The systematic biases of CMIP6 climate models: remote connections and impacts

Despite tremendous efforts made to improve model performance over the past several decades, climate models exhibit systematic errors or biases in the simulation of many aspects and regions of the climate system. These systematic biases indicate the misrepresentations of physical processes in the models, which can be amplified by feedbacks among different processes and/or climate components. The magnitude of the model biases is often similar to the magnitude of the climate change that have been observed in the past several decades in some regions and for some parameters. This gives rise to large uncertainties in the climate predictions and projections. Furthermore, using climate models with such biases for assessing future climate change implies an assumption that these biases are stationary over time. However, the assumption may not be justified due to the internal variability and the evolving background state of the climate system.

In this study, we investigate model biases of key climate variables with the aim of understanding their links with biases of the same or other variables at remote locations. We analyze the multi-model multi-member ensemble (MME) of CMIP6 historical runs. We first focus on the model bias of the Atlantic meridional overturning circulation (AMOC) and its links with remote oceanic biases (e.g., sea surface temperature, sea surface salinity), North Atlantic deep water formation, etc.) and sea ice extent, as well as the atmospheric biases. We assess to what extent these linkages may affect the AMOC change. We further explore the representations of the circulation modes (e.g., North Atlantic Oscillation) in the CMIP6 MME relative to the observations, with emphasis on understanding how the internal variability influences the representation of the circulation modes and their relationship with other climate variables, and how these relationships in turn impact the climate predictability.