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Abstract. Large-scale interaction between the three tropical ocean basins is an area of intense research that is often conducted through experimentation with numerical models. A common problem is that modeling groups use different experimental setups, which makes it difficult to compare results and delineate the role of model biases from differences in experimental setups. To address this issue, an experimental protocol for examining interaction between the tropical basins is introduced. The Tropical Basin Interaction Model Intercomparison Project (TBIMIP) consists of experiments in which sea surface temperatures (SSTs) are prescribed to follow observed values in selected basins. There are two types of experiments. One type, called standard pacemaker, consists of simulations in which SSTs are restored to observations in selected basins during a historical simulation. The other type, called pacemaker hindcast, consists of seasonal hindcast simulations in which SSTs are restored to observations during 12-month forecast periods. TBIMIP is coordinated by the Climate and Ocean – Variability, Predictability, and Change (CLIVAR) Research Focus on Tropical Basin Interaction. The datasets from the model simulations will be made available to the community to facilitate and stimulate research on tropical basin interaction and its role in seasonal-to-decadal variability and climate change. 

Abstract The modeling of weather and climate has been a success story. The skill of forecasts continues to improve and model biases continue to decrease. Combining the output of multiple models has further improved forecast skill and reduced biases. But are we exploiting the full capacity of state-of-the-art models in making forecasts and projections? Supermodeling is a recent step forward in the multimodel ensemble approach. Instead of combining model output after the simulations are completed, in a supermodel individual models exchange state information as they run, influencing each other’s behavior. By learning the optimal parameters that determine how models influence each other based on past observations, model errors are reduced at an early stage before they propagate into larger scales and affect other regions and variables. The models synchronize on a common solution that through learning remains closer to the observed evolution. Effectively a new dynamical system has been created, a supermodel, that optimally combines the strengths of the constituent models. The supermodel approach has the potential to rapidly improve current state-of-the-art weather forecasts and climate predictions. In this paper we introduce supermodeling, demonstrate its potential in examples of various complexity, and discuss learning strategies. We conclude with a discussion of remaining challenges for a successful application of supermodeling in the context of state-of-the-art models. The supermodeling approach is not limited to the modeling of weather and climate, but can be applied to improve the prediction capabilities of any complex system, for which a set of different models exists. 

Abstract Many fish and marine organisms are responding to our planet’s changing climate by shifting their distribution. Such shifts can drive international conflicts and are highly problematic for the communities and businesses that depend on these living marine resources. Advances in climate prediction mean that in some regions the drivers of these shifts can be forecast up to a decade ahead, although forecasts of distribution shifts on this critical time-scale, while highly sought after by stakeholders, have yet to materialise. Here, we demonstrate the application of decadal-scale climate predictions to the habitat and distribution of marine fish species. We show statistically significant forecast skill of individual years that outperform baseline forecasts 3–10 years ahead; forecasts of multi-year averages perform even better, yielding correlation coefficients in excess of 0.90 in some cases. We also demonstrate that the habitat shifts underlying conflicts over Atlantic mackerel fishing rights could have been foreseen. Our results show that climate predictions can provide information of direct relevance to stakeholders on the decadal-scale. This tool will be critical in foreseeing, adapting to and coping with the challenges of a changing future climate, particularly in the most ocean-dependent nations and communities. 

Abstract Jiang et al. (2023),https://doi.org/10.1029/2023gl103777argue that the apparent impact of the equatorial Atlantic on El Niño‐Southern Oscillation (ENSO) is a statistical artifact, and that the 6‐month lead correlation reported in previous studies stems from early developing ENSO events driving the equatorial Atlantic zonal mode (AZM) in boreal summer and maturing in winter. Closer examination, however, reveals that most AZM events develop too early to be driven by developing ENSO, and that the influence of decaying ENSO events has to be considered too. Thus, while early developing ENSO events may play a role, they do not fully explain observed AZM behavior. Our aim is not to argue for or against an AZM influence on ENSO, but rather to show that Jiang et al.’s analysis is insufficient to resolve this issue. More analysis will be needed for a deeper understanding of Atlantic‐Pacific interaction. 

The El Niño–Southern Oscillation (ENSO), which originates in the Pacific, is the strongest and most well-known mode of tropical climate variability. Its reach is global, and it can force climate variations of the tropical Atlantic and Indian Oceans by perturbing the global atmospheric circulation. Less appreciated is how the tropical Atlantic and Indian Oceans affect the Pacific. Especially noteworthy is the multidecadal Atlantic warming that began in the late 1990s, because recent research suggests that it has influenced Indo-Pacific climate, the character of the ENSO cycle, and the hiatus in global surface warming. Discovery of these pantropical interactions provides a pathway forward for improving predictions of climate variability in the current climate and for refining projections of future climate under different anthropogenic forcing scenarios.