Research Highlight

According to twenty-first century climate-model projections, greenhouse warming will intensify rainfall variability and extremes across the globe1,2,3,4. However, verifying this prediction using observations has remained a substantial challenge owing to large natural rainfall fluctuations at regional scales3,4. 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.

함유근 교수 삼프로tv 출연 [2023.07.19]

About Us

This lab (Ocean & Climate Science Lab) conducts studies

  • Climate projection & forecasts using a deep learning technique
  • Seasonal to decadal prediction by using a global coupled climate model
  • The development of the initialization system including the data assimilation and the optimal perturbation method for sub-seasonal
  • Sub-seasonal, interannual and decadal climate variability over the tropics (e.g., El Niño, AMOC)
  • Climate change/sensitivity after the global warming

To understand the physical mechanisms of the climate variability and the improvement of the seasonal predictability is the main aim of the research.