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Improvement and evaluation of simulated global biogenic soil NO emissions in an AC-GCM

机译:模拟全球生物土壤的改进与评价AC-GCM中没有排放的

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Biogenic NO emissions from soils (SNOx) play important direct and indirect roles in tropospheric chemistry. The most widely applied algorithm to calculate SNOx in global models was published 15 years ago by Yienger and Levy (1995), and was based on very few measurements. Since then, numerous new measurements have been published, which we used to build up a compilation of world wide field measurements covering the period from 1978 to 2010. Recently, several satellite-based top-down approaches, which recalculated the different sources of NOx (fossil fuel, biomass burning, soil and lightning), have shown an underestimation of SNOx by the algorithm of Yienger and Levy (1995). Nevertheless, to our knowledge no general improvements of this algorithm, besides suggested scalings of the total source magnitude, have yet been published. Here we present major improvements to the algorithm, which should help to optimize the representation of SNOx in atmospheric-chemistry global climate models, without modifying the underlying principals or mathematical equations. The changes include: (1) using a new landcover map, with twice the number of landcover classes, and using annually varying fertilizer application rates; (2) adopting a fraction of 1.0 % for the applied fertilizer lost as NO, based on our compilation of measurements; (3) using the volumetric soil moisture to distinguish between the wet and dry states; and (4) adjusting the emission factors to reproduce the measured emissions in our compilation (based on either their geometric or arithmetic mean values). These steps lead to increased global annual SNOx, and our total above canopy SNOx source of 8.6 Tg yr?1 (using the geometric mean) ends up being close to one of the satellite-based top-down approaches (8.9 Tg yr?1). The above canopy SNOx source using the arithmetic mean is 27.6 Tg yr?1, which is higher than all previous estimates, but compares better with a regional top-down study in eastern China. This suggests that both top-down and bottom-up approaches will be needed in future attempts to provide a better calculation of SNOx.
机译:生物生成的土壤(SNOX)的排放在对流层化学中起着重要的直接和间接作用。在15年前的Yienger和Levy(1995)上发表了最广泛应用的算法,以全球模型在全球模型中发布,并基于几分钟的测量。从那时起,已经发布了许多新的测量,我们曾经发布过,我们用于建立覆盖1978年至2010年期间的全球场测量的汇编。最近,几种基于卫星的自上而下方法,该方法重新计算了NOx的不同来源(通过Yienger和Levy算法(1995),化石燃料,生物量燃烧,土壤和闪电)显示出低估Snox(1995)的算法。然而,对于我们的知识没有这种算法的一般性改进,除了建议的总源幅度的缩放之外尚未发布。在这里,我们对算法提出了重大改进,这应该有助于优化大气化学全球气候模型中SNO的代表,而无需修改底层的原理或数学方程。这些变化包括:(1)使用新的Landcover地图,其中包含土地层数量的两倍,并使用每年变化的肥料应用率; (2)基于我们的测量汇编,采用施肥肥料的一小部分为1.0%; (3)使用体积土壤水分区分湿和干燥状态; (4)调整排放因子以重现汇编中的测量排放(基于其几何或算术平均值)。这些步骤导致全球每年斯诺克的增加,我们以上的天篷SNOX源8.6 TG YR?1(使用几何平均值)最终靠近卫星的自上而下方法之一(8.9 TG YR?1) 。使用算术平均值的上述冠层SNOX源是27.6 TG YR?1,比以前的所有估计更高,但在中国东部的区域自上而下研究更好地比较。这表明将来需要自上而下和自下而上的方法,以便更好地计算SNOX。

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