In this session, we aim at reviewing recent progress made with novel approaches of constraining ecosystem gross primary productivity, respiration and transpiration and at discussing their weaknesses and future steps required to reduce the uncertainty of present-day estimates. To this end, we are seeking contributions that use emerging constrains to improve the ability to quantify respiration and photosynthesis processes, transpiration and water use efficiency, from leaf to ecosystem and global scales.
Orals: Thu, 7 May, 16:15–18:00 | Room 2.95
The land carbon cycle-climate change (C4) feedback, which partially determines the future level of climate warming, depends on the temperature sensitivity of ecosystem respiration (ER). Most of Earth System Models (ESMs) predict a strong C4 feedback by using monotonic response functions, whereas recent empirical evidence strongly suggests that ER does not monotonically increase with temperature. Here we used a Data-Informed Ecosystem Respiration Model (DIERM) to estimate global ER, and found that ESMs in Coupled Model Intercomparison Project Phase 6 (CMIP6) CMIP 6 (i.e., Can-ESM5, CESM2-WACCM, CMCC-ESM2, MPI-ESM1-2, and Nor-ESM2) generally overestimate ER in places with high air temperatures (e.g., tropical and temperate regions). Moreover, the overestimation of ER by ESMs increases with the increasing air temperature under future climate scenarios. Compared with our data-driven approach, Can-ESM5, CESM2-WACCM, CMCC-ESM2, MPI-ESM1-2, and Nor-ESM2 over-estimated global ER by 98.7%, 45.0%, 31.5%, 51.4%, and 64.8%, respectively, under the SSP585 scenarios by 2100. Overall, this study highlights the importance of accounting for the unimodal (functions with one maximum) temperature response pattern on ER and suggests that current models do not accurately represent the response of ER to warming, which may contribute to the large uncertainty of projected warming in the future.
How to cite: Wang, S. and Niu, S.: Overestimating global ecosystem respiration by Earth System Models under future warming, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4634, https://doi.org/10.5194/egusphere-egu26-4634, 2026.
Plants take up carbon dioxide (CO2) through photosynthesis. How this will change with rising CO2 concentrations in the atmosphere will strongly determine future climate change. Yet, this is a process critically unconstrained at the global scale. An increase in the seasonal variations of atmospheric CO2 in recent decades indicates a positive trend in photosynthetic carbon uptake and a lengthening of the growing season in northern extra-tropical ecosystems. However, the biospheric characteristics behind these changes have not yet been fully explained.
We combined data‐driven seasonal cycles of plant productivity with carbon sinks across the range predicted by current biospheric process models to explain the seasonal variations of CO2 at high and low northern latitudes over the past 40 years. We find that increases in seasonal variations can only be explained by a larger gross primary productivity (GPP) of northern ecosystems than most current estimates, equivalent to (51 ± 2) Pg(C) a−1 around 2007, and by an increase of GPP about proportional (1.1 ± 0.3) to the increase in atmospheric CO2, also larger than most current estimates. Our results highlight the importance of the interplay between vegetation productivity and its seasonal variations, providing an improved constraint to estimate the future behaviour of the terrestrial carbon sink.
Reference
Cuntz M, Smith B, Canadell JG, Knauer J, and Haverd V (2025) Large and increasing biospheric productivity of northern ecosystems. Geophysical Research Letters, 52(14), e2025GL115983. https://doi.org/10.1029/2025gl115983
How to cite: Cuntz, M., Smith, B., Canadell, J. G., Knauer, J., and Haverd, V.: Large and Increasing Biospheric Productivity of Northern Ecosystems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5022, https://doi.org/10.5194/egusphere-egu26-5022, 2026.
Gross primary productivity (GPP) is the largest terrestrial carbon flux and is highly sensitive to global warming. Despite global warming, relationships of the optimum temperature of GPP and the maximum GPP rate remain uncertain. We investigated the drivers of maximum GPP trends during 2000-2019 using global observations of ground-based eddy-covariance and satellite-based sun-induced chlorophyll fluorescence. Although maximum GPP increased worldwide, its optimum temperature increased only in tropical and temperate regions, but remained unchanged globally, and in arid and cold regions. Thermal acclimation via shifting optimum temperature was constrained by atmospheric and soil dryness, explaining less than a fifth of the global rise in maximum GPP. In contrast, increasing maximum GPP trends were more strongly driven by stomatal regulation improving water-use efficiency (as determined by the stomatal slope at the ecosystem scale, G1) and canopy development (as determined by the leaf area index). These results challenge the expectation that thermal acclimation is essential for terrestrial carbon uptake, and reveal that dynamic plant physiological and structural trends are critical for improving carbon cycle predictions at the ecosystem to global scales.
How to cite: Grünzweig, J. and Xu, C.: Physiological and structural trends rather than photosynthetic optimum temperature explain the recent increase of terrestrial carbon uptake, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9048, https://doi.org/10.5194/egusphere-egu26-9048, 2026.
The P model is a parameter-sparse model for gross primary production (GPP) based on eco-evolutionary optimality principles. Here we describe a global implementation of the sub-daily P model, which separates the acclimation response of photosynthetic parameters to environmental variations (with an e-folding time scale of 15 days) from the rapid response of photosynthesis (with a time step of 30 minutes), together with a daily soil-moisture accounting scheme (SPLASHv1.0) and a semi-empirical response function describing how the influence of soil moisture on GPP varies systematically with climatic aridity. We assess the model’s ability to reproduce seasonal cycles of net ecosystem exchange (NEE), as inferred from spaceborne atmospheric CO2 measurements via the Global Carbon Assimilation System version 2 (GCAS2021). For simplicity, we assume net primary production (NPP) is a constant fraction of GPP, and constrain total annual heterotrophic respiration (RH) to match total annual NPP at each 0.5˚ grid cell. The response of RH to environmental variations is represented via a model that links RH to physical and biological processes involving oxygen transport and microbial activity, influenced by the soil water content and the temperature. This model mechanistically represents the nonlinear coupling of moisture and temperature dynamics, replacing the canonical “function times function” approach. The coupled model reproduces the observed spatial variation in amplitude and timing of NEE, with excellent agreement in extratropical regions. It also captures the interannual differences (over a time span of 10 years) in the seasonal cycle aggregated by the Fifth Assessment Report (AR5) geographic reference regions. In the tropics and some Southern regions, however, the large interannual variability in the inversion products results in a signal of the climatological seasonal cycle of NEE that is too small to assess model performance. Our results suggest that the temperature and moisture dependences of heterotrophic respiration, as well as primary production, are major controls of the seasonal cycle of NEE and that the observed global patterns in this cycle can be well captured by an extremely parameter-sparse model.
How to cite: Mengoli, G., Harrison, S. P., and Prentice, I. C.: A simple approach to apply an eco-evolutionary optimality model with a global climatological aridity function to predict the spatial and seasonal dynamics of net ecosystem exchange , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5848, https://doi.org/10.5194/egusphere-egu26-5848, 2026.
Gross primary productivity (GPP) represents the largest and one of the most dynamic components of the global carbon cycle, yet quantifying its magnitude and future trajectory remains a significant challenge due to the lack of direct measurements at scales beyond the leaf. Over the past 15 years, Solar-Induced chlorophyll Fluorescence (SIF) has emerged as a powerful photosynthetic tracer to bridge this gap, offering a unique opportunity to advance our predictive understanding of carbon-climate feedbacks and food security.
Technological advances have enabled the remote sensing of SIF from satellite platforms with unprecedented precision and resolution. When integrated with terrestrial biosphere models (TBMs), SIF provides a critical constraint for improving the parameterization of global photosynthesis and the coupled dynamics of the carbon and water cycles across both natural and managed ecosystems.
In this presentation, I will share our recent findings that empower SIF for multi-scale applications: (1) quantifying global GPP from tower networks to satellite over the globe, (2) enabling scalable crop yield predictions across diverse croppying systems management practices, and (3) partitioning net ecosystem exchange (NEE) and evapotranspiration (ET) across NEON sites spanning a wide range of hydroclimates and plant functional types. Central to these applications is the development of a Mechanistic Light Reaction (MLR) model that establishes a theoretical link between SIF and the actual electron transport rate. We demonstrate that this theory-informed approach significantly improves accuracy, scalability, and interpretability compared to conventional linear scaling and advanced machine learning algorithms, providing a robust framework for reducing uncertainties in ecosystem-scale flux estimates.
How to cite: Sun, Y., Luo, Z., Lee, S., and Kira, O.: Probing Global Photosynthesis for Food Security and Climate Mitigation: The Lens of Solar-Induced Chlorophyll Fluorescence (SIF), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11840, https://doi.org/10.5194/egusphere-egu26-11840, 2026.
Solar-induced chlorophyll fluorescence (SIF) is an electromagnetic signal emitted by photosynthetically active plants, serving as a proxy for photosynthesis. However, SIF measurements in tropical forests, which are vital carbon sinks, remain underexplored. This study conducted continuous multi-angle far-red SIF measurements in a tropical forest in Xishuangbanna, China, from July 2023 to August 2024, using a Multi-Fluo system. We retrieved SIF with four widely used algorithms, including three-band Fraunhofer Line Discrimination (3FLD), Band Shape Fitting (BSF), Spectral Fitting Method (SFM), and Singular Vector Decomposition (SVD). Results showed that BSF outperformed the others, with the strongest correlations with near-infrared radiance of vegetation (NIRvR) (R² = 0.89), absorbed photosynthetically active radiation (APAR) (R² = 0.86), and gross primary production (GPP) (R² = 0.66) at half-hourly scale. Furthermore, diurnal patterns of SIF, SIF yield and NIRvR-derived fluorescence yield (ΦF) were analyzed on cloudy and clear sky conditions. Interestingly, a hysteresis was observed in SIFBSF yield on sunny days. In addition, averaging data from 17 viewing azimuth angles (VAAs) could explain over 10% improvement for SIF-related relationships compared with single-angle results such as VAA of 0°. This study demonstrates the applicability of BSF for SIF retrieval in tropical forests and highlights the value of multi-angle measurements, providing foundational insights into understanding SIF dynamics in complex tropical ecosystems.
How to cite: Chen, M., Zhang, Z., Zhang, Y., Wu, L., and Wu, Y.: Multi-angle measurements of solar-induced chlorophyll fluorescence in tropical forest canopy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5289, https://doi.org/10.5194/egusphere-egu26-5289, 2026.
In recent years, solar-induced chlorophyll fluorescence (SIF) has emerged as a powerful indicator for observing terrestrial photosynthesis. However, most existing satellite-based SIF retrievals are characterized by relatively coarse spatial resolutions, typically at the kilometer scale or coarser. The Chinese Terrestrial Ecosystem Carbon Inventory Satellite, Goumang, launched in August 2022, addresses this limitation by carrying the SIF Imaging Spectrometer (SIFIS), the first spaceborne instrument specifically developed for global SIF observations. SIFIS offers both high spatial resolution (370 m × 800 m) and high spectral resolution (0.24 nm across 664–786 nm), while achieving SIF retrieval uncertainties (~0.48 mW m⁻² nm⁻¹ sr⁻¹) comparable to those of existing satellite SIF products. The radiance measurements and SIF retrievals from SIFIS were initially evaluated using airborne AisaIBIS observations. Furthermore, SIFIS-derived SIF exhibits strong spatial and temporal consistency with independent satellite SIF datasets, as well as high correlations with flux tower estimates of gross primary production (R² = 0.87). Overall, this novel SIF product provides new opportunities to investigate photosynthetic processes at fine spatial scales from space.
How to cite: Zhang, Z., Duveiller, G., Zhang, Y., Fu, A., Xu, J., Lin, J., and Zhang, X.: First Global Retrievals of Solar Induced Chlorophyll Fluorescence from the SIFIS Instrument onboard the Chinese Goumang Satellite, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5890, https://doi.org/10.5194/egusphere-egu26-5890, 2026.
The use of SIF to evaluate land surface models shows considerable promise to help constrain estimates of, and elucidate the processes that control Gross Primary Productivity (GPP) on large spatial scales. To use SIF effectively for this purpose, we argue that forward modelling of the observations from the land surface model – as opposed to, say, relying on empirical relationships with the modelled GPP – is desirable if we wish to understand structural deficiencies in the land surface model.
This presentation describes the prediction of SIF from JULES, the Joint UK Land Environment Simulator, which is the land surface scheme of the Hadley Centre climate models, and the UK Earth System Model (UKESM). We explain how we couple leaf-level SIF models to the biochemistry routines in JULES, and how we scale the emitted SIF to the canopy level using a vegetation radiative transfer scheme (L2SM) that is consistent with the physics inside JULES but also allows for radiative emissions within the canopy. The SIF scheme includes attenuation within the leaf, utilizing either modelled or observed leaf reflectance and transmittance spectra and can make predictions of the canopy leaving SIF at arbitrary wavelengths. Downregulation of fluorescence by water stress is also included.
We show results from JULES-SIF at regional and global scales, and make comparisons against TROPOSIF data. The results show generally good agreement and are sufficiently aligned with the observations that they are able to highlight areas where JULES is not correctly modelling the relevant environmental processes. Future directions for the JULES SIF module are explained, including accounting the directional component of the canopy leaving SIF.
How to cite: Quaife, T., Douglas, N., and McGuire, P.: Evaluation of JULES-SIF against TROPOMI data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9625, https://doi.org/10.5194/egusphere-egu26-9625, 2026.
High night temperatures (HNT) can depress the photosynthetic performance of the plants, with consequential reductions in crop yield. To quantify the impact of HNT on plant photosynthesis, understanding non-photochemical quenching (NPQ) behavior is crucial given its mechanistic link to the downregulation of photosynthesis. Yet, how HNT alters the dynamics of NPQ remains poorly understood. In this study, we used a controlled walk-in growth chamber with phenotyping equipment for whole plants to obtain NPQ and the quantum yield of photosynthesis (ΦPSII) images combined with imaging spectroscopy to investigate NPQ under controlled temperature conditions (pre-HNT and HNT). Tomato plants were consecutively exposed to 3-day phases with day/night temperatures of 35/20°C (pre-HNT, day 1-3), 35/28°C (HNT, day 4-6) and 35/20°C (recovery, day 7-9). HNT induced nocturnal NPQ elevation that persisted in the following day, resulting in consistently higher NPQ throughout the diurnal cycle compared to the pre-HNT (t-test, p<0.05). This carryover effect suggests a prolonged photoprotective state triggered by nighttime heat stress. Meanwhile, ΦPSII showed no nighttime difference among phases, but exhibited decreases near peak daytime temperatures. HNT further shifted the ΦPSII-fluorescence yield curve downward, resulting in lower fluorescence yield at similar ΦPSII values. A further objective was to monitor the change in NPQ through non-destructive image spectroscopy wherefore we employed partial least squares regression (PLSR) to estimate NPQ with using canopy reflectance (450-780 nm). Our PLSR results confirmed that NPQ can be estimated with an R2 of 0.93 based on canopy reflectance, and the predicted NPQ captured the HNT-induced increase during both night and day. From the variable importance in projection (VIP) analysis, we found that nighttime and daytime NPQ shared similar VIP peaks in green (500-600 nm) and red-edge (680-750 nm) region, indicating that consistent spectral features underlie NPQ dynamics regardless of light conditions. Our findings extend the understanding of how increased temperature activates NPQ dynamics and highlight that spectral reflectance contains informative signals for capturing temperature-driven photoprotective responses.
How to cite: Yeon, I., García-Martínez, C., Neuwirthová, E., Moncholi-Estornell, A., Cendrero-Mateo, M. P., Pescador-Dionisio, S., and Van Wittenberghe, S.: Quantifying the impact of high night temperature on NPQ dynamics using canopy reflectance, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15001, https://doi.org/10.5194/egusphere-egu26-15001, 2026.
Photosynthesis governs terrestrial carbon uptake and tightly couples carbon, energy and water exchange. However, any single observation has limited spatiotemporal coverage. Eddy-covariance CO2 exchange measurements, for instance, are still underrepresented in the tropics. At the global scale, model-based estimates of photosynthesis, often quantified as gross primary productivity (GPP), remain highly uncertain. Solar-induced chlorophyll fluorescence (SIF), carbonyl sulfide (COS or OCS), and carbon isotope discrimination (Δ¹³C) provide complementary windows into photosynthesis. They offer partially independent constraints on energy partitioning, conductance limitations, and diffusion–carboxylation controls. Breathing Earth System Simulator (BESS) is a remote-sensing-driven, process-based model that couples canopy carbon assimilation, evapotranspiration, and surface energy balance. Building on BESS, we (1) incorporate the Johnson–Berry model to provide a mechanistic yet parsimonious description of energy conversion within the electron transport system, enabling SIF simulation while accounting for photosynthetic control, cyclic electron flow, and non-photochemical quenching; (2) couple OCS exchange to BESS through shared conductance pathways (stomatal and boundary-layer) and biochemical capacity (Vcmax25℃), and implement an explicit mesophyll conductance scheme so that net CO₂ assimilation is computed from chloroplastic CO₂ concentration (Cc); (3) integrate a ¹³C discrimination module that mechanistically estimates Δ¹³C along the explicitly simulated CO₂ diffusion pathway from the atmosphere to the chloroplast, accounting for fractionation during boundary layer, stomatal, and mesophyll diffusion, as well as Rubisco carboxylation. By coupling SIF, OCS exchange, and Δ¹³C within a shared canopy gas-exchange and energy-balance framework, BESS is extended into a multi-tracer forward framework that generates internally consistent predictions of these tracers together with carbon-water fluxes. Based on this framework, we aim to: (1) evaluate whether multi-tracer integration improves simulations of carbon-water fluxes; (2) explore multi-constraint parameter optimization or data assimilation using independent observations to reduce uncertainty in photosynthesis estimates; and (3) quantify relationships between tracer signals and fluxes (e.g., GPP–SIF, GPP–OCS, SIF–OCS, Δ¹³C–GPP) and their responses to environmental variability.
How to cite: Zhang, H., Feng, H., Son, M., Ryu, Y., Berry, J., and Johnson, J.: Towards multi-tracer constraints on photosynthesis: unifying solar-induced chlorophyll fluorescence, carbonyl sulfide flux and carbon isotope discrimination in BESS framework, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15310, https://doi.org/10.5194/egusphere-egu26-15310, 2026.
Posters on site: Fri, 8 May, 14:00–15:45 | Hall X1
Global warming, primarily caused by rising levels of carbon dioxide (CO₂) concentration in the atmosphere, poses a severe threat to the Earth. Perhaps the most effective and important strategy is achieving carbon neutrality. While a reduction in carbon emissions is a go-to approach for achieving carbon neutrality, enhancing or conserving carbon uptake or sequestration through land vegetation—via photosynthesis, commonly known as gross primary production (GPP)—is equally crucial in reducing atmospheric CO₂ concentrations and mitigating global warming. Beyond direct and indirect human interventions, environmental drivers such as temperature, rainfall, vapor pressure deficit (VPD), solar radiation, and soil moisture (SM) significantly influence vegetation dynamics, influencing carbon sequestration. Therefore, identifying the interactions among various environmental drivers and GPP is vital for enhancing the accuracy of carbon budget estimations and refining climate-carbon feedback models to better project land-based carbon sinks under climate change. Despite this fact, there are very limited studies, especially in India, to investigate these relationships. Consequently, to ascertain the links between environmental drivers and GPP, we have adopted the data-driven causal technique rather than the conventional correlation approach. The results for average conditions in India indicate that root-zone SM has a strong connection with GPP, while rainfall presents a weaker connection with a larger lag than SM. It emphasizes the significance of irrigation in India's vegetation, as the country's land is predominantly occupied by shallow-rooted crops. Mean VPD demonstrates a moderate influence; in contrast, both mean temperature and net solar radiation show weaker effects, with almost equal lag time. The overall findings reveal the varying influences of different environmental variables on the GPP, offering crucial insights for improved regional land-atmosphere modeling to better replicate the carbon balance, thereby reducing the uncertainty associated with current estimates.
Keywords: Causal inference, climate-vegetation interactions, gross primary production (GPP)
How to cite: Babu, A. and Dhanya, C. T.: Understanding Time-Lagged Causal Effects of Environmental Drivers on Vegetation Carbon Uptake, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-430, https://doi.org/10.5194/egusphere-egu26-430, 2026.
The Arctic is warming approximately four times faster than the global average, driving rapid transformations across Arctic and Boreal ecosystems. Among these, modifications in vegetation structure, shrubbification, and changes in photosynthetic activity are particularly relevant, as vegetation strongly influences ecosystem–atmosphere carbon exchange. A warmer and increasingly CO₂-rich environment has stimulated photosynthetic activity and widespread greening; however, the persistence of these responses under continued climate warming remains uncertain.
In the ArcticSIF project, we investigate how tundra vegetation landscapes have evolved over the past two decades by examining multiple datasets of solar-induced chlorophyll fluorescence (SIF), gross primary productivity (GPP), ecosystem respiration (ER), and net CO₂ ecosystem exchange (NEE). Assessing the dynamics of these complementary records with meteorological datasets, we evaluate how carbon uptake and emission have shifted across different Arctic tundra regions during the growing season and to what extent SIF records support such an observation.
Our results show that in the circumpolar region, tundra landscapes exhibit a higher sensitivity to variations in air temperature compared to boreal ecosystems, with pronounced NEE shifts in graminoid and prostrate-shrub tundra environments modulated by growing season length. This study contributes to determining the speed at which some Arctic tundra ecosystems may shift from carbon sinks to carbon sources during the growing season in the context of Arctic warming and its influence on high-latitude carbon dynamics.
How to cite: Sabater, N., Kivimäki, E., Lipponen, A., Van Wittenberghe, S., Kolmonen, P., Thum, T., and Arola, A.: Chlorophyll fluorescence–based assessment of carbon sink–source shifts in Arctic tundra during the growing season, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2236, https://doi.org/10.5194/egusphere-egu26-2236, 2026.
Australia’s Mediterranean ecosystems are among the most climate-variable on Earth, experiencing recurrent droughts and heatwaves that strongly regulate carbon uptake and water use. Long-term eddy covariance measurements from Australia’s Terrestrial Ecosystem Research Network (TERN) and OzFlux provide critical observations of net ecosystem exchange (NEE) from several Mediterranean woodlands, yet major uncertainties remain in partitioning these fluxes into gross primary productivity (GPP) and ecosystem respiration (ER), and in separating transpiration from total evapotranspiration. Improving these estimates is essential for understanding how Mediterranean ecosystems respond to climate extremes and for constraining regional contributions to the global carbon and water cycles.
As part of TERN activities in Western Australia, we have deployed additional research infrastructure to improve and constrain ecosystem photosynthesis, respiration and transpiration measurements over several endemic Mediterranean woodland ecosystems. Instrumentation includes fixed terrestrial laser scanners to quantify daily changes in canopy structure, hyperspectral sensors to derive vegetation indices and measure sun-induced chlorophyll fluorescence (SIF), and distributed quantum sensor nodes to characterise within-canopy light absorption and scattering. Together, these measurements provide direct information on canopy architecture, photosynthetic activity, and radiation use efficiency – all of which are key drivers of carbon and water cycling.
We demonstrate how these novel observations improve interpretation of eddy covariance fluxes at the Boyagin wandoo woodland TERN site, as well as enhance constraints on photosynthetic dynamics during periods of heat and water stress. This work highlights the value of integrating proximal sensing with flux measurements to reduce uncertainty in ecosystem carbon and water fluxes and to strengthen links between ground-based observations and satellite-based products.
How to cite: Moore, C., Zandberg, J., Prajapati, P., Woodgate, W., and Beringer, J.: Multi-scale observations of carbon and water fluxes from a Mediterranean woodland ecosystem, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8895, https://doi.org/10.5194/egusphere-egu26-8895, 2026.
Understanding CO2 plant exchange is essential for quantifying its role in the global carbon cycle, predicting ecosystem responses to environmental change, and evaluating long-term growth under varying environmental conditions across several types of photosynthesis[1,2]. Plants exchange carbon dioxide with the atmosphere through three primary physiological processes: photosynthesis, which assimilates CO₂ during daylight to produce glucose and release O2 as a byproduct; photorespiration, a light-dependent process that recycles harmful byproducts of photosynthesis while releasing excess energy and CO2; mitochondrial respiration, which releases CO₂ and consume O2 to produce energy, occurring both day and night. These CO₂ fluxes are coupled with transpiration that facilitates the loss of water vapor from leaves through stomata[1]. These processes can be accurately quantified using gas-exchange techniques, in which a gas analyzer measures the exchange of CO₂ and H₂O between leaves and the atmosphere.
In this study, we employed a self-calibrated, optical sensor based on tunable diode laser spectroscopy to monitor plant CO₂ exchange in real time. The sensor consists of a quantum cascade laser emitting at 4.234 μm as the light source and a photodetector to measure CO2 absorption along an open optical path of 10 cm. Measurements were performed using an amplitude modulation approach with first-harmonic detection at 10 kHz, employing a phase-sensitive lock-in amplifier. The optical sensor was placed inside a transparent plexiglass enclosure (525x375x300 mm3) containing a plant to monitor CO₂ exchange with the surrounding environment. A temperature and humidity sensor was also installed inside the enclosure, while a non-dispersive infrared CO₂ sensor (SEFRAM 9825) outside the enclosure was used to track ambient CO₂, temperature, and humidity. Continuous measurements were performed over approximately 20 days, covering both daytime and nighttime periods outside the laboratory, in a dedicated open area to minimize disturbances from nearby activity. Measured CO₂ concentrations inside the enclosure reflected both plant exchange and diffusive transport driven by the concentration gradient with the external environment. A differential equation model accounting for these processes was developed and applied to the experimental data to quantitatively determine the plant’s net CO₂ exchange rate.
References
- Niu, Z., Ye, Z. W. Y., Huang, Q., Peng, C. & Kang, H. Accuracy of photorespiration and mitochondrial respiration in the light fitted by CO2 response model for photosynthesis. Front. Plant Sci. 16, 1455533 (2025).
- Busch, F. A., Ainsworth, E. A., Amtmann, A., Cavanagh, A. P., Driever, S. M., et al. A guide to photosynthetic gas exchange measurements: Fundamental principles, best practice and potential pitfalls. Plant Cell Environ. 47, 3344–3364 (2024).
How to cite: Olivieri, M., Zifarelli, A., Sampaolo, A., Spagnolo, V., and Patimisco, P.: Real-time monitoring of plant CO2 exchange using a direct absorption-based optical sensor, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18455, https://doi.org/10.5194/egusphere-egu26-18455, 2026.
Tree growth and water use are fundamental indicators of forest ecosystem functioning and are expected to respond differently to ongoing climate change. We analysed continuous stem radial growth and sap flow data from long-term monitoring sites at Mt. Taehwa and the Gwangneung forest in Korea, focusing on Pinus koraiensis and Quercus spp. stands at Mt. Taehwa during the period 2013–2024. Stem diameter growth was measured using custom dendrobands. To identify both short-term and carry-over climatic controls, annual transpiration and radial growth were related to air temperature, photosynthetically active radiation (PAR), and precipitation using a multi-window framework that distinguished current-year from previous-year climate effects. For P. koraiensis, annual transpiration showed a strong positive relationship with early-summer precipitation, indicating a direct water-supply control on water use. In contrast, transpiration in the oak stand was only weakly related to precipitation. Despite these contrasting transpiration responses, stem radial growth of both pine and oak species exhibited pronounced sensitivity to antecedent-year climate, demonstrating substantial carry-over effects. These results reveal a temporal decoupling between transpiration and growth and highlight the importance of climate memory in regulating stem growth across temperate forest types, providing new insights into forest vulnerability under increasing hydroclimatic variability.
How to cite: Lee, M., Lee, H., Hong, J., and Kim, H. S.: Species-specific coupling of transpiration and radial growth to climate in temperate forests of Korea, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20622, https://doi.org/10.5194/egusphere-egu26-20622, 2026.
The representation of gross primary production (GPP) in land surface models remains highly uncertain, despite GPP being a key driving component of the terrestrial carbon cycle (Gier et al., 2024). These uncertainties mainly arise from both the lack of direct measurements of GPP above the leaf scale and an incomplete representation of plant physiological processes (in terms of both parameter values and equations), in particular the links between carbon assimilation and nutrient availability.
Solar-induced chlorophyll fluorescence (SIF) has therefore emerged as a proxy of photosynthetic activity and of GPP by terrestrial ecosystems (Li et al., 2018). To further constrain parameters controlling photosynthetic activity, satellite-based SIF observations can be assimilated (from the TROPOSIF product, and, in the near future, the FLEX fluorescence product), as SIF provides information on plant physiological traits that regulate photosynthetic activity and GPP.
A fluorescence module previously developed for ORCHIDEE (Bacour et al., 2019) enables the simulation and assimilation of SIF observations. The ORCHIDEE-N land surface model now includes an explicit representation of the nitrogen cycle (Vuichard et al., 2019), allowing a more mechanistic description of photosynthesis through nitrogen limitations on key leaf traits controlling GPP, such as chlorophyll and Rubisco contents.
Integrating the fluorescence module into a model that explicitly represents leaf nitrogen limitation is expected to improve the simulation of both SIF and GPP by providing a more realistic description of chlorophyll content and photosynthetic capacity. In this study, an updated fluorescence module is implemented in ORCHIDEE-N to consistently link nitrogen availability, SIF, and photosynthetic activity.
We present a first intercomparison of these two model versions (with and without the nitrogen cycle) based on the seasonal cycles of GPP and SIF at seven observational sites in Europe. These sites are drawn from the AustroSIF database (Martini et al., in prep.), which integrates in situ measurements of eddy-covariance fluxes (used to estimate GPP), SIF, and pulse-amplitude modulated fluorescence measurements.
So far, neither the fluorescence model parameters nor those of the nitrogen-explicit module have been optimised in this new version. This preliminary study paves the way for assimilating both site-level data and satellite-derived SIF retrievals to further constrain the model.
Bacour, C., Maignan, F., et al. (2019). Improving estimates of gross primary productivity by assimilating solar‐induced fluorescence satellite retrievals in a terrestrial biosphere model using a process‐based SIF model. Journal of Geophysical Research: Biogeosciences, 124(11), 3281-3306.
Gier, B. K., et al. (2024). Representation of the terrestrial carbon cycle in CMIP6. Biogeosciences, 21(22), 5321-5360.
Li, X., et al. (2018). Solar‐induced chlorophyll fluorescence is strongly correlated with terrestrial photosynthesis for a wide variety of biomes: First global analysis based on OCO‐2 and flux tower observations. Global change biology, 24(9), 3990-4008.
Martini, D., et al., (in prep.). AustroSIF — A compilation of combined passive and active fluorescence data at flux tower sites across Europe.
Vuichard, N., et al. (2019). Accounting for carbon and nitrogen interactions in the global terrestrial ecosystem model ORCHIDEE (trunk version, rev 4999): Multi-scale evaluation of gross primary production. Geoscientific Model Development, 12(11), 4751-4779.
How to cite: Tuffery, L., Bacour, C., Martini, D., Wohlfahrt, G., Vuichard, N., Tartaglione, V., Viovy, N., and Maignan, F.: Assessing the impact of an explicit representation of the nitrogen cycle on SIF and GPP dynamics across European sites, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21999, https://doi.org/10.5194/egusphere-egu26-21999, 2026.
