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Impacts of land use/cover classification accuracy on regional climate simulations

机译:土地利用/覆盖物分类精度对区域气候模拟的影响

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摘要

Land use/cover change has been recognized as a key component in global change. Various land cover data sets, including historically reconstructed, recently observed, and future projected, have been used in numerous climate modeling studies at regional to global scales. However, little attention has been paid to the effect of land cover classification accuracy on climate simulations, though accuracy assessment has become a routine procedure in land cover production community. In this study, we analyzed the behavior of simulated precipitation in the Regional Atmospheric Modeling System (RAMS) over a range of simulated classification accuracies over a 3 month period. This study found that land cover accuracy under 80% had a strong effect on precipitation especially when the land surface had a greater control of the atmosphere. This effect became stronger as the accuracy decreased. As shown in three follow-on experiments, the effect was further influenced by model parameterizations such as convection schemes and interior nudging, which can mitigate the strength of surface boundary forcings. In reality, land cover accuracy rarely obtains the commonly recommended 85% target. Its effect on climate simulations should therefore be considered, especially when historically reconstructed and future projected land covers are employed.
机译:土地利用/覆盖变化被认为是全球变化的关键组成部分。各种土地覆盖数据集,包括历史重建的,最近观测到的和未来预测的,已被用于区域乃至全球范围的众多气候模拟研究中。然而,尽管准确性评估已成为土地覆被生产社区的常规程序,但很少有人关注土地覆被分类准确性对气候模拟的影响。在这项研究中,我们分析了在3个月内一系列模拟分类精度下区域大气模拟系统(RAMS)中模拟降水的行为。这项研究发现,在80%以下的土地覆盖精度对降水有很大影响,特别是当土地表面对大气的控制更大时。随着精度降低,此效果变得更强。如三个后续实验所示,对流方案和内部微调等模型参数化进一步影响了效果,这些参数化可以减轻表面边界强迫的强度。实际上,土地覆盖的准确性很少能达到通常推荐的85%的目标。因此,应考虑其对气候模拟的影响,尤其是在使用历史重建和未来预计的土地覆盖时。

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