Machine learning-based methods for the detection of anthropogenic influence (DAI) have been shown to overcome the reliance on trends 33, 34 and are even capable of detecting the human influence from weather data on a single day 35.
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Here, we aim to take these uncertainties fully into account, by making no assumptions about how to derive the anthropogenic signal from GCM data. In past research, spread in the response has been suppressed by assuming the anthropogenic fingerprint can be derived from the ensemble-mean change in extreme precipitation 32. These two effects create significant uncertainty in the character of the true anthropogenic signal. This spread, the model uncertainty, occurs alongside large internal variability in the models’ simulations of the historical period. Another key difficulty with traditional methods is that the models produce a large spread in the extreme precipitation response to historical anthropogenic forcing 31. In the case of extreme precipitation, traditional methods may be difficult to apply globally due to inordinately short records and large observational uncertainty, reflected in multiple global datasets produced with very different assumptions 27, 28, 29, 30. Thus, traditional D&A methods rely on long-term observations 24, 26. The presence of a signal that can be statistically distinguished from internal variability confirms the influence of external forcing. Projection of observations onto these fingerprints allows for detection of the signal 24, 25. Often, they initially extract the spatial or spatiotemporal patterns of climate-system response to anthropogenic forcing (so-called fingerprints) from an ensemble of global climate models (GCMs). These attempts are part of a larger category of studies known as Detection and Attribution (D&A) 22, 23, 24. Recent studies have detected anthropogenic influence in historical changes to extreme precipitation across the domains of North America 17, 18, Europe 18, 19, Asia 18, 19, 20, and Northern Hemisphere land areas as a whole 21. These changes in extreme precipitation may have already become apparent on a regional basis 14, 15, 16. Moreover, increased variation between wet and dry extremes is projected, which could have devastating societal impacts 12, 13. Future projections by climate models following climate change scenarios show a robust increase in extreme precipitation, globally and on regional scales 8, 9, 10, 11. However, circulation changes can act to enhance or reduce this increase 4, 5, 6, 7. This intensification is manifested in part through increased extreme precipitation as a result of greater atmospheric moisture with warming following the Clausius–Clapeyron relationship. Anthropogenic warming acts to intensify Earth’s hydrologic cycle 3. Machine learning efficiently generates multiple lines of evidence supporting detection of an anthropogenic signal in global extreme precipitation.Įxtreme precipitation can have devastating direct societal impacts such as flooding, soil erosion, and agricultural damage 1, as well as causing indirect health risks and impacts 2. Using machine learning methods that can account for these uncertainties and capable of identifying the time evolution of the spatial patterns, we find a physically interpretable anthropogenic signal that is detectable in all global observational datasets. Previous attempts to detect human influence on extreme precipitation have not incorporated model uncertainty, and have been limited to specific regions and observational datasets. Meanwhile, multiple global observational datasets of daily precipitation exist, developed using varying techniques and inhomogeneously sampled data in space and time. Models produce diverse magnitudes of precipitation response to anthropogenic forcing, largely due to differing schemes for parameterizing subgrid-scale processes. Large internal variability distorts this anthropogenic signal. The intensification of extreme precipitation under anthropogenic forcing is robustly projected by global climate models, but highly challenging to detect in the observational record.
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