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Deliberate enhancement of rainfall using desert plantations

机译:使用荒漠人工林故意增加降雨

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摘要

Large-scale afforestation is increasingly being considered as a negative emissions method for sequestering large quantities of atmospheric CO2. At the same time, regional weather modification methods, like cloud seeding, are being used to counteract increasing water scarcity in arid regions. Large-scale sustainable desert agroforestry plantations can contribute to climate change mitigation and can also be used to modify regional climate, particularly rainfall. Climate impacts from plantations need to be well understood before considering implementation. Typically, impact studies are attempted at continental or global scales and use coarse-resolution models, which suffer from severe systematic errors. This is highly problematic because decision makers should only countenance geoengineering schemes like global afforestation if impacts are understood on the regional scale. We posit the necessity of using high-resolution regional models with sophisticated representations of land–atmosphere feedback and vegetation. This approach allows for studying desert plantations and the process chain leading to climate modification. We demonstrate that large-scale plantations enhance regional clouds and rainfall and derive an index for predicting plantation impacts. Thus, desert plantations represent a unique environmental solution via predictable regional weather modification and carbon storage.
机译:越来越多的人将大规模植树造林作为隔离大量大气CO2的负排放方法。同时,诸如云播种之类的区域天气变化方法正在被用来应对干旱地区日益严重的缺水问题。大规模的可持续荒漠农林人工林可有助于缓解气候变化,还可用于改变区域气候,特别是降雨。在考虑实施之前,必须充分了解人工林对气候的影响。通常,影响研究尝试在大陆或全球范围内进行,并使用具有严重系统错误的粗分辨率模型。这是非常有问题的,因为如果在区域范围内了解影响,决策者应该只支持地球绿化等地球工程计划。我们认为有必要使用高分辨率的区域模型,并结合土地-大气反馈和植被的精细表示。这种方法允许研究沙漠人工林和导致气候变化的过程链。我们证明了大规模的人工林会增强区域云和降雨,并得出预测人工林影响的指数。因此,通过可预测的区域天气变化和碳储量,沙漠人工林代表了独特的环境解决方案。

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