PNNL

Abstract

We describe the development of the tangent linear (TL) and adjoint models of the MPAS-CO2 transport model, which is a global online chemical transport model developed upon the non-hydrostatic Model for Prediction Across Scales- Atmosphere (MPAS-A). The primary goal is to make the model system a valuable research tool for investigating atmospheric carbon transport and inverse modeling. First, we develop the TL code, encompassing all CO2 transport processes within the MPAS-CO2 forward model. Then, we construct the adjoint model using a combined strategy involving re-calculation and stor- age of the essential meteorological variables needed for CO2 transport. This strategy allows the adjoint model to undertake long-period integration with moderate memory demands. To ensure accuracy, the TL and adjoint models undergo vigorous verifications through a series of standard tests. The adjoint model, through backward-in-time integration, calculates the sen- sitivity of atmospheric CO2 observations to surface CO2 fluxes and the initial atmospheric CO2 conditions. To demonstrate the utility of the adjoint model, we conduct simulations for two types of atmospheric CO2 observations: tower-based in situ CO2 mixing ratio and satellite-derived column-averaged (XCO2 ). A comparison between the sensitivity to surface flux calcu- lated by the MPAS-CO2 adjoint model with its counterpart from Carbon Tracker-Lagrange (CT-L) reveals spatial agreement but notable magnitude differences. These differences, particularly evident for XCO2 , likely arise from differences in vertical mixing between the two systems. Moreover, this comparison highlights the substantial loss of information in the atmospheric CO2 observations due to CT-L’s simulation length and spatial domain limitations. Furthermore, the adjoint sensitivity analysis demonstrates that the sensitivities to both surface flux and initial CO2 conditions spread out throughout the entire north- ern hemisphere within a month. MPAS-CO2 forward, TL, and adjoint models stand out for their calculation efficiency and variable-resolution capability, making them competitive in computational cost. In conclusion, the successful development of the MPAS-CO2 TL and adjoint models, and their integration into the MPAS-CO2 system, establish the possibility of using MPAS’s unique features for investigating atmospheric CO2 transport sensitivity studies and for conducting inverse modeling with advanced methods such as variational data assimilation.