The variability and predictability of the Tropical Atlantic primary productivity remains little explored on interannual-to-decadal time scales. Here, we present the results of two studies, in which find a decadal scale variability in phytoplankton abundance that can be predicted three years ahead. The predictions are made with NorCPM, which is a fully coupled climate prediction model with ocean biogeochemistry that assimilates temperature and salinity to reconstruct past variability. From these reconstructions, predictions are initialized that are run freely ten years ahead. We find that the predictability is a result of nutrient pulses that are advected with the southern branch of the South Equatorial Current from the most southern part of the Atlantic, and that then get caught in the Equatorial undercurrent before they reach the surface in the Tropical Atlantic Ocean. A more detailed analysis is being done in order to pinpoint the underlying mechanisms in a forced ocean model, where we find a link to the Pan-Atlantic decadal oscillation.

The impacts of marine heatwaves (MHWs) on marine ecosystems can be amplified when combined with other extreme events. Here, we investigate the temporal-spatial distribution of compound events of MHW, high acidity and low chlorophyll in the equatorial and South Atlantic, using observation-based datasets and reanalysis products. We show that the frequency and intensity of these triple compound events have increased dramatically over the past two decades, peaking in the most recent years. We analysed the drivers of triple compound events for six regions and found that, for the Angola Front and Brazil-Malvinas Confluence regions, these events are associated with a poleward shift of the fronts. In the Agulhas Leakage region, an increase in warmer waters entering from the Indian Ocean leads to compound extremes. In the western equatorial and subtropical Atlantic, they are caused by changes in the air-sea heat fluxes, while in the eastern equatorial by a weakening of upwelling. In addition, triple compound events are widespread over the South Atlantic during El Niño events. This is important because MHWs can be predicted when they are associated with ENSO.

Tropical basin interactions and the climatic linkages between mid-to-high latitudes and the tropics are active research areas. These interactions include the influence of El Niño–Southern Oscillation (ENSO) on the tropical Indian and Atlantic oceans, the feedback from these basins on ENSO, the influence of the tropics on mid-to-high-latitude climates, and the feedback from higher latitudes on tropical climate variability. This review summarizes the current understanding of these relationships and key underlying physical processes. In particular, we assessed the current knowledge of tropical variability and the interactions between the tropics and extratropics, including ENSO variability and diversity, the influence of ENSO on the tropical Atlantic and Indian Oceans, interactions among tropical basins on different timescales, variability in the Atlantic meridional overturning circulation (AMOC), the effect of tropical basins on the AMOC, the relationship between the AMOC and Atlantic multidecadal variability, the influence of the AMOC on ENSO and tropical variability, and the impact of other mid-to-high-latitude processes on tropical variability. Although ENSO is the dominant mode of variability on interannual timescales, its characteristics are not stationary and can be influenced by processes from other tropical basins and mid-to-high latitudes. The strength and variations of these interactions among different tropical basins and latitudes can be modulated by changes in external forcing, whether of natural or anthropogenic origin, and may also be shaped by nonlinear interactions between different modes of internal variability.

As two highly sensitive climate zones in the world, the Arctic and Tibetan Plateau (TP) regions respectively exhibit significantly uneven spatial variability in surface air temperature (SAT) and greatly influence the Eurasian climate on the interannual timescale. However, despite the synchronized warming trends in these two regions, their interannual spatio-temporal connection remains unclear. In this study, we applied the singular value decomposition (SVD) method to ERA5 wintertime surface air temperature anomalies to explore the dominant modes of SAT covariability between the Arctic and TP. We identified two major interannual modes: the dipolar Arctic-uniform TP (DA-UTP) and the quadrupolar Arctic-dipolar TP (QA-DTP), which together explain 82

The El Niño–Southern Oscillation (ENSO) — describing shifts between warm El Niño and cold La Niña phases — has a substantial effect on the global climate. In this Review, we outline the mechanisms and climate impacts of ENSO in Africa, focusing on rainfall. ENSO’s influence varies strongly by season, region, phase, event and decade, highlighting complex dynamics and asymmetries. Although difficult to generalize, key characteristics include: anomalies across the Sahel in July–September, related to the tropospheric temperature mechanism; a strong dipole in anomalies between eastern and southern Africa during October–December (the short rain reason) and December–February, linked to interactions with the Indian Ocean Dipole and Indian Ocean Basin mode, respectively; and anomalies over southern Africa (with possible indications of opposite anomalies over East Africa) during March–May (the long rain season), associated with continuation of the Indian Ocean Basin mode. These teleconnections tend to be most pronounced for East Pacific El Niño and Central Pacific La Niña events, as well as during decades when interbasin interactions are strongest. Although challenging to simulate, climate models suggest that these impacts will strengthen in the future, manifesting as an increased frequency of ENSO-related dry and wet extremes. Given the reliance of much of Africa on rain-fed agriculture, resolving these relationships is vital, necessitating realistic simulation of regional circulations, ENSO and its interbasin interactions. The El Niño–Southern Oscillation (ENSO) has substantial impacts on the global climate. This Review outlines ENSO relationships with Africa, outlining their dynamics, impacts on precipitation and projected changes in the future.

Today, the global mean sea level (GMSL) stands ~ 20 cm higher than at the beginning of the last century, and the rate of sea-level rise has been accelerating in recent decades. Even a slight, globally uniform sea-level rise can notably impact atmospheric and oceanic circulations at climatic and potentially synoptic scales. However, the extent to which sea-level rise will influence extreme weather remains largely unknown. Here, we focus on East Asia and conduct climate model experiments to investigate the effects of GMSL rise on winter cold extremes. Our experiments demonstrate that GMSL rise promotes stronger and more frequent extreme cold events, and this influence is expected to strengthen significantly in the coming century. This effect is attributed to weakened mid-high latitude westerly winds and increased occurrence of blocking events over Eurasia. Our study presents evidence that GMSL rise can modify synoptic systems and intensify extreme events, suggesting that both coastal and inland countries are exposed to threats arising from GMSL rise.

Harmful algal blooms contaminate seafood with toxins and poison humans and wildlife upon consumption. Toxic algae niches are projected to expand in high latitudes, but how the frequency of their blooms will evolve is still little known. Here we use climate models, 14 years of observations and probabilistic models of toxic algae, to assess the frequency of harmful algal blooms in a future warmer world. The warmer ocean temperatures increase the blooms in spring and autumn. However, the blooms reduce in summer as surface waters become excessively warm. Freshening reduces the blooms of species confined to high salinity ranges and has no effect on increasing the blooms. In a 3 degrees C warmer world, the blooms of D. acuta might increase by 50% and A. tamarense complex reduce by 40% along the Norwegian coast. Therefore, humans and wildlife are likely to become more exposed to diarrheic toxins and less to paralytic toxins.

The amplified wintertime Arctic warming has accelerated in recent decades. However, whether and how the warming can cause Eurasian cooling remains debated. By identifying daily Arctic warming events, we find direct observational evidence that the Arctic warming tends to cause substantial Eurasian cooling and an increase in occurrence frequency of Eurasian cooling events with a roughly 2-day lag. We propose a mechanism explaining the causality. We find that the Arctic warming causes a large suppression in activities of daily weather disturbances (referred to as synoptic eddies) over high-latitude Eurasia. This produces a meridional dipole in geopotential height anomalies characterized by an equivalent-barotropic anomalous low (high) and a lower-level cooling (warming) over mid-latitude Eurasia (the Arctic) via a nonlinear eddy-to-mean flow feedback. The feedback induces near-surface northeasterly anomalies that enlarge the Eurasian cooling via cold advection. Thus, we conclude that the warm Arctic versus cold Eurasia is essentially an intrinsic dipole determined by synoptic eddy-mean flow interaction.

Understanding drivers of variability in oceanic primary productivity is essential to increase our understanding of the functioning of marine ecosystems and biogeochemical cycles. Here, interannual variability of satellite-derived chlorophyll-a (CHL) and its underlying oceanographic processes are analyzed in six coastal regions of the tropical and south Atlantic. Along the South American coast, sea-surface height (SSH) and alongshore velocity, proxies for surface flows, were identified as the main drivers. Along the African coast, variations in sea-surface temperature (SST) and SSH related to coastal upwelling, were the dominant drivers. Important links to the Tropical Southern Atlantic, Dipole Mode Index, Western Hemisphere Warm Pool, and Southern Oscillation Index indices were identified, indicating potential role of teleconnections in the CHL-variability. The identified driver-linked variables were used to reconstruct the regional CHL series using multi-linear regressions and a neural-network model. The multi-linear models were able to reproduce significant fractions of the observed CHL variance. In particular, a model based on eigenvalues from an empirical orthogonal function decomposition of SST, outperformed the others. The neural-network model shows the highest performance reproducing most of the CHL variance (> 70%), but it presents difficulty to deduce the relative importance of individual drivers. Beyond this fitting/training period, the multi-linear model show better results respect to the neural-network model, especially that based on oceanographic variables. These CHL-reconstruction models present the possibility to reproduce CHL in periods when its observation is unavailable and even to predict it in multi-year climate projections.

Atlantic and Benguela Niño events substantially affect the tropical Atlantic region, with far-reaching consequences on local marine ecosystems, African climates, and El Niño Southern Oscillation. While accurate forecasts of these events are invaluable, state-of-the-art dynamic forecasting systems have shown limited predictive capabilities. Thus, the extent to which the tropical Atlantic variability is predictable remains an open question. This study explores the potential of deep learning in this context. Using a simple convolutional neural network architecture, we show that Atlantic/Benguela Niños can be predicted up to 3 to 4 months ahead. Our model excels in forecasting peak-season events with remarkable accuracy extending lead time to 5 months. Detailed analysis reveals our model's ability to exploit known physical precursors, such as long-wave ocean dynamics, for accurate predictions of these events. This study challenges the perception that the tropical Atlantic is unpredictable and highlights deep learning's potential to advance our understanding and forecasting of critical climate events.

In January-February 2020, the EUREC4A-OA/ATOMIC experiment took place in the Northwest Tropical Atlantic with the overall goal of understanding the role of fine-scale processes in internal ocean dynamics and air-sea interaction. Four oceanographic ships, the French Atalante, the German Maria S. Merian and Meteor, and the US Ron Brown, were closely coordinated with airborne observations and autonomous ocean platforms (gliders, ©Saildrones, Argo floats, and drifters) to simultaneously measure the ocean and atmosphere from east of Barbados to the northern border of French Guyana. The multiple observations of the ocean, atmosphere, and their interface have revealed more complex ocean dynamics than expected, in particular a strong interaction between the Amazon River outflow (despite its reduced winter discharge), the North Brazil Current (NBC), and several mesoscale eddies (including the highly energetic NBC rings). This leads to even richer submesoscale dynamics that shape an important fraction of the air-sea exchange of heat, momentum, and CO2, and efficiently isolates the NBC northward flow waters from intense and continuous interactions with the atmosphere. Owing to the many complementary observations from ships and autonomous platforms, we have been able to quantify some of these processes, including the diurnal cycle and the 3D dynamics of different mesoscale eddies, as well as to map and quantify different terms of the air-sea fluxes and their impacts on the marine atmospheric boundary-layer water budget. The results have been widely used not only to validate numerical simulations of the region, but also to guide their analyses and to improve various numerical parameterizations. The collection of these observations was the result of an important international coordination between many different groups of ocean and atmospheric scientists. In addition, the special strategy for targeted data collection of meso- and submesoscale processes relied on daily planning of the field experiment and on detailed analysis of the near-real-time satellite data and the observations already obtained during the experiment, which was essential for providing the right snapshots of the ocean and atmosphere for the quantification of many processes. The lessons learned from this experiment will be implemented and extended in the upcoming major high-resolution oceanographic endeavor, the WHIRLS experiment, which will take place in June-July 2025, southwest of Africa.

Extreme Atlantic and Benguela Niño events continue to significantly impact the tropical Atlantic region, with far-reaching consequences for African climate and ecosystems. Despite attempts to forecast these events using traditional seasonal forecasting systems, success remains low, reinforcing the growing idea that these events are unpredictable. To overcome the limitations of dynamical prediction systems, we introduce a deep learning-based statistical prediction model for Atlantic and Benguela Niño events. Our convolutional neural network (CNN) model, trained on 90 years of reanalysis data incorporating surface and 100m-averaged temperature variables, demonstrates the capability to forecast the Atlantic and Benguela Niño indices with lead times of up to 3-4 months. Notably, the CNN model excels in forecasting peak-season events with remarkable accuracy extending up to 5 months ahead. Gradient sensitivity analysis reveals the ability of the CNN model to exploit known physical precursors, particularly the connection to equatorial dynamics and the South Atlantic Anticyclone, for accurate predictions of Benguela Niño events. This study challenges the perception of the Tropical Atlantic as inherently unpredictable, underscoring the potential of deep learning to enhance our understanding and forecasting of critical climate events.

We investigate the role of the subpolar North Atlantic (SPNA) for downstream predictability, using two decadal climate prediction systems. We use the subpolar extreme cold and fresh anomaly event developing in winter 2013/2014 as initial conditions and evaluate ensemble predictions of the two systems in the following decade. In addition, we perform ensemble pacemaker experiments where the models are forced toward observed ocean temperature and salinity anomalies in the SPNA from November 2014 through December 2019. The pacemaker experiments show improved skill along the Atlantic Water pathway, compared with the standard decadal predictions, and we therefore conclude that the correct description of the ocean in the SPNA is the key. The enhanced skill is most prominent in subsurface salinity in the form of propagating anomalies. Observations show that ocean anomalies propagate across the North Atlantic and further north along the Norwegian coast. Anomalous ocean temperatures can modify heat exchange between ocean and atmosphere and influence the circulation of the atmosphere. Since such anomalies can persist for months, it potentially opens a door to seasonal prediction of the atmosphere. However, climate models used for seasonal and decadal prediction fail to predict these propagating anomalies. In our study we aim to find causes for this lack of predictability. We use the record low temperatures observed in the North Atlantic in 2015 as a test case. We employ two climate prediction systems and make two types of predictions: one type is a standard prediction initialized with winter 2014/2015 ocean conditions; the other type is initialized in the same way and in addition, the model is kept close to observations in the subpolar gyre during the whole prediction, in the region where the cold blob was formed. Comparing the two types of predictions, we see that keeping the model close to observations in the subpolar gyre increases the predictability along the Norwegian coast. This points to the subpolar gyre as a key area where models need to be improved. Knowing temperature and salinity in the subpolar gyre increases skill downstream along the Atlantic Water pathway Salinity anomalies propagate northward, while temperature anomalies are more difficult to trace A pacemaker experiment has been used for decadal predictions for the first time

Abstract Jiang et al. (2023), https://doi.org/10.1029/2023gl103777 argue 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 coastal regions off Angola and Namibia are renowned for their highly productive marine ecosystems in the southeast Atlantic. In recent decades, these regions have undergone significant long-term changes. In this study, we investigate the variability of these long-term changes throughout the annual cycle and explore the underlying mechanisms using a 34-year (1982–2015) regional ocean model simulation. The results reveal a clear seasonal dependence of sea surface temperature (SST) trends along the Angolan and Namibian coasts, with alternating positive and negative trends. The long-term warming trend in the Angolan coastal region is mainly explained by a pronounced warming trend in the austral spring and summer (November-January), while the decadal trend off Namibia results from a counterbalance of an austral winter cooling trend and an austral summer warming trend. A heat budget analysis of the mixed-layer temperature variations shows that these changes are explained by a long-term modulation of the coastal currents. The Angolan warming trend is mainly explained by an intensification of the poleward coastal current, which transports more warm equatorial waters towards the Angolan coast. Off Namibia, the warming trend is attributed to a reduction in the northwestward Benguela Current, which advects cooler water from the south to the Namibian coast. These changes in the coastal current are associated with a modulation of the seasonal coastal trapped waves that are remotely-forced along the equatorial waveguide. These long-term changes may have significant implications for local ecosystems and fisheries.

Interannual global mean surface temperature (GMST) forecast provides critical insights into the economic and societal implications of climate variability. The pronounced GMST elevation in 2023-2024 indicates that the Earth may have accumulated enough heat to cause widespread disasters, underscoring the necessity for establishing accurate short-term GMST predictions to offer timely and sustainable public service. However, capturing high-frequency annual variability (ANV) component of GMST poses challenges due to its susceptibility to intraseasonal-to-interannual (ISI) noises, particularly across the Northern Hemisphere's mid-to-high latitudes. Averaging these ISI variations in November and December effectively enhances signal clarity, especially over oceans, and masks unpredictable noises on land. By forecasting the average GMST for November and December to extract ANV predictability, a strategy for annual GMST prediction was established. This approach successfully advanced precise GMST hindcasts by up to 2-months during 1980-2022, exceeding performance of existing climate models and boosting early warning for interannual GMST shifts.

Spring consecutive rainfall events (CREs) are key triggers of geological hazards in the Three Gorges Reservoir area (TGR), China. However, previous projections of CREs based on the direct outputs of global climate models (GCMs) are subject to considerable uncertainties, largely caused by their coarse resolution. This study applies a triple-nested WRF (Weather Research and Forecasting) model dynamical downscaling, driven by a GCM, MIROC6 (Model for Interdisciplinary Research on Climate, version 6), to improve the historical simulation and reduce the uncertainties in the future projection of CREs in the TGR. Results indicate that WRF has better performances in reproducing the observed rainfall in terms of the daily probability distribution, monthly evolution and duration of rainfall events, demonstrating the ability of WRF in simulating CREs. Thus, the triple-nested WRF is applied to project the future changes of CREs under the middle-of-the-road and fossil-fueled development scenarios. It is indicated that light and moderate rainfall and the duration of continuous rainfall spells will decrease in the TGR, leading to a decrease in the frequency of CREs. Meanwhile, the duration, rainfall amount, and intensity of CREs is projected to regional increase in the central-west TGR. These results are inconsistent with the raw projection of MIROC6. Observational diagnosis implies that CREs are mainly contributed by the vertical moisture advection. Such a synoptic contribution is captured well by WRF, which is not the case in MIROC6, indicating larger uncertainties in the CREs projected by MIROC6.

Marine Ecosystem Models (MEMs) are increasingly driven by Earth System Models (ESMs) to better understand marine ecosystem dynamics, and to analyze the effects of alternative management efforts for marine ecosystems under potential scenarios of climate change. However, policy and commercial activities typically occur on seasonal-to-decadal time scales, a time span widely used in the global climate modeling community but where the skill level assessments of MEMs are in their infancy. This is mostly due to technical hurdles that prevent the global MEM community from performing large ensemble simulations with which to undergo systematic skill assessments. Here, we developed a novel distributed execution framework constructed of low-tech and freely available technologies to enable the systematic execution and analysis of linked ESM/MEM prediction ensembles. We apply this framework on the seasonal-to-decadal time scale, and assess how retrospective forecast uncertainty in an ensemble of initialized decadal ESM predictions affects a mechanistic and spatiotemporal explicit global trophodynamic MEM. Our results indicate that ESM internal variability has a relatively low impact on the MEM variability in comparison to the broad assumptions related to reconstructed fisheries. We also observe that the results are also sensitive to the ESM specificities. Our case study warrants further systematic explorations to disentangle the impacts of climate change, fisheries scenarios, MEM internal ecological hypotheses, and ESM variability. Most importantly, our case study demonstrates that a simple and free distributed execution framework has the potential to empower any modeling group with the fundamental capabilities to operationalize marine ecosystem modeling. Climate change and human activities like fishing are affecting the balance of marine ecosystems and the services they provide. To understand impacts better, scientists use computer models that consider climate, ocean conditions, and ocean life. To make robust decisions, decision makers need robust science delivered by robust models. This requires running many computer simulations, but the complexity of marine ecosystems models makes this difficult. Typically, only institutions with sufficient financial and technical means can overcome these difficulties, which leaves the majority of marine ecosystem modelers wanting. Here we introduce a possible solution to overcome the difficulties, using only simple and free technologies to facilitate the systematic execution of complex ecosystem models across networks of computers, to allow any modeling group to perform these exercises. We demonstrate our solution by running a global marine ecosystem model, EcoOcean, hundreds of times to see how it is affected by variability in ocean conditions. Using available laptops and desktops, we can now complete this task in 30 hr; where prior this modeling task would have taken weeks to complete. It is conceptually simple solutions such as these that may make the process of marine ecosystem modeling easier and more operational around the globe, thus opening the door for scientific management breakthroughs. Most marine ecosystem modellers lack the skills and resources to systematically calibrate, validate and assess the models for uncertainty Here we present a low-tech and open source run framework to use any computer network as a distributed model execution and assessment system We use the framework to mass-execute an Earth System/Ecosystem Model ensemble to assess the ecosystem impact of ESM uncertainty