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<p>Rapid changes and increasing climatic variability across the widely varied Köppen-Geiger regions of northern Europe generate significant needs for adaptation. Regional planning needs high-resolution projected temperatures. This work presents an integrative statistical bias correction 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 independent historical observations (1951–2014) of day-to-day temperature metrics, extreme value distributions (99th percentile), and thermodynamic coupling (Diurnal Temperature Range). The ViT showed improved performance (Root Mean Squared Error (RMSE): 1.01 °C; <i>R</i><sup>2</sup>: 0.92), allowing for production of credible bias-corrected projections. Under the SSP5-8.5 scenario, the Dfc and Dfb climate zones are projected to warm by 4. 8 °C and 3. 9 °C (Summer <i>T</i><sub><i>m</i><i>a</i><i>x</i></sub>), respectively, by 2100, with expansion in the diurnal temperature range by more than 1. 5 °C. The Time of Emergence signal first appears in subarctic winter seasons (Dfc: ~ 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.</p>

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Deep learning-driven statistical bias correction for climate risk assessment of projected temperature extremes in the Nordic region

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月15日

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  • Parthiban Loganathan,
  • Elias Zea,
  • Ricardo Vinuesa,
  • Evelyn Otero

摘要

Rapid changes and increasing climatic variability across the widely varied Köppen-Geiger regions of northern Europe generate significant needs for adaptation. Regional planning needs high-resolution projected temperatures. This work presents an integrative statistical bias correction 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 independent historical observations (1951–2014) of day-to-day temperature metrics, extreme value distributions (99th percentile), and thermodynamic coupling (Diurnal Temperature Range). The ViT showed improved performance (Root Mean Squared Error (RMSE): 1.01 °C; R2: 0.92), allowing for production of credible bias-corrected projections. Under the SSP5-8.5 scenario, the Dfc and Dfb climate zones are projected to warm by 4. 8 °C and 3. 9 °C (Summer Tmax), respectively, by 2100, with expansion in the diurnal temperature range by more than 1. 5 °C. The Time of Emergence signal first appears in subarctic winter seasons (Dfc: ~ 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.