{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/climsim-a-large-multi-scale-dataset-for","title":"ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation","arxiv_id":"2306.08754","date":"2023-06-14","proceeding":"NeurIPS 2023 11","authors":["Sungduk Yu","Zeyuan Hu","Akshay Subramaniam","Walter Hannah","Liran Peng","Jerry Lin","Mohamed Aziz Bhouri","Ritwik Gupta","Björn Lütjens","Justus C. Will","Gunnar Behrens","Julius J. M. Busecke","Nora Loose","Charles I. Stern","Tom Beucler","Bryce Harrop","Helge Heuer","Benjamin R. Hillman","Andrea Jenney","Nana Liu","Alistair White","Tian Zheng","Zhiming Kuang","Fiaz Ahmed","Elizabeth Barnes","Noah D. Brenowitz","Christopher Bretherton","Veronika Eyring","Savannah Ferretti","Nicholas Lutsko","Pierre Gentine","Stephan Mandt","J. David Neelin","Rose Yu","Laure Zanna","Nathan Urban","Janni Yuval","Ryan Abernathey","Pierre Baldi","Wayne Chuang","Yu Huang","Fernando Iglesias-Suarez","Sanket Jantre","Po-Lun Ma","Sara Shamekh","Guang Zhang","Michael Pritchard"],"abstract":"Modern climate projections lack adequate spatial and temporal resolution due to computational constraints, leading to inaccuracies in representing critical processes like thunderstorms that occur on the sub-resolution scale. Hybrid methods combining physics with machine learning (ML) offer faster, higher fidelity climate simulations by outsourcing compute-hungry, high-resolution simulations to ML emulators. However, these hybrid ML-physics simulations require domain-specific data and workflows that have been inaccessible to many ML experts. As an extension of the ClimSim dataset (Yu et al., 2024), we present ClimSim-Online, which also includes an end-to-end workflow for developing hybrid ML-physics simulators. The ClimSim dataset includes 5.7 billion pairs of multivariate input/output vectors, capturing the influence of high-resolution, high-fidelity physics on a host climate simulator's macro-scale state. The dataset is global and spans ten years at a high sampling frequency. We provide a cross-platform, containerized pipeline to integrate ML models into operational climate simulators for hybrid testing. We also implement various ML baselines, alongside a hybrid baseline simulator, to highlight the ML challenges of building stable, skillful emulators. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res) and code (https://leap-stc.github.io/ClimSim and https://github.com/leap-stc/climsim-online) are publicly released to support the development of hybrid ML-physics and high-fidelity climate simulations.","url_abs":"https://arxiv.org/abs/2306.08754v6","url_pdf":"https://arxiv.org/pdf/2306.08754v6.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"climsim-a-large-multi-scale-dataset-for","repo_url":"https://github.com/leap-stc/climsim","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"jax","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"climsim-a-large-multi-scale-dataset-for","repo_url":"https://github.com/leap-stc/climsim-online","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2306.08754","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.08754"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/leap-stc/climsim-online","reach":{"status":"ok","spdx":"Apache-2.0"}},{"provenance":"deterministic:regex_extraction","url":"https://github.com/leap-stc/ClimSim","reach":{"status":"ok","spdx":"Apache-2.0"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/leap-stc/climsim","reach":{"status":"ok","spdx":"Apache-2.0"}}],"summary":{"unverified":3},"by_repo_kind":{"official":{"samples":3,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"561782c7b170b096","entry":"eice","repo":"leap-stc/ClimSim","repo_kind":"official","path":"climsim_utils/data_utils.py","file_url":"https://github.com/leap-stc/ClimSim/blob/HEAD/climsim_utils/data_utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"561782c7b170b096"}},{"code_sha256_prefix":"a00f0e3089f828f0","entry":"eliq","repo":"leap-stc/ClimSim","repo_kind":"official","path":"climsim_utils/data_utils.py","file_url":"https://github.com/leap-stc/ClimSim/blob/HEAD/climsim_utils/data_utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"a00f0e3089f828f0"}},{"code_sha256_prefix":"1dfe90af653014df","entry":"schedule","repo":"leap-stc/ClimSim","repo_kind":"official","path":"baseline_models/ED/training/ClimSIM_ED_1_3_train.py","file_url":"https://github.com/leap-stc/ClimSim/blob/HEAD/baseline_models/ED/training/ClimSIM_ED_1_3_train.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"1dfe90af653014df"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}