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在千米尺度上预测未来的雪变化,以利用机器学习和CMIP6多模型组合进行适应
Alessandro Damiani1, Noriko N Ishizaki1, Sarah Feron2
1NIES, Tsukuba, Japan.
The Science of the total environment
|January 25, 2025
概括
预计日本未来的雪深变化将减少高达25%,到本世纪中叶雪地覆盖的出现将延迟. 这项研究开发了一种新的缩放模型,以评估这些对冬季旅游和水资源的影响.
科学领域:
- 气候科学 气候科学
- 环境建模环境建模
- 遥感 遥感 遥感 遥感
背景情况:
- 由于传统气候模型的空间分辨率较低,对未来的雪地覆盖变化的准确评估受到阻碍.
- 高分辨率数据对于了解对水资源和冬季旅游业的局部影响至关重要.
研究的目的:
- 为日本使用卷积神经网络 (CNN) 开发超空间分辨率的雪深 (SD) 缩小模型.
- 为了估计未来的雪地覆盖变化,使用适用于合模型相互比较项目第6阶段 (CMIP6) 气候模拟的下调模型.
主要方法:
- 开发和训练一个基于CNN的缩小模型,使用观察到的参考雪深数据.
- 验证了模型与独立观测的连贯性,以及其重现空间分布和季节性的能力.
- 应用受过训练的模型来缩小CMIP6气候模型的一组.
主要成果:
- 缩小规模的组合精确地重现了观察到的雪深的空间分布和季节性.
- 预计冬季覆盖积雪面积的平均减少约为20%,春季初减少约25%.
- 观察到高度依赖的雪深变化和21世纪中叶在高排放场景下延迟的雪盖出现.
结论:
- 基于CNN的缩小模型提供了物理可信的,高分辨率的未来雪深的评估.
- 结果为与冬季旅游和水资源管理相关的适应战略提供了宝贵的见解.
- 该方法与区域气候模型相一致,对各种适应研究有益.
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