用深度学习提升北欧气温极端事件预测精度，助力气候适应决策。
Deep Learning-Driven Downscaling for Climate Risk Assessment of Projected Temperature Extremes in the Nordic Region
- 融合ViT、ConvLSTM与GeoStaNet模型，实现多区域高分辨率气温降尺度。
- 在挪威模型验证中误差仅1.01℃，2100年亚北极区升温达4.8℃，日温差扩大超1.5℃。
- 可为极地快速变化区提供站点级极端风险评估，适合政策制定者参考。
北欧多样气候区因快速气候变化和日益加剧的变率，亟需高分辨率温度预测支持区域规划。本文提出一种整合Vision Transformer（ViT）、Convolutional Long Short-Term Memory（ConvLSTM）与地理时空注意力网络（GeoStaNet）的集成降尺度框架。在涵盖温带海洋性（Cfb）、副极地海洋性（Cfc）、暖夏大陆性（Dfb）及亚北极（Dfc）气候区的十座气象站上进行评估，并采用深度学习-TOPSIS（DL-TOPSIS）多准则决策系统。利用挪威地球系统模型（NorESM2-LM）CMIP6数据，在1951–2014年间进行偏差校正，并与历史逐日温度指标和日较差统计量对比验证。结果表明，ViT模型表现最优（均方根误差：1.01℃；决定系数：0.92），可生成可信的降尺度预测。在SSP5-8.5情景下，到2100年，Dfc与Dfb气候区预计分别升温4.8℃和3.9℃，日温差扩大超过1.5℃。首次出现的“出现时间”信号出现在亚北极冬季（约2032年），凸显迫切适应需求。该框架提供站点级不确定性与极端事件估计，适用于高纬度快速环境变化区的适应政策制定。
原文摘要 · Abstract (English)
Rapid changes and increasing climatic variability across the widely varied Koppen-Geiger regions of northern Europe generate significant needs for adaptation. Regional planning needs high-resolution projected temperatures. This work presents an integrative downscaling framework that incorporates Vision Transformer (ViT), Convolutional Long Short-Term Memory (ConvLSTM), and Geospatial Spatiotemporal Transformer with Attention and Imbalance-Aware Network (GeoStaNet) models. The framework is evaluated with a multicriteria decision system, Deep Learning-TOPSIS (DL-TOPSIS), for ten strategically chosen meteorological stations encompassing the temperate oceanic (Cfb), subpolar oceanic (Cfc), warm-summer continental (Dfb), and subarctic (Dfc) climate regions. Norwegian Earth System Model (NorESM2-LM) Coupled Model Intercomparison Project Phase 6 (CMIP6) outputs were bias-corrected during the 1951-2014 period and subsequently validated against earlier observations of day-to-day temperature metrics and diurnal range statistics. The ViT showed improved performance (Root Mean Squared Error (RMSE): 1.01 degrees C; R^2: 0.92), allowing for production of credible downscaled projections. Under the SSP5-8.5 scenario, the Dfc and Dfb climate zones are projected to warm by 4.8 degrees C and 3.9 degrees C, respectively, by 2100, with expansion in the diurnal temperature range by more than 1.5 degrees C. The Time of Emergence signal first appears in subarctic winter seasons (Dfc: approximately 2032), signifying an urgent need for adaptation measures. The presented framework offers station-based, high-resolution estimates of uncertainties and extremes, with direct uses for adaptation policy over high-latitude regions with fast environmental change.
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