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The Extreme Climate Indices for Benghazi-Libya from Canadian Regional-Climate Model

机译:来自加拿大区域气候模式的班加西-利比亚极端气候指数

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摘要

Extreme weather events present serious threats to existing human life and resource systems. Eventssuch as flooding, extreme heat or cold, heavy or high winds have the ability to destroy, disrupt, anddisable vital links. In this article, some patterns of these indices were presented. Representativeconcentration pathway (RCP4.5) running by the Canadian Regional Climate Model (CanRCM4)during 2011-2041 was used to study the future trend of climate indices over Benghazi in Libya.Totally different extreme climate indices were analyzed like the number of heat and cold waves,summer days and tropical nights. It is clear that the numbers of summer days (SU25) and trop-ical nights (TR20) have a positive sign, which implies that in each year there is a rise by one dayand night. In addition, there is a major decrease within the trend of cold days and nights. Forprecipitation extreme indices, there are negative trends within the annual range of days: with theprecipitation amount ≥ 1mm (-0.73), the annual range of days ≥ 10mm (-0.02), and the annualrange of days ≥ 20mm (-0.022). Whereas for the annual total of daily precipitation > 95th percentile(0.041) there is a positive trend, and therefore the trend of the precipitation indices is insignificantas temperature indices, which may be induced to the uncertainty within the physics and param-eterization of the precipitation within the regional model. Projection of future climate extremeindices is an important step for stakeholders to put adaptation plans to reduce the vulnerabilityrisks upon society.
机译:极端天气事件对现有的人类生命和资源系统构成了严重威胁。洪水,极端高温或低温,大风或强风等事件都有能力破坏,破坏和使重要的联系失去能力。在本文中,介绍了这些索引的一些模式。利用加拿大区域气候模式(CanRCM4)在2011-2041年间运行的代表浓度路径(RCP4.5)研究了利比亚班加西地区气候指数的未来趋势,分析了完全不同的极端气候指数,例如冷热数量波浪,夏季和热带夜晚。显然,夏季(SU25)和热带夜晚(TR20)的数量呈正数,这意味着每年的昼夜增加。此外,白天和夜晚的寒冷趋势也有很大的减少。对于极端降水指数,年天数范围内呈负趋势:降水量≥1mm(-0.73),年天数≥10mm(-0.02)和年天数≥20mm(-0.022)。而对于年降水量> 95%(0.041)的年均呈正趋势,因此降水指数的趋势是微不足道的温度指数,这可能会导致降水的物理性和参数化不确定性在区域模型中。未来气候极端指数的预测是利益相关者制定适应计划以减少社会脆弱性风险的重要步骤。

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