This work presents observational evidence of a change in Atlantic‐Pacific Niños connection since the late 60's. Accordingly, summer Atlantic Niños (Niñas) alter the tropical circulation favoring the development of following‐winter Pacific Niñas (Niños). The same change is obtained in an ensemble of AGCM integrations in which SSTs in the Atlantic are the observed in 1949–2002 and those in the tropical Indo‐Pacific are from a coupled OGCM. The mechanism (for the positive Atlantic phase) involves the strengthening of the Walker circulation with ascending branch over the Atlantic and descending branch over the central Pacific. The enhanced surface divergence in the latter region shallows the equatorial thermocline triggering coupled processes, and favoring the development of a Pacific La Niña. Results could be linked to the reported 60's and 70's climate shifts; emphasizing the importance of tropical Atlantic for the success of seasonal forecast skill.

Seasonal climate forecasts occupy an intermediate zone between weather forecasting and climate projections. They share with the numerical weather prediction the difficulty of initializing the simulations with a realistic state of the atmosphere and the need to periodically verify different aspects of their quality, while additionally are burdened by uncertainties in feedback processes that also play a central role in constraining climate projections. Seasonal predictions have to deal also with the challenge of initializing all the components of the climate system (ocean, sea ice, and land surface). The value of skilful seasonal forecasts is obvious for many societal sectors and is currently being included in the framework of developing climate services. Seasonal forecasts will in addition be valuable by increasing the acceptance of climate projections among the general public. This advanced‐review article presents an overview of the state‐of‐the‐art in global seasonal predictability and forecasting for climate researchers and discusses fundamental advances to increase forecast quality in the near future. The article concludes with a list of challenges where seasonal forecasting is expected to focus on in the near future. WIREs Clim Change 2013, 4:245–268. doi: 10.1002/wcc.217 This article is categorized under: Climate Models and Modeling > Earth System Models Climate Models and Modeling > Knowledge Generation with Models Social Status of Climate Change Knowledge > Climate Science and Decision Making

Climate models are seen by many to be unverifiable. However, near-term climate predictions up to 10 years into the future carried out recently with these models can be rigorously verified against observations. Near-term climate prediction is a new information tool for the climate adaptation and service communities, which often make decisions on near-term time scales, and for which the most basic information is unfortunately very scarce. The Fifth Coupled Model Intercomparison Project set of co-ordinated climate-model experiments includes a set of near-term predictions in which several modelling groups participated and whose forecast quality we illustrate here. We show that climate forecast systems have skill in predicting the Earth's temperature at regional scales over the past 50 years and illustrate the trustworthiness of their predictions. Most of the skill can be attributed to changes in atmospheric composition, but also partly to the initialization of the predictions.

Abstract. Polar amplification – the phenomenon where external radiative forcing produces a larger change in surface temperature at high latitudes than the global average – is a key aspect of anthropogenic climate change, but its causes and consequences are not fully understood. The Polar Amplification Model Intercomparison Project (PAMIP) contribution to the sixth Coupled Model Intercomparison Project (CMIP6; Eyring et al., 2016) seeks to improve our understanding of this phenomenon through a coordinated set of numerical model experiments documented here. In particular, PAMIP will address the following primary questions: (1) what are the relative roles of local sea ice and remote sea surface temperature changes in driving polar amplification? (2) How does the global climate system respond to changes in Arctic and Antarctic sea ice? These issues will be addressed with multi-model simulations that are forced with different combinations of sea ice and/or sea surface temperatures representing present-day, pre-industrial and future conditions. The use of three time periods allows the signals of interest to be diagnosed in multiple ways. Lower-priority tier experiments are proposed to investigate additional aspects and provide further understanding of the physical processes. These experiments will address the following specific questions: what role does ocean–atmosphere coupling play in the response to sea ice? How and why does the atmospheric response to Arctic sea ice depend on the pattern of sea ice forcing? How and why does the atmospheric response to Arctic sea ice depend on the model background state? What have been the roles of local sea ice and remote sea surface temperature in polar amplification, and the response to sea ice, over the recent period since 1979? How does the response to sea ice evolve on decadal and longer timescales? A key goal of PAMIP is to determine the real-world situation using imperfect climate models. Although the experiments proposed here form a coordinated set, we anticipate a large spread across models. However, this spread will be exploited by seeking “emergent constraints” in which model uncertainty may be reduced by using an observable quantity that physically explains the intermodel spread. In summary, PAMIP will improve our understanding of the physical processes that drive polar amplification and its global climate impacts, thereby reducing the uncertainties in future projections and predictions of climate change and variability.

This work presents a description of the 1979–2002 tropical Atlantic (TA) SST variability modes coupled to the anomalous West African (WA) rainfall during the monsoon season. The time-evolving SST patterns, with an impact on WA rainfall variability, are analyzed using a new methodology based on maximum covariance analysis. The enhanced Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) dataset, which includes measures over the ocean, gives a complete picture of the interannual WA rainfall patterns for the Sahel dry period. The leading TA SST pattern, related to the Atlantic El Niño, is coupled to anomalous precipitation over the coast of the Gulf of Guinea, which corresponds to the second WA rainfall principal component. The thermodynamics and dynamics involved in the generation, development, and damping of this mode are studied and compared with previous works. The SST mode starts at the Angola/Benguela region and is caused by alongshore wind anomalies. It then propagates westward via Rossby waves and damps because of latent heat flux anomalies and Kelvin wave eastward propagation from an off-equatorial forcing. The second SST mode includes the Mediterranean and the Atlantic Ocean, showing how the Mediterranean SST anomalies are those that are directly associated with the Sahelian rainfall. The global signature of the TA SST patterns is analyzed, adding new insights about the Pacific–Atlantic link in relation to WA rainfall during this period. Also, this global picture suggests that the Mediterranean SST anomalies are a fingerprint of large-scale forcing. This work updates the results given by other authors, whose studies are based on different datasets dating back to the 1950s, including both the wet and the dry Sahel periods.

Abstract. The Stratosphere-troposphere Processes And their Role in Climate (SPARC) Quasi-Biennial Oscillation initiative (QBOi) aims to improve the fidelity of tropical stratospheric variability in general circulation and Earth system models by conducting coordinated numerical experiments and analysis. In the equatorial stratosphere, the QBO is the most conspicuous mode of variability. Five coordinated experiments have therefore been designed to (i) evaluate and compare the verisimilitude of modelled QBOs under presentday conditions, (ii) identify robustness (or alternatively the spread and uncertainty) in the simulated QBO response to commonly imposed changes in model climate forcings (e.g. a doubling of CO 2 amounts), and (iii) examine model dependence of QBO predictability. This paper documents these experiments and the recommended output diagnostics. The rationale behind the experimental design and choice of diagnostics is presented. To facilitate scientific interpretation of the results in other planned QBOi studies, consistent descriptions of the models performing each experiment set are given, with those aspects particularly relevant for simulating the QBO tabulated for easy comparison.

The possibility that Arctic sea ice loss weakens mid-latitude westerlies, promoting more severe cold winters, has sparked more than a decade of scientific debate, with apparent support from observations but inconclusive modelling evidence. Here we show that sixteen models contributing to the Polar Amplification Model Intercomparison Project simulate a weakening of mid-latitude westerlies in response to projected Arctic sea ice loss. We develop an emergent constraint based on eddy feedback, which is 1.2 to 3 times too weak in the models, suggesting that the real-world weakening lies towards the higher end of the model simulations. Still, the modelled response to Arctic sea ice loss is weak: the North Atlantic Oscillation response is similar in magnitude and offsets the projected response to increased greenhouse gases, but would only account for around 10% of variations in individual years. We further find that relationships between Arctic sea ice and atmospheric circulation have weakened recently in observations and are no longer inconsistent with those in models.

Abstract. Polar amplification – the phenomenon that external radiative forcing produces a larger change in surface temperature at high latitudes than the global average – is a key aspect of anthropogenic climate change, but its causes and consequences are not fully understood. The Polar Amplification Model Intercomparison Project (PAMIP) contribution to the Sixth Coupled Model Intercomparison Project (CMIP6, Eyring et al. 2016) seeks to improve our understanding of this phenomenon through a coordinated set of numerical model experiments documented here. In particular, PAMIP will address the following primary questions: 1. What are the relative roles of local sea ice and remote sea surface temperature changes in driving polar amplification? 2. How does the global climate system respond to changes in Arctic and Antarctic sea ice? These issues will be addressed with multi-model simulations that are forced with different combinations of sea ice and/or sea surface temperatures representing present day, pre-industrial and future conditions. The use of three time periods allows the signals of interest to be diagnosed in multiple ways. Lower priority tier experiments are proposed to investigate additional aspects and provide further understanding of the physical processes. These experiments will address the following specific questions: What role does ocean-atmosphere coupling play in the response to sea ice? How and why does the atmospheric response to Arctic sea ice depend on the pattern of sea ice forcing? How and why does the atmospheric response to Arctic sea ice depend on the model background state? What are the roles of local sea ice and remote sea surface temperature in polar amplification, and the response to sea ice, over the recent period since 1979? How does the response to sea ice evolve on decadal and longer timescales? A key goal of PAMIP is to determine the real world situation using imperfect climate models. Although the experiments proposed here form a coordinated set, we anticipate a large spread across models. However, this spread will be exploited by seeking emergent constraints in which model uncertainty may be reduced by using an observable quantity that physically explains the inter-model spread. In summary, PAMIP will improve our understanding of the physical processes that drive polar amplification and its global climate impacts, thereby reducing the uncertainties in future projections and predictions of climate change and variability.