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在亚马逊低地森林山谷的高分辨率利基建模下调空气温度:微气候R套餐的案例研究
M J Pohl1, L Lehnert2, B Thies1
1Laboratory for Climatology and Remote Sensing, Department of Geography, University of Marburg, Marburg, Germany.
PloS one
|November 7, 2024
概括
气候变化威胁着亚马逊的长植物. 像微气候这样的缩小模型难以准确模拟这些物种至关重要的夜间温度反转.
科学领域:
- 气候学 气候学 气候学
- 生态生态学 生态生态学
- 森林科学 森林科学 森林科学
背景情况:
- 亚马逊森林面临着气候和土地利用变化带来的威胁,影响着像树冠长植物这样依赖水分的物种.
- 夜间的 Katabatic 流动在亚马逊山谷中造成温度逆转和雾,支持更高的植物多样性.
- 像ERA5-Land这样的粗度分辨率气候模型不能充分反映这些微气候温度动态.
研究的目的:
- 为了评估R-Package微气候的准确性,在南美热带低地森林中将粗度分辨率的温度数据缩小到高空间分辨率.
- 评估模型模拟与高度相关的白天空气温度变化和温度逆转的能力.
主要方法:
- 与热带低地森林的气象站数据进行了微观气候降级 ERA5-Land 空气温度数据的比较.
- 经过测试的默认设置和修改的参数 (排放率值,时间框架) 以提高冷空气排水 (CAD) 事件的检测.
主要成果:
- 在默认设置下,微气候只能准确检测73/412 (干季) 和18/400 (潮季) 的夜间冷空气排水事件.
- 参数调整改善了对345/412 (干季) 和177/400 (雨季) CAD事件的检测.
- 这些算法在准确建模白天温度轨迹和高度温度梯度方面仍然存在困难.
结论:
- 微气候方案需要进行调整,以有效地模拟热带低地森林的温度动态.
- 改进的缩小模型对于精确的利基建模敏感物种,如树冠生植物是必不可少的.
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