Importance of dry deposition parameterization choice in global simulations of surface ozone
Importance of dry deposition parameterization choice in global simulations of surface ozone
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DOI：
10.5194/acp-19-14365-2019
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发表时间：
2019-06
影响因子：
6.3
通讯作者：
A. Y. H. Wong;J. Geddes;A. Tai;Sam J. Silva
中科院分区：
文献类型：
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作者：
A. Y. H. Wong;J. Geddes;A. Tai;Sam J. Silva
抽象的。干沉降是对流层臭氧的主要汇。越来越多的证据表明，臭氧干沉降将气象和水文学与臭氧空气质量积极联系起来。然而，对全球范围内不同臭氧干沉降参数化的性能以及参数化选择如何影响地表臭氧模拟的系统研究很少。在这里，我们展示了使用多个臭氧干沉降参数化对臭氧干沉降速度（vd）进行首次全球、数十年建模和评估的结果。我们使用代表全球臭氧干沉降建模当前方法的四种臭氧干沉降参数化对 1982 年至 2011 年的臭氧干沉降速度进行建模。我们在所有四个中使用一致的同化气象学、土地覆盖和卫星衍生的叶面积指数 (LAI)，因此模拟 vd 的差异完全是由于沉积模型结构的差异或关于如何处理每个土地类型的假设的差异。此外，我们利用化学输运模型预测的地表臭氧对 vd 的敏感性来估计臭氧干沉降速度的平均值和变异性对地表臭氧的影响。我们根据现场观察评估了四种不同参数化的估计 vd 值，虽然性能因土地覆盖类型而异，但我们的结果表明，没有一种参数化普遍优于其他参数化。据估计，参数化之间模拟平均 vd 的差异将导致北半球 (NH) 表面臭氧的差异为 2 至 5 ppbv，7 月份热带雨林的差异高达 8 ppbv，12 月份印度支那热带雨林和季节性干燥热带森林的差异高达 8 ppbv。人们发现，基于个体土地覆盖类型和水文气候的参数化特定偏差是造成这种差异的两个主要驱动因素。我们发现，在变暖和干燥（亚马逊流域南部、非洲南部大草原和蒙古）或绿化（高纬度地区）的驱动下，所有参数化的模拟七月白天 vd 的多年时间序列都存在统计上显着的趋势。 7 月白天 vd 的趋势估计为 yr−1 1 %，并导致 1982 年至 2011 年期间地表臭氧变化高达 3 ppbv。北半球7月白天平均vd的年际变异系数(CV)为5%~15%，其空间分布随干沉降参数化而变化。我们的灵敏度模拟表明，这可能对地表臭氧年际变化 (IAV) 产生 0.5 至 2 ppbv 的影响，但与长期臭氧通量观测相比，所有模型都倾向于低估年际 CV。我们还发现，某些干沉降参数化中的 IAV 对 LAI 更敏感，而在其他干沉降参数化中，IAV 对气候更敏感。与其他已发表的背景臭氧 IAV 估计值的比较证实，臭氧干沉降可能是自然表面臭氧变化的重要组成部分。我们的结果证明了臭氧干沉降参数化选择对表面臭氧建模的重要性以及vd的IAV对表面臭氧的影响，从而为不同时空尺度上臭氧干沉降的进一步测量、评估和模型数据集成提供了有力的依据。
Abstract. Dry deposition is a major sink of tropospheric ozone. Increasing evidence has shown that ozone dry deposition actively links meteorology and hydrology with ozone air quality. However, there is little systematic investigation on the performance of different ozone dry deposition parameterizations at the global scale and how parameterization choice can impact surface ozone simulations. Here, we present the results of the first global, multidecadal modelling and evaluation of ozone dry deposition velocity (vd) using multiple ozone dry deposition parameterizations. We model ozone dry deposition velocities over 1982–2011 using four ozone dry deposition parameterizations that are representative of current approaches in global ozone dry deposition modelling. We use consistent assimilated meteorology, land cover, and satellite-derived leaf area index (LAI) across all four, such that the differences in simulated vd are entirely due to differences in deposition model structures or assumptions about how land types are treated in each. In addition, we use the surface ozone sensitivity to vd predicted by a chemical transport model to estimate the impact of mean and variability of ozone dry deposition velocity on surface ozone. Our estimated vd values from four different parameterizations are evaluated against field observations, and while performance varies considerably by land cover types, our results suggest that none of the parameterizations are universally better than the others. Discrepancy in simulated mean vd among the parameterizations is estimated to cause 2 to 5 ppbv of discrepancy in surface ozone in the Northern Hemisphere (NH) and up to 8 ppbv in tropical rainforests in July, and up to 8 ppbv in tropical rainforests and seasonally dry tropical forests in Indochina in December. Parameterization-specific biases based on individual land cover type and hydroclimate are found to be the two main drivers of such discrepancies. We find statistically significant trends in the multiannual time series of simulated July daytime vd in all parameterizations, driven by warming and drying (southern Amazonia, southern African savannah, and Mongolia) or greening (high latitudes). The trend in July daytime vd is estimated to be 1 % yr−1 and leads to up to 3 ppbv of surface ozone changes over 1982–2011. The interannual coefficient of variation (CV) of July daytime mean vd in NH is found to be 5 %–15 %, with spatial distribution that varies with the dry deposition parameterization. Our sensitivity simulations suggest this can contribute between 0.5 to 2 ppbv to interannual variability (IAV) in surface ozone, but all models tend to underestimate interannual CV when compared to long-term ozone flux observations. We also find that IAV in some dry deposition parameterizations is more sensitive to LAI, while in others it is more sensitive to climate. Comparisons with other published estimates of the IAV of background ozone confirm that ozone dry deposition can be an important part of natural surface ozone variability. Our results demonstrate the importance of ozone dry deposition parameterization choice on surface ozone modelling and the impact of IAV of vd on surface ozone, thus making a strong case for further measurement, evaluation, and model–data integration of ozone dry deposition on different spatiotemporal scales.