Experimenting with modern ESM inherently requires a workflow organization to handle the multiple steps comprising of, but not limited to, execution, data governance, cleaning, and coordinating multiple machines. And for climate experiments, due to long scale of the simulations, workflows are even more critical. The community has thoroughly proposed enhancements for reducing the runtime of the models, but long has overlooked the time to response, which also takes into account the queue time. And, that is what we aim to optimize by wrapping jobs, which would otherwise be submitted individually, onto a single one. The intricate three-way interaction of the HPC system usage, scheduler policy, and user's past usage is the main challenge addressed here to analyze the impact of wrapping jobs.

How to cite: Giménez de Castro Marciani, M., Utrera, G., Castrillo, M., and Acosta, M. C.: Assessing Job Wrapping as an Strategy for Workflow Optimization on Shared HPC Platforms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1042, https://doi.org/10.5194/egusphere-egu24-1042, 2024.

At the Earth Sciences department of the Barcelona Supercomputing Center (BSC-ES) a variety of workflows are run for many different purposes like executing climate and atmospheric simulations, data downloading or performance evaluation. This implies having to deal with many different processes and time scales in different environments and machines.

To help conduct all these complex tasks, the Autosubmit workflow manager is used in the whole department as a unique framework. The fact that Autosubmit has been fully developed at the BSC-ES has led to the adoption of a co-design procedure between users, workflow developers and Autosubmit developers to fulfill the day-to-day department needs. The synergy and close collaboration among them allows the workflow engineers to gather the specific user requirements that later become new Autosubmit features available to everyone. Thanks to this continuous interaction at all levels, an efficient and very adaptable system could have been achieved, perfectly aligned with the constantly evolving user needs.

Here this collaborative strategy is presented from the workflow development point of view. Some real use cases and practical examples are used to show the positive impact it had in different operational and research projects, demonstrating how it can help in the achievement of high scientific productivity.

How to cite: Montané Pinto, G., Ferrer, E., Olid, M., Garcia, A., Bonet, G., and Mozaffari, A.: Collaboratively developing workflows at the BSC-ES, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2152, https://doi.org/10.5194/egusphere-egu24-2152, 2024.

The NOAA Global Systems Laboratory, Earth Prediction Advancement Division, Scientific Computing Branch team works on approaches to facilitate development of cloud-resolving, Earth system prediction systems suitable for the next generation of exascale high performance computing (HPC), including exploration of machine learning (ML) algorithms within our systems for improved performance and reduced computational cost. 

Our work is divided into two main categories: incremental - shorter term and innovative - longer term challenges. Work related to incremental changes focuses on existing NOAA algorithms and improvement of their performance on different architectures (e.g. adapting existing codes to run on GPUs). The more innovative aspects focus on development and evaluation of new algorithms and approaches to environmental modeling that simultaneously improve prediction accuracy, performance, and portability. For this purpose we have developed the GeoFLuid Object Workflow (GeoFLOW), a C++ framework with convective (and other)  dynamics, high order truncation, quantifiable dissipation, an option to use a variety of 2D and 3D grids, and excellent strong scaling and on-node properties. An evaluation of the use of ML-based emulators for different components of the earth system prediction models also forms an important part of our research. 

Finally, a large portion of our research and development activities involves building federated and unified workflows to facilitate both the effective use of distributed computing resources as well as easy configuration for nontrivial workflow applications in research and operations.

A comprehensive summary of these research and development activities will be presented.

How to cite: Jankov, I., Abdi, D., Bharwani, N., Carpenter, E., Harrop, C., Holt, C., Madden, P., Sliwinski, T., Rosenberg, D., and Bernardet, L.: Research and Development within the Scientific Computing Branch of NOAA’s Global Systems Laboratory, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6350, https://doi.org/10.5194/egusphere-egu24-6350, 2024.

Computer performance has increased immensely in recent years, but the ability to store data has hardly increased at all. The current version of meteorological reanalysis data ERA5 provided by the European Centre of Medium-Range Weather Forecasts (ECMWF) has increased by a factor of ∼80 compared to its predecessor ERA-Interim. This presents scientists with major challenges, especially if data covering several decades is to be stored on local computer systems. Accordingly, many compression methods have been developed in recent years with which data can be stored either lossless or lossy. Here we test three of these methods two lossy compression methods ZFP and Layer Packing (PCK) and the lossless compressor ZStandard (ZSTD) and investigate how the use of compressed data affects the results of Lagrangian air parcel trajectory calculations with the Lagrangian model for Massive-Parallel Trajectory Calculations (MPTRAC). We analysed 10-day forward trajectories that were globally distributed over the free troposphere and stratosphere. The largest transport deviations were derived when using ZFP with the largest compression. Using a less strong compression we could reduce the transport deviation and still derive a significant compression. Since ZSTD is a lossless compressor, we derive no transport deviations at all when using these compressed files, but do not loose much disk space using this compressor (reduction of ∼20%). The best result concerning compression efficiency and transport deviations is derived with the layer packing method PCK. The data is compressed by about 50%, but transport deviations do not exceed 40 km in the free troposphere and are even lower in the upper troposphere and stratosphere. Thus, our study shows that the PCK compression method would be valuable for application in atmospheric sciences and that with compression of meteorological reanalyses data files we can overcome the challenges of high demand of disk space from these data sets.

How to cite: Khosrawi, F. and Hoffmann, L.: Compression of meteorological reanalysis data files and their application to Lagrangian transport simulations , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10750, https://doi.org/10.5194/egusphere-egu24-10750, 2024.

The Urgent Tsunami Computing procedures discussed herein are designed to quantify potential hazards resulting from seismically-induced tsunamis following an earthquake, with a temporal scope ranging from minutes to a few hours. The presented workflow employs comprehensive simulations, encompassing the entire tsunami propagation process, while accounting for uncertainties associated with source parameters, tsunamigenesis and wave propagation dynamics. Within the EuroHPC eFlows4HPC project, we present a High-Performance Computing (HPC) workflow tailored for urgent tsunami computation in which the Probabilistic Tsunami Forecast (PTF) code has been restructured and adapted for seamless integration into a PyCOMPSs framework. This framework enables parallel execution of tasks and includes simulations from Tsunami-HySEA numerical model within a unified computational environment. Of particular significance is the workflow's capability to incorporate new datasets, such as focal mechanism data, seismic records, or real-time tsunami observations. This functionality facilitates an "on-the-fly" update of the PTF, ensuring that the forecasting model remains responsive to the latest information. The development of this workflow involves a systematic exploration of diverse scenarios, realistic simulations, and the assimilation of incoming data. The overarching goal is to rigorously diminish uncertainties, thereby producing updated probabilistic forecasts without compromising precision and enhancing risk mitigation efforts far from the seismic source. Improved risk management, achieved by informing decision-making in emergency situations, underscores the importance of this development. We will showcase the technical advancements undertaken to tailor the workflow for HPC environments, spanning from the developers' perspective to that of the end user. Additionally, we will highlight the scientific enhancements implemented to leverage the full potential of HPC capabilities, aiming to significantly reduce result delivery times while concurrently enhancing the accuracy and precision of our forecasts.

How to cite: Cordrie, L., Ejarque, J., Sánchez-Linares, C., Selva, J., Macías, J., Gibbons, S. J., Bernardi, F., Tonini, B., Badia, R. M., Scardigno, S., Lorito, S., Romano, F., Løvholt, F., Volpe, M., D'Anca, A., de la Asunción, M., and Magni, V.: A Dynamic HPC Probabilistic Tsunami Forecast Workflow for Real-time Hazard Assessment, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11167, https://doi.org/10.5194/egusphere-egu24-11167, 2024.

The Urgent Computing Integrated Services for Earthquakes (UCIS4EQ) introduces a fully automatic seismic workflow centered on rapidly delivering synthetic assessments of the impact of moderate to large earthquakes throughout physics-based forward simulations. This novel approach links High-Performance Computing (HPC), High-Performance Data Analytics (HPDA), and highly optimized numerical solvers. Its core objective lies in performing numerical simulations either during or right after an earthquake, accomplishing this task within a short timeframe, typically spanning from minutes to a few hours.

During multi-node execution, PyCOMPSs orchestrates UCIS4EQ’s distributed tasks and improves its readiness level towards providing an operational service. UCIS4EQ coordinates the execution of multiple seismic sources to account for input and model uncertainties. Its comprehensive scope provides decision-makers with numerical insights into the potential outcomes of post-earthquake emergency scenarios.

The UCIS4EQ workflow includes a fast inference service based on location-specific pre-trained machine learning models. Such learned models permit a swift analysis and estimation of the potential damage caused by an earthquake. Leveraging advanced AI capabilities endows our workflow with the  ability to rapidly estimate a seismic event's impact. Ultimately it provides valuable support for rapid decision-making during emergencies.

Through the integration of high performance computational techniques and pioneering methodologies, our hope is to see UCIS4EQ emerge as a useful instrument to make agile and well-informed post-event decisions in the face of seismic events.

With this study, we account for UCIS4EQ's continuous development through a number of case studies. These case studies will shed light on the most recent developments and applications of urgent computing seismic workflow, demonstrating its efficacy in providing rapid and precise insights into earthquake scenarios.

How to cite: Blanco-Prieto, R., Monterrubio-Velasco, M., Pienkowska, M., Ejarque, J., Bhihe, C., Zamora, N., and de la Puente, J.: Urgent Computing Integrated Services for Earthquakes , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15849, https://doi.org/10.5194/egusphere-egu24-15849, 2024.

Global kilometer-scale climate models produce vast amounts of output, posing challenges in efficient data utilization. For ICON, we addressed this by creating a consolidated and analysis-ready dataset in the Zarr format, departing from the previous cumbersome directory structure. This new dataset format provides a comprehensive overview of variables and time steps at one glance.

To ensure swift and ergonomic access to the dataset, we employ two key concepts: output hierarchies and multidimensional chunking. We remapped all output onto the HEALPix grid, facilitating hierarchical resolutions, and pre-computed temporal aggregations like daily and monthly averages. This enables users to seamlessly switch between resolutions, reducing computational burdens during post-processing.

Spatial chunking of high-resolution data further allows for efficient extraction of regional subsets, significantly improving the efficiency of common climate science analyses, such as time series and vertical cross-sections. While our efforts primarily integrate established strategies, the synergies achieved in resolution have shown a profound impact on the post-processing efficiency of our global kilometer-scale output.

In summary, our approach, creating a single analysis-ready dataset, pre-computing hierarchies, and employing spatial chunking, addresses challenges in managing and extracting meaningful insights from increasingly large model output. We successfully tested the new analysis-ready datasets during well-attended hackathons, revealing significant usability and performance improvements over a wide range of real-life applications.

How to cite: Kluft, L. and Kölling, T.: Building a useful dataset for ICON output, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17007, https://doi.org/10.5194/egusphere-egu24-17007, 2024.

The European Digital Twin of the Ocean, EDITO, is an initiative of the European Commission that aims to create a virtual representation of marine and coastal environments around the globe to assess future impacts of climate change and human activities, improving the accessibility of ocean knowledge. EDITO Infra is the backbone of EDITO. It provides the infrastructure where components of the EDITO digital twin are combined and integrated.

In this work, we describe how Autosubmit is integrated and used in EDITO Infra, as the back-end component of the Virtual Ocean Model Lab (VOML, a virtual co-working environment). Users of the digital twin can connect to the VOML to customize and build ocean models, run using cloud and HPC resources, and access applications deployed in the EDITO Infra that consume the output of the models. Besides usually being a local software that helps users to leverage remote resources, in this case we demonstrate Autosubmit's versatility with this scenario in which it is deployed using Docker and Kubernetes, tools used traditionally in cloud environments.

In this context, Autosubmit acts as a middleware between the applications, and HPC and Cloud resources. It manages experiments and workflows, connecting EuroHPC’s like MareNostrum 5 and Leonardo. It provides integration with different GUI’s (with a REST API), GIS systems, and other services.

How to cite: De Paula Kinoshita, B., Beltran Mora, D., G. Marciani, M., Tenorio Ku, L., and Castrillo, M.: Workflows with Autosubmit in EDITO, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20844, https://doi.org/10.5194/egusphere-egu24-20844, 2024.

The geophysical research community has developed a relatively large amount of numerical codes and scientific methodologies which are able to numerically simulate through physics the extreme behavior of the Earth systems (for example: volcanoes, tsunamis earthquakes, etc). Furthermore,
nowadays, large volumes of data have been acquired and, even near real-time data streams are accessible. Therefore, Earth scientist currently have on their hands the possibility of monitoring these events through sophisticated approaches using the current leading edge computational capabilities provided by pre-exascale computing infrastructures. The implementation and deployments of 12 Digital Twin Components (DTCs), addressing different aspects of geophysical extreme events is being carried out by DT-GEO, a project funded under the Horizon Europe programme (2022-2025). Each DTC is intended as self-contained entity embedding flagship simulation codes, Artificial Intelligence layers, large volumes of (real-time) data streams from and into data-lakes, data assimilation methodologies, and overarching workflows which will are executed independently or coupled DTCs in a centralized HPC and/or virtual cloud computing research infrastructure.

How to cite: Carbonell, R., Folch, A., Costa, A., Orlecka-Sikora, B., Lanucara, P., Løvholt, F., Macías, J., Brune, S., Gabriel, A.-A., Barsotti, S., Behrens, J., Gomez, J., Schmittbuhl, J., Freda, C., Kocot, J., Giardini, D., Afanasiev, M., Glaves, H., and Badía, R.: Digital Twining of Geophysical Extremes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21138, https://doi.org/10.5194/egusphere-egu24-21138, 2024.

Geo-simulation experiments (GSEs) are experiments allowing the simulation and exploration of Earth’s surface (such as hydrological, geomorphological, atmospheric, biological, and social processes and their interactions) with the usage of geo-analysis models (hereafter called ‘models’). Computational processes represent the steps in GSEs where researchers employ these models to analyze data by computer, encompassing a suite of actions carried out by researchers. These processes form the crux of GSEs, as GSEs are ultimately implemented through the execution of computational processes. Recent advancements in computer technology have facilitated sharing models online to promote resource accessibility and environmental dependency rebuilding, the lack of which are two fundamental barriers to reproduction. In particular, the trend of encapsulating models as web services online is gaining traction. While such service-oriented strategies aid in the reproduction of computational processes, they often ignore the association and interaction among researchers’ actions regarding the usage of sequential resources (model-service resources and data resources); documenting these actions can help clarify the exact order and details of resource usage. Inspired by these strategies, this study explores the organization of computational processes, which can be extracted with a collection of action nodes and related logical links (node-link ensembles). The action nodes are the abstraction of the interactions between participant entities and resource elements (i.e., model-service resource elements and data resource elements), while logical links represent the logical relationships between action nodes. In addition, the representation of actions, the formation of documentation, and the reimplementation of documentation are interconnected stages in this approach. Specifically, the accurate representation of actions facilitates the correct performance of these actions; therefore, the operation of actions can be documented in a standard way, which is crucial for the successful reproduction of computational processes based on standardized documentation. A prototype system is designed to demonstrate the feasibility and practicality of the proposed approach. By employing this pragmatic approach, researchers can share their computational processes in a structured and open format, allowing peer scientists to re-execute operations with initial resources and reimplement the initial computational processes of GSEs via the open web.

How to cite: Zhu, Z. and Chen, M.: Reproducing computational processes in service-based geo-simulation experiments, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3295, https://doi.org/10.5194/egusphere-egu24-3295, 2024.

We propose a framework-agnostic specification for contextualizing Docker containers in environmental research. Given a scientific context, containers are especially useful to combine scripts in different languages following different development paradigms. 

The specification standardizes inputs and outputs from and to containers to ease the development of new tools, retrace results and add a provenance context to scientific workflows. As of now we also provide templates for the implementation of new tools developed in Python, R, Octave and NodeJS, two different server applications to run the containers in a local or remote setting and a Python client to seamlessly include containers into existing workflows. A flutter template is in development, which can be used as a basis to build use-case specific applications for Windows, Linux, Mac, the Web, Android and iOS.

We present the specification itself, with a focus on ways of contributing, to align the specification with as many geoscientific use-cases as possible in the future. In addition a few insights into current implementations are given, namely the role of the compliant pre-processing tools in the generation of the CAMELS-DE dataset, as well as result presentation for a Machine learning application for predicting soil moisture. Both applications are presented at EGU as well. We use these examples to demonstrate how the framework can increase the reproducibility of associated workflows.

How to cite: Mälicke, M., Dolich, A., Manoj Jaseetha, A., Bischof, B., and Reid, L.: Using Docker for reproducible workflows, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15382, https://doi.org/10.5194/egusphere-egu24-15382, 2024.

Large-scale earth system simulations produce huge amounts of data. Due to limited I/O bandwidth and available storage space this data often needs to be reduced before writing to disk or storing permanently. Error-bounded lossy compression is an effective approach to tackle the trade-off between accuracy and storage space.

We are exploring and discussing error-bounded lossy compression based on tree-based adaptive mesh refinement (AMR) techniques. According to flexible error-criteria the simulation data is coarsened until a given error bound is reached. This reduces the number of mesh elements and data points significantly.

The error criterion may for example be an absolute or relative point-wise error. Since the compression method is closely linked to the mesh we can additionally incorporate geometry information - for example varying the error by geospatial region.

We implement these techniques as the open source tool cmc, which is based on the parallel AMR library t8code. The compression tool can be linked to and used by arbitrary simulation applications or executed as a post-processing step. As a first example, we couple our compressor with the MESSy and MPTRAC libraries.

We show different results including the compression of ERA5 data. The compressed sample datasets show better results in terms of file size than conventional compressors such as SZ and ZFP. In addition, our method allows for a more fine-grained error control.

How to cite: Böing, N., Holke, J., Hergl, C., Basermann, A., and Gassner, G.: Adaptive Data Reduction Techniques for Extreme-Scale Atmospheric Models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17154, https://doi.org/10.5194/egusphere-egu24-17154, 2024.

Many of the figures in the WGI contribution to the IPCC Sixth Assessment report (AR6) are derived from the data of multiple CMIP6 simulations.  For instance, a plot showing projections of global temperature change in Figure 2 of Chapter 4 of the IPCC AR6 is based on data from 183 CMIP6 simulation datasets. The figure helpfully tells us which CMIP6 experiments were used as input data but does not provide information about the models that ran the simulations. It is possible to deduce the specific input data from supplementary tables in the IPCC assessment report and from within the report’s annexes.  However, these information sources are not machine-accessible so are difficult to use for tracing purposes, and they are not sufficient to give credit as they do not enter indexing services, and they are difficult to find as they are not part of the printed report. Even if we gather this knowledge to create a navigable provenance network for the figure, we are still left with the unwieldy prospect of rendering 183 data citations for an outwardly simple plot.

We require a compact way to provide traceable provenance for large input data networks that makes transparent the specific input data used to create the CMIP6-based figures in IPCC AR6 and gives credit to modelling centres for the effort of running the simulations. The so-called complex citation discussed within the RDA Complex Citation Working Group. 

We present a pragmatic solution to the complex citation challenge that uses an existing public infrastructure technology, Zenodo.  The work establishes traceability by collating references to a figure’s input datasets within a Zenodo record and credit via Zenodo’s relatedWorks feature/DataCite’s relations which link to existing data objects through Persistent Identifiers (PIDs), in this case the CMIP6 data citations.   Whilst a range of PIDs exist to support connection between objects, the use of DOIs is widely used for citations and is well connected within the wider PID graph landscape and Zenodo provides a tool to create objects that utilise the DOI schema provided by DataCite.  CMIP6 data citations have sufficient granularity to assign credit, but the granularity is not fine enough for traceability purposes, therefore Zenodo reference handle groups are used to identify specific input datasets and Zenodo connected objects provide the join between them.

There is still work to be done to establish full visibility of credit referenced within the Zenodo records.  However, we hope to engage the community by presenting our pragmatic solution to the complex citation challenge, one that has the potential to provide modelling centres with a route to a more complete picture of the impact of their simulations.

How to cite: Pascoe, C., Stockhause, M., Parton, G., Fisher, E., MacRae, M., Kreuss, B., and Sitz, L.: A pragmatic approach to complex citations, closing the provenance gap between IPCC AR6 figures and CMIP6 simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20519, https://doi.org/10.5194/egusphere-egu24-20519, 2024.

The advancement of science is increasingly intertwined with complex computational processes [1]. Scientific workflows are at the heart of this evolution, acting as essential orchestrators for a vast range of experiments. Specifically, these workflows are central to the field of computational Earth Sciences, where they orchestrate a diverse range of activities, from cloud-based data preprocessing pipelines in environmental modeling to intricate multi-facility instrument-to-edge-to-HPC computational frameworks for seismic data analysis and geophysical simulations [2].

The emergence of continuum and cross-facility workflows marks a significant evolution in computational sciences [3]. Continuum workflows represent continuous computing access required for analysis pipelines, while cross-facility workflows extend across multiple sites, integrating experiments and
computing facilities. These cross-facility workflows, crucial for real-time applications, offer resiliency and stand as solutions for the demands of continuum workflows. Addressing continuum and cross-facility computing requires a focus on data, ensuring workflow systems are equipped to handle diverse data
representations and storage systems.

As we navigate the computing continuum, the pressing needs of contemporary scientific applications in Earth Sciences call for a dual approach: the recalibration of existing systems and the innovation of new workflow functionalities. This recalibration involves optimizing data-intensive operations and
incorporating advanced algorithms for spatial data analysis, while innovation may entail the integration of machine learning techniques for predictive modeling and real-time data processing in earth sciences. We offer a comprehensive overview of cutting-edge advancements in this dynamic realm, with a focus on computational Earth Sciences, including managing the increasing volume and complexity of geospatial data, ensuring the reproducibility of large-scale simulations, and adapting workflows to leverage emerging computational architectures.

[1] Ferreira da Silva, R., Casanova, H., Chard, K., Altintas, I., Badia, R. M., Balis, B., Coleman, T., Coppens, F., Di Natale, F., Enders, B., Fahringer, T., Filgueira, R., et al. (2021). A Community Roadmap for Scientific Workflows Research and Development. 2021 IEEE Workshop on Workflows in Support of Large-Scale Science (WORKS), 81–90. DOI: 10.1109/WORKS54523.2021.00016

[2] Badia Sala, R. M., Ayguadé Parra, E., & Labarta Mancho, J. J. (2017). Workflows for science: A challenge when facing the convergence of HPC and big data. Supercomputing frontiers and innovations, 4(1), 27-47. DOI: 10.14529/jsfi170102

[3] Antypas, K. B., Bard, D. J., Blaschke, J. P., Canon, R. S., Enders, B., Shankar, M. A., ... & Wilkinson, S. R. (2021, December). Enabling discovery data science through cross-facility workflows. In 2021 IEEE International Conference on Big Data (Big Data) (pp. 3671-3680). IEEE. DOI: 10.1109/BigData52589.2021.9671421

How to cite: Ferreira da Silva, R., Maheshwari, K., Skluzacek, T., Souza, R., and Wilkinson, S.: Advancing Computational Earth Sciences:Innovations and Challenges in Scientific HPC Workflows, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21636, https://doi.org/10.5194/egusphere-egu24-21636, 2024.

The Integrated Carbon Observation System (ICOS) and the Lund University department of Physical Geography and Ecosystem Science have developed the Lund University Modular Inversion Algorithm (LUMIA) that is being used for the inverse modelling of carbon and isotope resolved methane. The work is linked with past and present Horizon 2020 projects like DICE and AVENGERS with the overarching goal of supporting the Paris agreement goals.

The ICOS Carbon Portal (https://data.icos-cp.eu/portal) collects, maintains and supports a large range of greenhouse gas observations as well as some inventory and model data all of which have a unique persistent identifier each, which is a key pre-requisite for achieving a reproducible work-flow.

Here we present a self-documenting fully reproducible work-flow for our inverse carbon model LUMIA, that is based on frameworks discussed in EU initiatives like Copernicus CAMS and CoCO2 as well as our own experiences from actual work-flows routinely used in Australian court cases against illegal land use changes. We will show a live demonstration of the system including its graphical user interfaces and the created provenance and reproducibility meta-data.

How to cite: Meier, A., Monteil, G., Karstens, U., Scholze, M., and Vermeulen, A.: A fully automated reproducible self-documenting workflow implemented in our inverse regional carbon model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19437, https://doi.org/10.5194/egusphere-egu24-19437, 2024.