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As the number of cores in top computing facilities pushes into the millions, increased average frequency of hardware and software failures forces users to review their algorithms and systems in order to protect simulations from breakdown. This report surveys hardware, application-level and algorithm-level resilience approaches of particular relevance to time-critical numerical weather and climate prediction systems. A selection of applicable existing strategies is analysed, featuring interpolation-restart and compressed checkpointing for the numerical schemes, in-memory checkpointing, user-level failure mitigation and backup-based methods for the systems. Numerical examples showcase the performance of the techniques in addressing faults, with particular emphasis on iterative solvers for linear systems, a staple of atmospheric fluid flow solvers. The potential impact of these strategies is discussed in relation to current development of numerical weather prediction algorithms and systems towards the exascale. Trade-offs between performance, efficiency and effectiveness of resiliency strategies are analysed and some recommendations outlined for future developments. <\/jats:p>","DOI":"10.1177\/1094342021990433","type":"journal-article","created":{"date-parts":[[2021,2,12]],"date-time":"2021-02-12T02:58:04Z","timestamp":1613098684000},"page":"285-311","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":14,"title":["Resilience and fault tolerance in high-performance computing for numerical weather and climate prediction"],"prefix":"10.1177","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0732-7167","authenticated-orcid":false,"given":"Tommaso","family":"Benacchio","sequence":"first","affiliation":[{"name":"MOX\u00a0\u2013 Modelling and Scientific Computing, Dipartimento di Matematica, Politecnico di Milano, Milan, Italy"}]},{"given":"Luca","family":"Bonaventura","sequence":"additional","affiliation":[{"name":"MOX\u00a0\u2013 Modelling and Scientific Computing, Dipartimento di Matematica, Politecnico di Milano, Milan, Italy"}]},{"given":"Mirco","family":"Altenbernd","sequence":"additional","affiliation":[{"name":"Institute for Applied Analysis and Numerical Simulation and Cluster of Excellence \u2018Data-Driven Simulation Science\u2019, University of Stuttgart, Stuttgart, Germany"}]},{"given":"Chris D","family":"Cantwell","sequence":"additional","affiliation":[{"name":"Department of Aeronautics, Imperial College London, London, UK"}]},{"given":"Peter D","family":"D\u00fcben","sequence":"additional","affiliation":[{"name":"European Centre for Medium Range Weather Forecasts, Reading, UK"},{"name":"AOPP, Department of Physics, University of Oxford, Oxford, UK"}]},{"given":"Mike","family":"Gillard","sequence":"additional","affiliation":[{"name":"Loughborough University, Loughborough, UK"}]},{"given":"Luc","family":"Giraud","sequence":"additional","affiliation":[{"name":"HiePACS, Inria Bordeaux, Sud-Ouest, Talence, France"}]},{"given":"Dominik","family":"G\u00f6ddeke","sequence":"additional","affiliation":[{"name":"Institute for Applied Analysis and Numerical Simulation and Cluster of Excellence \u2018Data-Driven Simulation Science\u2019, University of Stuttgart, Stuttgart, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0359-5372","authenticated-orcid":false,"given":"Erwan","family":"Raffin","sequence":"additional","affiliation":[{"name":"CEPP\u00a0\u2013 Center for Excellence in Performance Programming, Atos, Rennes, France"}]},{"given":"Keita","family":"Teranishi","sequence":"additional","affiliation":[{"name":"Sandia National Laboratories, Livermore, CA, USA"}]},{"given":"Nils","family":"Wedi","sequence":"additional","affiliation":[{"name":"European Centre for Medium Range Weather Forecasts, Reading, UK"}]}],"member":"179","published-online":{"date-parts":[[2021,2,8]]},"reference":[{"key":"bibr1-1094342021990433","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpdc.2019.02.007"},{"key":"bibr2-1094342021990433","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-61982-8_3"},{"key":"bibr147-1094342021990433","doi-asserted-by":"publisher","DOI":"10.1002\/nla.2059"},{"key":"bibr3-1094342021990433","doi-asserted-by":"publisher","DOI":"10.1137\/15M1042115"},{"key":"bibr4-1094342021990433","doi-asserted-by":"publisher","DOI":"10.1109\/HiPC.2015.9"},{"key":"bibr5-1094342021990433","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPSW.2014.132"},{"key":"bibr6-1094342021990433","doi-asserted-by":"publisher","DOI":"10.1177\/1094342016684006"},{"key":"bibr7-1094342021990433","doi-asserted-by":"publisher","DOI":"10.1109\/PDP2018.2018.00032"},{"key":"bibr8-1094342021990433","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2004.2"},{"key":"bibr9-1094342021990433","unstructured":"Balay S, Abhyankar S, Adams MF, et al. 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