This session will bring together progress in observations, modeling, and applications to advance understanding of climate-ecosystems-atmospheric chemistry interactions. Topics include, but are not limited to:
• Observations: Integrating multi-source data (e.g., field measurements, remote sensing, flux networks, atmospheric monitoring) with controlled experiments (e.g., FACE, warming experiments) to identify key interactions;
• Modeling: Improving the multi-scale representations in Earth system models, developing high-resolution regional coupled models, and optimizing parameterizations of key processes such as biogenic emissions and biogeochemical feedbacks;
• Applications: Assessing how interactions between atmospheric aerosols/pollutants and ecosystems influence climate change mitigation and carbon neutrality goals, and quantifying the strength of the ecosystem-climate feedbacks to air quality under different scenarios, and evaluating the effectiveness and risks of aerosol/cloud-based geoengineering.
By addressing these topics, the session aims to advance understanding of coupled climate–ecosystem–chemistry processes, enhance predictive capability, and provide a stronger scientific foundation for climate adaptation and sustainable development.
Clouds and aerosols influence land carbon uptake by modifying the quantity and quality of light (direct vs diffuse). Diffuse light tends to enhance canopy photosynthesis and vegetation carbon uptake via the diffuse radiation fertilisation effect, but the net response depends on a balance with the concurrent reduction in total radiation. Global and regional effects remain poorly understood, with modelling studies disagreeing on the magnitude, sign, and spatial variability of impacts. One key source of uncertainty lies in ecosystem-climate feedbacks, which are triggered by the initial photosynthesis response to diffuse radiation and can initiate a cascade of effects that further modulate carbon fluxes and light conditions. Despite this potential importance, very few studies have implemented fully coupled simulations.
Here, we use the UK Earth System Model (UKESM), implemented with a coupled interactive diffuse radiation scheme, to investigate global and regional impacts of diffuse radiation on gross primary productivity (GPP). Between 1984-2008, diffuse radiation enhances global GPP by 557.6 PgC. The diffuse radiation effect of the 1991 Mount Pinatubo eruption resulted in substantial productivity effects compared to simulations driven by climatological mean volcanic aerosol. The interactive scheme resulted in similar global GPP compared to the standard UKESM configuration with a fixed diffuse fraction of 0.4, but with important regional differences including greater diffuse radiation fraction and GPP in boreal regions but reductions in the tropics. Climate feedbacks also show considerable regional variation, acting to either enhance or suppress the initial photosynthesis response to diffuse radiation. For example, in the tropics, reduced diffuse fraction leads to lower ET resulting in warming and drying trends that amplify reductions in GPP.
These results highlight how diffuse radiation and resulting climate feedbacks can significantly influence land carbon uptake. These effects may become even more important under potential solar radiation modification (SRM) scenarios. While our results imply that diffuse radiation effects of SRM may increase global GPP, the varying regional impacts indicate that the extent of this influence is likely to be sensitive to how and where additional aerosol forcing is applied. We therefore recommend that fully coupled simulations which include representation of diffuse radiation processes are needed to better evaluate potential ecosystem impacts of SRM, and to improve understanding of the complex relationship between clouds and aerosols and the global carbon cycle.
How to cite: McClory, S., Wiltshire, A., Mercado, L., Hancock, S., Spracklen, D., and Rap, A.: Diffuse radiation and climate feedback effects on the land carbon sink, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19855, https://doi.org/10.5194/egusphere-egu26-19855, 2026.
Natural vegetation is exposed to highly dynamic light environments driven by wind-induced canopy motion, cloud cover, and atmospheric aerosols. These processes generate diffuse radiation and rapid irradiance fluctuations, ranging from sub-second windflecks to hour-long cloudflecks. This variability strongly regulates photosynthesis and transpiration, yet most vegetation models still assume static canopies and instantaneous responses.
At the ecosystem scale, we present long-term observations from a boreal Scots pine forest showing that productivity is most strongly related to the absolute amount of diffuse light rather than diffuse fraction alone. Enhanced ecosystem uptake across both shoots and forest-floor vegetation emphasizes that diffuse radiation not only reorganizes light distribution throughout the canopy but may also modify leaf-level photosynthesis through canopy-mediated effects.
Controlled-environment experiments under defined fluctuating or diffuse irradiance regimes reveal that developmental acclimation to light variability represents a physiological compromise. This trade-off between carbon gain and water loss under realistic light dynamics has direct implications for water-use efficiency and drought responses in a warming climate. In parallel, field measurements reveal order-of-magnitude differences in wind-driven canopy motion among wheat cultivars under comparable wind speeds. Light fluctuations are thus not solely imposed by the atmosphere but are co-determined by plant structure and biomechanics.
Finally, we present new analyses of cloudfleck properties derived from multi-year, high-frequency measurements, revealing cloud-driven spectral irradiance fluctuations in temperate and boreal forests. These data stress that diffuse and direct radiation differ fundamentally in both directionality and spectral composition, extending beyond a simple binary and challenging the traditional direct–diffuse dichotomy used in models. Dynamic light is not noise around a mean state but a fundamental driver of ecosystem function. Accounting for its temporal, structural, and spectral complexity is essential for realistic predictions of vegetation responses to climate change.
How to cite: Durand, M., Neimane-Šroma, S., Gibbs, A. J., Zhuang, X., Lintunen, A., Ezhova, E., Hughes, N. M., Salmon, Y., Gibbs, J. A., Murchie, E. H., and Robson, T. M.: When wuthering winds create fluttering fields: plant canopies under diffuse and fluctuating light, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6811, https://doi.org/10.5194/egusphere-egu26-6811, 2026.
Leaf area index (LAI) is a key indicator of vegetation structure and plays a crucial role in global ecological and climate systems. The accuracy of satellite-based LAI retrievals critically depends on the effective separation of land surface and atmospheric signals in radiation measurements. Most traditional remote sensing algorithms retrieve LAI from land surface reflectance (LSR) products after atmospheric correction. In such schemes, uncertainties in aerosol optical depth (AOD) retrievals are implicitly propagated into LSR and subsequently into LAI estimates, leading to degraded accuracy and reduced spatiotemporal consistency, particularly under conditions of rapidly varying or heavy aerosol loadings.
In this study, we propose a joint retrieval framework that simultaneously estimates AOD, LSR, and LAI directly from FY-3D/MERSI top-of-atmosphere (TOA) reflectance observations. The approach employs an ensemble machine learning–based model, thereby avoiding the conventional decoupled treatment of atmospheric correction and vegetation parameter retrieval. By jointly optimizing atmospheric state variables and land surface biophysical parameters under a unified observational constraint, the method effectively suppresses the propagation of atmospheric correction uncertainties into LAI estimates.
Global retrievals for the period 2020–2023 demonstrate robust performance across a wide range of aerosol loading and observation conditions. The retrieved LAI captures reasonable spatial patterns and seasonal dynamics and shows good consistency with GLASS and MODIS LAI products. This study advances a novel land–atmosphere integrated inversion strategy and establishes a global-scale coupled aerosol-vegetation remote sensing dataset, which can serve as an important technique and data source for improving Earth system models and investigating ecosystem responses to global climate change.
How to cite: Dong, Y. and Li, J.: Joint retrieval of aerosol optical depth and leaf area index from FY-3D/MERSI measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17021, https://doi.org/10.5194/egusphere-egu26-17021, 2026.
Tropospheric ozone (O3) is a major air pollutant in China that threatens vegetation productivity and ecosystem functions. Quantifying O3-induced impacts on photosynthesis and stomatal conductance is crucial for understanding changes in carbon, water, and energy fluxes between the biosphere and atmosphere on regional and global scales. In recent decades, several parameterization schemes have been developed to describe the photosynthetic and stomatal responses to O3 exposure. However, a significant spread remains when applying different schemes in various model frameworks. In this study, we integrated six flux-based O3-vegetation damage parameterizations into SSiB4/TRIFFID, a well-established land surface model coupled with a dynamic vegetation model, to assess the impacts of O3 pollution on terrestrial ecosystems in China during the 2010s. Our results show that O3 pollution led to approximately a 20% reduction in GPP during the 2010s, with discrepancies ranging from 15% to 31% across different schemes. Comparison of the O3 damage schemes revealed substantial differences in vegetation O3 sensitivity across schemes and plant functional types (PFTs). When compared with observations, the newly developed schemes, such as L2024 and LMA-based approaches, showed more reliable O3 sensitivity, as evidenced by smaller biases relative to peer-reviewed observations. This improved performance can be attributed to the inclusion of a broader range of observational and experimental data, as well as key physiological parameters (e.g., LMA) to better capture O3 sensitivity. Furthermore, we found that the L2024 scheme exhibited strong inhibition of photosynthesis in the late growing season due to cumulative O3 exposure. By refining the "decay" process of O3 accumulation using leaf lifespan parameters and applying the "decay" and "heal" processes across all PFTs, we improved the spatial and temporal distribution of Gross Primary Productivity (GPP) simulations. This study highlights the importance of observational evidence and physiological insights in developing O3-vegetation damage parameterizations. Future efforts should focus on expanding observational and experimental data on O3 responses in China’s natural ecosystems to enhance O3 damage assessment and model development.
How to cite: Li, L., Qiu, B., Zhao, S., Miao, X., Chen, C., Chen, J., Ni, Y., and Guo, W.: Integrating Ozone–vegetation Damage Schemes into SSiB4/TRIFFID: Evaluation of Six Parameterizations and Refinement of Ozone Decay Process Across Plant Functional Types, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16938, https://doi.org/10.5194/egusphere-egu26-16938, 2026.
Abstract: Sulfur dioxide (SO₂) is a major air pollutant that damages terrestrial vegetation. It can suppress photosynthesis through sulfite/bisulfite toxicity and ROS-mediated oxidative stress, promoting stomatal closure and impairing chloroplast photochemistry and carbon fixation, as documented in laboratory and field fumigation studies. Here we develop, to our knowledge, the first observation-based parameterization of SO₂-induced photosynthetic damage, derived from 858 measurements compiled from peer-reviewed SO₂ fumigation experiments. The scheme captures observed relationships between accumulated SO₂ exposure above a concentration threshold, ASTα (α, nmol mol⁻¹), and relative leaf photosynthetic rate across broadleaf trees, needleleaf trees, shrubs, C₃ crops, C₄ crops, C₃ grasses, and C₄ grasses. We implement the parameterization in the Common Land Model (CoLM2024). Global simulations for 2003–2021 indicate that, relative to simulations without SO₂ damage, contemporary SO₂ exposure reduces global photosynthetic rate by ~10%. These results highlight the importance of representing SO₂-induced physiological stress in process-based large-scale models to improve assessment and projection of the global carbon cycle.
Keywords: sulfur dioxide (SO2); photosynthesis; terrestrial ecosystem; fumigation experiments; parameterization, land surface model
How to cite: Yuan, X. and Li, F.: Incorporating Experiment-Based SO₂ Damage to Photosynthesis into Regional and Global Land Process Models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4365, https://doi.org/10.5194/egusphere-egu26-4365, 2026.
Fires are a significant disturbance in Earth's systems. Smoke aerosols emitted from fires can cause environmental degradation and climatic perturbations, leading to exacerbated air pollution and posing hazards to public health. However, research on the climatic and health impacts of fire emissions is severely limited by the scarcity of air pollution data directly attributed to these emissions. Here, we develop a global daily fire-sourced PM2.5 concentration ([PM2.5]) dataset at a spatial resolution of 0.25° for the period 2000–2023, using the GEOS-Chem chemical transport model driven with two fire emission inventories, the Global Fire Emissions Database version 4.1 with small fires (GFED4.1s) and the Quick Fire Emission Dataset version 2.5r1 (QFED2.5). Simulated all-source [PM2.5] is bias-corrected using a machine learning algorithm, which incorporates ground observations from over 9000 monitoring sites worldwide. Then the simulated ratios between fire-sourced and all-source [PM2.5] at individual grids are applied to derive fire-sourced [PM2.5]. Globally, the average fire-sourced [PM2.5] is estimated to be 2.04 µg m−3 with GFED4.1s and 3.96 µg m−3 with QFED2.5. Both datasets show consistent spatial distributions with regional hotspots in central Africa and widespread decreasing trends over most areas. While the mean levels of fire-sourced [PM2.5] are much higher at low latitudes, fire episodes in the boreal regions can cause PM2.5 levels that are comparable to those of the tropics. This dataset serves as a valuable tool for exploring the impacts of fire-related air pollutants on climate, ecosystems, and public health, enabling accurate assessments and support for decision-making in environmental management and policy.
How to cite: Tian, C.: Global high-resolution fire-sourced PM2.5 concentrations for 2000–2023, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-741, https://doi.org/10.5194/egusphere-egu26-741, 2026.
Biogenic Volatile Organic Compounds (BVOCs) influence aerosol-cloud interactions and climate radiative impacts by changing the formation of Secondary Organic Aerosols (SOA) and atmospheric oxidation capacity. Current Earth System Models (ESMs) typically employ two approaches to BVOC emissions: prescribed offline emission inventories (e.g., MEGAN) or online calculated emissions that do not link to plant physiological processes or vegetation dynamics. To date, most ESMs generally lack a fully interactive coupling of plant BVOC emissions with photosynthesis-based ecosystem processes, vegetation dynamics, meteorology, and atmospheric chemistry, thus the quantified impacts on global/regional radiative forcing and climate patterns remain insufficiently understood.
In this study, we integrated a process-based vegetation BVOC emission scheme that is fully coupled with the TM5 atmospheric chemistry component within EC-Earth3-AerChem. Leveraging this interactive capability, we evaluate the impact by contrasting a simulation driven by prescribed offline inventories against this online experiment. Results for the boreal summer (JJA) indicate that while the online-coupled BVOC scheme captures the general global distribution of BVOCs, it significantly reshapes regional emission hotspots. Specifically, tropical forest source regions exhibit distinct spatial heterogeneity, characterized by an east-west dipole in the Amazon and a core-periphery contrast in the Congo Basin. This emission redistribution caused by online coupling further induces significant changes in SOA optical depth (diagnosed at 550 nm) and Cloud Condensation Nuclei (CCN) concentrations, accompanied by a widespread increase in mid-tropospheric (500 hPa) ozone across the tropics and subtropics.
With an online coupled BVOC scheme, the Shortwave Cloud Radiative Effect (SWCRE) becomes more negative (enhanced cloud cooling) over large areas, consistent with the spatial patterns of net Top-of-Atmosphere (TOA) radiation differences. The surface temperature response presents significant regional divergence, consistent with competing contributions in the radiative budget. Over the Congo Basin, warming signals are linked to widespread reductions in SOA and CCN, which weaken the aerosol cooling effect. In contrast, over parts of the Eastern Amazon, warming occurs despite increased SOA loading, suggesting that the greenhouse effect from enhanced tropospheric ozone overrides the local aerosol cooling potential. Meanwhile, cooling signals appear over ocean regions such as the North Atlantic, consistent with enhanced SWCRE. This suggests that interactive BVOC emissions reshape regional temperature responses primarily through combined BVOC–SOA–cloud and ozone feedbacks.
Overall, compared with the offline inventory approach, online coupled BVOC emissions in EC-Earth3-AerChem significantly change spatial patterns of regional radiative impact and temperature response, indicating that the dynamics in BVOC emissions themselves may be an important source of regional uncertainty in chemistry-coupled climate simulations.
How to cite: Wang, Z., Noije, T. V., Miller, P., Sager, P. L., and Tang, J.: Interactive versus inventory-based BVOC emissions reshape regional cloud-radiative and ozone feedbacks in EC-Earth3-AerChem-BVOC, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7447, https://doi.org/10.5194/egusphere-egu26-7447, 2026.
Climate change is expected to significantly alter photochemical activity through its impact on biosphere–atmosphere gas exchange. In particular, higher air temperatures and lower relative humidity (RH) over land are projected to enhance biogenic volatile organic compound (BVOC) emissions and surface ozone (O₃) concentrations, while simultaneously suppressing dry ozone deposition via both stomatal and non-stomatal pathways.
This study investigates the effects of warm and dry conditions on dry ozone deposition and BVOC emissions from terrestrial vegetation by synthesizing results from multiple field campaigns conducted between 2013 and 2023 in the eastern Mediterranean. Using eddy covariance and branch-level enclosure measurements, we quantified fluxes and mixing ratios of O₃, VOCs (dominated by BVOCs and measured using PTR-ToF-MS), and nitrogen oxides (NOₓ) across forested, urban, and coastal environments.
Our observations show that under very dry conditions (RH < 30%), the occurrence of positive (upward) O₃ fluxes increases, while the downward ozone deposition velocity increases logarithmically with RH in both urban and rural settings, driven by enhanced surface evaporation and dry air intrusion events from the upper troposphere¹,². Canopy- and branch-level measurements further reveal that under severe drought, instantaneous intraday variations in meteorological parameters serve as better proxies for BVOC emission rates than absolute meteorological values³,⁴. Additional coastal observations indicate substantial marine contributions to isoprene mixing ratios inland, consistent with recent increases in sea surface temperature in the Levantine Basin ⁵.
Collectively, these results point toward enhanced ozone formation and reduced surface removal under future warmer and drier climates, while providing new insights into the modeling of dry ozone deposition and BVOC emissions under warm and dry conditions.
References
1. Choi, D. et al., Sci. Total Environ., 2025. https://doi.org/10.1016/j.scitotenv.2025.180347
2. Li, Q. et al., Sci. Total Environ., 2019. https://doi.org/10.1016/j.scitotenv.2018.12.272
3. Li, Q. et al., Biogeosciences, 2024. https://doi.org/10.5194/bg-21-4133-2024, 2024
4. Li, Q. et al., Sci. Total Environ., 2025. https://doi.org/10.1016/j.scitotenv.2025.180423
5. Dayan, Chen, et al., Atmos. Chem. Phys.,2020. https://doi.org/10.5194/acp-20-12741-2020
How to cite: Tas, E., Li, Q., Gabay, M., Choi, D., and Dayan, C.: Effects of Warm and Dry Conditions on Ozone Deposition and Biogenic VOC Emissions in the Eastern Mediterranean, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12802, https://doi.org/10.5194/egusphere-egu26-12802, 2026.
Coastal salt marshes (CSMs) are vital blue carbon (BC) reservoirs, yet accurately quantifying their gross primary productivity (GPP) remains challenging due to limitations in terrestrial biosphere models (TBMs), which often overlook coastal-specific processes. Here, we present SAL-GPP, a process-based model that incorporates coastal-specific modules to capture the effects of salinity and temperature stress on photosynthesis, as well as light-use efficiency across salinity gradients in diverse CSM plant species. Model validation showed strong agreement with observations, with R² of 0.82 and model efficiencies of 0.82 and 0.74 for daily and seasonal GPP, respectively. Driven with global inputs, SAL-GPP produced high-resolution global simulations, yielding a mean annual GPP of 66.89 ± 11.68 TgC yr–1 (2011–2020), with 64% concentrated in key hotspots across the southeastern United States, western Europe, southeastern China, and Australia. From 2011 to 2016, global CSM GPP increased by 1.56 TgC yr–1, then declined, rebounded after 2018, and peaked at 71.45 ± 12.02 TgC yr–1 in 2020. Model evaluation showed that SAL-GPP outperformed existing remote sensing-based GPP products and TBMs at both site and grid levels. By explicitly incorporating coastal ecosystem dynamics, SAL-GPP supports global BC accounting and climate mitigation strategies aligned with nature-based solutions for carbon neutrality.
How to cite: Zhou, Z., Li, T., Yang, X.-Q., Chen, D., Han, G., Fan, X., Zhao, X., Wei, S., He, B., and Wang, G.: Supporting Blue Carbon Accounting: A Process-Based Productivity Model for Global Salt Marshes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2185, https://doi.org/10.5194/egusphere-egu26-2185, 2026.
In this study, we applied the monthly mean near-surface temperatures from the historical and future projections under the SSP585 and G6 sulfur scenarios. The bias-correction and multi-model ensemble averaging were applied to the monthly mean near-surface temperatures from four models and then used to investigate the impact of Stratospheric Aerosol Injection (SAI) on species migration to thermal environments similar to their historical adaptations. Thermal connectivity was used to quantify the migration capacity, with thermal exposure (TE) representing the cumulative temperature difference (°C) along the migration path and thermal velocity (TV) characterizing the minimum migration velocity (km yr⁻¹) required for species to track their historically adapted thermal environments. The results show that SAI exhibits a complex dual effect under the SSP585 scenario. SAI can successfully mitigate thermal stress in more than 80% global land area and provide an additional about 2% of the migratory zone globally. The mitigation effect was most significant in high-latitude regions. On the other hand, implementing SAI under the SSP585 scenario can lead to increased thermal stress in 4% of the land area increasing migration pressure in these regions. The results highlight that temperature response of SAI exhibits heterogeneous impact on thermal environments, necessitating the development of customized adaptation strategies tailored to different geographical regions in the future.
How to cite: Xu, X.: Impact of stratospheric aerosol injection on thermal environment shift, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8627, https://doi.org/10.5194/egusphere-egu26-8627, 2026.
Solar Radiation Modification (SRM) ranks among the most promising geoengineering approaches. Given that SRM scenarios are plausible, current understanding of its impacts relies heavily on model projections. However, significant risks emerge from limited knowledge and substantial model uncertainties, especially regarding terrestrial ecosystems, which are intrinsically linked to human well-being. Ecosystem response uncertainties stem from scenarios, atmospheric models, land surface models, etc. Fully coupled simulations make it impossible to disentangle these contributing factors for further refinement. To address this gap, we designed a new set of offline simulations using multiple land surface models to isolate uncertainty sources and refine the ecosystem response to SRM. We will present preliminary results from these experiments.
How to cite: Zhang, Y., Yuan, W., Xu, X., and Liu, D.: Understanding terrestrial ecosystems response to solar radiation modification, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6233, https://doi.org/10.5194/egusphere-egu26-6233, 2026.
Severe air pollution reduces ecosystem carbon assimilation by damaging vegetation with ozone (O3) and altering climate through aerosol effects, thereby exacerbating global warming. In response, China implemented the Clean Air Action (CAA) plan in 2013 to reduce anthropogenic emissions. Here we assessed the impact of CAA-induced air pollution reductions on net primary productivity (NPP) in China during 2014-2020 using multiple measurements, process-based models, and machine learning algorithms. The CAA plan led to a national NPP increase of 26.3±27.9 Tg C yr-1, with 20.1±10.9 Tg C yr-1 attributed to aerosol reductions, mainly driven by enhanced light availability from decreased black carbon and increased precipitation due to weakened aerosol climatic effects. The impact of O3 amelioration became more significant over time, surpassing the effects of aerosol reduction by 2020. Two machine learning models showed similar NPP recoveries of 42.8±26.8 Tg C yr-1 and 43.4±30.1 Tg C yr-1. Our study highlights significant carbon gains from controlling aerosols and surface O3, underscoring the co-benefits of air pollution regulation for public health and carbon neutrality in China.
How to cite: Yue, X. and Zhou, H.: Improved ecosystem productivity in China driven by declining aerosols and surface ozone, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3443, https://doi.org/10.5194/egusphere-egu26-3443, 2026.
Most existing studies simulate ecosystem influences on atmospheric pollutants using prescribed vegetation datasets, while feedbacks from atmospheric chemistry to terrestrial ecosystems are rarely represented. Here, we couple the chemistry-climate model ECHAM6.3–HAM2.3–MOZ1.0 (ECHAM-HAMMOZ) with the interactive Model for Air Pollution and Land Ecosystems (iMAPLE) to enable fully dynamic, two-way interactions between atmospheric chemistry and terrestrial ecosystems. Biogenic volatile organic compounds, including isoprene (IPP) and monoterpenes (MTP), are simulated by iMAPLE and interactively participate in chemical reactions in HAMMOZ. Simulated leaf area index and stomatal conductance modulate dry deposition velocities, thereby influencing atmospheric chemical concentrations. In turn, the modeled ozone affects vegetation productivity through stomatal uptake. Relative to the original model, the coupled system exhibits notable changes in atmospheric composition and ecosystem productivity. Enhanced IPP and MTP emissions reduce surface ozone concentrations in high-latitude regions, while dynamically simulated ozone variability induces a seasonal reduction in terrestrial gross primary productivity (GPP). In addition, spatial heterogeneity in stomatal conductance alters ozone dry deposition patterns. By explicitly representing these coupled feedback processes, the integrated ECHAM–HAMMOZ–iMAPLE framework improves the realism of biosphere–atmosphere interactions and provides a useful tool for studying atmosphere–terrestrial ecosystem coupling.
How to cite: Zhao, Y. and Yue, X.: Development and evaluation of ecosystem-atmospheric chemistry interactions in the ECHAM-HAMMOZ-iMAPLE model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3305, https://doi.org/10.5194/egusphere-egu26-3305, 2026.
Atmospheric Motion Vector (AMV) provides essential wind field information, playing a key role in typhoon path prediction and intensity analysis. However, the spatial resolution of current mainstream AMV products is relatively low, limiting their ability to meet the high-precision demands of meteorological services. While the China Meteorological Administration's visible channel AMV product achieves a relatively high spatial resolution of 6 km, its long computational time prevents it from being applied in real-time operational scenarios.In this study, we propose a GPU-accelerated high-resolution wind field retrieval algorithm, designed to address the computational bottleneck of the target tracking component within the AMV retrieval process. By decomposing the core calculations into parallel tasks, we leverage OpenACC to efficiently implement parallel computing. Additionally, to overcome the memory limitations of a single GPU, we design a block-based computational strategy, enabling multi-GPU processing for handling larger datasets.Experimental results show that the proposed algorithm achieves significant acceleration, with computational efficiency improved by more than ten times compared to traditional CPU implementations, while maintaining the retrieval accuracy. The algorithm also demonstrates excellent scalability, supporting a wide range of remote sensing data resolutions, from 4000 m down to 500 m. This work presents a feasible technical solution for real-time operational high-resolution AMV retrieval, enhancing the timeliness of typhoon monitoring and numerical weather prediction assimilation.
How to cite: Zhou, R.: Accelerating Target Tracking in Atmospheric Motion Vector Retrieval Using Openacc, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6062, https://doi.org/10.5194/egusphere-egu26-6062, 2026.
Vegetation plays a fundamental role in the terrestrial carbon-oxygen cycle, with gross primary productivity (GPP) coupled to biospheric O₂ release. Ongoing warming and increasing human activity may perturb this coupling by altering productivity, respiration, and land-surface processes. These effects are of particular concern on the Qinghai–Tibetan Plateau, where such high-elevation regions are highly sensitive to climatic forcing and anthropogenic changes. As a proxy for O2 exchange, near-surface relative oxygen concentration (ROC) reflects the combined effects of biogeochemical processes, anthropogenic activities, and atmospheric transport. Here, we integrate satellite-derived GPP estimates, reanalysis meteorological data, and land-cover classifications to quantify the variability of GPP and ROC across the Qinghai–Tibetan Plateau from 2001 to 2024. We find clear land-use-dependent patterns in the co-variation of GPP and ROC. In natural ecosystems such as grasslands and forests, GPP and ROC increase synchronously, whereas in built-up areas both GPP and ROC decrease. We also find that near-surface air temperature increases across all land-use classes and is strongly correlated with GPP and ROC variability in natural ecosystems. Overall, our results suggest that intensifying human activity simultaneously increase oxygen consumption and reduce the biospheric contribution to O2 production by constraining vegetation productivity. These patterns provide observational evidence of increasingly challenging conditions for livestock in high-elevation regions and of weakened ecosystem functioning under growing human impacts.
How to cite: Zhang, Y., Lee, S., and Zhang, W.: Contrasting productivity–oxygen co-variation in natural and human-influenced areas of the Qinghai–Tibetan Plateau, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8267, https://doi.org/10.5194/egusphere-egu26-8267, 2026.
Stratospheric Aerosol Injection (SAI) has been widely investigated as a potential Solar Radiation Management (SRM) strategy to offset global warming, with ensemble-based Earth System Model simulations such as the Geoengineering Large Ensemble Simulation (GLENS) providing key evidence for its climatic impacts. However, the reliability of these ensemble projections, particularly at regional scales, remains insufficiently assessed. This study presents a joint evaluation of the projection skill and statistical calibration of GLENS ensemble outputs over Southeast Asia, focusing on precipitation and near-surface temperature variables. First, ensemble skill is assessed against ERA5 reanalysis using rank histograms, confidence interval coverage, Continuous Ranked Probability Score (CRPS), and Brier Score. Results show that raw GLENS projections are systematically underdispersive and biased, with overly narrow uncertainty ranges that frequently fail to capture observations. Projection skill exhibits strong regional contrasts, with poorer precipitation performance over the Maritime Continent and weaker temperature skill over mainland Southeast Asia. These deficiencies indicate overconfident ensemble behavior and limit the direct usability of raw GLENS outputs for impact assessment and decision support. To address these limitations, Bayesian Model Averaging (BMA) is applied as a probabilistic post-processing method to calibrate monthly mean temperature projections. BMA substantially reduces systematic bias, corrects ensemble dispersion, and improves probabilistic reliability across most countries. Post-calibration CRPS values consistently decrease, and predictive distributions better represent observed variability. Overall, the combined results demonstrate that while GLENS captures large-scale climatic signals of SAI, statistical calibration is essential to reduce uncertainty and obtain reliable regional projections. This study highlights the importance of integrating ensemble verification and calibration to support robust interpretation of SRM impacts in climate-sensitive regions such as Southeast Asia.
How to cite: Kuswanto, H., Fatahillah, H. A., Utomo, C. R., Fithriasari, K., and Widhianingsih, T. D. A.: Reliability Assessment and Statistical Calibration of SAI Ensemble Projections in Southeast Asia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8408, https://doi.org/10.5194/egusphere-egu26-8408, 2026.
Fires and their carbon emissions have substantial impacts on land surface, climate systems, and air quality. However, long-term datasets with detailed spatiotemporal fire records remain limited, due to insufficient understanding of the climatic, ecosystem, and societal drivers of fire processes in current process-based models. Here, we employ a data-driven approach that integrates machine learning algorithms with outputs from eight fire models within the Fire Model Intercomparison Project (FireMIP) to reconstruct fire CO2 emissions from 1901 to 2012 and to assess the respective impacts of human activities, climate, and land cover change. Our MLA-based dataset reveals a global decline in fire-emitted CO2 at -7.45 ± 0.12 Tg C yr-2 (-0.29% yr-1), mainly in South America and Africa. Land use change emerges as the primary driver, reducing fire CO2 emissions by -6.07 ± 0.23 Tg C yr-2, followed by population growth, which contributes -3.60 ± 0.54 Tg C yr-2. Population growth typically suppresses fires in agricultural and urban areas but raises fire risks at rainforest edges where deforestation occurs. Although climate change has a limited impact on global fire CO2 reduction (-0.39 ± 0.19 Tg C yr-2), it remains a key driver for boreal fires, strongly influenced by precipitation changes. These findings underscore the need for robust data and informed management to support fire prevention and climate change mitigation efforts.
How to cite: Zhang, H., He, B., Zhu, J., and Tian, C.: Centennial-scale decline in global fire emissions driven by land use and population growth, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4888, https://doi.org/10.5194/egusphere-egu26-4888, 2026.
Crop straw management presents a critical trade-off in climate mitigation: balancing straw return for carbon sequestration against conversion into carbon-neutral bioenergy. The potential for agricultural systems to synergistically achieve both ambitious soil carbon growth and substantial greenhouse gas (GHG) reductions remains not fully understood. The integration of the Rothamsted Carbon Model (RothC) and a Bioenergy-Emission-Economic Model (BEE) enabled a systematic evaluation of the carbon balance effects of straw management in China (2021–2100) under varying soil organic carbon (SOC) targets. The results demonstrate that even under an ambitious 4 per mille SOC target, allocating a substantial share of straw resources to bioenergy production still yields robust climate mitigation benefits for the agricultural system. Under this target, the agricultural system exhibits significant climate mitigation potential, capable of fully offsetting China’s total agricultural GHG emissions. Spatial analysis further identifies East and Central China as priority regions for implementing this synergistic pathway, due to their abundant straw resources and relatively high carbon sequestration efficiency. These findings indicate that enhancing soil carbon and deploying straw-based bioenergy are not mutually exclusive, but can act as synergistic pillars for achieving agricultural carbon neutrality through spatially optimized allocation. The agricultural sector has the potential to evolve into a reliable system for climate mitigation that supports carbon neutrality while safeguarding soil health.
How to cite: Lu, X., Xu, Y., Lin, Z., Wang, G., and Skalsky, R.: Straw-based bioenergy for carbon-neutral agriculture with sustained soil carbon growth, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18063, https://doi.org/10.5194/egusphere-egu26-18063, 2026.
Atmospheric ammonia (NH₃) is a key air pollutant with high spatiotemporal variability, challenging the observation of its diurnal cycle. The Fengyun-4B Geostationary Interferometric Infrared Sounder (FY-4B/GIIRS) offers high-frequency measurements that capture this variability. We introduce a novel Multi-modal Fusion Transformer (MF-Transformer) to retrieve NH₃ total columns directly from hyperspectral radiances, meteorology, and ancillary data, circumventing costly radiative transfer simulations. Our retrievals are consistent with the IASI (Infrared Atmospheric Sounding Interferometer) NH₃ product (correlation coefficient, R=0.79) and Optimal Estimation (OE) retrievals (R=0.75), outperform benchmark machine learning models by ~20% in accuracy, and eliminate unphysical negative values. The method is orders of magnitude faster than OE approach, enabling global full-disk processing in tens of seconds. This advance allows the resolution of rapid NH₃ variations, demonstrating a transformative capability for operational monitoring.
How to cite: Xia, X., Min, M., Li, J., and Gao, L.: Diurnal Ammonia Mapping based on Deep Learning from Geostationary Hyperspectral Infrared Sounder Observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4840, https://doi.org/10.5194/egusphere-egu26-4840, 2026.
Enhancing SOC accumulation is considered as a pathway for mitigating climate change. However, projection of soil organic carbon (SOC) exhibits large uncertainties, and microbial activities, the key regulator of SOC dynamics, are omitted in most Earth System Models (ESMs). Here, we compare the traditional SOC scheme in ESMs (CENURY) with two microbial-explicit models, through constraining SOC stock and its stable component. Unlike net gains of SOC projected by CENTURY, the two microbial models consistently projected turning points of SOC change, and net loss of SOC for 21.9 ~ 61.4PgC were projected under SSP5-8.5 by 2100. SOC loss originates primarily from unprotected SOC in northern high-latitudes. SOC stabilization pathways, instead of the temperature sensitivity of SOC mineralization, drive the divergence among model projections. Our results indicate strong risk of SOC loss in northern high-latitudes, and ESMs should incorporate microbial-regulated SOC stabilization mechanisms as the priority to improve SOC projections.
How to cite: Liu, D. and Yichen, Z.: Microbial regulation of soil carbon stabilization shapes soil carbon projections, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1836, https://doi.org/10.5194/egusphere-egu26-1836, 2026.
As global temperature rises, the severity and frequency of droughts are projected to increase. Stratospheric aerosol injection (SAI) has been proposed as a potential solution to reduce surface temperatures, but its effectiveness in alleviating drought extremes remains unclear. Here, we assess the global impacts of SAI on drought extremes based on experiments from the Geoengineering Model Intercomparison Project phase 6 (GeoMIP6) and the Geoengineering Large Ensemble Project (GLENS). By 2100, the frequency of extreme droughts is projected to increase by 7.33 % under the high-emission Shared Socioeconomic Pathways 5 (SSP5-8.5) scenario relative to present day. SAI reduces this increase by 1.99 % in GeoMIP6, and by 1.80 % in GLENS compared with Representative Concentration Pathways 8.5 (RCP8.5). Attribution analyses show that SAI-induced cooling alone reduces extreme drought frequency by 3.42 % in GeoMIP6 and 4.28 % in GLENS relative to their respective high-emission scenarios, outweighing the 2.12 % increase driven by SAI-induced precipitation reductions under the same conditions. However, these rainfall deficits lead to substantial inequities in drought exposures. Compared to developed nations, countries with less development experience smaller reductions, or even increases, in economic and population exposure to extreme drought under SAI relative to SSP5-8.5 or RCP8.5. These findings suggest that the current SAI strategies in GeoMIP6 and GLENS may induce the risk of unintentionally worsening regional hydroclimatic disparities.
How to cite: Fu, W., Tian, C., Xu, R., and Guo, Y.: Mitigating global drought extremes through stratospheric aerosol geoengineering: spatial and socioeconomic disparities, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4584, https://doi.org/10.5194/egusphere-egu26-4584, 2026.
Understanding of the crop carbon balance across different time scales and corresponding responses to abiotic and biotic factors is crucial for improving carbon cycle models in the context of future climate change and management practices. In this study, we employed the Random Forest (RF) algorithm, Kolmogorov-Zurbenko filtering method and structural equation modeling (SEM) to quantify the effects of abiotic and biotic factors on CO2 fluxes at various time scales based on 7-years measurements. Our results revealed that O3 primarily manifested indirect effects on NEE and GPP via altering LAI on the daily and monthly scale, and that overall regulatory effect on CO2 fluxes developed greater as the time scale increased. Net radiation (Rn) was the most critical abiotic factor altering net ecosystem exchange (NEE) and gross primary productivity (GPP) at the half-hourly, daily, and monthly scales, with the exception of photosynthetically active radiation (PAR) controlling daily NEE and GPP in the rice system. It was innovatively found that LAI had little control on detrended daily CO2 fluxes, which was much lower than the monthly CO2 fluxes. Air temperature (Ta) was the most important abiotic factor for ecosystem respiration (Reco) at half-hourly and daily scale. For NEE, Reco, and GPP, the maximum explanation of SEM models was 70.10%, 79.60% and 76.20%, respectively. The SEM results indicated that at multiple time scales, Rn exerted significant direct and indirect effects on both NEE and GPP. LAI only showed a strong direct leading effect on NEE and GPP on the monthly scale. The findings we reported have the potential to further develop carbon cycle models of cropland ecosystems under climate change by clarifying the influence path of O3 on CO2 fluxes and highlighting the factors that dominate CO2 fluxes on various time scales.
How to cite: Zhang, K., Lu, Y., Gong, Y., Duan, C., and Zhang, F.: Variations and drivers of CO2 fluxes at multiple temporal scales of subtropical agricultural systems in the Huaihe river Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4794, https://doi.org/10.5194/egusphere-egu26-4794, 2026.
Posters virtual: Wed, 6 May, 14:00–18:00 | vPoster spot 5
EGU26-9022 | ECS | Posters virtual | VPS4
Nitrogen oxide emissions from cropland soil: spatiotemporal heterogeneity, carbon-nitrogen coupling mechanisms, and mitigation strategiesWed, 06 May, 14:06–14:09 (CEST) vPoster spot 5
Nitrogen oxides (N₂O, NO, and NO₂) serve as critical linkages connecting climate systems, ecosystems, and atmospheric chemistry, with soils acting as a primary natural source. Adopting a multi-scale framework spanning global, regional, and field scales, we systematically examine the spatiotemporal heterogeneity of nitrogen oxide emissions from cropland soils. Spatially, emissions exhibit a latitudinal gradient, decreasing from low to high latitudes, with hotspots concentrated in agriculturally intensive regions. Temporally, emissions display multi-scale rhythmic patterns aligned with crop growth stages, seasonal cycles, and diurnal variations, tightly coupled to soil carbon-nitrogen transformation processes. From the perspective of carbon-nitrogen coupling mechanisms, we reveal how land management practices—including nitrogen fertilization, conservation tillage, and precision irrigation—regulate emissions by modulating soil organic carbon content, carbon-nitrogen ratios, and pore structure. Concurrently, climate change drivers such as rising temperatures, elevated CO₂ concentrations, and extreme precipitation alter microbial-mediated carbon-nitrogen transformation efficiency, collectively shaping the core mechanisms governing nitrogen oxide emissions. A meta-analysis further investigates light effects on soil nitrogen oxide emissions, demonstrating significant impacts: light exposure increased N₂O and NO fluxes by 57.28% and 116.19%, respectively. Notably, heightened UV-B radiation reduced N₂O emissions by 6.85%, whereas shading increased them by 77.23%, with crop-specific responses observed. Mechanistically, light regulates emissions by modifying soil physicochemical properties and restructuring nitrogen-cycling microbial communities. Current emission mitigation faces challenges, including underdeveloped monitoring systems, limited prediction accuracy due to multifactor coupling complexities, and poor regional adaptability of existing technologies. Integrating multi-source data (field observations, remote sensing inversion, laboratory experiments) with advanced modeling approaches—such as climate-soil-crop coupling models and machine learning algorithms—offers viable pathways to enhance emission prediction precision and optimize mitigation strategies. Looking ahead, priorities include establishing multi-scale automated monitoring networks, developing carbon-nitrogen coupling-driven predictive models, promoting regionally tailored carbon sequestration and nitrogen emission reduction technologies, and combining policy incentives with public engagement to reduce uncertainties in global carbon-nitrogen cycle projections. These efforts aim to strengthen scientific support for sustainable agricultural development.
How to cite: Li, D. and Shen, W.: Nitrogen oxide emissions from cropland soil: spatiotemporal heterogeneity, carbon-nitrogen coupling mechanisms, and mitigation strategies, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9022, https://doi.org/10.5194/egusphere-egu26-9022, 2026.
EGU26-9373 | ECS | Posters virtual | VPS4
Integrated impacts of land use intensification on greenhouse gas emissions and soil microbial communities in the Taihu Lake Region: patterns, mechanisms, and implicationsWed, 06 May, 14:09–14:12 (CEST) vPoster spot 5
ABSTRACT
The Taihu Lake region has experienced rapid land use intensification, characterized by conversions from natural wetlands (NW) to conventional rice-wheat rotation fields (RW) and further to greenhouse vegetable fields (GH), driven by economic interests. While such transformations are widespread, their combined effects on greenhouse gas (GHG) emissions and underlying soil microbial mechanisms remain poorly understood. This integrated study addresses these gaps through multi-faceted analyses of GHG fluxes, soil microbial communities, and nitrogen (N)-cycling functional genes across NW, RW, and GH sites. Two-year in-situ field experiments revealed significant GHG emission shifts: land use intensification reduced methane (CH₄) emissions (NW: 970.66 ± 100.09 kg C ha⁻¹; RW: 896.71 ± 300.44 kg C ha⁻¹; GH: 71.23 ± 63.62 kg C ha⁻¹) but markedly increased nitrous oxide (N₂O) emissions (NW: 3.35 ± 0.44 kg N ha⁻¹; RW: 14.38 ± 4.09 kg N ha⁻¹; GH: 81.62 ± 4.89 kg N ha⁻¹). Global warming potential followed the order RW > NW > GH, indicating intensified comprehensive greenhouse effects during NW→RW conversion and mitigation during RW→GH conversion. Microbial community analyses showed land use intensification directly altered bacterial and fungal compositions, with stronger impacts on bacteria. Bacterial communities correlated closely with soil NO₃⁻-N, pH, and electrical conductivity, exhibiting decreased deterministic processes (opposite to fungi). Arable lands (RW/GH) displayed more complex microbial networks, and seasonal variations (notably summer) influenced microbial diversity and function, though less strongly than land use effects. Integrating quantitative PCR and metagenomics uncovered microbial mechanisms driving N₂O emissions: intensification reshaped N-cycling microbial communities, depleting nitrogen fixation, dissimilatory nitrate reduction to ammonium, and anammox marker genes in GH soils. Denitrifying communities segregated similarly to total N-cycling assemblages, with increased network complexity but divergent stability. Critically, intensification amplified N₂O emission potential by elevating Pseudomonadota harboring nirK/norB genes (and associated communities) while reducing nosZ (encoding N₂O reductase) abundance—directly linking microbial functional imbalance to emission increases. Collectively, this study demonstrates that land use intensification in the Taihu Lake region drives GHG emission trade-offs (reduced CH₄ but amplified N₂O) and restructures soil microbial communities and N-cycling functions. These findings highlight the need to prioritize microbial functional balance (e.g., restoring nosZ-carrying taxa) in mitigation strategies, providing critical insights for sustainable land management in wetland-agricultural transition zones.
Acknowledgment
This study was funded by the National Key Research and Development Program of China (2023YFF0805403, 2025YFD1700403) and National Natural Science Foundation of China (42377311).
How to cite: Shen, W., Xiong, R., Qin, D., and Gao, N.: Integrated impacts of land use intensification on greenhouse gas emissions and soil microbial communities in the Taihu Lake Region: patterns, mechanisms, and implications, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9373, https://doi.org/10.5194/egusphere-egu26-9373, 2026.
