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Abstract
The 2021 Pacific Northwest heatwave was so extreme as to challenge conventional statistical and climate-model-based approaches to extreme weather attribution. However, state-of-the-art operational weather prediction systems are demonstrably able to simulate the detailed physics of the heatwave. Here, we leverage these systems to show that human influence on the climate made this event at least 8 [2–50] times more likely. At the current rate of global warming, the likelihood of such an event is doubling every 20 [10–50] years. Given the multi-decade lower-bound return-time implied by the length of the historical record, this rate of change in likelihood is highly relevant for decision makers. Further, forecast-based attribution can synthesise the conditional event-specific storyline and unconditional event-class probabilistic approaches to attribution. If developed as a routine service in forecasting centres, it could provide reliable estimates of human influence on extreme weather risk, which is critical to supporting effective adaptation planning.
The 2021 Pacific Northwest Heatwave challenged standard attribution methods. The authors use a weather model that predicted the event to quantify human impact on the heat, suggesting that such models could be used broadly to assess changing weather risk.
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Details







1 University of Oxford, Atmospheric, Oceanic, and Planetary Physics, Department of Physics, Oxford, UK (GRID:grid.4991.5) (ISNI:0000 0004 1936 8948); Climate X Ltd., London, UK (GRID:grid.4991.5)
2 European Centre for Medium-Range Weather Forecasts, Earth System Predictability Section, Research Department, Reading, UK (GRID:grid.42781.38) (ISNI:0000 0004 0457 8766)
3 University of Oxford, Atmospheric, Oceanic, and Planetary Physics, Department of Physics, Oxford, UK (GRID:grid.4991.5) (ISNI:0000 0004 1936 8948); European Centre for Medium-Range Weather Forecasts, Earth System Predictability Section, Research Department, Reading, UK (GRID:grid.42781.38) (ISNI:0000 0004 0457 8766)
4 University of Oxford, Atmospheric, Oceanic, and Planetary Physics, Department of Physics, Oxford, UK (GRID:grid.4991.5) (ISNI:0000 0004 1936 8948); University of Bristol, School of Geographical Sciences, Bristol, UK (GRID:grid.5337.2) (ISNI:0000 0004 1936 7603)
5 University of Bristol, School of Geographical Sciences, Bristol, UK (GRID:grid.5337.2) (ISNI:0000 0004 1936 7603)
6 University of Bristol, School of Geographical Sciences, Bristol, UK (GRID:grid.5337.2) (ISNI:0000 0004 1936 7603); Royal Netherlands Meteorological Institute (KNMI), De Bilt, The Netherlands (GRID:grid.8653.8) (ISNI:0000 0001 2285 1082)
7 University of Oxford, Atmospheric, Oceanic, and Planetary Physics, Department of Physics, Oxford, UK (GRID:grid.4991.5) (ISNI:0000 0004 1936 8948)
8 University of Oxford, Atmospheric, Oceanic, and Planetary Physics, Department of Physics, Oxford, UK (GRID:grid.4991.5) (ISNI:0000 0004 1936 8948); European Centre for Medium-Range Weather Forecasts, Earth System Predictability Section, Research Department, Reading, UK (GRID:grid.42781.38) (ISNI:0000 0004 0457 8766); University of Oxford, National Centre for Atmospheric Science, Atmospheric, Oceanic, and Planetary Physics, Department of Physics, Oxford, UK (GRID:grid.4991.5) (ISNI:0000 0004 1936 8948)
9 University of Oxford, Atmospheric, Oceanic, and Planetary Physics, Department of Physics, Oxford, UK (GRID:grid.4991.5) (ISNI:0000 0004 1936 8948); University of Oxford, Environmental Change Institute, School of Geography and the Environment, Oxford, UK (GRID:grid.4991.5) (ISNI:0000 0004 1936 8948)