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Throughout its ~4.5 billion year history, the Earth has experienced periods of warming and cooling. However, the current drastic increase in global temperatures is well outside of the Earth’s cyclic norms, and evidence for human-caused global climate change is compelling. Paleoclimatology, the study of ancient climate conditions, provides ample evidence for human-caused global climate change by comparing recent conditions with those in the past.
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The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...
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Precipitation gravimetry is based on converting an analyte into a sparingly soluble precipitate, which is separated by filtration and weighed. An ideal precipitate should be pure, insoluble, of known composition, and easily filtered from the reaction mixture.
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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
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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.

关键词:
适应 适应 适应气候 气候 气候 气候 气候缩小规模缩小规模缩小规模雪 雪 雪 雪 雪 雪 雪

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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.