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Air–snow exchange of nitrate: a modelling approach to investigate physicochemical processes in surface snow at Dome?C, Antarctica

机译:硝酸盐的雪-雪交换:一种研究南极Dome?C表层雪中物理化学过程的建模方法

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Snowpack is a multiphase (photo)chemical reactor that strongly influences the air composition in polar and snow-covered regions. Snowpack plays a special role in the nitrogen cycle, as it has been shown that nitrate undergoes numerous recycling stages (including photolysis) in the snow before being permanently buried in the ice. However, the current understanding of these physicochemical processes remains very poor. Several modelling studies have attempted to reproduce (photo)chemical reactions inside snow grains, but these have relied on strong assumptions to characterise snow reactive properties, which are not well defined. Air–snow exchange processes such as adsorption, solid-state diffusion, or co-condensation also affect snow chemical composition. Here, we present a physically based model of these processes for nitrate. Using as input a 1-year-long time series of atmospheric nitrate concentration measured at Dome?C, Antarctica, our model reproduces with good agreement the nitrate measurements in the surface snow. By investigating the relative importance of the main exchange processes, this study shows that, on the one hand, the combination of bulk diffusion and co-condensation allows a good reproduction of the measurements (correlation coefficient ir/i?=?0.95), with a correct amplitude and timing of summer peak concentration of nitrate in snow. During winter, nitrate concentration in surface snow is mainly driven by thermodynamic equilibrium, whilst the peak observed in summer is explained by the kinetic process of co-condensation. On the other hand, the adsorption of nitric acid on the surface of the snow grains, constrained by an already existing parameterisation for the isotherm, fails to fit the observed variations. During winter and spring, the modelled concentration of adsorbed nitrate is respectively 2.5 and 8.3-fold higher than the measured one. A strong diurnal variation driven by the temperature cycle and a peak occurring in early spring are two other major features that do not match the measurements. This study clearly demonstrates that co-condensation is the most important process to explain nitrate incorporation in snow undergoing temperature gradient metamorphism. The parameterisation developed for this process can now be used as a foundation piece in snowpack models to predict the inter-relationship between snow physical evolution and snow nitrate chemistry.
机译:Snowpack是一个多相(光)化学反应器,会严重影响极地和积雪地区的空气成分。 Snowpack在氮循环中起着特殊的作用,因为已经证明硝酸盐在永久地埋在冰中之前,在雪中经历了许多循环阶段(包括光解)。但是,目前对这些理化过程的理解仍然很差。几项建模研究试图重现雪粒内部的(光)化学反应,但这些研究均依赖于未明确定义的表征雪反应性的强大假设。空气-雪交换过程,例如吸附,固态扩散或共冷凝,也会影响雪的化学成分。在这里,我们介绍了硝酸盐这些过程的基于物理的模型。使用在南极洲Dome?C测得的长达1年的大气硝酸盐浓度的时间序列作为输入,我们的模型可以很好地重现地表雪中的硝酸盐测量值。通过研究主要交换过程的相对重要性,这项研究表明,一方面,体扩散和共冷凝的组合可以很好地再现测量值(相关系数 r ?= (0.95),正确的幅度和时间来确定夏季雪中硝酸盐的峰值浓度。在冬季,表雪中的硝酸盐浓度主要由热力学平衡驱动,而夏季观测到的峰值由共冷凝的动力学过程解释。另一方面,受等温线已经存在的参数化约束,硝酸在雪粒表面的吸附无法适应观察到的变化。在冬季和春季,模拟的硝酸盐吸附浓度分别比实测浓度高2.5倍和8.3倍。温度周期驱动的强烈的昼夜变化和早春出现的峰值是与测量结果不匹配的另外两个主要特征。这项研究清楚地表明,共凝结是解释经历温度梯度变质的雪中硝酸盐掺入的最重要过程。为该过程开发的参数化现在可以用作积雪模型中的基础,以预测积雪的物理演变与积雪的硝酸盐化学之间的相互关系。

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