Monthly to seasonal prediction of tropical Atlantic sea surface temperature with statistical models constructed from observations and data from the Kiel Climate Model

We explore the predictability of tropical Atlantic sea surface temperature (SST) and the potential influence of climate model bias on SST predictions over the tropical Atlantic. Two statistical methods are used to examine the skill in forecasting tropical Atlantic SST anomalies (SSTAs): linear inverse modeling (LIM) and analogue forecast (AF). The statistical models are trained either with observations or with data from two control integrations of the Kiel Climate Model (KCM), which only differ with respect to the resolution of its atmospheric component. Observed SSTAs suggest that Tropical Atlantic climatic changes are potentially predictable at lead times of up to 6 months over large parts of the Tropical Atlantic. The SSTAs from the KCM version employing a high-resolution atmosphere model (KCM-HRES) is potentially predictable at a level comparable to that derived from the observations, whereas the SSTAs from the KCM version employing a low-resolution atmosphere model (KCM-LRES) is considerably less potentially predictable. We show that the enhanced potential predictability in the former KCM version can be very likely related to the improved representation of ENSO-like dynamics and its seasonality. We used the statistical models in true forecast mode, i.e. the prediction schemes were trained from data independent of the forecast period. Using observed SSTAs to train the LIM yields significant skill in forecasting observed SSTAs at lead times of up to 4 months across all calendar months, which is mostly restricted to the northern and equatorial western Tropical Atlantic. Similar patterns, but with lower skill, are found when the models’ SSTAs are used, in which LIM trained with the KCM-HRES generally yields higher skills than that from the KCM-LRES. Applying AF yields significant skills in predicting observed SSTAs over the same regions, but the forecast skills are considerably smaller. When the SSTAs together with either sea level pressure (SLP) anomalies or dynamic sea level (DSL) anomalies from the KCM are used to construct the statistical models, the prediction of observed equatorial Atlantic SSTAs can be improved, with significant skill enhancement at lead times of up to 4 months in limited regions. An optimal initial SSTA pattern is found, which results in the largest transient anomaly growth over the entire domain. Independent of external forces, this amplification is developed internally; meaning that the seasonal forecast might be more sensitive to initial conditions than currently thought