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@ARTICLE{Schlund:1050041,
      author       = {Schlund, Manuel and Andela, Bouwe and Benke, Jörg and
                      Comer, Ruth and Hassler, Birgit and Hogan, Emma and
                      Kalverla, Peter and Lauer, Axel and Little, Bill and
                      Loosveldt Tomas, Saskia and Nattino, Francesco and Peglar,
                      Patrick and Predoi, Valeriu and Smeets, Stef and Worsley,
                      Stephen and Yeo, Martin and Zimmermann, Klaus},
      title        = {{A}dvanced climate model evaluation with {ESMV}al{T}ool
                      v2.11.0 using parallel, out-of-core, and distributed
                      computing},
      journal      = {Geoscientific model development},
      volume       = {18},
      number       = {13},
      issn         = {1991-959X},
      address      = {Katlenburg-Lindau},
      publisher    = {Copernicus},
      reportid     = {FZJ-2025-05755},
      pages        = {4009 - 4021},
      year         = {2025},
      abstract     = {Earth system models (ESMs) allow numerical simulations of
                      the Earth's climate system. Driven by the need to better
                      understand climate change and its impacts, these models have
                      become increasingly sophisticated over time, generating vast
                      amounts of data. To effectively evaluate the complex
                      state-of-the-art ESMs and ensure their reliability, new
                      tools for comprehensive analysis are essential. The
                      open-source community-driven Earth System Model Evaluation
                      Tool (ESMValTool) addresses this critical need by providing
                      a software package for scientists to assess the performance
                      of ESMs using common diagnostics and metrics. In this paper,
                      we describe recent significant improvements of ESMValTool's
                      computational efficiency, which allow a more effective
                      evaluation of these complex ESMs and also high-resolution
                      models. These optimizations include parallel computing
                      (execute multiple computation tasks simultaneously),
                      out-of-core computing (process data larger than available
                      memory), and distributed computing (spread computation tasks
                      across multiple interconnected nodes or machines). When
                      comparing the latest ESMValTool version with a previous not
                      yet optimized version, we find significant performance
                      improvements for many relevant applications running on a
                      single node of a high-performance computing (HPC) system,
                      ranging from 2.3 times faster runs in a multi-model setup up
                      to 23 times faster runs for processing a single
                      high-resolution model. By utilizing distributed computing on
                      two nodes of an HPC system, these speedup factors can be
                      further improved to 3.0 and 44, respectively. Moreover,
                      evaluation runs with the latest version of ESMValTool also
                      require significantly less computational resources than
                      before, which in turn reduces power consumption and thus the
                      overall carbon footprint of ESMValTool runs. For example,
                      the previously mentioned use cases use 2.3 (multi-model
                      evaluation) and 23 (high-resolution model evaluation) times
                      less resources compared to the reference version on one HPC
                      node. Finally, analyses which could previously only be
                      performed on machines with large amounts of memory can now
                      be conducted on much smaller hardware through the use of
                      out-of-core computation. For instance, the high-resolution
                      single-model evaluation use case can now be run with 8 GB
                      of available memory despite an input data size of 35 GB,
                      which was not possible with earlier versions of ESMValTool.
                      This enables running much more complex evaluation tasks on a
                      personal laptop than before.},
      cin          = {JSC},
      ddc          = {550},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / 2A5 - Exascale Earth
                      System Modeling (CARF - CCA) (POF4-2A5) / USMILE -
                      Understanding and Modelling the Earth System with Machine
                      Learning (855187) / ESM2025 - Earth system models for the
                      future (101003536) / IS-ENES3 - Infrastructure for the
                      European Network for Earth System modelling - Phase 3
                      (824084) / EUCP - European Climate Prediction system
                      (776613) / ESiWACE3 - Center of excellence for weather and
                      climate phase 3 (101093054)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-HGF)POF4-2A5 /
                      G:(EU-Grant)855187 / G:(EU-Grant)101003536 /
                      G:(EU-Grant)824084 / G:(EU-Grant)776613 /
                      G:(EU-Grant)101093054},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.5194/gmd-18-4009-2025},
      url          = {https://juser.fz-juelich.de/record/1050041},
}