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

Abstract

Abstract. Climate change mitigation efforts require information on the current greenhouse gas atmospheric concentrations and their sources and sinks. Carbon dioxide (CO2) is the most abundant anthropogenic greenhouse gas. Its variability in the atmosphere is modulated by the synergy between weather and CO2 surface fluxes, often referred to as CO2 weather. It is interpreted with the help of global or regional numerical transport models, with horizontal resolutions ranging from a few hundreds of kilometres to a few kilometres. Changes in the model horizontal resolution affect not only atmospheric transport but also the representation of topography and surface CO2 fluxes. This paper assesses the impact of horizontal resolution on the simulated atmospheric CO2 variability with a numerical weather prediction model. The simulations are performed using the Copernicus Atmosphere Monitoring Service (CAMS) CO2 forecasting system at different resolutions from 9 to 80 km and are evaluated using in situ atmospheric surface measurements and atmospheric column-mean observations of CO2, as well as radiosonde and SYNOP observations of the winds. The results indicate that both diurnal and day-to-day variability of atmospheric CO2 are generally better represented at high resolution, as shown by a reduction in the errors in simulated wind and CO2. Mountain stations display the largest improvements at high resolution as they directly benefit from the more realistic orography. In addition, the CO2 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. We conclude that increasing horizontal resolution matters for modelling CO2 weather because it has the potential to bring together improvements in the surface representation of both winds and CO2 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 CO2 and wind modelling error with resolution is not linear, making it difficult to quantify the improvement beyond the tested resolutions. Finally, we show that the high-resolution simulations are useful for the assessment of the small-scale variability of CO2 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 CO2. For these reasons, the high-resolution CO2 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.

On what scales can GOSAT flux inversions constrain anomalies in terrestrial ecosystems?

“…It is possible that, although the model transport errors influence the flux estimates, the resolution-dependent transport processes are not sensitive to NEE IAV for the time period considered here. However, both model resolutions examined in this study are quite coarse, and horizontal resolu-tion becomes increasingly important at smaller spatial scales (Agustí-Panareda et al, 2019).…”

Development and evaluation of CO<sub>2</sub> transport in MPAS-A v6.3

“…3.4). Two sets of MPAS simulations are conducted: the first set simulations covers four ACT campaign seasons (2016-2018) for evaluation using high-resolution airborne measurements; the second set covers January and July of 2014 for evaluation using near-surface CO 2 measurements and comparison with the IFS simulation results reported in Agusti-Panareda et al (2019). In the following model evaluation, https://doi.org/10.5194/gmd-2020-265 Preprint.…”

“…4), the MPAS simulated horizontal wind fields have larger errors in both January and July of 2014. For instance, the IFS 80 km resolution simulation has a vector wind RMSE about 4.5 m/s at the 200 hPa level in January while MPAS results in 5.16 m/s at the same pressure level.We then compare MPAS simulated CO 2 with hourly measurements from 50 stations that were used for the IFS model evaluation inAgusti-Panareda et al (2019). The information of the 50 stations, including location, elevation, intake height, and reference is listedTable 7.…”

“…For a given station this result in 744 (24 × 31) hourly measurements per month at the maximum. FollowingPatra et al (2008) andAgusti-Panareda et al (2019), MPAS hourly outputs are horizontally sampled by taking the nearest land cell to a given station. The resulting statistics (RMSE, bias, and STDE) for the MPAS simulation covering the month of January 2014, along with their counterparts from the IFS 9 km and 80 km resolution simulations(Agusti-Panareda et al, 2019), are listed in…”

Supplementary material to "Development and evaluation of CO<sub>2</sub> transport in MPAS-A v6.3"

“…Comparison of ECWMF IFS CO2 simulated hourly CO2 statistics with MPAS results for the month of January 2014. RMSE, STDE, and Bias of IFS 9km and 80km are reproduced from supplementTable S1ofAgusti-Panareda et al (2019) with permission.…”