A new approach for parameter optimization in land surface model

In this study, a new parameter optimization method was used to investigate the expansion of conditional nonlinear optimal perturbation (CNOP) in a land surface model (LSM) using long-term enhanced field observations at Tongyu station in Jilin Province, China, combined with a sophisticated LSM (common land model, CoLM). Tongyu station is a reference site of the international Coordinated Energy and Water Cycle Observations Project (CEOP) that has studied semiarid regions that have undergone desertification, salination, and degradation since late 1960s. In this study, three key land-surface parameters, namely, soil color, proportion of sand or clay in soil, and leaf-area index were chosen as parameters to be optimized. Our study comprised three experiments: First, a single-parameter optimization was performed, while the second and third experiments performed triple- and six-parameter optimizations, respectively. Notable improvements in simulating sensible heat flux (SH), latent heat flux (LH), soil temperature (TS), and moisture (MS) at shallow layers were achieved using the optimized parameters. The multiple-parameter optimization experiments performed better than the single-parameter experminent. All results demonstrate that the CNOP method can be used to optimize expanded parameters in an LSM. Moreover, clear mathematical meaning, simple design structure, and rapid computability give this method great potential for further application to parameter optimization in LSMs.