The 2019 Raikoke volcanic eruption: Part 1 Dispersion model simulations and satellite retrievals of volcanic sulfur dioxide
The 2019 Raikoke volcanic eruption: Part 1 Dispersion model simulations and satellite retrievals of volcanic sulfur dioxide
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DOI：
10.5194/acp-2020-889
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发表时间：
2020-10
影响因子：
6.3
通讯作者：
J. de Leeuw;A. Schmidt;C. Witham;N. Theys;Isabelle A. Taylor;R. Grainger;R. Pope;J. Haywood;Martin Osborne;N. Kristiansen
中科院分区：
文献类型：
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作者：
J. de Leeuw;A. Schmidt;C. Witham;N. Theys;Isabelle A. Taylor;R. Grainger;R. Pope;J. Haywood;Martin Osborne;N. Kristiansen
摘要。火山爆发会对社会造成严重破坏，数值模型对于预测喷发物质的扩散至关重要。在这里，我们评估了英国气象局的数值大气扩散模拟环境（NAME）在模拟2019年6月21日至22日Raikoke火山喷发（48.3°N, 153.2°E）期间二氧化硫（SO2）云的扩散方面的技能和局限性。此次火山喷发释放了约1.5 0.2 Tg的SO2，这是自2011年纳布罗火山喷发以来最大的SO2火山喷发。我们模拟了整个北半球（NH）火山SO2云的时间演变，并将我们的模式模拟结果与对流层监测仪器（TROPOMI）和红外大气探测干涉仪（IASI）卫星SO2产品的高分辨率SO2测量结果进行了比较。我们发现，NAME准确地模拟了火山爆发后2-3周内二氧化硫云的观测位置和水平范围，但在其标准配置下，无法捕捉火山云内高浓度区域的范围和精确位置。使用分数技能分数作为模型技能的度量，NAME显示了在喷发后12-17天内模拟云的水平范围的技能，其中二氧化硫的垂直柱密度（VCD）（以多布森单位，DU）高于1du。对于20 DU以上的SO2 vcd，主要以SO2云内的小尺度特征被观测到，该模型仅显示出2-4天的技能。对这些高浓度区域的较低技能的部分解释是，与TROPOMI检索相比，NAME模式模拟的SO2云过于分散。将NAME中使用的标准扩散参数降低四分之一，在模拟的前五天内，模型技能略有提高，但在更长的时间尺度上，与TROPOMI的测量结果相比，模拟的二氧化硫云仍然过于扩散。我们发现，NAME模拟的nh -平均SO2质量负荷的时间演变强烈依赖于排放到平流层下层的SO2质量的比例，这对于2019年Raikoke火山喷发来说是不确定的。当向平流层下层（11-18 km）和对流层上层（8-11 km）分别排放0.9-1.1 Tg和0.4-0.7 Tg时，NAME和TROPOMI在北半球的SO2质量负荷峰值（1.4-1.6 Tg）相似，平均SO2 e折叠时间为14-15 d。我们的工作证明了使用高分辨率卫星检索来识别和纠正像NAME这样的色散模型的局限性的巨大潜力，这将最终有助于改善火山SO2云的色散建模工作。
Abstract. Volcanic eruptions can cause significant disruption to society and numerical models are crucial for forecasting the dispersion of erupted material. Here we assess the skill and limitations of the Met Office’s Numerical Atmospheric-dispersion Modelling Environment (NAME) in simulating the dispersion of the sulfur dioxide (SO2) cloud from the 21–22 June 2019 eruption of the Raikoke volcano (48.3° N, 153.2° E). The eruption emitted around 1.5 0.2 Tg of SO2, which represents the largest volcanic emission of SO2 into the stratosphere since the 2011 Nabro eruption. We simulate the temporal evolution of the volcanic SO2 cloud across the Northern Hemisphere (NH) and compare our model simulations to high-resolution SO2 measurements from the Tropospheric Monitoring Instrument (TROPOMI) and the Infrared Atmospheric Sounding Interferometer (IASI) satellite SO2 products. We show that NAME accurately simulates the observed location and horizontal extent of the SO2 cloud during the first 2–3 weeks after the eruption, but is unable, in its standard configuration, to capture the extent and precise location of very high-concentration regions within the volcanic cloud. Using the Fractional Skill Score as metric for model skill, NAME shows skill in simulating the horizontal extent of the cloud for 12–17 days after the eruption where vertical column densities (VCD) of SO2 (in Dobson Units, DU) are above 1 DU. For SO2 VCDs above 20 DU, which are predominantly observed as small-scale features within the SO2 cloud, the model shows skill on the order of 2–4 days only. The lower skill for these high-concentration regions is partly explained by the model-simulated SO2 cloud in NAME being too diffuse compared to TROPOMI retrievals. Reducing the standard diffusion parameters used in NAME by a factor of four results in a slightly increased model skill during the first five days of the simulation, but on longer timescales the simulated SO2 cloud remains too diffuse when compared to TROPOMI measurements. We find that the temporal evolution of the NH-mean SO2 mass burden simulated by NAME strongly depends on the fraction of SO2 mass emitted into the lower stratosphere, which is uncertain for the 2019 Raikoke eruption. When emitting 0.9–1.1 Tg of SO2 into the lower stratosphere (11–18 km) and 0.4–0.7 Tg into the upper troposphere (8–11 km), both NAME and TROPOMI show a similar peak in SO2 mass burden (1.4–1.6 Tg of SO2) with an average SO2 e-folding time of 14–15 days in the NH. Our work demonstrates the large potential of using high-resolution satellite retrievals to identify and rectify limitations in dispersion models like NAME, which will ultimately help to improve dispersion modelling efforts of volcanic SO2 clouds.