Ensemble-based observation impact of surface CO₂ concentration observations on analysis and forecast of atmospheric CO₂ concentrations over East Asia

Abstract. The effect of assimilated surface CO₂ concentration observations on the analysis and forecast errors of model CO₂ concentrations was evaluated using the data assimilation (DA)-forecast system combining the Weather Research and Forecasting model coupled with Chemistry and modified Data Assimilation Research Testbed. To investigate the impact of surface CO₂ observations, four observing system simulation experiments (OSSEs) were conducted in July 2019. The impact of CO₂ observations on the CO₂ concentration analysis was calculated using self-sensitivity. Average self-sensitivity of four OSSEs was 21.0 %, implying that surface CO₂ observations provided average 21.0 % information to the CO₂ concentration analysis. Self-sensitivity was highly correlated with the root mean square error of the analysis and hourly variability in the surface CO₂ observations at each observation site. The impact of CO₂ observations on reducing forecast errors, calculated using nonlinear forecast error reduction (NER), showed that NER with DA was reduced by average 17.0 % compared with that without DA. Linear forecast error reduction, calculated using the ensemble forecast sensitivity to observation (EFSO) impact, showed that the EFSO impact was greater at surface CO₂ observation sites with higher self-sensitivity and active vegetation types. Average fraction of beneficial observations for all experiments was 68.9 % (66.3 %) for 6 h (12 h) forecasts, implying that more than half of the assimilated CO₂ observations contributed to reducing forecast errors. The assessment of observation impact on the CO₂ concentration analysis and forecast can be useful for monitoring and estimating atmospheric CO₂ concentrations, optimizing surface CO₂ fluxes, and designing atmospheric CO₂ observation networks.