On the correspondence between seasonal forecast biases and long-term climate biases in sea surface temperature
Scientists at Lawrence Livermore National Laboratory, along with collaborators from five other modeling groups, examined the correspondence between mean sea surface temperature (SST) biases in seasonal hindcasts and long-term climate simulations from five global climate models (GCMs) to diagnose the degree to which systematic SST biases develop on seasonal time scales. They also proposed a set of criteria for identifying whether a hindcast approach is useful for a specific regional bias study.
Diagnosing causes of systematic SST errors in long-term, fully coupled GCM simulations is challenging because of the non-linear feedback processes. Studying the growth of biases in the hindcasts may provide important clues to the causes and ultimately the amelioration of these biases. Further, initialized seasonal hindcasts can profitably be used in the development of high-resolution coupled ocean-atmosphere models.
The analysis suggests that most robust climatological SST biases begin to form within 6 months of a realistically initialized integration, although the growth rate varies with location, time, and model. In regions with large biases, interannual variability and ensemble spread is much smaller than the climatological bias. Additional ensemble hindcasts of the Community Earth System Model with a different initialization method suggest that initial conditions do matter for the initial bias growth, but the overall global bias patterns are similar after 6 months. A hindcast approach is more suitable to study biases over the tropics and sub-tropics than over the extra-tropics because of smaller initial biases and faster bias growth. The rapid emergence of SST biases makes it likely that fast processes with times scales shorter than the seasonal time scales in the atmosphere and upper ocean are responsible for a substantial part of the climatological SST biases.