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!

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.

Separating models from observations, and from each other

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)

Main findings and future directions

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.

How do aerosols impact our climate?

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.

Challenges in Aerosol Modeling

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.

Interactive aerosols in High-Resolution Climate Models

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.

Practical Implications and Future Research

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

How Cordex can help us build the next generation of climate services

– a talk by Daniela Jacobs

On Monday, March 4th 2024, Prof. Dr. Daniela Jacob, Director of the Climate Service Center Germany (GERICS), held an inspiring keynote at the opening event of the 4th km-scale hackathon. She painted a picture of the challenges posed by climate change, affecting the lives of all of us, and how the Coordinated Regional Climate Downscaling Experiment (CORDEX) and the Earth Visualization Engine (EVE) could be the next steps towards an international, joint approach to address and tackle the challenging times ahead.

Recent decades have shown that only half a degree of rising global temperature was enough to induce immense changes in the frequency and intensity of droughts, heat waves and extreme precipitation. These developments continue in the coming years causing extreme events in places where they had never happened before. The current youth will, throughout their lifetime, experience a change in temperature of 4-6°C if we do not act urgently on mitigation of greenhouse gas, threatening their livelihood and potentially impacting their physical and mental health.

The climate modeling community holds both the privilege and the duty to understand what lies ahead of us. Developing adaptation measures that are specific to various places and contexts requires enhanced integration of global and local impact models. To do so it is not sufficient to only learn from the past but we will need to utilize scenarios to understand the future climate risks and mitigate their impacts at regional and local levels.

The Current Approach

This is where CORDEX plays a vital role. For the last 15 years, the Coordinated Regional Climate Downscaling Experiment has been advancing climate science through regional climate down scaling in a global partnership framework. Its relatively coarse scale of 12 to 25km for each continent embeds some higher resolution domains, creating information that can be integrated at the local scale to support sustainable decision-making.

By sharing this information, consulting, discussing and collaborating with different experts in a sustainable way, scientists are able to move to finer and finer scales, revealing weather and climate details that were not previously captured in the models.

The challenges we face now are the inequity of access to information, the mismatch between the scale of information available and that needed to answer the questions being asked, the low impact of users in generating information, as well as the very limited ability of users to interact with information.

The way forward

EVE aims to address these issues by creating „an international federation of centres of excellence to empower all people to respond to the immense and urgent challenges posed by climate change“ in an international, collaborative venture (Stevens et al., 2024). The concepts upon which EVE is being developed could revolutionize the way stakeholders interact with information for modeling and advances in knowledge creation. The plan is to build a digital infrastructure that will take advantage of the latest scientific and technological developments for the production and sharing of climate information. EVE is designed to engage all societal actors in assessing climate risks and supporting adaptation processes. It combines real-world data, simulations and AI to create an interactive, multi-tiered information system that uses high-performance computing to develop the most sophisticated simulations of future climate change.

The experience gained with CORDEX can be built upon to provide a solid foundation for EVE, contributing to and shaping its development. In return, EVE could serve as an integration platform supporting climate services by allowing quick interaction and analysis.

Typically, Earth system models employ grid spacing of 100 or 150 km to represent processes on land, in the atmosphere, in the oceans and sea-ice, which could result in imprecise climate projections. By using a fifty-fold finer horizontal grid with a 3 km scale – or storm resolving models –, nextGEMS is trying to reach an advanced level of resolution in climate modelling. These types of models can realistically project critical small-scale climate processes that have been neglected or represented empirically through parametrisations before, possibly introducing errors or bringing unclearness to the models.

Hence, nextGEMS is currently simulating the climate at resolutions never seen before with the objective of improving two already existing models: ICON and IFS.

What is ICON?

The ICOsahedral Nonhy-drostatic – also known as ICON – model was developed by the Max Planck Institute for Meteorology and the German Weather Service. Primarily, it was established for the simulation of the components of the Earth system and their interactions at kilometre and sub-kilometre scales on global and regional domains, according to Hohenegger et al. (2023). In other words, a model like ICON has the capacity to substantially represent terrestrial and marine vegetation that grows and dies. For instance: atmospheric chemistry; carbon, nitrogen, sulphur, and phosphorus cycles; and dynamical ice sheets.

An interesting feature of the model is that it has been able to run coupled, simulating the system interactions between the ocean, atmosphere, and land. Furthermore, the study executed by Hohenegger et al. (2023) showed the model can run coupled for one year at uncommon scales: for a few months with a grid spacing of 2.5 km and for a few days with a grid spacing of 1.25 km. In that way, ICON has made it possible to simulate the biogeochemical processes happening both on land and on the ocean, showing its influence on carbon flows.

What is IFS? 

The Integrated Forecasting System (IFS), developed by the European Centre for Medium-Range Weather Forecasts (ECMWF) is a model used to produce skilful medium-range weather forecasts. In other words, it provides forecasts for a period extending from about three to seven days in advance for the ECMWF Member and Co-operating States. Moreover, the model has opened up the possibility of providing broader environmental services, such as monitoring climate change or forecasting risks that involve floods, air pollution and wildfires (Maskell, 2022). 

For instance, air quality and increasing levels of greenhouse gases in the atmosphere are important concerns. In that sense, IFS is used to produce forecasts of European air quality; information for the solar energy sector; and monitoring of the ozone layer. Taking that into account, through nextGEMS the model can be improved to reproduce encompassed interactions among atmosphere, land, and ocean or sea ice at a high level of detail.

In the attempt of improving these models, projects like nextGEMS are aiming to provide a wide range of environmental possibilities and climate knowledge to society. For instance, through the enhancement of ICON and IFS, decisions like where it is possible to install solar panels or where can fisheries take place are likely to be better assessed, ideally reducing hazards and reinforcing benefits.

References:

Hohenegger, C., Korn, P., Linardakis, L., Redler, R., Schnur, R., Adamidis, P., Bao, J., Bastin, S., Behravesh, M., Bergemann, M., Biercamp, J., Bockelmann, H., Brokopf, R., Brüggemann, N., Casaroli, L., Chegini, F., Datseris, G., Esch, M., Geet, G., … Stevens, B. (2023). ICON-Sapphire: simulating the components of the Earth system and their interactions at kilometer and subkilometer scales. Geoscientific Model Development, 16(2), 779-811. https://doi.org/10.5194/gmd-16-779-2023

Maskell, K. (2022, April 7). Global numerical modelling at the heart of ECMWF’s forecasts. ECMWF. https://www.ecmwf.int/en/about/media-centre/focus/2022/global-numerical-modelling-heart-ecmwfs-forecasts