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

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.

nextGEMS is working on high-resolution simulations (see post) and part of the projects’ goals is to make this novel science adequate and accessible for society, with the aim to reduce the climate action gap. To achieve this, nextGEMS is working around two main societal challenges: the uptake of renewable energy in national decarbonisation processes and the sustainability of fisheries and marine ecosystems. In this article we discuss our work on energy, and how we are creating links with the energy community using nextGEMS data outputs as our basis.

Diverse Perspectives: Envisioning Energy Transitions

The world’s shift towards decarbonization is intensifying, driven by ambitious goals from supranational initiatives such as in the context of the European Union. Due to its complexity, there is a rich societal debate about the best pathways towards decarbonization, and especially the role of renewable energies in it. One of the sources of uncertainty on how these different pathways might unfold is given by the evolution of our climate, which interplays with policy decisions by amplifying or reducing the impact they are expected to have.

There are several research methods available to scholars which allow for taking a plurality of perspectives into account, as well as the evolution of our climate conjointly, and one of these is scenario modelling. In NextGEMS, we have implemented this methodology to study the interlinkage between climate, energy and socio-political dimensions using as case study the national integrated energy and climate plans, which is a policy present across EU Member States.

Engaging with a diversity of stakeholders

Given the diversity of the actors who directly or indirectly intervene in the policy process and the plurality of perspectives and interests they might bring in, scholars are incredibly using participatory approaches to avoid limiting the range of futures we envisage. Renewables‘ pivotal role in the energy transition equally demands for a co-production approach, as the best form to ensure that all voices are heard. This method starts by constructing together ‘storylines’, qualitative descriptions of our collective future, and thus considering multifaceted narratives that might include societal concerns, biodiversity considerations, land use competition, and democratic decision-making in the construction of decarbonization pathways.

Spain’s Case Study: A Closer Look at Energy Transition Challenges

In the context of the cycle 3 hackathon held at Universidad Complutense de Madrid (see news archive), we organised a stakeholder workshop focused on the energy sector in Spain. Employing a co-production framework, the workshop aimed at generating storylines that, later on, would be used for scenario modelling. Preparatory steps involved engagements with energy experts, brainstorming sessions on renewable energy optimization, and systematic literature review to identify key discourses. Stakeholder mapping and interviews with 50 individuals shaped the workshop’s direction, culminating in 22 participants contributing to the co-production of storylines.

The workshop revolved around three broad storylines—actual, integrative, and distributive: playing with the current system, integrating biodiversity concerns and adding in the role of energy communities. Participants engaged in discussions, challenging and enhancing these storylines, evaluating their pros and cons within the context of their own positions -being these the public, private, third and academic sectors. For 2.5h, stakeholders deliberated around these aspects and proposed forms to evaluate the storylines as well as for their operationalization (see more information about the workshop here).

NextGEMS is working now in this operationalisation, so stay tuned for the results of this study!

Post by Eulàlia Baulenas (BSC)

by Thomas Rackow*, Daniel Klocke**, and the MPI-M and ECMWF-AWI modelling teams***

The nextGEMS model development is structured into simulation cycles. Each simulation cycle is followed by a hackathon, where simulation results are evaluated extensively by the nextGEMS community. The first nextGEMS hackathon in Berlin in October 2021 analysed the very first simulations. Based on the results, the two models participating in the project, IFS and ICON, were updated significantly for the cycle 2 simulations. These simulations where just completed and the unique datasets are waiting for the community to analyse them at the upcoming hackathon at the end of June. 
In case of IFS, besides updates to the atmospheric model component (read more here) and a more realistic treatment of snow, another update has been the use of a higher-resolution ocean that resolves eddies over large parts of the globe. Eddies in the ocean impact exchanges of energy and matter across the ocean-atmosphere interface, they transport heat both horizontally and vertically, and they were shown to alter projections of global climate in a warming world.

Figure 1: Ocean resolution used in the IFS-FESOM simulations for Cycle 2. The grid points of the NG5 configuration are concentrated in higher latitudes in order to resolve ocean eddies over larger areas of the globe compared to a more homogeneous distribution of grid points.

The operational high-resolution 9km forecasts at ECMWF include an ocean that applies a ¼ degree resolution (about 25km at the equator). While many coupled effects such as the atmospheric and oceanographic interaction during tropical cyclone conditions (Mogensen et al. 2017) can be realistically simulated at this resolution, ocean eddies are still only ‘permitted’ in mid-latitudes compared to the even coarser 1 degree standard-resolution climate models. This is far from the goal to explicitly resolve mesoscale ocean eddies all around the globe and is a potential source of many long-standing biases in climate models. Importantly, mesoscale features can also affect the predictability of European weather downstream of the Gulf Stream area.

A recent development for the IFS Cycle 2 simulations is a nextGEMS grid (NG5) for FESOM2, which was designed to be of equivalent size to the ICON-5km ocean grid with about 7.5 million surface nodes (Figure 1). Making use of the multi-resolution capability of FESOM2, relatively more surface nodes were concentrated in higher latitudes in order to extend the area where eddies are resolved – from the mid-latitudes into higher latitudes. Similar to the initialization strategy in ICON, the ocean grid has been spun up for several years with ERA5 forcing until 20 January 2020, the common starting point of the nextGEMS simulations, before being coupled to IFS. 

Early preliminary analyses of Cycle 2 compare rather well to the ERA5 reanalysis, OSI-SAF, and observational data, such as a long 40-day forecast of sea ice concentration evolution (Figure 2). We are looking very much forward to the next Hackathon in Vienna later this month where in-depth analyses from all project partners might reveal new surprises – both in terms of weaknesses but also in terms of novel strengths that only this new generation of climate models can provide.

Figure 2: 40-day forecast of sea ice concentration in the coupled IFS-FESOM Cycle 2 simulation with 2.5km IFS and FESOM2-NG5 (left), compared to data from OSI SAF (right). (figure kindly provided by Lorenzo Zampieri, NCAR, https://ncar.ucar.edu)

* Scientist at ECMWF

** Model development at MPI-M
*** Special thanks to all participants of the 1st nextGEMS hackathon

by Tobias Becker* and ECMWF modelling teams**

One of the advantages of having a community of people looking at the behaviour of our models is that it makes us look at old problems in a new light. A good example is the first nextGEMS hackathon in October 2021, at which we found that both of our nextGEMS models, ICON and the Integrated Forecasting System (IFS), do not conserve energy well. The atmospheric energy leakage amounts to 6.6 W m-2 in ICON and 6.3 W m-2 in the IFS, at 4 to 5 km resolution with no parametrisation of deep convection. Analysis quickly showed that most of the energy imbalance in the IFS is related to water non-conservation, and that this issue gets worse when spatial resolution is increased and when the parametrisation of deep convection is switched off. Figure 1 shows that the nextGEMS Cycle 1 simulations with the IFS have an artificial source of water in the atmosphere, which is responsible for 4.6% and 10.7% of the total precipitation, in the simulation with deep convection parameterisation at 9 km (the configuration used for ECMWF’s operational high resolution ten-day forecasts) and without deep convection parameterisation at 4 km, respectively.

Figure 1: Daily mean water non-conservation in the IFS, computed as the difference between evaporation and precipitation, subtracted from the time derivative of total column water, as a fraction of precipitation. Results are shown as a function of lead time for NextGEMS Cycle 1 and Cycle 1.2 simulations, with and without deep convection parameterisation, at 9 km and 4 km, started on January 20, 2020.

The water non-conservation of the IFS had been known for a long time, given that the departure point interpolation of the Semi-Lagrangian advection scheme used in the IFS is non-conserving. However, while this issue was acknowledged to be detrimental for the accuracy of climate integrations (Roberts et al., 2018), so far it was thought that it does not affect the quality of numerical weather forecasts which span timescales ranging from a few hours to seasons ahead. Further analysis after the hackathon by the modelling teams at ECMWF has shown that about 50% of this artificial atmospheric water source is created as water vapour. The additional water vapour not only affects the radiation energy budget of the atmosphere, but can also cause energy non-conservation when heat is released through condensation. The other 50% of water is created as cloud liquid, cloud ice, rain or snow. The artificial source of water is related to higher-order interpolation in the semi-Lagrangian advection scheme, causing spurious extrema. For the moist species, spurious minima can result in negative values, which are clipped, leaving the spurious maxima to increase condensate mass.

To address the problem of water non-conservation in the IFS, three adjustments were required: a small bug fix in the IFS code, a switch from cubic to linear horizontal interpolation  in the advection scheme for cloud liquid, cloud ice, rain and snow, and most importantly, the activation of a global tracer mass fixer for all moist species, including water vapour. Activating tracer mass fixers increases the computational cost of running a simulation, but we succeeded to find an accurate yet cost-effective setup that uses a Finite Differences approach rather than a Finite Elements approach to calculate the vertical integrals for the tracer mass fixers. Note that tracer mass fixers assure global mass conservation, so locally tracer non-conservation is still possible but is expected to have been significantly reduced. Figure 1 shows that with these three model changes, global water non-conservation is essentially eliminated (less than 0.1%) in our new nextGEMS simulations (labelled Cycle1.2), while the global energy budget imbalance has reduced to less than 1 W m-2 (not shown). 

Importantly, global water conservation turns out to be beneficial not only for long integrations, but also for the quality of ECMWF’s medium-range weather forecasts. Preliminary results suggest that the model changes performed to fix the water and energy imbalances improve the skill scores of the medium-range weather forecasts for many variables, but most robustly for precipitation. Figure 2 shows that the mean absolute error against rain gauge measurements is about 2-3% smaller in 9 km forecasts that ensure global water conservation compared to the default 9 km forecasts.

Figure 2: Scaled differences between forecasts with and without global water conservation with respect to the mean absolute error in precipitation against rain gauge measurements over the Northern Hemisphere, as a function of lead time. Forecasts are run at 9 km resolution for summer 2020 and winter 2021.


* Alexander von Humboldt Fellow at ECMWF
** Special thanks to Thomas Rackow, Xabier Pedruzo, Irina Sandu, Richard Forbes, Michail Diamantakis, Peter Bechtold, Inna Polichtchouk
and to the participants of the 1st nextGEMS hackathon


Here you can make settings regarding data protection.