic and respiration components through a series of numerical operations and environmental constraints. Flux measurements in association with carbon isotopic ratios can provide a unique way to partition NEE. We have measured the concentration and isotopic ratio of CO2 and the eddy covariance‐based measurements to obtain the respiratory and photosynthetic fluxes of CO2 in a tropical forest in India. In addition, methane (CH4) concentration and its isotopic ratio have been measured to identify if trees can be considered a CH4 source in this ecosystem. Key Points The net ecosystem exchange was measured using an eddy‐covariance technique in a forest in India Separately the greenhouse gases isotopic data were collected and used to determine the “isofluxes” Partitioning of the respiratory and the photosynthetic fluxes was carried out

The exchange of momentum, heat and trace gases between atmosphere and surface is mainly controlled by turbulent fluxes. Turbulent mixing is usually parametrized using Monin–Obukhov similarity theory (MOST), which was derived for steady turbulence over homogeneous and flat surfaces, but is nevertheless routinely applied to unsteady turbulence over non-homogeneous surfaces. We study four years of eddy-covariance measurements at a highly heterogeneous alpine valley site in Finse, Norway, to gain insights into the validity of MOST, the turbulent transport mechanisms and the contributing coherent structures. The site exhibits a bimodal topography-following flux footprint, with the two dominant wind sectors characterized by organized and strongly negative momentum flux, but different anisotropy and contributions of submeso-scale motions, leading to a failure of eddy-diffusivity closures and different transfer efficiencies for different scalars. The quadrant analysis of the momentum flux reveals that under stable conditions sweeps transport more momentum than the more frequently occurring ejections, while the opposite is observed under unstable stratification. From quadrant analysis, we derive the ratio of the amount of disorganized to organized structures, that we refer to as organization ratio (OR). We find an invertible relation between transfer efficiency and corresponding organization ratio with an algebraic sigmoid function. The organization ratio further explains the scatter around scaling functions used in MOST and thus indicates that coherent structures modify MOST. Our results highlight the critical role of coherent structures in turbulent transport in heterogeneous tundra environments and may help to find new parametrizations for numerical weather prediction or climate models.

We investigate the components of radiation and energy balance and heat storage flux in an urban area of Central Amazonia, during the wet and dry seasons of 2022. Detailed radiation and turbulent energy fluxes measurements were conducted using a 30-meter micrometeorological tower. The analyses include an assessment of the energy balance closure, incorporating the urban canopy heat storage term. The main findings were: (i) A footprint analysis showed that during the wet season, the primary energy flux sources were from the impermeable surfaces, while in the dry season, in addition to impermeable surfaces, green areas also influenced the fluxes; (ii) Incoming shortwave radiation was significantly higher during the dry season; (iii) Albedo was higher in dry season compared to the wet season; (iv) Latent heat flux showed low sensitivity to seasonal variability, compared to sensible heat flux; (v) Energy balance closure significantly improved with the inclusion of urban canopy heat storage and soil heat flux, highlighting their critical roles in reducing energy imbalances. The measurements presented in this study are the first Eddy Covariance measurements for an urban region of the Amazon. These results are important for urban climate modeling in tropical regions, providing insights into the impacts of urbanization in the Amazon region.

Coastal wetlands are increasingly vital carbon sinks, helping mitigate atmospheric CO2 and slow global warming. However, we have limited knowledge about the carbon sink capacity of coastal wetlands, whereby developing advanced skills for predicting CO2 fluxes of coastal wetlands is critical. Here, by employing recent cutting-edge achievements in artificial intelligence, we evaluated three automated machine learning (AutoML) platforms, including Lazy Predict, H2O AutoML and fast and lightweight automated machine learning, for predicting monthly gross primary production (GPP), ecosystem respiration (RE), and net ecosystem exchange (NEE) in China’s of mangrove and saltmarsh coastal wetlands with multi-source satellite observations. Our results indicate that these AutoML platforms effectively predicted GPP, RE, and NEE, with superior performance for GPP and RE compared to NEE. For individual predictions across 14 sites, the testing set yielded average determination coefficient (R2) values of 0.74, 0.79, and 0.63, and root mean square error values of 0.83, 0.45, and 0.76 gC m−2s−1 for GPP, RE, and NEE, respectively. Cross-site predictions performed better for saltmarsh (average R2: 0.86, 0.84, and 0.76 for GPP, RE, and NEE) than mangrove ecosystems (average R2: 0.72, 0.76, and 0.59). In addition, ensemble ML models, particularly on the Lazy Predict platform, significantly outperformed individual models. Feature important analyses revealed that vegetation variables (leaf area index and fraction of absorbed photosynthetically active radiation) play pronouncedly important roles in mangrove ecosystems, followed by climate variables (air temperature (Ta) and precipitation) with considerably important roles, while Ta dominated in saltmarsh ecosystems, with vegetation variables but playing a lesser role. Our study offers valuable insights for utilizing AutoML techniques to enhance CO2 flux predictions and regional budget estimations for coastal wetlands, potentially advancing strategies for monitoring large-scale coastal ‘blue carbon’ dynamics.

The Qinghai–Tibet Plateau (QTP) plays a crucial role in the terrestrial carbon cycle, but the gross primary productivity (GPP) estimates for the region remain highly uncertain due to limited flux observations and modeling challenges. Here, we integrated 65.2 site years of eddy covariance data from 19 flux sites with multi-source remote sensing observations to develop a data driven GPP model for the QTP. Eleven machine learning algorithms from two automated machine learning (AutoML) platforms, H2O AutoML and FLAML, were evaluated to construct an ensemble model named AutoML. The model showed strong performance at site-level across alpine meadow, steppe, wetland, and shrub ecosystems, achieving R2 up to 0.95 and RMSE as low as 0.42 g C m−2 d−1. By validating extracted site-level GPP values from the upscaling GPP datasets against with flux observations, AutoML-GPP demonstrates overall superior or equivalent performance over global GPP products (FLUXCOM X-base, GOSIF, and FluxSat). Regional upscaling estimated a mean annual total GPP of 374.20 Tg C yr−1 from 2002 to 2018, with a slight upward trend of 0.08 Tg C yr−1. Spatially, higher GPP occurred mainly in the eastern QTP, with anomalies linked to climate extremes in 2008, 2010, and 2015. AutoML-GPP effectively captures climate-induced interannual anomalies in the QTP’s GPP, coinciding with GOSIF-GPP and FluxSat GPP, and outperforming the recent released well-known global upscaling flux dataset FLUXCOM X-base. This study provides improved GPP estimation for the QTP, offering new insights into carbon cycling and climate–vegetation interactions.

Warming-induced carbon loss via ecosystem respiration ( R e ) is probably intensifying in the alpine grassland ecosystem of the Tibetan Plateau owing to more accelerated warming and the higher temperature sensitivity of R e ( Q 10 ). However-little is known about the patterns and controlling factors of Q 10 on the plateau, impeding the comprehension of the intensity of terrestrial carbon–climate feedbacks for these sensitive and vulnerable ecosystems. Here, we synthesized and analyzed multiyear observations from 14 sites to systematically compare the spatiotemporal variations of Q 10 values in diverse climate zones and ecosystems, and further explore the relationships between Q 10 and environmental factors. Moreover, structural equation modeling was utilized to identify the direct and indirect factors predicting Q 10 values during the annual, growing, and non-growing seasons. The results indicated that the estimated Q 10 values were strongly dependent on temperature, generally, with the average Q 10 during different time periods increasing with air temperature and soil temperature at different measurement depths (5 cm, 10 cm, 20 cm). The Q 10 values differentiated among ecosystems and climatic zones, with warming-induced Q 10 declines being stronger in colder regions than elsewhere based on spatial patterns. NDVI was the most cardinal factor in predicting annual Q 10 values, significantly and positively correlated with Q 10 . Soil temperature ( T s ) was identified as the other powerful predictor for Q 10 , and the negative Q 10 – T s relationship demonstrates a larger terrestrial carbon loss potentiality in colder than in warmer regions in response to global warming. Note that the interpretations of the effect of soil moisture on Q 10 were complicated, reflected in a significant positive relationship between Q 10 and soil moisture during the growing season and a strong quadratic correlation between the two during the annual and non-growing season. These findings are conducive to improving our understanding of alpine grassland ecosystem carbon–climate feedbacks under warming climates.

Understanding the relationships between climate and carbon exchange by terrestrial ecosystems is critical to predict future levels of atmospheric carbon dioxide because of the potential accelerating effects of positive climate–carbon cycle feedbacks. However, directly observed relationships between climate and terrestrial CO2 exchange with the atmosphere across biomes and continents are lacking. Here we present data describing the relationships between net ecosystem exchange of carbon (NEE) and climate factors as measured using the eddy covariance method at 125 unique sites in various ecosystems over six continents with a total of 559 site-years. We find that NEE observed at eddy covariance sites is (1)a strong function of mean annual temperature at mid-and high-latitudes, (2)a strong function of dryness at mid-and low-latitudes, and (3)a function of both temperature and dryness around the mid-latitudinal belt (45°N). The sensitivity of NEE to mean annual temperature breaks down at ∼ 16 °C (a threshold value of mean annual temperature), above which no further increase of CO2 uptake with temperature was observed and dryness influence overrules temperature influence.

As an important carbon sink to mitigate global climate change, the role of arid and semiarid grassland ecosystem has been widely reported. Precipitation and temperature changes have a dramatic impact on the carbon balance. However, the study of wind speed has long been neglected. Intuitively, wind speed regulates the carbon balance of grassland ecosystems by affecting the opening of vegetation stomata as well as near-surface moisture and temperature. It is sufficient that there is a need to conduct field observations to explore the effect of wind speed on the carbon balance in arid and semiarid grassland. Therefore, we conducted observations of carbon fluxes and corresponding climate factors using an eddy covariance system in a typical steppe in Inner Mongolia from 2017 to 2021. The research contents include that, (i) we depicted the changing patterns of carbon fluxes and climate factors at multiple time scales; (ii) we simulated the net ecosystem carbon balance (NECB) based rectangular hyperbolic model and compared it with the observed net ecosystem exchange values; (iii) we quantified the mediated effect of wind speed on NECB by adopting structural equation modeling; (iv) we used the constrained line method to explore what wind speed intervals might have the greatest carbon sequestration capacity of vegetation. The results were as follows, (i) the values of NECB for the five years of the study period were 101.95, −48.21, −52.57, −67.78, and −30.00 g C m −2 yr −1 , respectively; (ii) if we exclude the inorganic carbon component of the ecosystem, we would underestimate the annual carbon balance by 41.25, 2.36, 20.59, 22.06 and 43.94 g C m −2 yr −1 ; (iii) the daytime wind speed during the growing season mainly influenced the NECB of the ecosystem by regulating soil temperature and vapor pressure deficit, with a contribution rate as high as 0.41; (iv) the grassland ecosystem had the most robust carbon sequestration capacity of 4.75 μ mol m −2 s −1 when the wind speed was 2–3 m s −1 . This study demonstrated the significant implications of wind speed variations on grassland ecosystems.

We describe pragmatic and reliable methods to examine the influence of patch-scale heterogeneities on the uncertainty in long-term eddy-covariance (EC) carbon flux data and to scale between the carbon flux estimates derived from land surface optical remote sensing and directly derived from EC flux measurements on the basis of the assessment of footprint climatology. Three different aged Douglas-fir stands with EC flux towers located on Vancouver Island and part of the Fluxnet Canada Research Network were selected. Monthly, annual and interannual footprint climatologies, unweighted or weighted by carbon fluxes, were produced by a simple model based on an analytical solution of the Eulerian advection-diffusion equation. The dimensions and orientation of the flux footprint depended on the height of the measurement, surface roughness length, wind speed and direction, and atmospheric stability. The weighted footprint climatology varied with the different carbon flux components and was asymmetrically distributed around the tower, and its size and spatial structure significantly varied monthly, seasonally and inter-annually. Gross primary productivity (GPP) maps at 10-m resolution were produced using a tower-mounted multi-angular spectroradiometer, combined with the canopy structural information derived from airborne laser scanning (Lidar) data. The horizontal arrays of footprint climatology were superimposed on the 10-m-resolution GPP maps. Monthly and annual uncertainties in EC flux caused by variations in footprint climatology of the 59-year-old Douglas-fir stand were estimated to be approximately 15-20% based on a comparison of GPP estimates derived from EC and remote sensing measurements, and on sensor location bias analysis. The footprint-variation-induced uncertainty in long-term EC flux measurements was mainly dependent on the site spatial heterogeneity. The bias in carbon flux estimates using spatially-explicit ecological models or tower-based remote sensing at finer scales can be estimated by comparing the footprint-weighted and EC-derived flux estimates. This bias is useful for model parameter optimizing. The optimization of parameters in remote-sensing algorithms or ecosystem models using satellite data will, in turn, increase the accuracy in the upscaled regional carbon flux estimation.

This study examines the seasonal and diurnal variations of carbon dioxide and energy fluxes over three land cover types of Nepal by using the eddy covariance method from March to November 2016. The surface energy balance closures were moderate with the values of about 56%, 61%, and 64% closure at Kirtipur, Simara, and Tarahara sites respectively. The monthly average values of net radiation flux and latent heat flux peaked in August at Kirtipur and Tarahara sites whereas in June at the Simara site respectively. The maximum monthly average measured sensible heat flux was 37 W m−2, 43.6 W m−2, and 36.3 W m−2 in April for all the sites whereas soil heat flux was 5.1 W m−2 and 2.9 W m−2 in April for Kirtipur and Simara sites and 6.2 W m−2 in June for the Tarahara site. The magnitude of diurnal peak of net ecosystem CO2 exchange (NEE) reached up to 11.04 μmol m−2 s−1 at Kirtipur, 15.04 μmol m−2 s−1 at Simara, and 10.44 μmol m−2 s−1 at Tarahara sites respectively. Among the three study sites, the ecosystem at the Kirtipur site was a good carbon source; the ecosystems at Simara and Tarahara sites were low and good carbon sink in the growing season. In addition, all three different land cover ecosystem were carbon source when accounted for the measurement period.