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

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

The km-scale Hackathon was co-organized by the three climate science projects EERIE, WarmWorld, 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.

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).

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.  

References

Cattiaux, J., Douville, H., Schoetter, R., Parey, S., & Yiou, P. (2015). Projected increase in diurnal and interdiurnal variations of European summer temperatures. Geophysical Research Letters, 42(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 Meteorology, 145(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.