用深度学习修正气候模型对孟加拉湾海温与海平面的预测偏差。
Data Driven Deep Learning for Correcting Global Climate Model Projections of SST and DSL in the Bay of Bengal
- 基于历史数据训练深度神经网络，输入为去趋势气候投影，输出为再分析数据。
- 相比传统统计方法，海温误差降0.15℃，动态海平面误差降0.3米。
- 可推广至长期气候预测，适合关注区域气候影响的研究者。
气候变化改变海洋条件，尤其是温度和海平面。在孟加拉湾，这些变化影响季风降水和海洋生产力，对印度经济至关重要。在第六阶段耦合模式比较计划（CMIP6）中，全球气候模型（GCMs）采用不同共享社会经济路径（SSPs）进行未来气候预测。然而，2015–2024年间，这些模型与海洋再分析系统（ORAS5）在孟加拉湾存在显著差异：海表温度（SST）的均方根误差（RMSE）为1.2℃，动态海平面（DSL）为1.1米。本文提出一种新的数据驱动深度学习模型进行偏差校正。该模型以去气候态的月度气候投影为输入，对应月份的ORAS5数据为输出，利用1950–2014年历史数据训练，2015–2020年未来数据验证，2021–2023年测试。相比传统的等距累积分布函数（EDCDF）统计校正方法，本方法使SST的RMSE降低0.15℃，DSL降低0.3米。模型进一步用于校正2024–2100年的投影，并通过月、季节及十年平均值与变率分析，揭示了修正后投影所展现的新动态特征。
原文摘要 · Abstract (English)
Climate change alters ocean conditions, notably temperature and sea level. In the Bay of Bengal, these changes influence monsoon precipitation and marine productivity, critical to the Indian economy. In Phase 6 of the Coupled Model Intercomparison Project (CMIP6), Global Climate Models (GCMs) use different shared socioeconomic pathways (SSPs) to obtain future climate projections. However, significant discrepancies are observed between these models and the reanalysis data in the Bay of Bengal for 2015-2024. Specifically, the root mean square error (RMSE) between the climate model output and the Ocean Reanalysis System (ORAS5) is 1.2C for the sea surface temperature (SST) and 1.1 m for the dynamic sea level (DSL). We introduce a new data-driven deep learning model to correct for this bias. The deep neural model for each variable is trained using pairs of climatology-removed monthly climate projections as input and the corresponding month's ORAS5 as output. This model is trained with historical data (1950 to 2014), validated with future projection data from 2015 to 2020, and tested with future projections from 2021 to 2023. Compared to the conventional EquiDistant Cumulative Distribution Function (EDCDF) statistical method for bias correction in climate models, our approach decreases RMSE by 0.15C for SST and 0.3 m for DSL. The trained model subsequently corrects the projections for 2024-2100. A detailed analysis of the monthly, seasonal, and decadal means and variability is performed to underscore the implications of the novel dynamics uncovered in our corrected projections.
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