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 (R_H) to match total annual NPP at each 0.5˚ grid cell. The response of R_H to environmental variations is represented via a model that links R_H 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 SIF_BSF 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 (Φ_PSII) 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, Φ_PSII showed no nighttime difference among phases, but exhibited decreases near peak daytime temperatures. HNT further shifted the Φ_PSII-fluorescence yield curve downward, resulting in lower fluorescence yield at similar Φ_PSII 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 (V_cmax25℃), 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.