Coordination to understand and reduce global model biases by U.S. and Chinese institutions

A U.S.-China Coupled Model Intercomparison Workshop: the first bilateral workshop to coordinate diagnoses of global climate models and resolution of key biases with 60 attendees from three U.S. and six Chinese modeling instiututions; 23-25 August 2017, Beijing, China.

Systematic biases in coupled ocean–atmosphere models and Earth system models (ESMs) impact their fidelity to predict climate variability and future changes. These biases will affect the simulation results in the upcoming phase 6 of the Coupled Model Intercomparison Project (CMIP6; Eyring et al. 2016). Complementary to the broad efforts in the international community to confront these biases, there are benefits of focused collaborations by a smaller number of modeling groups to diagnose, understand, and investigate specific biases of mutual interests. These collaborations allow for discussions of model development priorities and coordinated process-oriented diagnostics and numerical experiments. Recognizing the benefits of such collaborations, representatives of modeling centers from the United States and China held a joint workshop in Beijing, China, on biases in coupled models. The meeting was jointly organized by the Chinese Academy of Sciences and National Oceanic and Atmospheric Administration (NOAA) and involved scientists from major U.S. and Chinese modeling institutions participating in CMIP6.

While the U.S. models participating in CMIP6 are widely known, some of the Chinese models are not. The workshop gave scientists in the United States, and in other countries through this workshop summary, an opportunity to become better acquainted with the development undertaken by the modeling institutions in China. Some of the Chinese models build off component models, such as the ocean, atmosphere, land, and sea ice models publicly available from institutions in the United States or other countries, with varying degrees of heritage and similarities. The totality of these models offers unique opportunities to compare and understand biases in coupled models when one or more components are replaced.