Yoo-Keun Ham
Seoul National University · 環境科学
研究室紹介
Professor Yoo-Keun Ham's research lab specializes in climate system dynamics, with a focus on improving the simulation and prediction of major climate phenomena such as the El Niño–Southern Oscillation (ENSO), the Madden–Julian Oscillation (MJO), and tropical rainfall variability. The lab integrates advanced machine learning techniques—particularly convolutional neural networks—with climate modeling and observational data to detect climate change signals and correct model biases. A central theme is leveraging intermodel diversity in climate models to enhance the realism of climate projections, especially under global warming scenarios. The lab also investigates the role of tropical sea surface temperatures and atmospheric moisture dynamics in modulating global climate variability.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15Abstract Two distinct roles of the Atlantic sea surface temperatures (SSTs), namely, the North Tropical Atlantic (NTA) SST and the Atlantic Niño, on the El Niño–Southern Oscillation (ENSO) variability are investigated using the observational data from 1980 to 2010 and coupled model experiments. It appears that the NTA SST and the Atlantic Niño can be used as two independent predictors for predicting the development of ENSO events in the following season. Furthermore, they are likely to be linked
Abstract Many climate models struggle with a poor simulation of the Madden‐Julian Oscillation (MJO), especially its propagation across the Maritime Continent (MC). This study quantitatively evaluates the robustness of MJO propagation over the MC in climate models that participated in Coupled Model Intercomparison Project Phase 5 (CMIP5) and Phase 6 (CMIP6) with a newly developed MC propagation metric. The results show that the CMIP6 models simulate MJO propagation over the MC more realistically
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 neur
Although deep learning has achieved a milestone in forecasting the El Niño-Southern Oscillation (ENSO), the current models are insufficient to simulate diverse characteristics of the ENSO, which depends on the calendar season. Consequently, a model was generated for specific seasons which indicates these models did not consider physical constraints between different target seasons and forecast lead times, thereby leading to arbitrary fluctuations in the predicted time series. To overcome this pr
Abstract In this study, a new methodology is developed to improve the climate simulation of state-of-the-art coupled global climate models (GCMs), by a postprocessing based on the intermodel diversity. Based on the close connection between the interannual variability and climatological states, the distinctive relation between the intermodel diversity of the interannual variability and that of the basic state is found. Based on this relation, the simulated interannual variabilities can be improve
Abstract This study examines the relationship between the intermodel diversities of the present climate climatology and those of El Niño–Southern Oscillation (ENSO) amplitude change under global warming in the Coupled Model Intercomparison Project Phase 5 (CMIP5) models. The models with increased ENSO amplitude under greenhouse warming (i.e., “ENSO‐amplified models”) tend to simulate a twentieth century stronger climatological Intertropical Convergence Zone and South Pacific Convergence Zone ove