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We quantify seasonal prediction skill of tropical winter rainfall in 14 climate forecast systems. High levels of seasonal prediction skill exist for year-to-year rainfall variability in all tropical ocean basins. The tropical East Pacific is the most skilful region, with very high correlation scores, and the tropical West Pacific is also highly skilful. Predictions of tropical Atlantic and Indian Ocean rainfall show lower but statistically significant scores. We compare prediction skill (measured against observed variability) with model predictability (using single forecasts as surrogate observations). Model predictability matches prediction skill in some regions but it is generally greater, especially over the Indian Ocean. We also find significant inter-basin connections in both observed and predicted rainfall. Teleconnections between basins due to El Niño–Southern Oscillation (ENSO) appear to be reproduced in multi-model predictions and are responsible for much of the prediction skill. They also explain the relative magnitude of inter-annual variability, the relative magnitude of predictable rainfall signals and the ranking of prediction skill across different basins. These seasonal tropical rainfall predictions exhibit a severe wet bias, often in excess of 20 of mean rainfall. However, we find little direct relationship between bias and prediction skill. Our results suggest that future prediction systems would be best improved through better model representation of inter-basin rainfall connections as these are strongly related to prediction skill, particularly in the Indian and West Pacific regions. Finally, we show that predictions of tropical rainfall alone can generate highly skilful forecasts of the main modes of extratropical circulation via linear relationships that might provide a useful tool to interpret real-time forecasts. © 2018 Crown copyright, Met Office Weather © 2018 Royal Meteorological Society This article is published with the permission of the Controller of HMSO and the Queen˘2019s Printer for Scotland

Variability in the tropical Atlantic Ocean is dominated by the seasonal cycle. A defining feature is the migration of the inter-tropical convergence zone into the northern hemisphere and the formation of a so-called cold tongue in sea surface temperatures (SSTs) in late boreal spring. Between April and August, cooling leads to a drop in SSTs of approximately 5°. The pronounced seasonal cycle in the equatorial Atlantic affects surrounding continents, and even minor deviations from it can have striking consequences for local agricultures. Here, we report how state-of-the-art coupled global climate models (CGCMs) still struggle to simulate the observed seasonal cycle in the equatorial Atlantic, focusing on the formation of the cold tongue. We review the basic processes that establish the observed seasonal cycle in the tropical Atlantic, highlight common biases and their potential origins, and discuss how they relate to the dynamics of the real world. We also briefly discuss the implications of the equatorial Atlantic warm bias for CGCM-based reliable, socio-economically relevant seasonal predictions in the region.

Near-inertial oscillations are ubiquitous in the ocean and are believed to play an important role in the global climate system. Studies on wind power input to near-inertial motions (WPI) have so far focused primarily on estimating the time-mean WPI, with little attention being paid to its temporal variability. In this study, a combination of atmospheric reanalysis products, a high-resolution ocean model and linear regression models are used to investigate for the first time the relationship between interannual variability of WPI in the North Atlantic and the North Atlantic Oscillation (NAO), motivated by the idea that the NAO serves as a good indicator for storminess over the North Atlantic and that storms account for the majority of WPI. It is found that WPI at low and high latitudes of the North Atlantic is significantly correlated to the NAO, owing to its influence on the configuration of the storm track. Positive (negative) NAO conditions are associated with increased WPI in the subpolar (subtropical) ocean. Basin-wide WPI is found to be significantly enhanced under negative NAO conditions, but is not significantly different from the climatological average under positive NAO conditions. This indicates a weak inverse relationship between basin-wide WPI and the NAO, contradicting intuitive expectations. The asymmetric impact of the NAO on basin-wide WPI results from greater sensitivity of WPI to near-inertial wind forcing at lower latitudes due to the variation of the Coriolis parameter with latitude.