In the present era, scientific computation is gradually becoming a primary research method, with an increasing number of researchers engaging in simulation studies on various high-performance computing platforms. Scientific workflows play a crucial role in organizing these complex research tasks effectively. However, poorly managed scientific workflows can lead to wastage of HPC com- putational resources. Researchers using scientific workflows for their studies hope to simplify operations and minimize additional learning costs. The parameter optimization of Earth Sys- tem Models (ESM) poses specific challenges due to its complexity, exacerbating these issues. To address these challenges, we propose a scientific workflow management framework for surrogate- based ESM parameter optimization. In this framework, various tools are deployed, and multiple management mechanisms are established to enhance control over different stages of the workflow cycle. The goal is to conserve platform resources and improve the user experience. We monitor a team engaged in tuning CAM parameters before and after adopting the framework, and the results demonstrate significant improvements in various metrics after deploying the framework. This validates that our proposed scientific workflow management framework effectively addresses the challenges in user operations and resource management during surrogate-based ESM optimization processes. It holds demonstrative significance in showcasing the potential of our framework.