Abstract As cities embark upon greenhouse gas (GHG) mitigation efforts, there is an increasing need for accurate quantification of urban emissions. In urban areas, transport and dispersion is particularly difficult to simulate using current mesoscale meteorological models due, in part, to added complexity from surface heterogeneity and fine spatial/temporal scales. It is generally assumed that the errors in GHG estimation methods in urban areas are dominated by errors in transport and dispersion. Other significant errors include, but are not limited to, those from assumed emissions magnitude and spatial distribution. To assess the predictability of simulated trace gas mole fractions in urban observing systems using a numerical weather prediction model, we employ an Eulerian model that combines traditional meteorological variables with multiple passive tracers of atmospheric carbon dioxide (CO2) from anthropogenic inventories and a biospheric model. The predictability of the Eulerian model is assessed by comparing simulated atmospheric CO2 mole fractions to observations from four in situ tower sites (three urban and one rural) in the Washington DC/Baltimore, MD area for February 2016. Four different gridded fossil fuel emissions inventories along with a biospheric flux model are used to create an ensemble of simulated atmospheric CO2 observations within the model. These ensembles help to evaluate whether the modeled observations are impacted more by the underlying emissions or transport. The spread of modeled observations using the four emission fields indicates the model's ability to distinguish between the different inventories under various meteorological conditions. Overall, the Eulerian model performs well; simulated and observed average CO2 mole fractions agree within 1% when averaged at the three urban sites across the month. However, there can be differences greater than 10% at any given hour, which are attributed to complex meteorological conditions rather than differences in the inventories themselves. On average, the mean absolute error of the simulated compared to actual observations is generally twice as large as the standard deviation of the modeled mole fractions across the four emission inventories. This result supports the assumption, in urban domains, that the predicted mole fraction error relative to observations is dominated by errors in model meteorology rather than errors in the underlying fluxes in winter months. As such, minimizing errors associated with atmospheric transport and dispersion may help improve the performance of GHG estimation models more so than improving flux priors in the winter months. We also find that the errors associated with atmospheric transport in urban domains are not restricted to certain times of day. This suggests that atmospheric inversions should use CO2 observations that have been filtered using meteorological observations rather than assuming that meteorological modeling is most accurate at certain times of day (such as using only mid-afternoon observations).

中文翻译：

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调查城市地区二氧化碳模拟中的变异性和误差来源

摘要 随着城市开始进行温室气体 (GHG) 减排工作，越来越需要准确量化城市排放。在城市地区，使用当前的中尺度气象模型来模拟运输和扩散特别困难，部分原因是由于地表异质性和精细的空间/时间尺度增加了复杂性。一般认为，城市地区温室气体估算方法的误差主要由运输和扩散误差决定。其他重大误差包括但不限于来自假设排放量和空间分布的误差。使用数值天气预报模型评估城市观测系统中模拟痕量气体摩尔分数的可预测性，我们采用欧拉模型，该模型将传统气象变量与来自人为清单的大气二氧化碳 (CO2) 的多种被动示踪剂和生物圈模型相结合。欧拉模型的可预测性是通过将模拟的大气 CO2 摩尔分数与 2016 年 2 月华盛顿特区/马里兰州巴尔的摩地区四个原位塔站点（三个城市和一个农村）的观测值进行比较来评估的。 四种不同的网格化石燃料排放清单与生物圈通量模型一起用于在模型内创建模拟大气 CO2 观测的集合。这些集合有助于评估模拟观测是否更多地受到潜在排放或运输的影响。使用四个发射场模拟观测的分布表明模型' 区分不同气象条件下不同清单的能力。总体而言，欧拉模型表现良好；模拟和观察到的平均 CO2 摩尔分数在一个月内三个城市地点的平均值一致在 1% 以内。但是，在任何给定的小时内可能存在大于 10% 的差异，这归因于复杂的气象条件，而不是清单本身的差异。平均而言，模拟与实际观察相比的平均绝对误差通常是四个排放清单中模拟摩尔分数标准偏差的两倍。这一结果支持了假设，在城市领域，相对于观测的预测摩尔分数误差主要由模式气象学中的误差而不是冬季月份潜在通量的误差所主导。因此，与改善冬季月份的通量先验相比，最大限度地减少与大气传输和扩散相关的误差可能有助于提高温室气体估算模型的性能。我们还发现，与城市领域大气传输相关的误差不限于一天中的某些时间。这表明大气反演应该使用已经使用气象观测过滤的 CO2 观测，而不是假设气象建模在一天中的某些时间最准确（例如仅使用午后观测）。与改善冬季月份的通量先验相比，最大限度地减少与大气传输和扩散相关的误差可能有助于提高温室气体估算模型的性能。我们还发现，与城市领域大气传输相关的误差不限于一天中的某些时间。这表明大气反演应该使用已经使用气象观测过滤的 CO2 观测，而不是假设气象建模在一天中的某些时间最准确（例如仅使用午后观测）。与改善冬季月份的通量先验相比，最大限度地减少与大气传输和扩散相关的误差可能有助于提高温室气体估算模型的性能。我们还发现，与城市领域大气传输相关的误差不限于一天中的某些时间。这表明大气反演应该使用已经使用气象观测过滤的 CO2 观测，而不是假设气象建模在一天中的某些时间最准确（例如仅使用午后观测）。