About ACM Gordon Bell Prize for Climate Modelling

ACM presented a 26-member team with the ACM Gordon Bell Prize for Climate Modelling in recognition of their project “Computing the Full Earth System at 1 km Resolution.” The award honors innovative contributions to parallel computing toward solving the global climate crisis.

Climate change is responsible for more extreme hurricanes, more destructive wildfires, severe droughts, and increased human disease, among other harmful outcomes. Experts warn that if carbon emissions are not significantly reduced within a few decades, the damage to the Earth’s ecosystem will be irreversible.

Among the most effective tools scientists have developed to understand climate change are digital simulations of the Earth. These simulations are produced by developing specific algorithms to run on the world’s most powerful supercomputers. But simulating how human activity influences the climate has been an extraordinarily difficult challenge. A mind-boggling number of variables need to be taken into consideration—such as the cycles of water, energy, and carbon, how those factors relate to each other, and how diverse physical, biological, and chemical processes interact over space and time. For these reasons, previous state-of-the-art simulations have not been able to achieve what is referred to as a “Full Earth System” simulation.

The Gordon Bell Climate Prize-winning team reached a landmark this year by being the first team ever to develop a Full Earth Simulation at 1 km (extremely high) Resolution. In their introduction, they explain, “We present the first-ever global simulation of the full Earth system at 1.25 km grid spacing, achieving highest time compression with an unseen number of degrees of freedom. Our model captures the flow of energy, water, and carbon through key components of the Earth system: atmosphere, ocean, and land. To achieve this landmark simulation, the team harnessed the power of 8192 GPUs on Alps and 4096 GPUs on JUPITER, two of the world’s largest GH200 superchip installations.”

The groundbreaking innovations the team employed to make the Full Earth Simulation possible include (1) exploiting functional parallelism by efficiently mapping components to specialized heterogeneous systems and (2) simplifying the implementation and optimization of an important component by separating its implementation in Fortran from the optimization details of the target architecture.

In the conclusion to their paper they write, “This has enormous and enduring potential to provide full global Earth system information on local scales about the implications of future warming for both people and eco-systems, information that otherwise would not exist.”

Team Members 

The members of the team are: Daniel Klocke, René Redler, Reiner Schnur, Helmuth Haak, Luis Kornblueh, Cathy Hohenegger, Nils Brüggemann, and Bjorn Stevens (all of the Max Planck Institute for Meteorology); Claudia Frauen, Jan Frederik Engels, and Hendryk Bockelmann (all of Deutsches Klimarechenzentrum); Dmitry Alexeev (NVIDIA),; Fatemeh Chegini (University of Hamburg), Manoel Römmer, Lars Hoffmann, Sabine Griessbach, Mathis Bode, and Andreas Herten (all of Forschungszentrum Jülich), Jonathan Coles, Miguel Gila, and William Sawyer (all of the Swiss National Supercomputing Centre); as well as Alexandru Calotoiu, Torsten Hoefler, Yakup Budanaz, Pratyai Mazumder, Marcin Copik, and Benjamin Weber (all of ETH Zurich).

The ACM Gordon Bell Prize for Climate Modelling was presented at the International Conference for High Performance Computing, Networking, Storage and Analysis (SC25), held in St. Louis, Missouri.

ACM today presented a 12-member team with the ACM Gordon Bell Prize for Climate Modelling for their project “Boosting Earth System Model Outputs and Saving PetaBytes in Their Storage Using Exascale Climate Emulators.” The award recognizes innovative parallel computing contributions toward solving the global climate crisis.

The members of the team are: Sameh Abdulah, Marc G. Genton, David E. Keyes, Zubair Khalid, Hatem Ltaief, Yan Song, Greorgiy L. Stenchikov and Ying Sun (all of King Abdullah University of Science and Technology, Saudi Arabia); Allison H. Baker (NSF National Center for Atmospheric Research, USA); George Bosilca, (NVIDIA, USA); Qinglei Cao (St. Louis University, USA); and Stefano Castruccio (University of Notre Dame, USA).

Scientists have warned that global warming, caused by the human use of fossil fuels, is reaching a crisis point. Experts assert that the prevalence of more intense storms, hurricanes, droughts, wildfires, as well as a loss of biodiversity, are signs that the crisis is worsening rapidly. They warn that, if not urgently addressed, global warming poses an existential threat to life on Earth.

Using computational tools to better understand the rate and impacts of climate change is considered a valuable tool in developing strategies to address the problem. While climate modelling has been a scientific practice since the 1950’s, recently introduced exascale supercomputers (which can process a quintillion calculations each second) offer the opportunity to understand climate change at a far more advanced level than ever before. With the use of exascale computers, computer scientists and climate scientists have developed extremely high-resolution Earth System Models (ESM’s).

ESM’s offer great promise in understanding the Earth’s climate but they are computationally expensive—i.e., they require a great deal of computation time and energy, and they require a tremendous amount of storage for the massive quantity of data they generate.

To address this problem, the prize-winning team presented the design and implementation of an exascale climate emulator for addressing the escalating computational and storage requirements of high-resolution Earth System Model simulations. In computing, emulators allow for a dynamic interplay between different computers—with one computer system (called the host) behaving like another computer system (the guest). The growing use of emulators in climate modeling has become more common as emulators can combine and enhance efficient algorithms to handle large datasets, as well as the distribution of computations across multiple processors.

Climate emulators have come to play a pivotal role in alleviating the computational burden and storage requirements associated with climate modeling and simulations. Because the resolution of a climate model is impacted by the trade-off between the computational costs and the representation of the climate system, improving both computational and data storage challenges in a high-performance computer allows for more advanced climate modeling capabilities.

The ACM Gordon Bell Prize for Climate Modelling winning team contends their emulator could save several petabytes of computing storage space. By way of comparison, one petabyte is equal to the storage capacity of approximately 170 top-end servers.

The team’s ultra-high resolution model of the earth’s climate included 54,486,360 spatial locations around the globe, as well as 318 billion hourly and 31 billion daily observations.

The team achieved its results using high performance computing methods called Spherical Harmonic Transform (SHT) and Cholesky factorization. In the introduction to their paper, the Prize-winning team wrote: “We utilize the spherical harmonic transform to stochastically model spatio-temporal variations in climate data. This provides tunable spatio-temporal resolution and significantly improves the fidelity and granularity of climate emulation, achieving an ultra-high spatial resolution of 0.034 ^◦ (∼3.5 km) in Space.”

The team ran mixed-precision computations on a PaRSEC dynamic runtime system, running on 9,025 nodes on Frontier, 1,936 nodes on Alps, 1,024 nodes on Leonardo, and 3,072 nodes on Summit, with the hybrid Flop/s rates 0.976 EFlop/s, 0.739 EFlop/s, 0.243 EFlop/s, and 0.375 EFlop/s, respectively.

The team concludes that their exascale climate emulator holds significant potential for the climate community, advancing climate research and policy making. They also maintain that their work holds significant potential in advancing the development of machine learning (ML) and AI-driven methods for forecasting or prediction applications in climate science.

ACM, the Association for Computing Machinery, presented a nineteen-member team with the inaugural  ACM Gordon Bell Prize for Climate Modelling for their project, “The Simple Cloud-Resolving E3SM Atmosphere Model Running on the Frontier Exascale System.” The new award aims to recognize innovative parallel computing contributions toward solving the global climate crisis.

The members of the team are: Mark A. Taylor, Luca Bertagna, Conrad Clevenger, James G. Foucar, Oksana Guba, Benjamin R. Hillman, Andrew G. Salinger (all of Sandia National Laboratories); Peter M. Caldwell, Aaron S. Donahue, Noel Keen, Christopher R. Terai, Renata B. McCoy, David C. Bader (all of Lawrence Livermore National Laboratory); Jayesh Krishna, Danqing Wu (both of Argonne National Laboratory); Matthew R. Norman, Sarat Sreepathi (both of Oakridge National Laboratory); James B. White III (Hewlett Packard Enterprise); and L. Ruby Leung (Pacific Northwest National Laboratory).

To develop the most effective carbon emission reduction policies, governments are working with scientists to better understand the relationship between carbon emissions, the earth’s atmosphere, and climate change. Because of the mind-boggling number of variables in understanding climate phenomena (e.g., temperature, humidity, precipitation), scientists have increasingly used powerful supercomputers to process all these variables in order to develop high resolution simulations.

Climate scientists are especially interested in understanding convective clouds (clouds that are formed by the process of warmer air rising above a less dense atmosphere). Deep convective clouds (which can be many kilometers thick) are particularly important to simulate correctly because they drive the tropical overturning circulation and modulate energy transfer over much of the planet.

A class of algorithmic models known as global cloud-resolving models (GCRMs) have been used to attempt to simulate deep convective clouds and have been accurate in certain instances such as providing simulations of short time periods or limited physical areas. But the Prize-winning team notes that the drawbacks of GCRM’s include the fact that running these algorithms on existing supercomputers has been slow and computationally expensive (e.g., the algorithms require too many steps).

The team proves that by using just-introduced exascale supercomputers along with a new algorithmic model they have introduced, the longstanding challenge of developing efficient and accurate simulations of deep convective clouds can be accomplished. The prize-winning team introduces the new algorithmic model, “Simple Cloud Resolving E3SM Atmosphere Model (SCREAM).“

The ACM Gordon Bell Prize for Climate Modelling aims to recognize innovative parallel computing contributions toward solving the global climate crisis. Climate scientists and software engineers are evaluated for the award based on the performance and innovation in their computational methods.