Tag: luohao

Impact of Ocean, Sea Ice or Atmosphere Initialization on Seasonal Prediction of Regional Antarctic Sea Ice

Xiu, Y., Wang, Y., Luo, H., Garcia-Oliva, L., Yang, Q. 2025: Impact of ocean, sea ice or atmosphere initialization on seasonal prediction of regional Antarctic sea ice. JAMES. https://doi.org/10.1029/2024MS004382

Summary: This study investigates how the atmosphere, ocean, or sea ice observations affect the seasonal prediction of Antarctic sea ice. We analyze three sets of predictions from the Norwegian Climate Prediction Model, each integrating different data sets of the atmosphere, ocean, or sea ice. Initially, we assess the seasonal cycles, trends, and variability of Antarctic sea ice in these data sets. We found that including atmosphere observations gave the best seasonal cycle compared to the observed sea ice. However, the linear trend in sea ice when including atmospheric data is poorly reproduced in the western Southern Ocean. Regarding variability, including the combined ocean and sea ice data gave the best performance. Next, we assess the accuracy of regional Antarctic sea ice prediction. We found that the accuracy varies with region and season. Austral winter predictions in western Antarctic have some skill up to a year in advance, while those in the eastern Antarctic are less reliable. Predictions based on atmosphere data are generally more accurate than those based on ocean or ocean/sea-ice data, especially when predicting from July or October. Interestingly, once ocean data is used, involving additional sea ice data improves sea ice concentration in the reanalysis but not in the predictions.

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Ice-kNN-South: A Lightweight Machine Learning Model for Antarctic Sea Ice Prediction

Lin, Y., Yang, Q., Li, X., Dong, X., Luo, H., Nie, Y., et al. 2025: Ice-kNN-south: A lightweight machine learning model for Antarctic sea ice prediction. JAMES. https://doi.org/10.1029/2024JH000433

Summary: Antarctic sea ice has undergone a transition, with more frequent extreme minimum events observed since 2014, emphasizing the ongoing need for accurate predictions. We developed a lightweight machine learning model called Ice-k-nearest neighbor (kNN)-South to improve Antarctic sea ice prediction. Compared with commonly used benchmarks, such as anomaly persistence, climatology, and the European Centre for Medium-Range Weather Forecasts predictions, the Ice-kNN-South shows skillful predictions for almost 90 lead days, especially in summer. Even in years with extreme minimum sea ice areas, Ice-kNN-South shows strong skill in predicting Antarctic sea ice cover. Additionally, due to its minimal computational resource requirements, Ice-kNN-South shows promise for operational and real-time Antarctic sea ice prediction applications.

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Ocean–atmosphere coupled Pacific Decadal variability simulated by a climate model

Luo H, Zheng F, Keenlyside N, Zhu J. 2020: Ocean–atmosphere coupled Pacific Decadal variability simulated by a climate model. Clim Dyn. https://doi.org/10.1007/s00382-020-05248-9 .

Summary: Currently, the mechanisms for Pacific Decadal Oscillation (PDO) are still disputed, and in particular the atmosphere response to the ocean in the mid-latitude remains a key uncertainty. In this study, we investigate two potential feedbacks—a local positive and a delayed negative—for the PDO based on a long-term control simulation using the ECHAM5/MPI-OM coupled model, which is selected because of reproduces well the variability of PDO. The positive feedback is as follows. In the PDO positive phase, the meridional sea surface temperature (SST) gradient is intensified and this strengthens the lower level atmospheric baroclinicity in the mid-latitudes, leading to the enhancement of Aleutian low and zonal wind. These atmospheric changes reinforce the meridional SST temperature gradient through the divergence of ocean surface currents. The increased heat flux loss over the anomalously warm water and decreased heat flux loss over the anomalously cold water in turn reinforce the lower atmospheric meridional temperature gradient, baroclinicity and atmospheric circulation anomalies, forming a local positive feedback for the PDO. The delayed negative feedback arises, because the intensified meridional SST gradient also generates an anticyclonic wind stress in the central North Pacific, warming the upper ocean by Ekman convergence. The warm upper ocean anomalies then propagate westward and are transported to the mid-latitudes in the western North Pacific by the western boundary current. This finally reduces the meridional SST gradient, 18 years after the peak PDO phase. These results demonstrate the significant contributions of the meridional SST gradient to the PDO’s evolution.

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