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.

ICON model
Overview of the ICON Sapphire model configuration. Retrieved from Hohenegger et al. (2023)

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.

IFS model
Illustration of the ECMWF Earth system approach. Retrieved from Maskell (2022)

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.


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. ECMWFhttps://www.ecmwf.int/en/about/media-centre/focus/2022/global-numerical-modelling-heart-ecmwfs-forecasts

Max-Planck Institute for Meteorology (MPI), Hamburg, 4th – 8th March, 2024

The km-scale Hackathon was co-organized by the three climate science projects EERIEWarmWorld, and nextGEMS and welcomed over 130 professionals from different scientific backgrounds, levels of expertise, diverse nationalities and a variety of institutions.

Participants were creating valuable output while hacking and got some inspiring input by talks and keynotes throughout the week.

On the first day, Eulàlia Baulenas explained what is needed to transform knowledge created in climate science into tangible actions and scientists Rohit Gosh and Dian Putrasaham shared the current state of the Earth System Models used in the EERIE project. Meteorologist Daniela Jacob held a key note about the challenges posed by climate change, affecting the lives of all of us and how the Earth Visualisation Enging (EVE) and the Coordinated Regional Downscaling Experiment (CORDEX) could be one step towards an international, joint approach to address and tackle these difficult times ahead. 

A second key note was delivered by Sarah Kang, the Climate Dynamics Director at MPI, on Wednesday evening. She presented her work on understanding the physical processes driving observed, unexplained cooling trends in the tropical Pacific.

Additionally, Daniel Klocke talked about the Icosahedral Nonhydrostatic Weather and Climate Model (better known as ICON), used by all three of the organizing projects. He introduced some of the aspects of the model that were improved since the last Hackathon and gave an idea about which challenges still need to be combated in the near future. Similarly, Thomas Rackow updated the participants on the improvements of the Integrated Forecasting System (IFS) used in the nextGEMS project.

As a little treat, special side events were organized. These included a communal dinner, a world café, trips to the Wind Tunnel at the University of Hamburg and the DKRZ supercomputer Levante and a short Yoga-session to refresh the participants bodies and minds.

The closing session on Friday was used by the thematic groups to share their observations, analysis, challenges, and suggestions. Participants also engaged in a discussion about their opinions or concerns regarding the Earth System Models, future publications, and possible collaborations between different projects. This day was particularly special due to it coinciding with the International Women’s Day on March 8th, opening up the opportunity to especially celebrate the achievements of our female scientists, organizers and supporters.

by Jonathan Wille from the Swiss Federal Institute of Technology in Zurich (ETH Zurich)

Temperature changes within a day (diurnal temperature range) and the day-to-day temperature changes (inter-diurnal temperature variability) have major impacts on agricultural practices and energy providers. Large swings in temperature can introduce heat and water stress for crop yields while creating sudden spikes in energy demand  (Lobell, 2007). Thus, properly simulating temperature variability in both the present and future climate is essential for projecting potential global warming impacts on daily heat stress. The enhanced resolution of the nextGEMS models offer a more detailed picture on local temperature variability thus potentially enabling communities to better prepare for future temperature variations. Before this can be realized, we must test the realism of these processes in the nextGEMS IFS and ICON models (cycle 3) in the present climate to ensure their future projections can be considered reliable. 

Temperature variability in mountainous regions 

Focusing first on the high-resolution capabilities of the IFS and ICON models, we see that possessing a horizontal resolution of ~5 km allows both models to capture the variations in the DTR (diurnal temperature range) across the complex topography of the European Alpes during the wintertime months of December, January, and February (Figure 1a and 1b). To verify if the values shown here can be considered realistic, we use the Copernicus European Regional ReAnalysis (CERRA) which uses past measurements and data assimilation to create a high-resolution depiction of temperature behavior. When compared with the nextGEMS IFS and ICON, we see that the ICON has a DTR that is much larger than observed in CERRA, while the IFS is closer to the reanalysis temperature behavior (Figure 1c and 1d). When looking at the IDTV (inter-diurnal temperature variability), similar patterns in the ICON and IFS biases are observed. These differences which are greatest in the winter months may be the result of the ICON having too few clouds thus creating greater daily temperature variability, but further testing is needed to verify this.

Figure 1: The average diurnal temperature range for DJF (December, January, and February) for a) ICON and b) IFS models along with their respective differences (depicted as difference in standard deviations) from the CERRA reanalysis (c, d).

A global look at temperature variability 

To see whether these patterns in temperature variability and associated biases in the European Alpes are isolated examples or representative of the broader globe, we repeated a similar analysis globally using the ERA5 reanalysis with the nextGEMS ICON and IFS interpolated to a lower resolution.  Within the ICON model, there is a great deal of spatial variability, but some patterns emerge. For instance, the high DTR bias in the European Alpes are observed again in the South American Andes and parts of the Himalayas (Figure 1a and 1c). There is also a strong gradient between overestimated and underestimated DTR separating the higher and lower latitudes respectively in the Northern hemisphere during winter. The DTR simulated in the IFS model is generally closer to the ERA5 reanalysis aside from notable areas of overestimation in equatorial regions of Southern America and Africa (Figure 1b and 1d).

Figure 2: The global differences in average diurnal temperature range for(a, c) ICON and (b, d) IFS for (a ,b) DJF (December, January, and February) and (c, d) JJA (June, July, and August) when compared with the ERA5 reanalysis.

Final thoughts

These preliminary results demonstrate the added benefits in resolving temperature variability in complex terrain using the nextGEMS storm-resolving Earth system models. The ability to resolve temperature variability within individual mountain valleys and peaks will prove beneficial to the communities residing in these areas when planning for future changes in temperature. This analysis also reveals areas where the nextGEMS models’ temperature behavior differs from observations, especially in the ICON model. While these biases are sometimes significant, similar biases also appear in coarser resolution CMIP5 simulations (Cattiaux et al., 2015), highlighting the challenge of accurately simulating daily temperature variability.  


Cattiaux, J., Douville, H., Schoetter, R., Parey, S., & Yiou, P. (2015). Projected increase in diurnal and interdiurnal variations of European summer temperatures. Geophysical Research Letters42(3), 899–907. https://doi.org/10.1002/2014GL062531
Lobell, D. B. (2007). Changes in diurnal temperature range and national cereal yields. Agricultural and Forest Meteorology145(3), 229–238. https://doi.org/10.1016/j.agrformet.2007.05.002

The key goal of the nextGEMS project is to produce the first-ever km-scale simulations that span a 30-year period, i.e., “one climate”. This produces an immense amount of output data even for single variables, in fact, the number of grid points in a single world map exceeds the number of pixels of a screen by far. To keep this dataset manageable, hierarchical output, chunking, and the HEALPix grid can help.

Hierarchical output involves generating multiple copies of output at different resolutions, just as the functionality of online map services. This spatial hierarchy enables users to access data at resolutions appropriate for their analysis needs, optimizing data loading and hence analysis speed. By storing data in a hierarchical structure, storage requirements remain efficient, because coarser levels occupying significantly less space compared to finer resolutions.

The hierarchical output is accompanied by horizontal chunking of data, which facilitates regional access without the need to load global fields. This approach enables targeted analysis and reduces processing time, particularly for regional-scale investigations, contributing to enhanced workflow efficiency in climate model output analysis.

Both hierarchical output and horizontal chunking can only be applied accurately and efficiently, if the data is stored on an appropriate grid. The HEALPix grid offers a structured pixelation of a sphere based on quadrilaterals with equal area. The structure of the grid ensures that data-locality maps to regional locality, which is necessary for efficient chunking. The equal-area property reduces rounding errors and eliminates numerical smoothing when computing the different levels of the output hierarchy.

Further information and examples on how to efficiently work with the new output hierarchy can be found on easy.gems. In the meantime, check out the short movie by M. Veerman (WUR) showing the surface longwave irradiance with different HEALPix zoom levels.

by Alexander J. Baker from the National Centre for Atmospheric Science and Department of Meteorology at the University of Reading

Tropical cyclones, including hurricanes and typhoons, rank among the most costly natural hazards. Recent research has highlighted an increasing trend in the destructiveness of tropical cyclones over the last few decades, as well as the disproportionately high exposure of socioeconomically underprivileged populations to their impacts globally. Simulating realistic tropical cyclones is therefore a vital ambition of weather and climate modelling—and of the nextGEMS project. Mainly, because while the physical processes governing a tropical cyclone’s lifecycle are relatively well understood, many of these processes occur at scales below those resolved by global climate models. Consequently, it is anticipated that increasing the resolution of models will help simulate more realistic tropical cyclones.

What is a tropical cyclone?

Tropical cyclones are born from ‘seeds’—tropical waves or rotating clusters of individual thunderstorms—in a process lasting from hours to weeks. This occurs at low latitudes over warm tropical oceans and, usually, at least a thousand kilometres from the equator because it is where planetary rotation is sufficient to aggregate convective activity into a coherent whirling phenomenon called vortex. Once formed, tropical cyclones mature, increasing in intensity. Typically, they move westward and poleward before reaching the midlatitudes, where they weaken over a cooler ocean surface or transform into frontal weather systems.

Understanding simulated tropical cyclones

In research carried out the University of Reading, we used a tracking algorithm to identify tropical cyclones in nextGEMS data. We analysed various simulated tropical cyclone characteristics, and I’ll describe two key ones here: the frequency of tropical cyclones and how quickly they intensify. Both characteristics are related to cyclone predictability and societal impacts.

Simulated tropical cyclone frequency is affected by both model resolution and model physics (see bar chart). For ECMWF’s Integrated Forecasting System (IFS) model, the annual number of tropical cyclones is closer to observations at higher resolutions in both cycle 2 and 3 simulations. Cyclone frequency also changes with ocean model / resolution. IFS simulations in cycle 3 at 9 km resolution in the atmosphere reveal an increase in cyclone frequency when using the Finite-Element/volume Sea ice-Ocean Model (FESOM) ocean model at 5km, compared with the Nucleus for European Modelling of the Ocean (NEMO) model at 0.25 º. The MPI-M’s Icosahedral Non-hydrostatic Weather and Climate (ICON) model shows little sensitivity to changes in atmospheric resolution, but shows significant sensitivity to model physics. Simulated cyclone frequency is too high in cycle 2 but is closer to observations in cycle 3.

Bar graph Alex Baker
Annual tropical cyclone frequency (“nTC”) in observations (“IBTrACS”) over the period 1980–2022 (black) and nextGEMS simulations performed in cycles 2 and 3. Bars represent global statistics and hatched areas represent the Northern Hemisphere.

On the other hand, how quickly tropical cyclones intensify increases with finer atmospheric resolution (see graph). At low resolution (e.g., the IFS at 28 km), even the most intense cyclones mature too slowly and reach a peak wind speed that is much lower than observed in the real world. When resolution is increased, however, the rate of intensification becomes much more realistic, and mimics observations closely at resolutions of 4.4 km (for the IFS) and 5 km (for ICON). nextGEMS simulations also contain examples of rapid intensification of tropical cyclones, forecasts of which are still frequently error prone. Finer resolution may therefore be a key component of improving forecasts of such events.

Graph Alex Baker
Tropical cyclone intensity in the days before and after their peak intensity (indicated by the grey dashed line) in IBTrACS observations over the period 1980–2022 (black) and nextGEMS simulations. Two measures of intensity are shown: (a) maximum wind speed (“vmax”) and (b) minimum central pressure (“pmin”). This analysis was performed for the strongest ten percent of both observed and simulated cyclones.

Data produced by model-development cycles in nextGEMS have demonstrated that tropical cyclone realism improves significantly with an increase in atmospheric resolution from that of previous-generation, HighResMIP-type models to a few kilometres. The forthcoming production of 30-year simulations in nextGEMS will help better understand the overall role of intense tropical cyclones in the climate system.


Baker, A. J., Vannière, B., and Vidale, P. L. On the realism of tropical cyclones simulated in global storm-resolving climate models. To be submitted to Geophysical Research Letters.


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