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Anthropogenic fingerprints in daily precipitation revealed by deep learning.

ABSTRACT: According to twenty-first century climate-model projections, greenhouse warming will intensify rainfall variability and extremes across the globe^1-4. However, verifying this prediction using observations has remained a substantial challenge owing to large natural rainfall fluctuations at regional scales^3,4. Here we show that deep learning successfully detects the emerging climate-change signals in daily precipitation fields during the observed record. We trained a convolutional neural network (CNN)⁵ with daily precipitation fields and annual global mean surface air temperature data obtained from an ensemble of present-day and future climate-model simulations⁶. After applying the algorithm to the observational record, we found that the daily precipitation data represented an excellent predictor for the observed planetary warming, as they showed a clear deviation from natural variability since the mid-2010s. Furthermore, we analysed the deep-learning model with an explainable framework and observed that the precipitation variability of the weather timescale (period less than 10 days) over the tropical eastern Pacific and mid-latitude storm-track regions was most sensitive to anthropogenic warming. Our results highlight that, although the long-term shifts in annual mean precipitation remain indiscernible from the natural background variability, the impact of global warming on daily hydrological fluctuations has already emerged.

SUBMITTER: Ham YG

PROVIDER: S-EPMC10567562 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Anthropogenic fingerprints in daily precipitation revealed by deep learning.

Ham Yoo-Geun YG Kim Jeong-Hwan JH Min Seung-Ki SK Kim Daehyun D Li Tim T Timmermann Axel A Stuecker Malte F MF

Nature 20230830 7982

According to twenty-first century climate-model projections, greenhouse warming will intensify rainfall variability and extremes across the globe<sup>1-4</sup>. However, verifying this prediction using observations has remained a substantial challenge owing to large natural rainfall fluctuations at regional scales<sup>3,4</sup>. Here we show that deep learning successfully detects the emerging climate-change signals in daily precipitation fields during the observed record. We trained a convoluti ...[more]

PMID: 37648861

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Project description:Hydro-meteorological monitoring through satellites in arid and semi-arid regions is constrained by the coarse spatial resolution of precipitation data, which impedes detailed analyses. The objective of this study is to evaluate various machine learning techniques for developing a downscaling framework that generates high spatio-temporal resolution precipitation products. Focusing on the Hai River Basin, we evaluated three machine learning approaches-Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Back Propagation (BP) neural networks. These methods integrate environmental variables including land surface temperature (LST), Normalized Difference Vegetation Index (NDVI), Digital Elevation Model (DEM), Precipitable Water Vapor (PWV), and albedo, to downscale the 0.1° spatial resolution Global Precipitation Measurement (GPM) product to a 1 km resolution. We further refined the results with residual correction and calibration using terrestrial rain gauge data. Subsequently, utilizing the 1 km annual precipitation, we employed the moving average window method to derive monthly and daily precipitation. The results demonstrated that the XGBoost method, calibrated with Geographical Difference Analysis (GDA) and Kriging spatial interpolation, proved to be the most accurate, achieving a Mean Absolute Error (MAE) of 58.40 mm for the annual product, representing a 14 % improvement over the original data. The monthly and daily products achieved MAE values of 11.61 mm and 1.79 mm, respectively, thus enhancing spatial resolution while maintaining accuracy comparable to the original product. In the Hai River Basin, key factors including longitude, latitude, DEM, LST_night, and PWV demonstrated greater importance and stability than other factors, thereby enhancing the model's precipitation prediction capabilities. This study provides a comprehensive assessment of the annual, monthly, and daily high-temporal and high-spatial resolution downscaling processes of precipitation, serving as an important reference for hydrology and related fields.

Dataset Information

Anthropogenic fingerprints in daily precipitation revealed by deep learning.

Publications

Anthropogenic fingerprints in daily precipitation revealed by deep learning.

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