Ocean & Climate Science Laboratory

News

• 함유근 전남대 교수 공동연구팀, 전 지구 ‘극한 호우’ 원인 규명 – 교수신문 (kyosu.net)

• 전남대 연구팀, 겨울철 한반도 기후 예측 14개월 앞서 가능 – 연합뉴스 (yna.co.kr)

• [차세대리더-의·과학] 함유근 전남대 지구환경과학부 교수 – 시사저널 (sisajournal.com)

• 전남대 함유근 교수, ‘2020 젊은과학자상’ 수상 – 시사매거진 (sisamagazine.co.kr)

• 전남대, ‘국가연구개발 우수성과 100선’ 최우수성과 선정 – 대학저널 (dhnews.co.kr)

• 올해 ‘국가연구개발 우수성과 100선’에 기상 연구 관련 3건 선정 (news1.kr)

Research Highlight

According to twenty-first century climate-model projections, greenhouse warming will intensify rainfall variability and extremes across the globe^1,2,3,4. 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.
(more: https://doi.org/10.1038/s41586-023-06474-x)

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.