Towards monitoring CO 2 source-sink distribution over India via inverse modelling: Quantifying the fine-scale spatiotemporal variability of atmospheric CO 2 mole fraction

Improving the estimates of CO 2 sources and sinks over India through inverse methods calls for a comprehensive atmospheric monitoring system involving atmospheric transport models that realistically account for 15 atmospheric CO 2 variability along with good coverage of ground-based monitoring stations. This study investigates the importance of representing fine-scale variability of atmospheric CO 2 in models for the optimal use of observations through inverse modelling. The unresolved variability of atmospheric CO 2 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 20 (~ 100 km) are considerable (median values of 1.5 ppm and 0.4 ppm for the surface and column CO 2 , 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 25 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 CO 2 variability in models over the Indian subcontinent to better understand processes regulating CO 2 sources and sinks. The efficacy of a simple parameterization scheme is further 30 demonstrated to capture these unresolved variations in coarse models. dependence on seasons and are mainly determined by dry and wet seasons for nearly 65 to 70 % of the country’s area except over north-eastern and south-western (Western Ghats) regions of India. In India, wet season crops (Kharif crops cultivated from June to November) including Rice, Millets, and Maize 100 mainly depend on monsoon rain. Dry season crops (Rabi crops, e.g. Wheat, Barley, and Mustard, cultivated from November to April,) are less water-dependent and primarily rely on irrigation (DAC/MA 2015). Therefore, employing a higher resolution modelling over the Indian subcontinent is desirable to better account for fine-scale variations generated by both mesoscale transport processes and surface flux patterns. This study focuses on accounting for unresolved sub-grid scale variability when employing current generation 105 global models. Assimilation of observations in an inverse framework requires the characterization of these error structures at relevant scales that can be utilized to retrieve source-sink distribution over India. The main objectives of this paper are to describe and quantify the expected spatiotemporal variability of atmospheric CO 2 that is not resolved by the current generation global models, quantify to what extent these variations cause uncertainty in flux estimations, and assess how these uncertainties can be minimized by modelling the sub-grid 110 variations in the global models. Specifically, we address the following questions: 1) how good is the level of agreement among global transport models that are used in current generation inversion systems for predicting atmospheric CO 2 concentrations over the Indian subcontinent? 2) how large are the variations of atmospheric CO 2 that are unresolved by global and regional models, which operate at different spatial scales from 4 ° × 4 ° to 0.5 ° × 0.5 ° ? 3) what is the role of seasonal changes on generating different patterns in these sub-grid variations 115 of CO 2 ? 4) how much is the uncertainty in the inverse-based flux estimation caused by these unresolved and the height up to this positive gradient is are for (both has the largest positive gradient among these models (~1ppm). It shows the maximum concentration at around m height and then a decrease in concentration. CarbonTracker also shows this positive gradient in the surface layers up to a height of 900 m. But the gradient is much smaller compared to the two models. Among these four models, CarboScope does not exhibit this tendency in the lower atmosphere. Its concentration decreases linearly from the surface as the height increases. variability, in which different points from different parts of the domain are binned together on the basis of their standard deviation of topography. Each bin is created with a size of 50 m variation in terrain height. The linear fit is estimated between the average value of topographic variability within a bin and the average value of representation error of the corresponding points in the bin. Our results show that the terrain heterogeneity alone can explain about 20-48% of the surface representation errors over the domain. In a similar way, we have estimated the influence of topographic variability on representation error in the column-averaged model simulations. It is found that topography alone can explain 45-52 % of representation errors in the column-averaged simulations. Emission hotspots can also lead to significant CO 2 variability near the surface as large as ≈ 8 ppm. Larger values are typically associated with the nocturnal shallow boundary layer dynamics and the stronger respiration signal with considerable flux variability. These findings are consistent with Pillai et al. (2010), which show that there exist spatial differences in the sub-grid variability for both surface and column CO 2 . Although the magnitude of the sub-grid variability of the total column is an order of magnitude smaller than the variability at the surface, the 655 spatial pattern remains similar for both, owing to the dominance of surface heterogeneity in topography and fluxes. With the underlying assumptions, the total uncertainty in optimized fluxes solely due to the unresolved sub-grid variations is estimated to be 3.1 to 10.3% of the total NEE while utilizing pseudo-data from nine observation stations over India. Increasing the spatial and temporal resolutions of the transport models can generally capture the mesoscale features and associated CO 2 gradients, thereby reducing the representation 660 error. Increasing the model’s resolution from 1 ° to 0.5 ° has shown an improvement in capturing variability with representation error reduction of 33% and 36% for summer time and winter time, respectively. By showing the existence of unresolved variability in 0.5 ° resolution with a similar spatial pattern of error as of 1 ° spatial resolution, we demonstrate the need for a much finer resolution than 0.5 ° for representing the atmospheric CO 2 variability over India. However, merely increasing the resolution without having a realistic representation of terrain heterogeneity and flux (both natural and anthropogenic) variability would not be beneficial. The uncertainties in the high-resolution fluxes can worsen the model's skills, whose effect would not be more pronounced at coarser resolutions due to the diffusive nature of fluxes, as seen in Agustí-Panareda et al. (2019). A parameterization scheme with explanatory variables of sub-grid variations of terrain, biospheric and anthropogenic fluxes is shown to capture a considerable fraction of expected representation error in the global model. The proposed method is easy to implement in the coarse models as it does not require computationally expensive transport simulations at high resolution. As we see a significant dependence of the distribution of sub-grid variability on terrain variations, our results reinforce the requirement for using accurate DEMs in the atmospheric transport models. The biosphere flux variability explains about 62 to 80% of the surface representation errors over the Indian region, indicating the need for using precise high-resolution surface fluxes. Overall, we show that the mesoscale transport mechanisms and flux variability contribute to fine-scale CO 2 variations that the current-generation models cannot resolve. Our findings indicate that the models need to be critically improved to capture mesoscale variations associated with horizontal and vertical transport and fine-scale flux variability to maximize the potential of highly precise and accurate measurements. Our results provide a baseline for overcoming the shortcomings mentioned above and accounting for the realistic distribution of atmospheric CO 2 to improve the estimation of surface fluxes through inverse modelling.