Yoo-Geun Ham

Compound events (CEs), defined as the co-occurrence of multiple climate extremes, often amplify societal and environmental impacts beyond those of individual extremes. While carbon dioxide removal (CDR) is increasingly considered essential for achieving global climate targets, the response of CEs to CDR pathways remains poorly understood. We investigate changes in three types of CEs—consecutive day and night heatwave event (CDNHE), compound drought and heatwave event (CDHE), and compound precipitation and wind event (CPWE)—under a CO ₂ ramp-up and ramp-down experiment using CESM1 with 28 ensemble members. Although atmospheric CO ₂ returns to its initial concentration, the global mean of temperature, precipitation, and wind speed (WS), as well as the frequencies of all three CEs, have not returned to their initial levels by 2300, indicating incomplete recovery. Over the final 30 years of the ramp-down period, a recovery deficit of approximately 15%–25% remains. CDNHE increases across low-latitude regions and the Southern Hemisphere in association with persistent SST warming, enhanced surface heating and increased atmospheric moisture, while decreases over Greenland and northern and western Europe coincide with cooling associated with the delayed recovery of Atlantic meridional overturning circulation. CDHE increases in tropical and mid-latitude dry regions through land–atmosphere feedbacks involving soil moisture depletion, increased vapor pressure deficit, and enhanced sensible heat flux. CPWE exhibits a more spatially heterogeneous and temporally delayed response, with its spatial changes more closely associated with precipitation than with WS. Regional increases in CEs can exceed those of both individual components by 20–35 percentage points, revealing changes that may be overlooked when individual extremes are considered separately. Our results highlight the importance of accounting for compound climate hazards and their delayed recovery when evaluating CO ₂ removal pathways.

Anticipating marine ecosystem changes is critical for enabling communities to adapt to climate fluctuations and for predicting future climate by considering interactions between Earth's physical and biogeochemical fields. Earth System Models (ESMs) capture large-scale physical–biogeochemical coupling, but their biogeochemical prediction skill varies substantially across regions and lead times due to sparse observational records, structural uncertainties in biogeochemical models. Here, we develop a deep learning-based prediction system to forecast surface chlorophyll concentrations across all Large Marine Ecosystems (LMEs) at monthly to annual timescales with lead times up to two years. Trained on multi-decadal simulations from various climate models and a coupled physical–biogeochemical reanalysis from a data assimilative ESM run, the system demonstrates skillful chlorophyll predictions comparable to ESM-based dynamic forecasts. The prediction skill is associated with physical-biogeochemical coupling processes triggered by large-scale climate variability, consistent with the mechanisms previously identified in dynamical forecasts. Furthermore, predicted chlorophyll anomalies are significantly linked to interannual variability in fish catch in several LMEs, demonstrating the promise of data-driven biogeochemical forecasting to support adaptive, climate-informed marine resource management.

Global meteorological agencies provide seasonal forecasts based on indicators such as El Niño–Southern Oscillation and Indian Ocean variability. The NOAA operates forecasts up to 9 months, while the ECMWF offers extended predictions up to 13 months. However, these physics-based forecasting systems suffer from the chaotic nature of the atmosphere, leading to a rapid decline in forecast accuracy as the lead times increase. This study proposes a novel seasonal forecasting framework utilizing neural ordinary differential equations (Neural ODEs). Through comparative experiments with the North American Multi-Model Ensemble model, we demonstrate that Neural ODEs significantly reduce computational costs and mitigate cumulative errors, thereby enhancing long-term forecasting capability. Furthermore, we employ multifractal detrended fluctuation analysis to quantify the memory characteristics, revealing the intrinsic limits of predictability in the climate system.

Artificial intelligence has advanced global weather forecasting, outperforming traditional numerical models in both accuracy and computational efficiency. Nevertheless, extending predictions beyond subseasonal timescales requires the development of deep learning (DL)–based ocean-atmosphere coupled models that can realistically simulate complex oceanic responses to atmospheric forcing. This study presents KIST-Ocean, a DL-based global three-dimensional ocean general circulation model. Comprehensive evaluations demonstrate the model’s robust ocean simulation skill and efficiency. Moreover, it reproduces ocean responses, such as Kelvin and Rossby wave propagation, and vertical motions induced by wind stress curl, demonstrating its ability to represent key atmospherically forced ocean dynamics underlying climate phenomena, including the El Niño–Southern Oscillation. These findings reinforce confidence in DL-based global weather and climate models by demonstrating their capacity to capture essential ocean-atmosphere relationships. Building on this foundation, the present study paves the way for extending DL-based modeling frameworks toward integrated Earth system simulations, thereby offering substantial potential for advancing long-range climate prediction capabilities.

Anticipating marine ecosystem changes is critical for enabling communities to adapt to climate fluctuations and for predicting future climate by considering interactions between Earth’s physical and biogeochemical fields. Earth System Models (ESMs) simulate Earth’s multi-facet features, but their predictive capabilities remain limited due to sparse biogeochemical observations and structural uncertainties in marine biogeochemical models. Here, we develop a deep learning–based prediction system to forecast surface chlorophyll concentrations across all Large Marine Ecosystems (LMEs). Trained on multi-decadal simulations from various climate models and a coupled physical–biogeochemical reanalysis from a data assimilative ESM run, the system demonstrates skillful chlorophyll predictions comparable to ESM-based dynamic forecasts. The prediction skill arises from physical-biogeochemical coupling processes triggered by large-scale climate variability, consistent with the mechanisms previously identified in dynamical forecasts. Furthermore, predicted chlorophyll anomalies are significantly linked to interannual variability in fish catch in several LMEs, demonstrating the promise of data-driven biogeochemical forecasting to support adaptive, climate-informed marine resource management.

Anticipating marine ecosystem changes is critical for enabling communities to adapt to climate fluctuations and for predicting future climate by considering interactions between Earth’s physical and biogeochemical fields. Earth System Models (ESMs) simulate Earth’s multi-facet features, but their predictive capabilities remain limited due to sparse biogeochemical observations and structural uncertainties in marine biogeochemical models. Here, we develop a deep learning–based prediction system to forecast surface chlorophyll concentrations across all Large Marine Ecosystems (LMEs). Trained on multi-decadal simulations from various climate models and a coupled physical–biogeochemical reanalysis from a data assimilative ESM run, the system demonstrates skillful chlorophyll predictions comparable to ESM-based dynamic forecasts. The prediction skill arises from physical-biogeochemical coupling processes triggered by large-scale climate variability, consistent with the mechanisms previously identified in dynamical forecasts. Furthermore, predicted chlorophyll anomalies are significantly linked to interannual variability in fish catch in several LMEs, demonstrating the promise of data-driven biogeochemical forecasting to support adaptive, climate-informed marine resource management.

Anticipating marine ecosystem changes is critical for enabling communities to adapt to climate fluctuations and for predicting future climate by considering interactions between Earth’s physical and biogeochemical fields. Earth System Models (ESMs) simulate Earth’s multi-facet features, but their predictive capabilities remain limited due to sparse biogeochemical observations and structural uncertainties in marine biogeochemical models. Here, we develop a deep learning–based prediction system to forecast surface chlorophyll concentrations across all Large Marine Ecosystems (LMEs). Trained on multi-decadal simulations from various climate models and a coupled physical–biogeochemical reanalysis from a data assimilative ESM run, the system demonstrates skillful chlorophyll predictions comparable to ESM-based dynamic forecasts. The prediction skill arises from physical-biogeochemical coupling processes triggered by large-scale climate variability, consistent with the mechanisms previously identified in dynamical forecasts. Furthermore, predicted chlorophyll anomalies are significantly linked to interannual variability in fish catch in several LMEs, demonstrating the promise of data-driven biogeochemical forecasting to support adaptive, climate-informed marine resource management.

Reliable prediction of climate variables and high-impact extremes in the midlatitudes is crucial for climate risk assessment, agricultural planning, water resource management, and disaster preparedness. However, conventional deep learning–based approaches for midlatitude climate prediction trained with dynamical climate models (e.g., CMIP models) can cause systematic errors in capturing the observed climate-relevant signals, ultimately limiting prediction skill. These limitations highlight the need to improve midlatitude prediction by detecting climate signals solely from the limited numbers of reliable observational climate data. To address the challenge of limited training samples, we employ the model-agnostic meta-learning (MAML) algorithm along with domain-knowledge-based data augmentation to predict mid-latitude winter temperatures. The proposed data augmentation is purely based on the observed data by defining the labels using large-scale climate variabilities associated with the target variable. The MAML-applied convolutional neural network (CNN) demonstrates superior correlation skills for winter temperature anomalies compared to a reference model (i.e., the CNN without MAML) and state-of-the-art dynamical forecast models across all target lead months during the boreal winter seasons. Moreover, occlusion sensitivity results reveal that the MAML model better captures the physical precursors that influence mid-latitude winter temperatures, resulting in more accurate predictions.

Anticipating marine ecosystem changes is critical for enabling communities to adapt to climate fluctuations and for predicting future climate by considering interactions between Earth’s physical and biogeochemical fields. Earth System Models (ESMs) simulate Earth’s multi-facet features, but their predictive capabilities remain limited due to sparse biogeochemical observations and structural uncertainties in marine biogeochemical models. Here, we develop a deep learning–based prediction system to forecast surface chlorophyll concentrations across all Large Marine Ecosystems (LMEs). Trained on multi-decadal simulations from various climate models and a coupled physical–biogeochemical reanalysis from a data assimilative ESM run, the system demonstrates skillful chlorophyll predictions comparable to ESM-based dynamic forecasts. The prediction skill arises from physical-biogeochemical coupling processes triggered by large-scale climate variability, consistent with the mechanisms previously identified in dynamical forecasts. Furthermore, predicted chlorophyll anomalies are significantly linked to interannual variability in fish catch in several LMEs, demonstrating the promise of data-driven biogeochemical forecasting to support adaptive, climate-informed marine resource management.