Development and evaluation of high-resolution regional CO2 data assimilation-forecast system in East Asia

In this study, a high-resolution CO₂ data assimilation (DA)-forecast system was developed to improve atmospheric CO₂ concentration simulations in East Asia. The Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) was used for simulating regional CO₂ concentrations and the ensemble adjusted Kalman filter (EAKF) in modified Data Assimilation Research Testbed (DART) was used for assimilating CO₂ concentration observations. To evaluate the performance of the developed DA-forecast system, observing system simulation experiment (OSSE) was performed in January and July 2019. Four experiments, which assimilated pseudo surface CO₂ observations from four observation site networks, were conducted to avoid the influence of observation site distributions. In January and July 2019, the ratios of the root mean square error (RMSE) to the ensemble total spread for surface CO₂ concentrations were 1.00 and 0.97, respectively. By assimilating surface CO₂ concentrations, the bias and RMSE of simulated CO₂ concentrations reduced by 1.23 ppm and 1.24 ppm in January and 1.41 ppm and 1.84 ppm in July, implying the stability of the developed DA-forecast system. Among four experiments, the experiment with an evenly distributed observation site network showed the smallest RMSE for surface CO₂ concentration. The RMSE of the experiment with the existing CO₂ observation network was greater than that with the evenly distributed observation network, but was smaller than that without DA. While the DA using the evenly distributed observation network showed the best performance for simulating CO₂ concentrations in East Asia, the DA using the existing surface CO₂ observation network also improved CO₂ simulation performance compared to the experiment without DA.

Acknowledgments

This study was supported by a National Research Foundation of Korea (NRF) grant funded by the South Korean government (Ministry of Science and ICT) (Grant 2021R1A2C1012572) and the Yonsei Signature Research Cluster Program of 2024 (2024-22-0162).