Two months ago, from October 14 to 18, Wageningen University and Research (WUR) hosted the nextGEMS Hazard Hackathon. Nearly 80 participants from 17 countries across three continents traveled to the Netherlands for this unique event.
Unlike previous hackathons that divided participants based on the nextGEMS working groups Storms and Land, Storms and Oceans, Storms and Radiation, and Storms and Society, this event took a fresh approach. Participants were organized into challenge groups focused on specific hazard-related topics, such as efficient data handling, the energy sector, fire weather, and extreme precipitation and temperature. These groups delivered remarkable insights and visualizations. Take a look for yourself:
Led by Lukas Brunner and Olivia Martius, this group focused on providing global extreme indices for the HEALPix zoom level 9. They developed highly detailed plots, such as a comparison of surface temperature fields from the ICON and IFS models. One visualization revealed significant discrepancies of the annual maximum temperatures (txx) between the two models that were especially pronounced in North America and Australia. These results are likely due to differences in how the models simulate land-atmosphere interactions.
Coordinated by Menno Veerman and Edgar Dolores-Tesillos, this team analyzed weather-dependent energy production, in particular solar and wind energy. They explored the spatial patterns of each of these around the world and found that there is more capacity to produce wind energy over the oceans than on land, and a larger solar energy capacity in regions closer to the equator. In a case study approach, the team also discovered distinct spatial patterns of solar and wind energy production across Spain. Additionally, the researchers progressed a trend analysis for the region of Spain, to assess how the energy production capacity might change over time.
The team led by Ralf Hand and Chiel van Heerwaarden focused on evaluating the potential of nextGEMS models to simulate realistic fire-prone weather conditions. They also sought to identify the factors driving potential changes in wildfire risk in the future. During their work, the team successfully modeled fire weather indices (FWI) as used by DWD, and also observed that humidity trends remain constant over time. However, they noted differences in the calculations produced by the IFS and ICON models, which require further investigation. Following this hackathon, the scientists plan to rerun these calculations using higher-resolution data to better understand how coarse versus high-resolution data impacts the results.
Jonathan Wille, Jasper Denissen, and Birgit Suetzl led the extreme precipitation and temperatures and urban heat challenge. This group examined the simulation of temperature and precipitation extremes at different levels, from the global to the local scale. The participants explored various topics within this broader frame, including the visualization of urban heat extremes, future changes in extreme precipitation behavior, and the connection between precipitation extremes and river runoff in alpine regions. They found that changes in the frequency of heavy precipitation events depend on the rarity of the event and the modeling approach. For instance, a 1-in-3-year event occurs 5% more frequently in IFS simulations and 20% more frequently in ICON simulations. The researchers also discovered that these changes vary by the region in which the precipitation events occur, with heavy precipitation events in the Northern Hemisphere becoming more frequent at locations further away from the equator.
In addition to working on their group challenges, participants engaged in several enriching side events. Paolo Davini and Matteo Nurisso from CNR-ISAC introduced the model evaluation framework AQUA, developed as part of the Destination Earth initiative (DestinE). During a workshop on energy storylines conducted by Eulàlia Baulenas and Dragana Bojovic, participants debated which nextGEMS data would be relevant for energy industry stakeholders and how the project could help them make more informed decisions. Experts like Nuria Sanchez from Iberdrola and Hester Biemans from WUR shared captivating insights on topics such as renewable energy and food security.
On the final day, the Storms and Society working group presented their ongoing efforts in knowledge co-production and communication strategies. Their outputs aim to bridge research and policy-making through storylines, policy briefs, and accessible Science Explainers that communicate complex research to the public in simple terms.
Hackathons like this Hazard Hackathon foster collaboration, innovation, and knowledge sharing, as emphasized by Bjorn Stevens, Director of the Max Planck Institute for Meteorology. Stevens highlighted how nextGEMS contributes to broader climate modeling projects, including EERIE, WarmWorld, and DestinE. Thanks to the efforts of the nextGEMS community, DestinE successfully launched its system in June 2023, with its data now accessible to the nextGEMS community and the wider academic community via a newly released DestinE platform.
However, not only scientific input and outcomes were at the focus of the Hazard Hackathon. The organizers also prioritized inclusivity by offering pronoun stickers for all attendees and rainbow lanyards for LGBTQIA2S+ community members and allies. These thoughtful gestures aimed to foster respect and acceptance for the diverse gender identities and sexual orientations within the nextGEMS community. For further reading on supporting the Queer community, attendees were encouraged to consult the HRC report on Being an LGBTQ+ Ally or explore resources provided by the EGU Pride group, which supports Queer individuals in geosciences and their allies.
Following three years of intensive knowledge creation, hacking, and collaboration, the nextGEMS project is now transitioning into its final phase. During the recent gathering, Bjorn Stevens initiated a discussion about the future of the nextGEMS community and its potential evolution beyond the project’s official timeline. As part of this dialogue, he announced an unprecedented event: the World Climate Research Programme Global KM-scale Hackathon.
This groundbreaking global hackathon is scheduled to take place from May 12–17, 2025, and will be hosted by multiple climate modeling institutes across the globe, including locations in Australia, Brazil, Argentina, China, Europe, India, Japan, North America, and South Africa. This unique, multi-continental approach highlights the collaborative and inclusive spirit of the climate research community.
To stay updated on the nextGEMS project and future events, including the final nextGEMS Hackathon, visit our news section and follow our social media channels.
Easy Gems is an online platform that consolidates information on high-resolution climate simulations produced by the nextGEMS project and other European initiatives, such as European Eddy-Rich ESMs (EERIE), WarmWorld, and the DYAMOND initiative. Developed by the German Climate Computing Center (DKRZ), this platform serves as a repository of best practices and a comprehensive guide to create climate models. According to DKRZ Senior Scientist Florian Ziemen, one of the key aspects behind the creation of Easy Gems was “the idea was to have one place where you can find everything you need when you want to analyze high-resolution simulations.”
At the same time, the goal was to build something that was not tied strictly to a single project, thereby ensuring the sustainability of the platform and its data even after individual projects conclude. This approach prevents the platform from ending up in the „website dumpster,“ Ziemen explained. As a result, Easy Gems is designed to extend beyond individual projects by offering access to all simulation data—or „simulation gems“—hosted at DKRZ, while also functioning as a how-to resource or e-book.
Since nextGEMS drives the development of two European storm-resolving Earth-system models — the ECMWF Integrated Forecasting System (IFS) and the Icosahedral Nonhydrostatic Weather and Climate Model (ICON) — Easy Gems includes details on three development cycles of these Earth System Models, as well as some pre-final simulations. This encompasses simulations at various horizontal resolutions and evolving model configurations.
Additionally, the platform offers guidance on logging data, plotting data, and applying a variety of best practices in data processing. It is entirely user-driven, meaning that the Easy Gems community actively contributes and keeps the content up-to-date. Beyond serving as a guide, the platform acts as a comprehensive documentation tool for the project, providing access to project outputs that are further illustrated and explained.
The platform is organized into three main sections: Simulations, Processing, and Contribute. The Simulations section provides detailed information on the simulations currently available, while the Processing section offers tips and example scripts for data handling. Finally, the Contribute section explains how users can collaborate and become part of this community-driven effort.
Easy Gems encourages contributions from anyone, regardless of their background. Contributions can range from reporting errors and requesting additional articles to suggesting clearer descriptions or improved illustrations. To contribute and interact with Easy Gems, users need a DKRZ account. Additionally, additions require approval from other members of the community to ensure that the input is correct and avoid redundancies. Currently, there are 3,260 registered accounts, with approximately 490 users granted access to all the data hosted on the platform.
If you’re interested, feel free to check out Easy Gems here!
To date, numerous studies have explored the connection between water stored in the soil, also called soil moisture, and precipitation. Using coarse-resolution global climate models, these studies have consistently found a positive feedback between soil moisture and precipitation. In other words, wet soils favor rain. And as rain itself wets the soil, a positive feedback between rain and soil moisture is maintained: soil moisture matters!
In a pioneer study, Hohenegger et al. (2009), looked for the first time at this same soil moisture-precipitation feedback in km-scale simulations conducted over the Alpine region and found the opposite result: dry soils favor rain. Hohenegger et al. delved into the reasons behind these conflicting findings and related them to how different models represent convection. Coarse-resolution models rely on simplified statistical representations, known as parametrization, to describe convective processes. In contrast, kilometer-scale models represent convection explicitly by solving the underlying fluid dynamical equation (a mathematical description that explain how liquids and gases move and behave). Yet, due to computational constraints, the study of Hohenegger et al. (2009) could only simulate the climate over a small region. It could not be excluded that the lateral boundary conditions (LBCs) required at the border of that region, which are taken from a coarse-resolution global model, may spuriously affect the sign of the soil moisture-precipitation feedback (What are LBCs? Read: Davies, 2013).
Building on these insights, Lee and Hohenegger, in their 2024 study, sought to overcome the limitations of Hohenegger et al. (2009) and following studies. They employed a global, coupled climate model with explicit convection and a 5km resolution. Using the storm-resolving version of the ICON model allowed Lee and Hohenegger to represent the feedback more accurately than coarse-resolution models by representing convection explicitly, while allowing the large-scale circulation to freely evolve and interact with convection by using a global domain (a simulation over the full Earth). Their remarkable findings suggest that precipitation is less influenced by soil moisture and evapotranspiration (the combined effect of evaporation, the transport of moisture from the earth surface directly to the air, and transpiration, the transport of moisture from the soil to the air via plants) than coarse-resolution climate models have led us to believe.
The study revealed several key points:
When compared with observational data, the global, coupled storm-resolving model provided more accurate representations of the strength of the correlation between soil moisture and precipitation for over 80% of the locations, suggesting that this type of model may be better suited for global precipitation modeling.
These findings indicate that coarse-resolution climate models may overestimate the role of land cover change and of the land surface in general for precipitation. They challenge our understanding of climate over land and may indicate that precipitation patterns may be more robust than previously thought.
For more on this topic, find the entire publication here.
References:
Hohenegger, C., Brockhaus, P., Bretherton, C. S., & Schär, C. (2009). The Soil Moisture–Precipitation Feedback in Simulations with Explicit and Parameterized Convection. Journal of Climate, 22(19), 5003-5020. DOI: 10.1175/2009JCLI2604.1
Lee, J. & Hohenegger, C. (2024). Weaker land–atmosphere coupling in global storm-resolving simulation. Proceedings of the National Academy of Sciences (PNAS), 21(12). DOI: 10.1073/pnas.2314265121
Nowadays, Machine Learning (ML) can assist in identifying climate models based on daily output. How? Firstly, it is important to understand ML as the process of developing algorithms that enable computers to learn and make predictions or decisions based on data, without being explicitly programmed for each scenario. In that context, ML techniques, such as Convolutional Neural Networks (or CNNs), are increasingly utilized in Climate Science to evaluate climate models; identify model characteristics; and assess model performance in comparison to observational data.
The study “Identifying climate models based on their daily output using machine learning”, by researchers Lukas Brunner and Sebastian Sippel, shed light on the use of ML classifiers – such as the CNNs mentioned before. Specifically, on how they can be trained to robustly identify climate models using daily temperature output.
By analyzing individual daily temperature maps, ML methods can separate models from observations and from each other, even in the presence of considerable noise from internal variability on specific weather timescales (Brunner & Sippel, 2023). Internal variability refers to the fluctuations in the climate system that arise from various processes within the Earth’s atmosphere, oceans, and land surfaces that we might refer to as weather. Hence, the ML approach allows for the identification of models and observations based on short timescales, providing new ways to evaluate and interpret model differences.
The study used daily temperature maps from 43 Coupled Model Intercomparison Projects (CMIP6) models and four different observational datasets. Additionally, ICON-Sapphire, one of the Earth system models developed by nextGEMS, was utilized as an experimental km-scale model. With that basis, two different statistical and ML methods were used to separate models from observations, and from each other.
Firstly, through logistic regression, the researchers were able to distinguish between models and observations because it allows the appreciation of the learned coefficients (Brunner & Sippel, 2023). The coefficients learned by the logistic regression classifier reveal that many well-known climatological model biases are already emerging as important for identifying daily maps. Nonetheless, other regions like the Arctic are not relevant for daily classification at all.
It is important to mention that logistic regression is a linear method and, after bias correction with the mean seasonal cycle, it is no longer skilful. To complement logistic regression, a second methodnamely CNN, specially due to the possibility of obtaining more trainable parameters that can also lean more complex, non-linear relationships within the data. (Brunner & Sippel, 2023)
Some of the main results of this research work are related to the high accuracy achieved by CNN classifiers in identifying models and observational datasets, even when faced with complex classification tasks. Overall accuracy of 83% was achieved in identifying 43 models and four observational datasets (Brunner & Sippel, 2023). Moreover, CNNs could pick up unique patterns specific to each dataset, enabling successful separation from other datasets. Generally, it is important to take away that dependencies between models – and observations – emerge even on daily time scales.
On another note, Brunner and Sippel (2023) clarified that misclassifications often occurred within model families or were related to common “ancestors”, indicating shared features among related datasets. However, the study revealed the ability of the CNN to correctly identify a significant portion of test samples, even those from distant time periods and under different climate scenarios.
The authors anticipate a planned follow-up study that aims to analyze the origin of classification skill in more detail, using explainable ML techniques and domain-specific approaches from Climate Sciences. In other words, the follow-up study will investigate the coupling of atmosphere and ocean; surface energy balance in models; and targeted masking of regions to understand model performance dependencies.
If you are interested in working with this method, feel free to do so! The researchers have made the code used in the paper freely available on Github.
References:
Brunner, L., & Sippel, S. (2023). Identifying climate models based on their daily output using machine learning. Environmental Data Science, 2. https://doi.org/10.1017/eds.2023.23
Aerosols, defined as tiny particles suspended in the atmosphere, play a pivotal role in the Earth’s climate system. Despite their minuscule size, often smaller than a human hair, these particles exert a significant influence on the planet’s climate. They originate from natural sources, including sea spray, dust storms, and wildfires, as well as human activities, such as industrial emissions and transportation.
Aerosols affect the climate through both direct and indirect mechanisms. They can absorb or scatter solar radiation, and act as nuclei for cloud droplet formation. These dynamics result in:
– Cooling the Earth’s surface by reflecting solar radiation.
– Warming the atmosphere by absorbing heat.
– Influencing cloud formation, brightness, and lifetime.
While aerosols generally contribute to cooling the Earth, quantifying this effect is a complex scientific challenge due to uncertainties, particularly related to indirect effects.
Accurately incorporating aerosol interactions within climate simulations has been a persistent difficulty. There are coarse-scale climate simulations with interactive aerosol models, but these models are very expensive. Moreover, they are not feasible to be used in km-scale climate simulations. Therefore, nowadays aerosols in km-scale climate simulations are not interactive but prescribed based on historical data. This approach fails to account for real-time atmospheric processes, such as the deposition of aerosols by winds and precipitation. In other words, this appeal overlooks critical dynamics.
New high-resolution climate models, like those developed in the nextGEMS project, are addressing the limitations mentioned above. These models resolve essential processes in the Earth’s system down to a few kilometers, enabling detailed simulations of phenomena such as thunderstorms and tropical cyclones.
nextGEMS aims to integrate aerosols interactively within these advanced climate models. The process begins with a complex aerosol module, which operates at coarse resolutions. Scientists then simplify the micro-physical aerosol processes, before coupling the simplified version with the new climate model.
This research has produced a streamlined and efficient model, making the aerosol simulations more accurate and usable. The model’s design facilitates understanding and adaptation for other researchers, enabling detailed simulations of long-term processes on a global scale. It now includes intricate processes, such as the transport of aerosols by winds, cloud formation, precipitation, and the scattering or absorption of solar radiation.
The nextGEMS model provides a groundbreaking tool for examining specific events or broader phenomena related to aerosol movement through the Earth system. It improves our understanding of aerosol-cloud-radiation interactions and helps quantify aerosols‘ cooling effects more precisely. This advancement is crucial for better predicting both short-term weather events and long-term climate trends.
Practical applications of this model include investigating how future wildfires across regions like the Congo and the Amazon could affect local cloud formation and precipitation, or assessing the potential damage tropical cyclones may cause to coastal areas of Japan and Florida. Additionally, simulation with interactive aerosols could help us to better understand the radiative forcing and therefore better estimate by how much aerosols actually cool the Earth.
Future research will focus on tracking phenomena over time with high-resolution data and conducting regional studies in areas with unique characteristics or significant events. Furthermore, it will allow the performing of long-term simulations to explore different emissions scenarios. Collaboration with other projects and scientists will continuously refine the model, fostering interdisciplinary research within and beyond the nextGEMS initiative.
Understanding aerosols and their interactions with our climate, we magnify our ability to predict and mitigate the impacts of Climate Change, contributing to a sustainable future for our planet.
Source: Weiss, P., Herbert, R., and Stier, P. (2024). ICON-HAM-lite: simulating the Earth system with interactive aerosols at kilometer scales. EGUsphere [preprint]. DOI: 10.5194/egusphere-2024-3325.
Visualisations created by: Latest Thinking GmbH
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