Modelling CO<sub>2</sub> weather – why horizontal resolution matters

Abstract

<p><strong>Abstract.</strong> Climate change mitigation efforts require information on the current greenhouse gas atmospheric concentrations and their sources and sinks. Carbon dioxide (CO<sub>2</sub>) is the most abundant anthropogenic greenhouse gas. Its variability in the atmosphere is modulated by the synergy between weather and CO<sub>2</sub> surface fluxes, often referred to as CO<sub>2</sub> weather. It is interpreted with the help of global or regional numerical transport models, with horizontal resolutions ranging from a few hundreds of km to a few km. Changes in the model horizontal resolution affect not only atmospheric transport, but also the representation of topography and surface CO<sub>2</sub> fluxes. This paper assesses the impact of horizontal resolution on the simulated atmospheric CO<sub>2</sub> variability with a numerical weather prediction model. The simulations are performed using the Copernicus Atmosphere Monitoring Service (CAMS) CO<sub>2</sub> forecasting system at different resolutions from 9 km to 80 km and are evaluated using in situ atmospheric surface measurements and atmospheric column-mean observations of CO<sub>2</sub>, as well as radiosonde and SYNOP observations of the winds.</p> <p>The results indicate that both diurnal and day-to-day variability of atmospheric CO<sub>2</sub> are generally better represented at high resolution, as shown by a reduction in the errors in simulated wind and CO<sub>2</sub>. Mountain stations display the largest improvements at high resolution as they directly benefit from the more realistic orography. In addition, the CO<sub>2</sub> spatial gradients are generally improved with increasing resolution for both stations near the surface and those observing the total column, as the overall inter-station error is also reduced in magnitude. However, close to emission hotspots, the high resolution can also lead to a deterioration of the simulation skill, highlighting uncertainties in the high resolution fluxes that are more diffuse at lower resolutions.</p> <p>We conclude that increasing horizontal resolution matters for modelling CO<sub>2</sub> weather because it has the potential to bring together improvements in the surface representation of both winds and CO<sub>2</sub> fluxes, as well as an expected reduction in numerical errors of transport. Modelling applications like atmospheric inversion systems to estimate surface fluxes will only be able to benefit fully from upgrades in horizontal resolution if the topography, winds and prior flux distribution are also upgraded accordingly. It is clear from the results that an additional increase in resolution might reduce errors even further. However, the horizontal resolution sensitivity tests indicate that the change in the CO<sub>2</sub> and wind modelling error with resolution is not linear, making it difficult to extrapolate the results beyond the tested resolutions.</p> <p>Finally, we show that the high resolution simulations are useful for the assessment of the small-scale variability of CO<sub>2</sub> which cannot be represented in coarser resolution models. These representativeness errors need to be considered when assimilating in situ data and high resolution satellite data such as Greenhouse gases Observing Satellite (GOSAT), Orbiting Carbon Observatory-2 (OCO-2), the Chinese Carbon Dioxide Observation Satellite Mission (TanSat) and future missions such as the Geostationary Carbon Observatory (GeoCarb) and the Sentinel satellite constellation for CO<sub>2</sub>. For these reasons, the high resolution CO<sub>2</sub> simulations provided by the CAMS in real-time can be useful to estimate such small-scale variability in real time, as well as providing boundary conditions for regional modelling studies and supporting field experiments.</p>

Carbon Dioxide Distribution, Origins, and Transport Along a Frontal Boundary During Summer in Mid‐Latitudes

“…Patra et al (2008) and Sarrat et al (2007) evaluated multiple global and regional scale numerical models and found that they were able to represent the observed synoptic scale CO 2 variability from tower and aircraft measurements. In order to improve the representation of atmospheric transport, the above studies suggest the use of higher horizontal and vertical resolution numerical models coupled with CO 2 fluxes with high temporal and spatial resolution (Agustí-Panareda et al, 2019;Geels et al, 2007). Numerical models running at global scale resolutions (>100s of km) represent mesoscale and microscale weather events through parameterizations of physical transport processes (Carvalho et al, 2014).…”

“…In order for the inversion process to be accurate, these numerical transport models need to infer CO 2 sources and sinks with high accuracy (Gurney et al, 2002). Evaluating the numerical models using CO 2 observation help determine the uncertainty in the ability of the models to reproduce the carbon cycle (Agustí-Panareda et al, 2019;H. W. Chen et al, 2019;Chevallier et al, 2019;Díaz Isaac et al, 2014.…”

Examining CO2 Model Observation Residuals Using ACT‐America Data

“…While the WRF-Chem resolutions used in this studies had a 27 km resolution and surface fluxes were at 1 1 E    resolution, RMSDs of order 5 ppm encountered for ACT were comparable to CAMS RMSDs at 9 km. At the same time, WRF RMSDs were larger than those of CT at the coarser 1-degree resolution, conflicting with results found by Agustí-Panareda et al (2019). One potential explanation for this discrepancy is the fact that while neither WRF nor CT are capable of directly resolving convective cells, WRF has a sufficiently high resolution to resolve features of warm and cold fronts.…”

“…Model resolution is also an important factor for model performance. A comparison of forecasting system showed a 1.8-2.5 ppm reduction of RMSD (corresponding to 33%), when reducing horizontal model resolution from 80 to 9 km (Agustí-Panareda et al, 2019). This was attributed to both better representation of modeled wind fields (i.e., transport) and spatial variability in surface carbon fluxes.…”

Development of a Mesoscale Inversion System for Estimating Continental‐Scale CO2 Fluxes

“…This study used only those observations that occurred between noon and 4 pm local solar time, when tower measurements are representative of the entire convective boundary layer (Bakwin et al., 1998; Davis et al., 2003) and modeling of the ABL is less uncertain (Agustí‐Panareda et al., 2019; Chen et al., 2019; Geels et al., 2007).…”