Abstract. Improving the estimates of CO₂ sources and sinks over India through inverse methods calls for a comprehensive atmospheric monitoring system involving atmospheric transport models that realistically account for atmospheric CO₂ variability along with good coverage of ground-based monitoring stations. This study investigates the importance of representing fine-scale variability of atmospheric CO₂ in models for the optimal use of observations through inverse modelling. The unresolved variability of atmospheric CO₂ in coarse models is quantified by using WRF-Chem simulations at a spatial resolution of 10 km × 10 km. We show that the representation errors due to unresolved variability in the coarse model with a horizontal resolution of one degree (~ 100 km) are considerable (median values of 1.5 ppm and 0.4 ppm for the surface and column CO₂, respectively) compared to the measurement errors. The monthly averaged surface representation error reaches up to ~5 ppm, which is comparable to a quarter to half of the magnitude of seasonal variability. Representation error shows a strong dependence on multiple factors such as time of the day, season, terrain heterogeneity, and changes in meteorology and surface fluxes. By employing a first-order inverse modelling scheme using pseudo observations from nine tall tower sites over India, we show that the Net Ecosystem Exchange (NEE) flux uncertainty solely due to unresolved variability is in the range of 3.1 to 10.3 % of the total NEE of the region. By estimating the representation error and its impact on flux estimations during different seasons, we emphasize the need for taking account of fine-scale CO₂ variability in models over the Indian subcontinent to better understand processes regulating CO₂ sources and sinks. The efficacy of a simple parameterization scheme is further demonstrated to capture these unresolved variations in coarse models.

中文翻译：

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通过逆向建模监测印度 CO2 源汇分布：量化大气 CO2 摩尔分数的精细时空变化

摘要。_{通过逆向方法改进对印度 CO 2}源和汇的估计需要一个全面的大气监测系统，该系统涉及大气传输模型，该模型实际考虑了大气 CO ₂变化以及地面监测站的良好覆盖范围。本研究探讨了在模型中表示大气 CO ₂精细尺度变化的重要性，以便通过逆向建模优化使用观测值。大气CO _{2的未解决变异性}通过使用 WRF-Chem 模拟在 10 km × 10 km 的空间分辨率下量化粗模型中的数据。_{我们表明，由于水平分辨率为 1 度（~100 km）的粗模型中未解决的可变性导致的表示误差是相当大的（表面和柱 CO 2}的中值分别为 1.5 ppm 和 0.4 ppm，分别）与测量误差相比。月平均地表表示误差高达约 5 ppm，相当于季节性变化幅度的四分之一到一半。表示误差显示出对多种因素的强烈依赖性，例如一天中的时间、季节、地形异质性以及气象和地表通量的变化。通过使用来自印度九个高塔站点的伪观测的一阶反演建模方案，我们表明仅由于未解决的可变性导致的净生态系统交换 (NEE) 通量不确定性在总 NEE 的 3.1% 到 10.3% 范围内该地区的。通过估计表示误差及其对不同季节通量估计的影响，我们强调需要考虑精细尺度的 CO ₂印度次大陆模型的可变性，以更好地了解调节 CO ₂源和汇的过程。进一步证明了简单参数化方案的功效可以捕获粗模型中这些未解决的变化。