Widely used in studies ranging from ecophysiology dynamics to global estimates using models and remote sensing data, FLUXNET datasets have become key to scientific research and applications. More frequently updated and high-quality FLUXNET data collections are ever more pressing, serving opportunities with new technologies and new real-world applications including nature-based and technological climate solutions, carbon credit verification, support to agriculture decision systems, and ecological forecasting. The three major FLUXNET releases (FLUXNET2015, LaThuile in 2007, and Marconi 2000) have been widely used by the scientific community, academia and industry. Nonetheless, release cycles of 7-10 years have become a major limiting factor, given the demand for continuously updated collections for anchoring remote sensing (calibration and validation), models (from hindcasts to forecasting), and real-world applications requiring near-real-time data. Regional flux networks have sought to expand the datasets by increasing the number of sites, variables and metadata shared, by improving data quality, and by moving toward open data principles via the recent adoption of the CC-BY data license. New network-level data products are being released, either regularly (e.g., AmeriFlux, ICOS, NEON, TERN datasets), or in response to specific demands (e.g. Drought2018 and WarmWinter2020 in ICOS). These data are processed using the shared and jointly maintained ONEFlux pipeline, making data products fully compatible and interoperable. However, mechanisms for global access to regional network data at a global scale are still challenging for users. The continuous development of the FAIRness and related data discovery tools further supports new strategies to create, maintain, and continuously update FLUXNET datasets. At the same time, inequity in data use, credit, recognition, and contribution is still a significant challenge that must be highlighted and solved. Here, we present a roadmap for how future FLUXNET synthesis datasets can be constructed and shared. We demonstrate a data discovery and access tool, look into benefits for data providers and users, and highlight data usage and availability. Open discussion of these challenges and solutions is encouraged.

https://shuttle-demo.fluxnet.org/

How to cite: Pastorello, G., Beringer, J., Durden, D., Trotta, C., Cheah, Y.-W., Isaac, P., Sturtevant, C., and Papale, D.: Global FLUXNET Datasets: Past Usage, Opportunities, and New Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14485, https://doi.org/10.5194/egusphere-egu24-14485, 2024.

Climate change is having an accelerating global impact through the increased frequency, magnitude and duration of droughts, fires, floods and other extreme climatic events. The most vulnerable populations bear the greatest brunt of these impacts. The societal solutions to this crisis depend also on how scientific research can address the air quality-climate-health nexus. Observations are needed as a foundation for air quality and climate services to address UN Sustainable Development Goals (SDGs). The atmospheric observing capabilities in most countries in Low- and Middle Income Countries (LMIC) remain often sketchy and heterogeneous, are established based on opportunities, and often not designed for integration into the operational infrastructures of the National Meteorological and Hydrological Services. As a result, operation often lacks sustainability and compatibility, and data are not easily and widely available. This is true for meteorological and climatological observations, but is even more pronounced for the complex instrumentation required to monitor greenhouse gases and short-lived climate pollutants. The development of standardised observations in sustainable research infrastructures (RIs) can overcome some of these issues.

The Horizon Europe funded KADI project (Knowledge and climate services from an African observation and Data research Infrastructure) aims to provide the conceptual framework for the future implementation of an All-African RI that delivers the science-based services to fully address the requirements of the Paris agreement and the SDGs.

The KADI project works towards the development of a comprehensive design for a pan-African climate observation system and research infrastructure using the climate services identified and required by key stakeholders as a guiding design principle. Knowledge is compiled and gaps identified through the SEACRIFOG collaborative inventory tool, the OSCAR/Surface, OSCAR/Space and OSCAR/Requirements tools, as well as a comprehensive survey and other stakeholder engagement. A pilot project focused on Kenya collects and integrates information on user requirements, existing and past observing capabilities, and services. Based on extensive engagement with stakeholders who use or provide weather, climate and atmospheric composition services, lessons-learnt and best practices for future endeavours will be distilled. The outputs from this will further inform the strategic design of the long-term observational and data infrastructures required.

The results so far suggest that services need to cover diverse requirements of a wide range of stakeholders. Sustainable standardized observations are a critical foundation. Sustainability requires long-term commitment of the operating institution at various organizational levels. Information derived from observations is often required with short lead times. Twinning programs and personnel exchange between new and established stations or laboratories can be effective to advance and transition new monitoring capabilities into full operation. 

The presentation will introduce the approaches and first results.

How to cite: Klausen, J., Danioth, S., Nying’uro, P., Kimutai, J., Thiong’o, K., Steinbacher, M., Merbold, L., Käyhkö, N., Saunders, M., Bilola, T., Salmon, E., and Kutsch, W. L.: Taking stock of observing capabilities for designing a pan-African atmospheric and climate research infrastructure (KADI): Lessons learnt from Kenya and best practices., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16993, https://doi.org/10.5194/egusphere-egu24-16993, 2024.

The Mobile-Tensaw Delta comprises one of the United States most important urban-influenced coastal systems. Known as the “North America’s Amazon”, the Mobile-Tensaw Delta has experienced significant anthropogenic disturbance such as from logging, land- cover change, and hydrologic modification. This region is also extremely susceptible to the consequences of climate change. On the land, these consequences include temporal trends and variability in temperature, precipitation, evapotranspiration and primary productivity. Whereas from the water, stressors include sea level rise, increasing salinity, changing period and frequency of wetland inundation, and changing sedimentation regimes. In combination, trends and variability in the environment are expected to increase plant water stress and alter water and carbon cycling processes in the Mobile-Tensaw Delta. Eddy covariance is of great use to several regulatory and commercial applications, related to environmental and water management, industrial monitoring, agricultural production, and other areas where directly measured energy, water vapor or gas exchanges, emissions and budgets are of interest. Major flux measurement networks exist to provide global synthesis, which allows interpretation of one particular site in the context of world-wide observations. Automated and semi-automated technical tools are now also available to expand the use of automated flux stations, individually and as a part of cross shared flux networks, into modelling and remote sensing with global coverage and local resolution. In the JAGFLUX network, we are designing and implementing a structure of eddy covariance towers throughout the Mobile-Tensaw Delta in order to understand the responses of different terrestrial ecosystems on releasing/absorbing water to and from the atmosphere and emitting/absorbing carbon to and from the atmosphere. The idea is to create a network of eddy covariance flux towers over hardwood evergreen forests, wetlands, marshes and also agricultural areas in the surroundings. These towers, together with remote sensing (satellite) data and modeling we will be able to investigate, e.g., how different ecosystems in the Delta are behaving, both spatially and temporally, in terms of acting a sink or source of carbon. Moreover, the measurements will help to improve fundamental, process-level understanding of the vegetation structure across the Mobile-Tensaw Delta, serving as a basis for future studies addressing the future link between forest degradation, water-use efficiency, and climate change in the region.

How to cite: de Oliveira, G., Hellenkamp, S., and Lehrter, J.: Development of JAGFLUX: An eddy covariance flux tower network in the Mobile-Tensaw Delta, the second largest delta in the United States, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13042, https://doi.org/10.5194/egusphere-egu24-13042, 2024.

Worldwide, peatlands have been transformed from carbon sinks to carbon sources due to years of intensive agriculture and livestock farming, requiring low water tables. In the Netherlands, emissions from drained organic soils mount up to around 3% of all national greenhouse gas emissions, and account for 4.6 to 7 Mt CO₂ annually. As part of the 2019 national climate agreement, the Dutch government set a specific mitigation target for these emissions of 1 Mt per year by 2030. In light of this target, the Netherlands Research Progamme on Greenhouse gas dynamics in Peatlands and organic soils (Dutch: NOBV) investigates the efficiency of proposed mitigation measures, and aims to contribute valuable scientific findings to the politically and socially sensitive debate around this issue. The focus of our study aligns with the NOBV's objective to enhance the understanding and quantification of drivers of regional emissions.

To research regional fluxes, NOBV incorporates a new approach: Eddy Covariance (EC) measurements are taken from a low-flying ultra-light aircraft. Fluxes of CO­₂, momentum, sensible and latent heat are measured, as well as meteorological variables. Weather permitting, the airborne surveys are done twice a week since 2020, over the three main fen meadow areas in the Netherlands. The crosswind, parallel flight tracks ensure that the footprints overlap, thus cover the full area. Additionally to the airborne data collection, a large EC tower network has been established with both stationary and mobile systems, encompassing 25 measurement sites.

In this study, we combine airborne and tower flux data, to make use of their different strengths: spatial heterogeneity and temporal continuity, respectively. We use footprint analysis to extract the corresponding spatial information from maps, remote sensing, and a daily soil-water information product. Using this data, we train a boosted regression tree (BRT) machine learning algorithm. Feature selection and hyperparameter tuning are applied as model optimization techniques, and subsequently Shapley values and various simulations are used to interpret the model’s outputs.

Related to the public debate and other studies on emissions from organic soils, we specifically investigate the influence of water table dynamics. A first analysis shows that during nighttime and at high incoming photosynthetically active radiation, every 10 centimeters lowering of efficient water table depth leads to 3.7 tonnes CO₂ ha^-1 yr^-1, which corresponds to current estimates. We will present these, and further results, showing what and how determines the CO₂ fluxes from drained fen meadows in the Netherlands.

How to cite: van der Poel, L., Franssen, W., Bataille, L., Hutjes, R., and Kruijt, B.: Analysing airborne CO2 flux measurements in relation to spatiotemporal characteristics of drained fen meadows in the Netherlands with machine learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10322, https://doi.org/10.5194/egusphere-egu24-10322, 2024.

The Carbon Dew Community of Practice is an international non-profit representing carbon and climate experts from over 160 organizations. Our vision is to anchor fair and equitable climate solutions in direct atmospheric measurements of GHG transfers to or from the atmosphere. Our mission is to facilitate technology transfer by providing a medium for public and private entities to work together towards common goals. We strive to translate surface-atmosphere science into real-world impacts and innovate industry practices with the best available science. To achieve this, we support the integration and coordination of existing capabilities and resources for enhancing the measurement and quantification of GHG emissions and removals.

Initial Endeavors:

• Formation of a well-rounded community representing all pertinent stakeholders and experts in climate solutions and GHG emissions trading.
• Launching collective contributions to workshops and conferences aimed at educating on the significance of direct GHG measurements for equitable climate solutions and emissions trading.
• Collaborative creation of responses to government policy and funding proposals, co-authoring publications, and piloting projects to test the efficacy of specific methodologies.
• Future phases will involve efforts towards comprehensive recommendations or protocols, ensuring a balance across environmental, economic, financial, and regulatory aspects to achieve practical and fair climate solutions globally.

Key Stakeholders:

• Natural and managed ecosystems contributing to global-scale carbon sequestration and storage services.
• Growers, ranging from small-scale farmers to large farm corporations, with substantial potential for reducing carbon emissions.
• Industries across food, oil, and gas sectors, holding significant potential for carbon emission reduction.
• Municipalities and local governments empowered to curtail carbon emissions through regulations and community-focused incentives.
• For-profit entities like financial consultants, carbon traders, and tech innovators capable of incentivizing emission reduction while generating profits.
• National governments and global non-profits serving as regulators and facilitators to drive societal improvements and incentivize emission reduction.

This presentation offers a progress report on the latest available tools and other developments in practical technology transfer of flux tools from academia to wider society, the latest adoption examples from FAO to the oil and gas sector, and a progress report on the latest activities by the CarbonDew Community.

How to cite: Burba, G., Metzger, S., and Community, C.: Pioneering Direct Flux Measurements for Immediate Societal Benefits: Latest Tools, Developments, and Community of Practice, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1265, https://doi.org/10.5194/egusphere-egu24-1265, 2024.

The Eddy Covariance (EC) method allows for the monitoring of carbon, water, and energy fluxes between Earth’s surface and atmosphere. Due to it’s varying interdependent data streams and abundance of data as a whole, EC is naturally suited to Artificial Intelligence (AI) approaches. The integration of AI and EC will likely play a crucial role in the climate change mitigation and adaptation goals defined in the Sustainable Development Goals (SDGs) of the Agenda 2030.

To aid this, we present a scoping review in which the novelty of various AI techniques in environmental science from the past two decades has been collected. Overall, we find a clear positive trend in the quantity of research in this area, particularly in the last five years. We also find a lack of uniformity in available techniques, due to the diverse technologies and variables employed across environmental conditions and ecosystems.

We suggest that future progress in this field requires an international, collaborative effort involing computer scientists and ecologists. Modern DL techniques such as Transformers and generative AI must be investigated to find how they may benefit our field. A forward-looking strategy must be formed for the optimal utilization of AI combined with EC to define the future actions in flux monitoring in the face of climate change.

Keywords: eddy covariance, artificial intelligence, flux monitoring, machine learning, deep learning, climate change.

How to cite: Lucarini, A., Lo Cascio, M., Marras, S., Spano, D., and Sirca, C.: Eddy Covariance and Artificial Intelligence: a review, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12579, https://doi.org/10.5194/egusphere-egu24-12579, 2024.

We present an approach to using a machine learning based regression model to estimate CO₂ fluxes at 30 meter spatial resolution. The method uses eddy covariance measurements of CO₂ obtained from in situ stations (FLUXNET) as primary reference data.  Multispectral satellite observations collected by Landsat are combined with meteorological information to form feature vectors that are used as predictor variables. The XGBoost machine learning algorithm is used to train the regression models on a per-land cover basis. The resulting models can be used to estimate CO₂ fluxes wherever Landsat satellite imagery is available.  Moreover, the approach provides a framework that is extensible to other satellite imagery types and will improve in accuracy as more primary reference data becomes available.  We present results of the method as applied to examples in the agricultural sector.

How to cite: Granat, R., Dara, A., Demidov, O., and Toth, G.: Estimating CO2 Flux at 30 Meter Resolution Using Machine Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13899, https://doi.org/10.5194/egusphere-egu24-13899, 2024.

Quantifying CO2 fluxes over terrestrial land is crucial to better understand the global carbon cycle and the contribution of ecosystems to climate change. In addition, ecosystems such as croplands and forests have the potential to sequester carbon in the soil and vegetation. Robust tools to simulate CO2 fluxes with high accuracy are needed to identify best practices and management for carbon sequestration.
In this study, the Net Ecosystem Exchange (NEE) from different networks (ICOS, NEON) is used to develop machine learning (ML) approaches to simulate daily CO2 fluxes. These biome specific approaches use as input high spatial and temporal resolution optical remote sensing products combined with meteorological data. The biomes considered are cropland, deciduous forest, evergreen forest and grassland. Different ML models were tested and the ExtraTreesRegression model seems to be better suited for all biomes except grassland where an SVR model was more appropriate. The features identified as most important among the remote sensing products are NDVI and NDMI while among meteorological variables, global radiation, air temperature and fraction of diffuse radiation appears as more relevant.
The predicted results show good agreement with daily observations, with R2 of 0.82 over cropland. The performance of the model in simulating CO2 fluxes over forests is more contrasted with good accuracy over deciduous forests (R2 of 0.72) but low confidence over evergreen forests (R2 of 0.29). Finally the model was also applied to grassland, but the small size of the dataset combined with the high heterogeneity of soil and climatic conditions of grassland sites led to low correlation with observations (R2 of 0.44).
This work demonstrates the potential of a machine learning-based method to assess CO2 fluxes across different biomes, and should be further explored due to its ease of use and application.

How to cite: Goussard, B., Pique, G., and Dussot, S.: Estimation of CO2 fluxes across different biomes using machine learning approaches, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19586, https://doi.org/10.5194/egusphere-egu24-19586, 2024.

In March of 2022, AP News and other news outlets reported that the Earth's poles were experiencing simultaneous heatwaves.  Portions of the Arctic were more than 30 degrees Celsius warmer than expected, while some locations in Antarctica were  40 degrees Celsius warmer than average.  While some suspect this freakish outcome results from human-induced climate change, others have scoffed at this suggestion.  With this attribution uncertainty in mind, this paper seeks to understand the drivers in hourly temperatures at the Ny-Ålesund station (NYA) in Svalbard (Latitude: 78.9227,  Longitude: 11.9273)  and the Neumayer Station (GVN) in Antarctica (Latitude: -70.6500, Longitude: -8.2500).

 Based on one-minute data from the Baseline Surface Radiation Network (BSRN), the hourly temperature data for NYA and GVN from January 1, 1999, through December 31, 2019, were calculated.  Hourly averages of the following radiation variables were also calculated: Short-Wave Downward (SWD), Long-Wave Downward(LWD),  Short-Wave Upward (SWU), and  Long-Wave Upward (LWU).   The hourly net radiation flux at the Earth's surface was then calculated as SWD + LWD – SWU – LWU.  This variable is of interest because it is recognized as an important driver of the weather and climate system.  The analysis also uses the hourly CO₂ concentration data for Svalbard reported by the  Integrated Carbon Observation System (ICOS) from January 1, 2004 through December 2019.

The analysis proceeds by employing the Vector Autoregressive Regression (VAR) method.  The general approach considers K variables specified as linear functions of p of their lags and p lags of the other K - 1 variables.  Using this methodology, one can subsequently test for Granger Causality.  The methodology is based on whether the lagged values of some variable X are useful in predicting the current value of some variable Y. Because of its focus on the lagged values, the methodology does not contest the truism that the correlation between two contemporaneous variables does not imply causation.

The VAR/Granger methodology is first applied here to model the possible relationship between hourly CO₂ concentrations and the hourly net radiation flux levels at  NYA.      In this case,  there is strong statistical evidence that hourly CO₂ concentrations at NYA have Granger Causal implications for the hourly net radiation flux at NYA.  Consistent with this finding, the out-of-sample hourly net radiation flux predictions for NYA are more accurate than a persistence forecast when the lagged CO₂ concentrations are included in the analysis.   

The following evidence is also presented: the hourly net radiation flux at NYA has Granger Causal implications for the hourly temperature at NYA, the hourly net radiation flux at NYA in the Polar region has  Granger Causal implications for the net radiation flux at the GVN station in Antarctica, and the hourly temperatures at NYA and GVN exhibit two-way Granger Causality.  In short, the analysis in the paper supports the view that the atmospheric and meteorological conditions at any location are highly interrelated with conditions elsewhere and that the pace of freakish weather conditions is likely to increase as CO₂ concentrations continue to rise.

How to cite: Forbes, K.: CO2 Concentrations and the Freakish Heatwaves at the Poles: A Preliminary Analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19726, https://doi.org/10.5194/egusphere-egu24-19726, 2024.