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    Nature Climate Change:气候变化背景下极端干旱对植被生产力的影响

    陆地总初级生产总值(gross primary production, GPP)是全球植被生长和粮食生产的基础,它影响着生态系统碳平衡,在调节大气CO2中发挥着重要作用。尽管未来几十年较高的CO2浓度可以增加GPP,但土壤水分利用率降低、热胁迫和干旱可能会降低这种CO2的施肥效益。为了更好地理解未来干旱将如何影响全球范围内的GPP,研究人员分析了13个地球系统模型的输出,结果表明,在21世纪,极端干旱对GPP的影响比轻度和中度干旱的影响更大。在中高排放情景下,到本世纪最后四分之一年(2075-2099年),由于极端干旱发生频率的急剧增加,极端干旱造成GPP的减少幅度预计将比历史时期(1850-1999年)的高达3倍。相比之下,轻度和中度干旱导致的GPP减少量预计不会大幅增加。研究分析表明,随着大气变暖,全球碳循环面临极端干旱的高风险;然而,未来有利的气候环境条件也可以促进GPP,从而可以潜在地缓解极度干旱的负面影响。

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    好文速递:局部城市气候的全球多模式预测

    摘要:针对气候驱动风险的有效城市规划依赖于针对特定建筑景观的强劲气候预测。由于全球规模的地球系统模型中几乎没有普遍的城市代表性,因此没有这种预测。在这里,我们结合了气候建模和数据驱动方法,以提供二十一世纪全球城市气候的多模型预测。结果表明,气候变化下某些地区的城市变暖特定水平的模型间鲁棒性。在高排放情景下,据估计,到本世纪末,美国,中东,中亚北部,中国东北,南美内陆和非洲的城市将经历超过4 开尔文(K)的实质性变暖,比区域变暖还要大。世纪,具有很高的跨模型信心。我们的发现突出表明,对于气候敏感型发展,需要对本地城市气候进行多模式全球预测,并支持绿色基础设施干预,以作为大规模减少城市高温压力的有效手段。

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    Google Earth Engine——美国1950-2099年降水、气温含预测数据集(1km)分辨率

    The NASA NEX-DCP30 dataset is comprised of downscaled climate scenarios for the conterminous United States that are derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5, see Taylor et al. 2012) and across the four greenhouse gas emissions scenarios known as Representative Concentration Pathways (RCPs, see Meinshausen et al. 2011) developed for the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). The purpose of these datasets is to provide a set of high resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate conditions.

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    Google Earth Engine——NASA NEX-DCP30数据集(降水和气温)由美国本土的降尺度气候情景组成1km分辨率

    The NASA NEX-DCP30 dataset is comprised of downscaled climate scenarios for the conterminous United States that are derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5, see Taylor et al. 2012) and across the four greenhouse gas emissions scenarios known as Representative Concentration Pathways (RCPs, see Meinshausen et al. 2011) developed for the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). The purpose of these datasets is to provide a set of high resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate conditions.

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