On the Correspondence between Seasonal Forecast Biases and Long-Term Climate Biases in Sea Surface Temperature

Abstract

The correspondence between mean sea surface temperature (SST) biases in retrospective seasonal forecasts (hindcasts) and long-term climate simulations from five global climate models is examined to diagnose the degree to which systematic SST biases develop on seasonal time scales. The hindcasts are from the North American Multi-Model Ensemble and the climate simulations are from the Coupled Model Intercomparison Project. 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. Studying the growth of biases 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.

Evaluation of ECMWF-SEAS5 Seasonal Temperature and Precipitation Predictions over South America

“…Despite numerous advances and constant development of climate modeling, errors are intrinsic to the process. However, several studies point to different causes for the deviations, such as deficiency in SST simulation, errors in the initialization of soil moisture conditions, and inappropriate physical parameterization [3,[34][35][36]78]. In addition, improved extreme events prediction requires a deep understanding of drought and flood mechanisms, refined observations from data assimilation, better parameterizing techniques, efficient ensemble methodologies, and proper uncertainty quantification [17].…”

Extended seasonal prediction of spring precipitation over the Upper Colorado River Basin

“…The CanSIPSv2 model, the best among the three NMME models, shows moderate skills for the lead time less than eight months, with correlation ~ 0.40, MAPE ~ 25%, and HSS > 40%. The lower prediction skills of the NMME models at longer lead times are probably linked to initial conditions for these lead times and model drifts (e.g., Hermanson et al 2018;Manzanas 2020;Ma et al 2021).…”

“…8a), consistent with typical prediction skills of dynamic models when lead time is longer (e.g., Yang 2014, Zhao et al 2015). Such low skills at long lead times may be associated with (1) initial conditions for these lead times (as initial conditions are responsible for the initial bias growth; Ma et al 2021) and (2) model drifts (e.g., asymptotic, overshooting, and inverse drift; Hermanson et al 2018;Manzanas 2020). The CanSIPSv2 shows the best performance among the three models, and the prediction skill of the correlation is comparable to that of the ANN with the retrospective approach for LD1-LD5.…”

“…4). The low prediction skills of the NMME models at longer lead times may be associated with initial conditions for these lead times and model drifts (e.g., Hermanson et al 2018;Manzanas 2020;Ma et al 2021).…”

A Data Set for Intercomparing the Transient Behavior of Dynamical Model‐Based Subseasonal to Decadal Climate Predictions

“…Additional studies have related biases that develop following initialization to those in long-term simulations unconstrained by initialization (Hermanson et al, 2018;Ma et al, 2014Ma et al, , 2020 and inconsistencies between sea ice and ocean initial conditions (Cruz-García et al, 2021).…”