Abstract
Prediction skill for the seasonal tropical cyclone (TC) activity in the Northern Hemisphere is investigated using the coupled climate forecast system (version 1.0) of Nanjing University of Information Science and Technology (NUIST-CFS1.0). This assessment is based on the seven-month (May to November) hindcasts consisting of nine ensemble members during 1982–2019. The predictions are compared with the Japanese 55-year Reanalysis and observed tropical storms in the Northern Hemisphere. The results show that the overall distributions of the TC genesis and track densities in model hindcasts agree well with the observations, although the seasonal mean TC frequency and accumulated cyclone energy (ACE) are underestimated in all basins due to the low resolution (T106) of the atmospheric component in the model.
NUIST-CFS1.0 closely predicts the interannual variations of TC frequency and ACE in the North Atlantic (NA) and eastern North Pacific (ENP), which have a good relationship with indexes based on the sea surface temperature. In the western North Pacific (WNP), NUIST-CFS1.0 can closely capture ACE, which is significantly correlated with the El Niño—Southern Oscillation (ENSO), while it has difficulty forecasting the interannual variation of TC frequency in this area. When the WNP is further divided into eastern and western subregions, the model displays improved TC activity forecasting ability. Additionally, it is found that biases in predicted TC genesis locations lead to inaccurately represented TC—environment relationships, which may affect the capability of the model in reproducing the interannual variations of TC activity.
摘 要
本文评估了南京信息工程大学耦合气候预报系统 (1.0 版, NUIST-CFS1.0) 对 1982–2019 年 5–11 月北半球主要海域的热带气旋 (TC) 季节活动的预测技巧. 评估发现 NUIST-CFS1.0 预测的热带气旋生成密度和路径密度的总体分布与观测有较好的一致性, 但各海域的季节平均热带气旋生成频率和累积气旋能量 (ACE) 有所低估, 这可能跟该耦合模式中大气模式部分的空间分辨率 (T106) 较低有关. 此外, NUIST-CFS1.0 能够很好的预测北大西洋 (NA) 和东北太平洋 (ENP) 热带气旋生成频率和累积气旋能量的年际变化, 这主要是因为该海域的热带气旋活动与海温相关的指数有显著的相关关系. 在西北太平洋 (WNP), 由于西北太平洋的累积气旋能量与厄尔尼诺-南方涛动 (ENSO) 有显著的相关性, 模式可以很好的预测累积气旋能量的年际变化, 但模式对西北太平洋热带气旋生成频率年际变化的预测技巧很低. 当西北太平洋被划分为东、 西两部分子区域时, NUIST-CFS1.0 显示出更好的热带气旋季节活动预测能力. 研究还发现, 预测热带气旋生成位置的偏差会导致热带气旋活动与环境因子的关系表述不准确, 这可能会影响模式对热带气旋季节活动年际变化的预测能力.
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Data availability statement The IBTrACS data can be obtained from https://www.ceei.oaaa.ovv/poouccss/intrnaatinaal-best-track-archive?name=ib-v4-access. The JRA55 reanalysis data are available at https://rda.ucar.edu/datasets/ds628.1/. The OISST data can be downloaded from https://www.ncei.noaa.gov/products/optimum-interpolation-sst. The NUIST-CFS1.0 prediction dataset on which this paper is based is too large to be retained or publicly archived with available resources. Documentation and methods used to support this study are available from Jing-jia LUO (jingjia_luo@hotmail.com) at Nanjing University of Information Science and Technology, Nanjing, China. The TC dataset and model variables used in the paper are uploaded at https://pan.baidu.com/s/1dMLctISZ5AXFSh9sYxbe6g?pwd=P023.
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Acknowledgements
This research was supported in part by the National Key Research and Development Program of China (Grant No. 2020YFA0608000) and the Nature Science Foundation of China (Grant Nos. 42005002, 42030605, and 42105003). We acknowledge the High-Performance Computing Center of Nanjing University of Information Science and Technology for its support of this work.
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• NUIST-CFS1.0 can closely predict the interannual variations of TC activity over the WNP, ENP, and NA (except TC frequency over the WNP).
• The good skill of NUIST-CFS1.0 in reproducing TC activity is attributed to its good performance in predicting tropical SST-based variables.
• Biases in predicted TC genesis locations lead to inaccurately represented TC—environment relationships, which may affect the capability of the model in reproducing the interannual variations of TC activity.
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Peng, K., Luo, JJ. & Liu, Y. Prediction of Seasonal Tropical Cyclone Activity in the NUIST-CFS1.0 Forecast System. Adv. Atmos. Sci. 40, 1309–1325 (2023). https://doi.org/10.1007/s00376-023-2213-8
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DOI: https://doi.org/10.1007/s00376-023-2213-8
Key words
- seasonal tropical cyclone activity
- interannual variation
- global ocean-atmosphere coupled forecast system