In an attempt to advance the understanding of the Earth's weather and climate by representing deep convection explicitly, we present a global, four‐month simulation (November 2018 to February 2019) with ECMWF's hydrostatic Integrated Forecasting System (IFS) at an average grid spacing of 1.4 km. The impact of explicitly simulating deep convection on the atmospheric circulation and its variability is assessed by comparing the 1.4 km simulation to the equivalent well‐tested and calibrated global simulations at 9 km grid spacing with and without parametrized deep convection. The explicit simulation of deep convection at 1.4 km results in a realistic large‐scale circulation, better representation of convective storm activity, and stronger convective gravity wave activity when compared to the 9 km simulation with parametrized deep convection. Comparison of the 1.4 km simulation to the 9 km simulation without parametrized deep convection shows that switching off deep convection parametrization at a too coarse resolution (i.e., 9 km) generates too strong convective gravity waves. Based on the limited statistics available, improvements to the Madden‐Julian Oscillation or tropical precipitation are not observed at 1.4 km, suggesting that other Earth system model components and/or their interaction are important for an accurate representation of these processes and may well need adjusting at deep convection resolving resolutions. Overall, the good agreement of the 1.4 km simulation with the 9 km simulation with parametrized deep convection is remarkable, despite one of the most fundamental parametrizations being turned off at 1.4 km resolution and despite no adjustments being made to the remaining parametrizations.

This study integrates machine learning and particle‐resolved aerosol simulations to develop emulators that predict submicron aerosol mixing state indices from the Earth system model (ESM) simulations. The emulators predict aerosol mixing state using only quantities that are predicted by the ESM, including bulk aerosol species concentrations, which do not by themselves carry mixing state information. We used PartMC‐MOSAIC as the particle‐resolved model and NCAR's CESM as the ESM. We trained emulators for three different mixing state indices for submicron aerosol in terms of chemical species abundance (χ_a), the mixing of optically absorbing and nonabsorbing species (χ_o), and the mixing of hygroscopic and nonhygroscopic species (χ_h). Our global mixing state maps show considerable spatial and seasonal variability unique to each mixing state index. Seasonal averages varied spatially between 13% and 94% for χ_a, between 38% and 94% for χ_o, and between 20% and 87% for χ_h with global annual averages of 67%, 68%, and 56%, respectively. High values in one index can be consistent with low values in another index depending on the grouping of species and their relative abundance, meaning that each mixing state index captures different aspects of the population mixing state. Although a direct validation with observational data has not been possible yet, our results are consistent with mixing state index values derived from ambient observations. This work is a prototypical example of using machine learning emulators to add information to ESM simulations.

This report highlights significant strides made by Oak Ridge National Laboratory’s Oak Ridge LeadershipComputing Facility (OLCF) in advancing computational research and infrastructure. Through the AdvancedComputing Ecosystem (ACE) strategic initiative, OLCF has been successfully integrated with DOE’sIntegrated Research Infrastructure (IRI) program, establishing itself as a critical framework for enhancingscientific computing capabilities across various domains. This report outlines the activities, accomplishments,and future directions of ACE, emphasizing its role in developing cutting-edge technologies, supportingscience pilots, and fostering collaborations that drive scientific innovation.One of the key achievements of ACE in FY24 has been the establishment of a robust testbed environmentthat facilitates the development and validation of new computing technologies. This testbed, integratedinto the larger IRI ecosystem through Energy Sciences Network (ESnet), allows for seamless collaborationamong DOE’s Advanced Scientific Computing Research (ASCR) facilities, including the Argonne LeadershipComputing Facility (ALCF), the National Energy Research Scientific Computing Center (NERSC), andJefferson Lab (JLab). The testbed has been instrumental in enabling science pilots to experiment withinnovative methodologies in a controlled environment, thereby accelerating the translation of research intopractical applications.ACE has been instrumental in advancing a series of science pilots using computational resources fromOLCF. These pilots span a wide range of scientific domains, from basic energy sciences to biological andenvironmental research, demonstrating the versatility and impact of the ACE framework. By integratingcutting-edge technologies, such as AI-driven analytics and real-time data processing, with HPC, these pilotshave enabled significant advancements in scientific workflows. For instance, projects like LCLStreamand DELERIA have showcased the potential of ACE in optimizing experimental designs, acceleratingdata analysis, and driving innovations that are critical for addressing some of the most complex scientificchallenges of IRI pathfinder projects.ACE has also made significant progress in foundational technology development, particularly in areas suchas data movement, interface design, and scheduling. These advancements have been key in addressingthe complex requirements of modern scientific workflows, ensuring that OLCF remains at the forefront ofhigh-performance computing (HPC). The report details several ongoing projects, including the developmentof the OLCF Facility API, Data Streaming technologies, and the Zambeze distributed orchestration system,which together provide a secure and flexible framework for managing computational resources across adistributed infrastructure.In addition to technical achievements, ACE has played a central role in outreach and engagement activi-ties. Through participation in conferences, hackathons, and training sessions, the ACE team has activelycontributed to the broader scientific community. These efforts have not only facilitated the exchange ofknowledge but have also strengthened collaborations with other DOE facilities and research institutions,further enhancing the impact of the ACE program.Looking ahead, the report outlines strategic plans for FY25, which include continued support for sciencepilots, further integration with ASCR facilities, and the exploration of new technologies. These initiatives aredesigned to ensure that ACE remains a dynamic and adaptable platform, capable of meeting the evolvingneeds of the scientific community. The ongoing collaboration with ORNL’s INTERSECT initiative and theplanned technology refreshes underscore ACE’s commitment to maintaining its leadership in HPC.

The U.S. Department of Energy's Office of Scientific and Technical Information